A farewell to brake reaction times? Kinematics-dependent brake response in naturalistic rear-end emergencies

Should I Stay or Should I Go? Cognitive Modeling of Left-Turn Gap Acceptance Decisions in Human Drivers

OBJECTIVE

We aim to bridge the gap between naturalistic studies of driver behavior and modern cognitive and neuroscientific accounts of decision making by modeling the cognitive processes underlying left-turn gap acceptance by human drivers.

BACKGROUND

Understanding decisions of human drivers is essential for the development of safe and efficient transportation systems. Current models of decision making in drivers provide little insight into the underlying cognitive processes. On the other hand, laboratory studies of abstract, highly controlled tasks point towards noisy evidence accumulation as a key mechanism governing decision making. However, it is unclear whether the cognitive processes implicated in these tasks are as paramount to decisions that are ingrained in more complex behaviors, such as driving.

RESULTS

The drivers' probability of accepting the available gap increased with the size of the gap; importantly, response time increased with time gap but not distance gap. The generalized drift-diffusion model explained the observed decision outcomes and response time distributions, as well as substantial individual differences in those. Through cross-validation, we demonstrate that the model not only explains the data, but also generalizes to out-of-sample conditions.

CONCLUSION

Our results suggest that dynamic evidence accumulation is an essential mechanism underlying left-turn gap acceptance decisions in human drivers, and exemplify how simple cognitive process models can help to understand human behavior in complex real-world tasks.

APPLICATION

Potential applications of our results include real-time prediction of human behavior by automated vehicles and simulating realistic human-like behaviors in virtual environments for automated vehicles.

Modeling road user response timing in naturalistic traffic conflicts: A surprise-based framework

There is currently no established method for evaluating human response timing across a range of naturalistic traffic conflict types. Traditional notions derived from controlled experiments, such as perception-response time, fail to account for the situation-dependency of human responses and offer no clear way to define the stimulus in many common traffic conflict scenarios. As a result, they are not well suited for application in naturalistic settings. We present a novel framework for measuring and modeling response times in naturalistic traffic conflicts applicable to automated driving systems as well as other traffic safety domains. The framework suggests that response timing must be understood relative to the subject's current (prior) belief and is always embedded in, and dependent on, the dynamically evolving situation. The response process is modeled as a belief update process driven by perceived violations to this prior belief, that is, by surprising stimuli. The framework resolves two key limitations with traditional notions of response time when applied in naturalistic scenarios: (1) The strong situation dependence of response timing and (2) how to unambiguously define the stimulus. Resolving these issues is a challenge that must be addressed by any response timing model intended to be applied in naturalistic traffic conflicts. We show how the framework can be implemented by means of a relatively simple heuristic model fit to naturalistic human response data from real crashes and near crashes from the SHRP2 dataset and discuss how it is, in principle, generalizable to any traffic conflict scenario. We also discuss how the response timing framework can be implemented computationally based on evidence accumulation enhanced by machine learning-based generative models and the information-theoretic concept of surprise.

Exploratory analysis of evasion actions of powered two-wheeler conflicts at unsignalized intersection

The study investigates the braking and steering evasions of powered two-wheelers (PTWs) during severe conflicts observed at an unsignalized intersection. Traffic conflicts were detected using a surrogate safety indicator called anticipated collision time (ACT). Then the peak-over-threshold approach was used to identify the severe conflicts and the evasive actions. Conflicts between right-turning PTWs and through-moving vehicles, through-moving PTWs crossing through-moving vehicles, and merging/diverging PTWs were analyzed using the minimum ACT (ACTmin), maximum deceleration rate (DRmax), maximum yaw rate (YRmax), and time of evasive action (TEA). The evasive actions were classified into five categories: driver/rider error, no-evasion, braking-only, steering-only, and both braking and steering. Analysis reveals that right-turning PTWs experience higher crash risk (0.7 %) than the other movements. PTW riders primarily employ extreme steering maneuvers (greater than 13 degrees/s) to evade conflicts, whereas braking rates lie in the normal ranges (less than 1.5 m/s2). The time of evasive action varies between 2.04 and 2.44 s, with the right-turning PTW riders responding early. Through-moving riders commit errors while evading severe conflicts and perform fewer evasive actions than right-turning and merging/diverging riders. Right-turning riders perform more steering-only evasions than braking-only, whereas the riders involved in the other two conflicts execute more braking-only evasions. These findings suggest that conflict type influences riders' braking and steering responses. Hence, future applications in advanced driver/rider assistance systems and training programs should consider appropriate evasive action strategies for different conflict types.

Applying the Accumulator model to predict driver's reaction time based on looming in approaching and braking conditions

INTRODUCTION

The prediction of when the driver will react to a change in the lead vehicle motion is critical for assessing rear-end crash risk using car-following models. Past studies have assumed constant reaction time and driver's continuous reaction. However, these assumptions are not valid as the driver's reaction time can vary in different car-following situations and the driver does not continuously react to the lead vehicle motion. Thus, this study predicted the driver's reaction time using the Wiedemann car-following model and the Accumulator model. The Accumulator model assumes the driver's start of reaction based on the accumulation of looming and thereby reflects the driver's intermittent reaction.

METHOD

Fifty drivers' behavior was observed using a driving simulator in two scenarios: (1) approach and follow a moving lead vehicle and (2) approach a stopped lead vehicle. The Accumulator model predicted the reaction times based on different looming variables (angular velocity and tau-inverse), lead vehicle type (car and truck), and lead vehicle brake lights (on or off).

RESULTS

The Accumulator model showed lower prediction errors of the reaction time than the Wiedemann model, which assumes reaction based on the fixed looming threshold. The Accumulator model predicted the reaction times more accurately when it was calibrated with the angular velocity due to width and height of lead vehicles. Moreover, the Accumulator model with tau-inverse produced the smallest prediction error of reaction times among different Accumulator models and the Wiedemann model when lead vehicle brake lights were on.

CONCLUSIONS

This study demonstrates that the Accumulator model is a promising method of predicting the driver's reaction time in car-following situations, which affects rear-end crash risk.

PRACTICAL APPLICATIONS

The Accumulator model can be incorporated into a car-following model for the prediction of reaction times and can estimate the rear-end collision risk of vehicles more accurately.

Road traffic safety assessment in self-driving vehicles based on time-to-collision with motion orientation

Traffic conflict analysis based on Surrogate Safety Measures (SSMs) helps to estimate the risk level of an ego-vehicle interacting with other road users. Nonetheless, risk assessment for autonomous vehicles (AVs) is still incipient, given that most of the AVs are currently prototypes and current SSMs do not directly apply to autonomous driving styles. Therefore, to assess and quantify the potential risk arising from AV interactions with other road users, this study introduces the TTC_mo (Time-to-Collision with motion orientation), a metric that considers the yaw angle of conflicting objects. In fact, the yaw angle represents the orientation of the other road users and objects detected by the AV sensors, enabling a better identification of potential risk events from changes in the motion orientation and position through the geometric analysis of the boundaries for each detected object. Using the 3D object detection data annotations available from the publicly available AV datasets nuScenes and Lyft5 and the TTC_mo metric, we find that at least 8% of the interactions with objects detected around the AV present some risk level. This is meaningful, since it is possible to reduce the proportion of data analyzed by up to 60% when replacing regular TTC by our improved TTC computation.

Application of naturalistic driving data: A systematic review and bibliometric analysis

The application of naturalistic driving data (NDD) has the potential to answer critical research questions in the area of driving behavior assessment, as well as the impact of exogenous and endogenous factors on driver safety. However, the presence of a large number of research domains and analysis foci makes a systematic review of NDD applications challenging in terms of information density and complexity. While previous research has focused on the execution of naturalistic driving studies and on specific analysis techniques, a multifaceted aggregation of NDD applications in Intelligent Transportation System (ITS) research is still unavailable. In spite of the current body of work being regularly updated with new findings, evolutionary nuances in this field remain relatively unknown. To address these deficits, the evolutionary trend of NDD applications was assessed using research performance analysis and science mapping. Subsequently, a systematic review was conducted using the keywords "naturalistic driving data" and "naturalistic driving study data". As a result, a set of 393 papers, Published between January 2002-March 2022, was thematically clustered based on the most common application areas utilizing NDD. the results highlighted the relationship between the most crucial research domains in ITS, where NDD had been incorporated, and application areas, modeling objectives, and analysis techniques involving naturalistic databases.

Modeling driver's evasive behavior during safety-critical lane changes: Two-dimensional time-to-collision and deep reinforcement learning

Lane changes are complex driving behaviors and frequently involve safety-critical situations. This study aims to develop a lane-change-related evasive behavior model, which can facilitate the development of safety-aware traffic simulations and predictive collision avoidance systems. Large-scale connected vehicle data from the Safety Pilot Model Deployment (SPMD) program were used for this study. A new surrogate safety measure, two-dimensional time-to-collision (2D-TTC), was proposed to identify the safety-critical situations during lane changes. The validity of 2D-TTC was confirmed by showing a high correlation between the detected conflict risks and the archived crashes. A deep deterministic policy gradient (DDPG) algorithm, which could learn the sequential decision-making process over continuous action spaces, was used to model the evasive behaviors in the identified safety-critical situations. The results showed the superiority of the proposed model in replicating both the longitudinal and lateral evasive behaviors.

Repeated conditionally automated driving on the road: How do drivers leave the loop over time?

The goal of this on the road driving study was to investigate how drivers adapt their behavior when driving with conditional vehicle automation (SAE L3) on different occasions. Specifically, we focused on changes in how fast drivers took over control from automation and how their gaze off the road changed over time. On each of three consecutive days, 21 participants drove for 50 min, in a conditionally automated vehicle (Wizard of Oz methodology), on a typical German commuting highway. Over these rides the take-over behavior and gaze behavior were analyzed. The data show that drivers' reactions to non-critical, system initiated, take-overs took about 5.62 s and did not change within individual rides, but on average became 0.72 s faster over the three rides. After these self-paced take-over requests a final urgent take-over request was issued at the end of the third ride. In this scenario participants took over rapidly with an average of 5.28 s. This urgent take-over time was not found to be different from the self-paced take-over requests in the same ride. Regarding gaze behavior, participants' overall longest glance off the road and the percentage of time looked off the road increased within each ride, but stayed stable over the three rides. Taken together, our results suggest that drivers regularly leave the loop by gazing off the road, but multiple exposures to take-over situations in automated driving allow drivers to come back into loop faster.

Factors That Affect Drivers' Perception of Closing and an Immediate Hazard

OBJECTIVE

To measure the looming threshold for when drivers perceive closing and an immediate hazard and determine what factors affect these thresholds.

BACKGROUND

Rear-end collisions are a common type of crash. One key issue is determining when drivers first perceive they need to react. The looming threshold for closing and an immediate hazard are critical perceptual thresholds that reflect when drivers perceive they need to react.

METHOD

Two driving simulator experiments examined whether engaging in a cell phone conversation and whether the complexity of the roadway environment affect these thresholds for the perception of closing and immediate hazard. Half of the participants engaged in a cognitive task, the last letter task, to emulate a cell phone conversation, and all participants experienced both simple and complex roadway environments.

RESULTS

Drivers perceived an immediate hazard later when engaged in a cell phone conversation than when not engaged in a conversation but only when the driving task was relatively less demanding (e.g., simple roadway, slow closing velocity). Compared to simple scenes, drivers perceived closing and an immediate hazard later for complex scenes but only when closing velocity was 30 mph (48.28 km/h) or greater.

CONCLUSION

Cell phone conversation can affect when drivers perceive an immediate hazard when the roadway is less demanding. Roadway complexity can affect when drivers perceive closing and an immediate hazard when closing velocity is high.

APPLICATION

Results can aid accident analysis cases and the design of driving automation systems by suggesting when a typical driver would respond.

Differences in frequency of occurrence, event characteristics, and pre-impact vehicle kinematics between crashes, near-crashes, and single vehicle conflicts in a large-scale naturalistic driving study

Objective: Motor vehicle crashes result in egregious personal injury, mortality, and economic cost but are relatively rare in naturalistic observations. There is, however, evidence of strong relationships between crashes and less severe (but more common) "surrogate" events (e.g., near-crashes). Despite this strong relationship, there can still be some important differences in findings when these surrogate events are investigated in lieu of, or combined with, crashes. Therefore, it is relevant to describe and quantify differences between crashes and crash-surrogate events. Consequently, the focus of this investigation was to establish how crashes and crash surrogate events in a large-scale naturalistic driving study compare in terms of frequency of occurrence, event characteristics, and pre-impact vehicle kinematics.Methods: Crashes, near-crashes, and single-vehicle conflicts (SVCs) derived from the Second Strategic Highway Research Program Naturalistic Driving Study were coded to summarize the environmental and contributing variables involved. The original coding for these events was downsized to the variables of interest, and those variables underwent recoding to simplify the coded options. Additional variables based on the kinematic characteristics for each event were also derived and analyzed. Multinomial logistic regression was used to assess the contributions of these different variables toward classification of an event as a crash, near-crash, or SVC.Results: The regression model comparing crashes with near-crashes and SVCs identified several variables that allowed differentiation between crashes and these surrogates, primarily the pre-incident maneuver of the subject vehicle and the evasive maneuver that was executed by the driver. Kinematic variables prior to event onset, however, were not predictive of event outcome.Conclusions: The results suggest that important differences exist between crashes and their near-crash surrogates, and between crashes and SVCs. These results, however, should not discourage the analysis of surrogate events, which still provide useful information in prevention and mitigation of crash circumstances. This investigation highlights how crashes are different from two types of surrogate events and provides information that may allow for more precise analysis of these surrogate events in the future.

Can non-crash naturalistic driving data be an alternative to crash data for use in virtual assessment of the safety performance of automated emergency braking systems?

INTRODUCTION

Developers of in-vehicle safety systems need to have data allowing them to identify traffic safety issues and to estimate the benefit of the systems in the region where it is to be used, before they are deployed on-road. Developers typically want in-depth crash data. However, such data are often not available. There is a need to identify and validate complementary data sources that can complement in-depth crash data, such as Naturalistic Driving Data (NDD). However, few crashes are found in such data. This paper investigates how rear-end crashes that are artificially generated from two different sources of non-crash NDD (highD and SHRP2) compare to rear-end in-depth crash data (GIDAS).

METHOD

Crash characteristics and the performance of two conceptual automated emergency braking (AEB) systems were obtained through virtual simulations - simulating the time-series crash data from each data source.

RESULTS

Results show substantial differences in the estimated impact speeds between the artificially generated crashes based on both sources of NDD, and the in-depth crash data; both with and without AEB systems. Scenario types also differed substantially, where the NDD have many fewer scenarios where the following-vehicle is not following the lead vehicle, but instead catches-up at high speed. However, crashes based on NDD near-crashes show similar pre-crash criticality (time-to-collision) to in-depth crash data.

CONCLUSIONS

If crashes based on near-crashes are to be used in the design and assessment of preventive safety systems, it has to be done with great care, and crashes created purely from small amounts of everyday driving NDD are not of much use in such assessment.

PRACTICAL APPLICATIONS

Researchers and developers of in-vehicle safety systems can use the results from this study: (a) when deciding which data to use for virtual safety assessment of such systems, and (b) to understand the limitations of NDD.

Why Do Drivers' Collision Avoidance Maneuvers Tend to Cause SUVs to Sideslip or Rollover on Horizontal Curve and Grade Combinations?-An Analysis of the Causes Based on a Modified Multibody Dynamics Model

The extent to which drivers' collision avoidance maneuvers affect the safety margins of sideslip and rollover is not captured by road geometric design theory. To quantify the effects of drivers' collision avoidance maneuvers on the safety margins of sport utility vehicles (SUVs) on horizontal curve and grade combinations, a modified 8-degree-of-freedom multibody model based on SUVs was developed. The model was then used to calculate the design safety margins of sideslip and rollover for steady states and the actual safety margins for collision avoidance maneuvers. Subsequently, the design safety margin reduction rate (the difference between the design and actual safety margins divided by the design safety margin) was calculated and used to assess the safety margins. The results showed that the safety margins of SUVs were significantly reduced by braking, lane changing, and lane changing with braking. The marginal effects indicated that the greater the deceleration and the shorter the lane change duration, the greater the effect on the safety margins, particularly the sideslip safety margin. Furthermore, when the SUV was driven at 80 km·h^-1 on grades with a horizontal curve radius of 270 m and 400 m, the sideslip safety margin with emergency braking (deceleration over -4.5 m·s^-2) was reduced by 71% and 21%, and the rollover safety margin was reduced by 11% and 5%, respectively. Under these conditions, an emergency lane change (lane change duration less than 2 s) caused the SUV to sideslip and reduced the rollover safety margin by 47% (curve radius 270 m) and 45% (curve radius 400 m). Therefore, drivers' collision avoidance maneuvers are a factor that cannot be neglected in alignment design.

On the importance of driver models for the development and assessment of active safety: A new collision warning system to make overtaking cyclists safer

The total number of road crashes in Europe is decreasing, but the number of crashes involving cyclists is not decreasing at the same rate. When cars and bicycles share the same lane, cars typically need to overtake them, creating dangerous conflicts-especially on rural roads, where cars travel much faster than cyclists. In order to protect cyclists, advanced driver assistance systems (ADAS) are being developed and introduced to the market. One of them is a forward collision warning (FCW) system that helps prevent rear-end crashes by identifying and alerting drivers of threats ahead. The objective of this study is to assess the relative safety benefit of a behaviour-based (BB) FCW system that protects cyclists in a car-to-cyclist overtaking scenario. Virtual safety assessments were performed on crashes derived from naturalistic driving data. A series of driver response models was used to simulate different driver reactions to the warning. Crash frequency in conjunction with an injury risk model was used to estimate the risk of cyclist injury and fatality. The virtual safety assessment estimated that, compared to no FCW, the BB FCW could reduce cyclists' fatalities by 53-96% and serious injuries by 43-94%, depending on the driver response model. The shorter the driver's reaction time and the larger the driver's deceleration, the greater the benefits of the FCW. The BB FCW also proved to be more effective than a reference FCW based on the Euro NCAP standard test protocol. The findings of this study demonstrate the BB FCW's great potential to avoid crashes and reduce injuries in car-to-cyclist overtaking scenarios, even when the driver response model did not exceed a comfortable rate of deceleration. The results suggest that a driver behaviour model integrated into ADAS collision threat algorithms can provide substantial safety benefits.

Computational modeling of driver pre-crash brake response, with and without off-road glances: Parameterization using real-world crashes and near-crashes

When faced with an imminent collision threat, human vehicle drivers respond with braking in a manner which is stereotypical, yet modulated in complex ways by many factors, including the specific traffic situation and past driver eye movements. A computational model capturing these phenomena would have high applied value, for example in virtual vehicle safety testing methods, but existing models are either simplistic or not sufficiently validated. This paper extends an existing quantitative driver model for initiation and modulation of pre-crash brake response, to handle off-road glance behavior. The resulting models are fitted to time-series data from real-world naturalistic rear-end crashes and near-crashes. A stringent parameterization and model selection procedure is presented, based on particle swarm optimization and maximum likelihood estimation. A major contribution of this paper is the resulting first-ever fit of a computational model of human braking to real near-crash and crash behavior data. The model selection results also permit novel conclusions regarding behavior and accident causation: Firstly, the results indicate that drivers have partial visual looming perception during off-road glances; that is, evidence for braking is collected, albeit at a slower pace, while the driver is looking away from the forward roadway. Secondly, the results suggest that an important causation factor in crashes without off-road glances may be a reduced responsiveness to visual looming, possibly associated with cognitive driver state (e.g., drowsiness or erroneous driver expectations). It is also demonstrated that a model parameterized on less-critical data, such as near-crashes, may also accurately reproduce driver behavior in highly critical situations, such as crashes.

Keeping the driver in the loop through semi-automated or manual lane changes in conditionally automated driving

In the current study we investigated if drivers of conditionally automated vehicles can be kept in the loop through lane change maneuvers. More specifically, we examined whether involving drivers in lane-changes during a conditionally automated ride can influence critical take-over behavior and keep drivers' gaze on the road. In a repeated measures driving simulator study (n = 85), drivers drove the same route three times, each trial containing four lane changes that were all either (1) automated, (2) semi-automated or (3) manual. Each ride ended with a critical take-over situation that could be solved by braking and/or steering. Critical take-over reactions were analyzed with a linear mixed model and parametric accelerated failure time survival analysis. As expected, semi-automated and manual lane changes throughout the ride led to 13.5% and 17.0% faster maximum deceleration compared to automated lane changes. Additionally, semi-automated and manual lane changes improved the quality of the take-over by significantly decreasing standard deviation of the steering wheel angle. Unexpectedly, drivers in the semi-automated condition were slowest to start the braking maneuver. This may have been caused by the drivers' confusion as to how the semi-automated system would react. Additionally, the percentage gaze off-the-road was significantly decreased by the semi-automated (6.0%) and manual (6.6%) lane changes. Taken together, the results suggest that semi-automated and manual transitions may be an alarm-free instrument which developers could use to help maintain drivers' perception-action loop and improve automated driving safety.

A methodology for assessing driver perception-response time during unanticipated cross-centerline events

OBJECTIVE

The purpose of this study was to present a methodology that utilizes naturalistic driving data to measure the driver response to an unanticipated driving scenario, a cross-centerline event.

METHODS

Forward-facing video from naturalistic driving was used to determine when the cross-centerline event occurred. Then, the recorded acceleration and yaw rate data were used to identify the start of braking and steering evasive actions, respectively. A deceleration threshold of -0.1 g was defined as the braking onset, and a yaw rate of 2 deg/s was defined as the steering onset. Perception-response times (PRTs) were derived using these inputs.

RESULTS

17 cross-centerline events were identified from the naturalistic driving database. The drivers in all analyzed events applied the brakes, and 11 of the 17 drivers performed a steering maneuver. However, the average steering PRT (0.39 s) was faster than the average braking PRT (0.84 s).

CONCLUSIONS

Based naturalistic data from cross-centerline encroachment scenarios, the average driver steering PRT was faster than the average driver braking PRT. Both the driver's median braking and steering PRT was faster in these real-world scenarios than in similar test track or simulator studies. Future analyses should investigate which action is attempted first and the effect of time to contact on driver response.

Accumulation of continuously time-varying sensory evidence constrains neural and behavioral responses in human collision threat detection

Evidence accumulation models provide a dominant account of human decision-making, and have been particularly successful at explaining behavioral and neural data in laboratory paradigms using abstract, stationary stimuli. It has been proposed, but with limited in-depth investigation so far, that similar decision-making mechanisms are involved in tasks of a more embodied nature, such as movement and locomotion, by directly accumulating externally measurable sensory quantities of which the precise, typically continuously time-varying, magnitudes are important for successful behavior. Here, we leverage collision threat detection as a task which is ecologically relevant in this sense, but which can also be rigorously observed and modelled in a laboratory setting. Conventionally, it is assumed that humans are limited in this task by a perceptual threshold on the optical expansion rate-the visual looming-of the obstacle. Using concurrent recordings of EEG and behavioral responses, we disprove this conventional assumption, and instead provide strong evidence that humans detect collision threats by accumulating the continuously time-varying visual looming signal. Generalizing existing accumulator model assumptions from stationary to time-varying sensory evidence, we show that our model accounts for previously unexplained empirical observations and full distributions of detection response. We replicate a pre-response centroparietal positivity (CPP) in scalp potentials, which has previously been found to correlate with accumulated decision evidence. In contrast with these existing findings, we show that our model is capable of predicting the onset of the CPP signature rather than its buildup, suggesting that neural evidence accumulation is implemented differently, possibly in distinct brain regions, in collision detection compared to previously studied paradigms.

Impact of the connected vehicle environment on tunnel entrance zone

The drastic changes of the space environment at the tunnel entrance can lead to frequent accidents with higher levels. The connected vehicle environment provides drivers with surrounding traffic information and improve their driving behavior by helping them make safe decisions efficiently. As such, this study is to examine the effects of the connected vehicle environment on driving behavior and safety at the tunnel entrance zone. To this end, this research simulates a connected vehicle environment and provides driving aids through the Human-Machine Interface (HMI). Secondly, 40 participants with diverse backgrounds drove the simulator under two different driving conditions: HMI-OFF (traditional driving environment) and HMI-ON (connected vehicle environment). Finally, indicators are selected from speed control, stability and urgency to analyze the impact of the connected vehicle environment on drivers' behaviors and safety at the warning zone and tunnel entrance zone. The results show that in the connected vehicle environment, the drivers' speed control in the warning zone is improved and their deceleration behavior is advanced. The driver's speed control and stability are improved while the danger level of the accident is reduced 100 m ahead of the tunnel entrance. Besides, the driver's speed control and stability have been both improved 300 m after the tunnel entrance. Overall, in the connected vehicle environment, the driver can recognize the tunnel in advance and adjust his driving speed in time to ensure his safety at the tunnel entrance. The results of this study play a critical role in the design and research of warning systems in a connected vehicle environment, and will also guide vehicle manufacturers in designing safety-related functions of automated vehicles. In this research, a connected vehicle environment test platform based on driving simulation technology is constructed and tested in specific tunnel entrance scenarios, which provides a reference for realizing active protection of vehicles at the tunnel entrance.

Mining patterns of near-crash events with and without secondary tasks

The engagement of secondary tasks, like using a phone or talking to passengers while driving, could introduce considerable risks to driving safety. This study utilizes a near-crash dataset extracted from a naturalistic driving study to explore the patterns of near-crash events with or without the involvement of secondary tasks as a surrogate approach to understand the impact of these behaviors on traffic safety. The dataset contains information about driver behaviors, such as secondary tasks, vehicle maneuvers, other conflict vehicles' maneuvers before and during near-crash events, and the driving environment. The patterns for near-crashes with or without the involvement of secondary tasks are mined by adopting the apriori association rule algorithm. Finally, the mined rules for the near-crash events with or without the involvement of the secondary tasks are analyzed and compared. The results demonstrate that near-crashes with the involvement of secondary tasks often occur with drivers in a relatively stable and presumably predictable environment, such as an interstate highway with a constant speed. This type of near-crash is highly associated with the leading vehicle's sudden slowing or stopping since there is no expectation of any interruptions for these drivers performing the secondary tasks. The most common evasive maneuver in this kind of emergency is braking. Near-crashes without the involvement of secondary tasks is often associated with lane-changing behavior and sideswipe incidents. With shorter reaction time and awareness of the driving environment, the drivers in this type of near-crash can often make more complex maneuvers, like braking and steering, to avoid a collision. Understanding the patterns of these two types of near-crash incidents could help safety researchers, traffic engineers, and even vehicle designers/engineers develop countermeasures for minimizing potential collisions caused by secondary tasks or improper lane changing behaviors.

Steering or braking avoidance response in SHRP2 rear-end crashes and near-crashes: A decision tree approach

OBJECTIVE

The paper presents a systematic analysis of drivers' crash avoidance response during crashes and near-crashes and developed a machine learning-based predictive model that can determine driver maneuver using pre-incident driver behavior and driving context.

METHODS

We analyzed 286 naturalistic rear-end crashes and near-crashes from the SHRP2 naturalistic driving study. All the events were manually reduced using face video (face and forward) and kinematic responses. In this paper, we developed new reduction variables that enhanced the understanding of drivers' gaze behavior and roadway attention behavior during these events. These features reflected how the event criticality, measured using time to collision, related to drivers' pre-incident behavior (secondary behavior, gaze behavior), and drivers' perception of the event (physical reaction and maneuver). The imperative understanding of such relations was validated using a random forest- (RF) based classifier, which efficiently predicted if a driver was going to brake or change the lane as an avoidance maneuver.

RESULTS

The RF presented in this paper effectively explored the nonlinear patterns in the data and was highly accurate (∼96 %) in its prediction. A further analysis of the RF model showed that six features played a pivotal role in the decision logic. These included the drivers' last glance duration before the event, last glance eccentricity, duration of 'eyes on road' immediately before the event, the time instance and criticality when the driver perceives the threat as well as acknowledge the threat, and possibility of an escape path in the adjacent lane. Using partial dependency plots, we also showed how different thresholds of these feature variables determined the drivers' maneuver intention.

CONCLUSIONS

In this paper we analyzed driving context, drivers' behavior, event criticality, and drivers' response in a unified structure to predict their avoidance response. To the best of our knowledge, this is the first such effort where large-scale naturalistic data (crashes and near crashes) was analyzed for prediction of drivers' maneuver and determined key behavioral and contextual factors that contribute to this avoidance maneuver.

Exploring the combined effects of driving situations on freeway rear-end crash risk using naturalistic driving study data

The causes and the crash-generating processes of freeway rear-end (FRE) crashes are complicated. Previous studies have highlighted the many contributing factors to crash occurrences on freeways, such as traffic flow conditions, driver-following behavior, driver attention allocation, driver characteristics, the driving environment, and drivers' interactions with surrounding vehicles, etc. Nevertheless, few studies have looked into the combined effects of these factors on FRE crash risk as a whole. This study focuses on characterizing the sequential crash generating process of the interactions between traffic flow conditions, roadway attributes, driver behavior, event attributes, and precipitating events in FRE crashes. A sequential modeling framework for modeling the sequential and combined effects on FRE crash risk was constructed by applying structural equation modeling (SEM). The Second Highway Strategic Research Program (SHRP2) Naturalistic Driving Study (NDS) data was utilized for this purpose as this data provides extensive information concerning what happened before crashes and near-crashes. A total of 17 and 433 FRE crashes and near-crashes, respectively, were included in this study. It was found that (1) FRE crashes were associated with the sequential and combined effects of those factors above; (2) certain types of speed oscillations were identified as precursors to sudden braking when vehicles ahead decelerated or stopped-and-went; and (3) many factors were identified as being associated with driver perception time and crash occurrence.

Detection and response to critical lead vehicle deceleration events with peripheral vision: Glance response times are independent of visual eccentricity

Studies show high correlations between drivers' off-road glance duration or pattern and the frequency of crashes. Understanding drivers' use of peripheral vision to detect and react to threats is essential to modelling driver behavior and, eventually, preventing crashes caused by visual distraction. A between-group experiment with 83 participants was conducted in a high-fidelity driving simulator. Each driver in the experiment was exposed to an unexpected, critical, lead vehicle deceleration, when performing a self-paced, visual-manual, tracking task at different horizontal visual eccentricity angles (12°, 40° and 60°). The effect of visual eccentricity on threat detection, glance and brake response times was analyzed. Contrary to expectations, the driver glance response time was found to be independent of the eccentricity angle of the secondary task. However, the brake response time increased with increasing task eccentricity, when measured from the driver's gaze redirection to the forward roadway. High secondary task eccentricity was also associated with a low threat detection rate and drivers were predisposed to perform frequent on-road check glances while executing the task. These observations indicate that drivers use peripheral vision to collect evidence for braking during off-road glances. The insights will be used in extensions of existing driver models for virtual testing of critical longitudinal situations, to improve the representativeness of the simulation results.

Predicting driver reaction time and deceleration: Comparison of perception-reaction thresholds and evidence accumulation framework

Prediction of driver reaction to the lead vehicle motion based on the perception-reaction time (PRT) is critical for prediction of rear-end crash risk. This study determines PRT at various spacings in approaching and braking conditions, and examines the association of PRT and deceleration rate with crash risk. For these tasks, a total of 50 drivers' behavior was observed in a driving simulator experiment with 4 different scenarios - reaction to a decelerating lead vehicle, reaction to a stopped lead vehicle, perception of a lead vehicle's speed change, and perception of a slow-moving lead vehicle. The study tested three hypotheses of PRT including perception and reaction thresholds and the evidence accumulation framework using a visual variable (tau-inverse). It was found that the drivers neither reacted after a specific PRT from the start of perception nor reacted at a specific value of tau-inverse. Rather, the drivers generally reacted when the accumulation of evidence (tau-inverse) over time reached a threshold. It was also found that the magnitude of deceleration rate depends on the tau-inverse at the start of braking and hence, higher crash risk was associated with higher level of urgency and insufficient brake force rather than longer PRT. This study demonstrates that the evidence accumulation framework is a promising method of predicting driver reaction in approaching and braking conditions for different types of lead vehicle, and the level of urgency is important for predicting the probability of crash.

Predicting takeover response to silent automated vehicle failures

Current and foreseeable automated vehicles are not able to respond appropriately in all circumstances and require human monitoring. An experimental examination of steering automation failure shows that response latency, variability and corrective manoeuvring systematically depend on failure severity and the cognitive load of the driver. The results are formalised into a probabilistic predictive model of response latencies that accounts for failure severity, cognitive load and variability within and between drivers. The model predicts high rates of unsafe outcomes in plausible automation failure scenarios. These findings underline that understanding variability in failure responses is crucial for understanding outcomes in automation failures.

How Do Drivers Respond to Silent Automation Failures? Driving Simulator Study and Comparison of Computational Driver Braking Models

OBJECTIVE

This paper aims to describe and test novel computational driver models, predicting drivers' brake reaction times (BRTs) to different levels of lead vehicle braking, during driving with cruise control (CC) and during silent failures of adaptive cruise control (ACC).

BACKGROUND

Validated computational models predicting BRTs to silent failures of automation are lacking but are important for assessing the safety benefits of automated driving.

METHOD

Two alternative models of driver response to silent ACC failures are proposed: a looming prediction model, assuming that drivers embody a generative model of ACC, and a lower gain model, assuming that drivers' arousal decreases due to monitoring of the automated system. Predictions of BRTs issued by the models were tested using a driving simulator study.

RESULTS

The driving simulator study confirmed the predictions of the models: (a) BRTs were significantly shorter with an increase in kinematic criticality, both during driving with CC and during driving with ACC; (b) BRTs were significantly delayed when driving with ACC compared with driving with CC. However, the predicted BRTs were longer than the ones observed, entailing a fitting of the models to the data from the study.

CONCLUSION

Both the looming prediction model and the lower gain model predict well the BRTs for the ACC driving condition. However, the looming prediction model has the advantage of being able to predict average BRTs using the exact same parameters as the model fitted to the CC driving data.

APPLICATION

Knowledge resulting from this research can be helpful for assessing the safety benefits of automated driving.

Modelling discomfort: How do drivers feel when cyclists cross their path?

Many cyclist fatalities occur on roads when crossing a vehicle path. Active safety systems address these interactions. However, the driver behaviour models that these systems use may not be optimal in terms of driver acceptance. Incorporating explicit estimates of driver discomfort might improve acceptance. This study quantified the degree of discomfort experienced by drivers when cyclists crossed their travel path. Participants were instructed to drive through an intersection in a fixed-base simulator or on a test track, following the same experimental protocol. During the experiments, three variables were controlled: 1) the car speed (30, 50 km/h), 2) the bicycle speed (10, 20 km/h), and 3) the bicycle-car encroachment sequence (bicycle clears the intersection first, potential 50 %-overlap crash, and car clears the intersection first). For each trial, a covariate, the car's time-to-arrival at the intersection when the bicycle appears (TTA_vis), was calculated. After each trial, the participants were asked to report their experienced discomfort on a 7-point Likert scale ranging from no discomfort (1) to maximum discomfort (7). The effect of the three controlled variables and the effect of TTA_vis on drivers' discomfort were estimated using cumulative link mixed models (CLMM). Across both experimental environments, the controlled variables were shown to significantly influence discomfort. TTA_vis was shown to have a significant effect on discomfort as well; the closer to zero TTA_vis was (i.e., the more critical the situation), the more likely the driver reported great discomfort. The prediction accuracies of the CLMM with all three controlled variables and the CLMM with TTA_vis were similar, with an average accuracy between 40 and 50 % for the exact discomfort level and between 80 and 85 % allowing deviations by one step. Our model quantifies driver discomfort. Such model may be included in the decision-making algorithms of active safety systems to improve driver acceptance. In fact, by tuning system activation times depending on the expected level of discomfort that a driver would experience in such situation, a system is not likely to annoy a driver.

Adverse Behavioral Adaptation to Adaptive Forward Collision Warning Systems: An Investigation of Primary and Secondary Task Performance

Advanced driver assistance systems can effectively support drivers but can also induce unwanted effects in behavior. The present study investigates this adverse behavioral adaptation in adaptive Forward Collision Warning (FCW) systems. Other than conventional FCW systems that provide warnings based on static Time-To-Collision (TTC) thresholds, adaptive FCW systems consider the driver's need for support by adjusting warning thresholds according to distraction. A neglected question is how drivers adapt their behavior when they use adaptive FCW systems under realistic conditions, i.e., when warnings occur infrequently but system functionality is anticipated. Forty-eight participants drove with two different FCW systems (adaptive vs. non-adaptive) while working on a secondary in-vehicle task in a driving simulator. During the main part of the experiment, no brake events occurred and hence FCW functioning was largely anticipated. Additionally, visual system feedback about the driver's distraction state was manipulated between groups. Participants had significantly shorter minimal time-headways and TTCs when driving with the adaptive relative to the non-adaptive system. Participants with system feedback about distraction state spent generally more time with engaging in the secondary task. These results indicate behavioral adaptation which, however, is restricted to the task that is specifically supported by the system, namely longitudinal control.

How do drivers negotiate intersections with pedestrians? The importance of pedestrian time-to-arrival and visibility

Forward collision warning (FCW) and autonomous emergency braking (AEB) systems are increasingly available and prevent or mitigate collisions by alerting the driver or autonomously braking the vehicle. Threat-assessment and decision-making algorithms for FCW and AEB aim to find the best compromise for safety by intervening at the "right" time: neither too early, potentially upsetting the driver, nor too late, possibly missing opportunities to avoid the collision. Today, the extent to which activation times for FCW and AEB should depend on factors such as pedestrian speed and lane width is unknown. To guide the design of FCW and AEB intervention time, we employed a fractional factorial design, and determined how seven factors (crossing side, car speed, pedestrian speed, crossing angle, pedestrian size, zebra-crossing presence, and lane width) affect the driver's response process and comfort zone when negotiating an intersection with a pedestrian. Ninety-four volunteers drove through an intersection in a fixed-base driving simulator, which was based on open-source software (OpenDS). Several parameters, including pedestrian time-to-arrival and driver response time, were calculated to describe the driver response process and define driver comfort boundaries. Linear mixed-effect models showed that driver responses depended mainly on pedestrian time-to-arrival and visibility, whereas factors such as pedestrian size, zebra-crossing presence, and lane width did not significantly influence the driver response process. Drivers released the accelerator pedal in 99.8 % of the trials and braked in 89 % of the trials. Forty-six percent of the drivers changed their negotiation strategy (proportion of pedal braking to engine braking) to minimize driving effort over the course of the experiment. In fact, 51 % of the of the inexperienced drivers changed their response strategy whereas only 40 % of the experienced drivers did; nevertheless, all drivers behaved similarly, independent of driving experience. The flexible and customizable driving environment provided by OpenDS may be a viable platform for behavioural experiments in driving simulators. Results from this study suggest that visibility and pedestrian time-to-arrival are the most important variables for defining the earliest acceptable FCW and AEB activations. Fractional factorial design effectively compared the influence of seven factors on driver behaviour within a single experiment; however, this design did not allow in-depth data analysis. In the future, OpenDS might become a standard platform, enabling crowdsourcing and favouring repeatability across studies in traffic safety. Finally, this study advises future design and evaluation procedures (e.g. new car assessment programs) for FCW and AEB by highlighting which factors deserve further investigation and which ones do not.

Quantifying accident risk and severity due to speed from the reaction point to the critical conflict in fatal motorcycle accidents

In fatal road vehicle accidents motorcycles are overrepresented per vehicle kilometre travelled. Fatal accidents involving motorcycles contain mode specific characteristics, and in common with fatal accidents involving all road users, speed typically presents as a significant contributory factor. The aim of the present study is to provide quantitative estimates for the contribution of speed in situations commencing from the reaction location to the safety critical event involving a motorcyclist and resulting in a fatal accident. The contribution of speed to the resulting accident risk and accident severity is considered from this reaction point. A speed-squared versus stopping distance domain, termed the severity-risk space, is examined to determine the accident measures. The defined accident measures, namely, accident risk, accident severity and accident severity risk are calculated for sixteen fatal accidents from a police dataset of recent UK motorcycle accidents. The estimates of the defined measures are provided in terms relative to values estimated for the vehicle travelling at the speed limit at the safety critical event. The relative accident risk in response to a safety critical situation shows a partial speed dependent reaction phase and a speed-squared dependent braking phase and ranges from 1.3 to 2.8. The speed-squared dependent accident severity measure ranges from 1.4 to 7.3 at pre-impact speeds. The relative accident severity risk shows speed squared to speed cubed dependency components during the reaction phase and a speed to the power of four dependent braking phase and ranges from 2.3 to 22.8. In eight cases the collision would have been avoided had the motorcyclist been travelling at the speed limit at the critical point and in the other eight cases the relative accident severity at impact ranged from 1.4 to 17.2. The speed-squared versus stopping distance domain provides an informative parameter space for considering the accident risk and accident severity dimensions of road user accidents.

The role of off-path glances: A quantitative analysis of rear-end conflicts involving Chinese professional truck drivers as the striking partners

INTRODUCTION

Rear-end crashes are one of the most frequent crash types in China, leading to significant economic and societal losses. The development of active safety systems - such as Automatic Emergency Braking System (AEBS) - could avoid or mitigate the consequences of these crashes in Chinese traffic situations. However, a clear understanding of the crash causation mechanisms is necessary for the design of these systems.

METHOD

Manually coded variables were extracted from a naturalistic driving study conducted with commercial vehicles in Shanghai. Quantitative analyses of rear-end crashes and near crashes (CNC) were conducted to assess the prevalence, duration, and location of drivers' off-path glances, the influence of lead vehicle brake lights on drivers' last off-path glance, and driver brake onset, and the influence of off-path glances and kinematic criticality on drivers' response to conflicts.

RESULTS

The results indicate that the Chinese truck drivers in our study rarely engage in distracting activities involving a phone or other handheld objects while driving. Instead, they direct their off-path glances mainly toward the mirrors, and the duration of off-path glances leading to critical situations are shorter compared to earlier analyses performed in Western countries. The drivers also often keep small margins.

CONCLUSIONS

Overall, the combination of short time headway with off-path glances directed toward the mirror originates visual mismatches which, associated to a rapid change in the kinematic situation, cause the occurrence of rear-end CNC. When drivers look back toward the road after an off-path glance, a fast response seems to be triggered by lower values of looming compared to previous studies, possibly because of the short time headways. Practical Application: The results have practical implications for the development of driver models, for the design of active safety systems and automated driving, and for the design of campaigns promoting safe driving.

Toward Computational Simulations of Behavior During Automated Driving Takeovers: A Review of the Empirical and Modeling Literatures

OBJECTIVE

This article provides a review of empirical studies of automated vehicle takeovers and driver modeling to identify influential factors and their impacts on takeover performance and suggest driver models that can capture them.

BACKGROUND

Significant safety issues remain in automated-to-manual transitions of vehicle control. Developing models and computer simulations of automated vehicle control transitions may help designers mitigate these issues, but only if accurate models are used. Selecting accurate models requires estimating the impact of factors that influence takeovers.

METHOD

Articles describing automated vehicle takeovers or driver modeling research were identified through a systematic approach. Inclusion criteria were used to identify relevant studies and models of braking, steering, and the complete takeover process for further review.

RESULTS

The reviewed studies on automated vehicle takeovers identified several factors that significantly influence takeover time and post-takeover control. Drivers were found to respond similarly between manual emergencies and automated takeovers, albeit with a delay. The findings suggest that existing braking and steering models for manual driving may be applicable to modeling automated vehicle takeovers.

CONCLUSION

Time budget, repeated exposure to takeovers, silent failures, and handheld secondary tasks significantly influence takeover time. These factors in addition to takeover request modality, driving environment, non-handheld secondary tasks, level of automation, trust, fatigue, and alcohol significantly impact post-takeover control. Models that capture these effects through evidence accumulation were identified as promising directions for future work.

APPLICATION

Stakeholders interested in driver behavior during automated vehicle takeovers may use this article to identify starting points for their work.

Situational influences on response time and maneuver choice: Development of time-critical scenarios

Findings concerning drivers' response times to sudden events vary considerably across studies due to different experimental setups and situational characteristics, such as expectancy of an event and urgency to react. While response times are widely reported in the literature, understanding of drivers' choice of maneuvers in time-critical situations is limited. Standardized test scenarios could enhance the comparability of studies and help in attaining a better understanding of driver behavior in these situations. In an effort to achieve these improvements, three driving simulator studies (N = 131) were conducted to investigate drivers' response time and maneuver choice under a range of situational conditions. Each study took place in a specific environmental setting (urban, rural, and highway) and incorporated one unexpected and 12 subsequent events (increased expectancy). Four different time-critical scenarios were used to evoke different driver responses. In three scenarios, obstacles suddenly entered the roadway (braking, steering, or both possible). A fourth scenario comprised the sudden braking of a leading vehicle (only braking possible). Half of the drivers performed a cognitive secondary task. To validate the findings, results from an additional field test (N = 14) were compared to the results from the simulated urban environment. As expected, response choice was influenced by scenario characteristics (available braking distance and room for evasive maneuvers). Braking maneuvers were more frequent in settings with lower speed limits (urban) while steering maneuvers were found at higher speed limits (highway). Responses to suddenly appearing obstacles were fastest in the urban setting at 540-680 ms; these responses were 200-300 ms slower in the rural and highway settings. Response times increased by 100-200 ms when drivers responded to braking leading vehicles rather than obstacles. Braking responses were 200-350 ms slower and steering responses were 90-200 ms slower when drivers responded to an unexpected event rather than subsequent events. The cognitive secondary task had no significant effect. The simulated environment and the field test produced comparable response behavior. The current study provides reference numbers that help to establish a set of standardized test scenarios for future studies. On basis of this study, nine scenarios are recommended for the context of time-critical crash avoidance maneuvers. Such standardized test scenarios could improve the comparability of future studies on response time and maneuver choice.

Influence of Taillight Brightness on the Ability to Recognize Closing Speed, Closing Distance, and Closing vs. Separating

Rear-end crashes contribute to a large percentage of fatal collisions in the United States. However, every rear-end collision cannot be classified as a single type of crash. Some crashes may be caused due to human error while some crashes may be attributed to a human inability to recognize closing speed well. Observers were shown two 4-second video clips of a commercial vehicle closing on a slow-moving vehicle on an unlit highway. The lead vehicle was depicted at distances of 91m (300 ft), 128m (420 ft) and 152m (500 ft). Closing speeds of 40 km/h (25 mph) and 105 km/h (65 mph) were depicted. The taillights on the lead vehicle were randomly shown as bright, or 80% dimmer which is typical of older taillights or aged retroreflective materials. Results showed that observers’ ability to recognize closing from separating worsened with increased distance, dimmer taillights and lower closing speeds. Observers perceived brighter taillights to be closer. Also, at greater distances, observers did not recognize closing speeds as well.

Using perceptual cues for brake response to a lead vehicle: Comparing threshold and accumulator models of visual looming

Previous studies have shown the effect of a lead vehicle's speed, deceleration rate and headway distance on drivers' brake response times. However, how drivers perceive this information and use it to determine when to apply braking is still not quite clear. To better understand the underlying mechanisms, a driving simulator experiment was performed where each participant experienced nine deceleration scenarios. Previously reported effects of the lead vehicle's speed, deceleration rate and headway distance on brake response time were firstly verified in this paper, using a multilevel model. Then, as an alternative to measures of speed, deceleration rate and distance, two visual looming-based metrics (angular expansion rate θ˙ of the lead vehicle on the driver's retina, and inverse tau τ^-1, the ratio between θ˙ and the optical size θ), considered to be more in line with typical human psycho-perceptual responses, were adopted to quantify situation urgency. These metrics were used in two previously proposed mechanistic models predicting brake onset: either when looming surpasses a threshold, or when the accumulated evidence (looming and other cues) reaches a threshold. Results showed that the looming threshold model did not capture the distribution of brake response time. However, regardless of looming metric, the accumulator models fitted the distribution of brake response times better than the pure threshold models. Accumulator models, including brake lights, provided a better model fit than looming-only versions. For all versions of the mechanistic models, models using τ^-1 as the measure of looming fitted better than those using θ˙, indicating that the visual cues drivers used during rear-end collision avoidance may be more close to τ^-1.

A computational model for driver's cognitive state, visual perception and intermittent attention in a distracted car following task

We present a computational model of intermittent visual sampling and locomotor control in a simple yet representative task of a car driver following another vehicle. The model has a number of features that take it beyond the current state of the art in modelling natural tasks, and driving in particular. First, unlike most control theoretical models in vision science and engineering-where control is directly based on observable (optical) variables-actions are based on a temporally enduring internal representation. Second, unlike the more sophisticated engineering driver models based on internal representations, our model explicitly aims to be psychologically plausible, in particular in modelling perceptual processes and their limitations. Third, unlike most psychological models, it is implemented as an actual simulation model capable of full task performance (visual sampling and longitudinal control). The model is developed and validated using a dataset from a simplified car-following experiment (N = 40, in both three-dimensional virtual reality and a real instrumented vehicle). The results replicate our previously reported connection between time headway and visual attention. The model reproduces this connection and predicts that it emerges from control of action uncertainty. Implications for traffic psychological models and future developments for psychologically plausible yet computationally rigorous models of full natural task performance are discussed.

Crash probability estimation via quantifying driver hazard perception

Crash probability estimation is an important method to predict the potential reduction of crash probability contributed by forward collision avoidance technologies (FCATs). In this study, we propose a practical approach to estimate crash probability, which combines a field operational test and numerical simulations of a typical rear-end crash model. To consider driver hazard perception characteristics, we define a novel hazard perception measure, called as driver risk response time, by considering both time-to-collision (TTC) and driver braking response to impending collision risk in a near-crash scenario. Also, we establish a driving database under mixed Chinese traffic conditions based on a CMBS (Collision Mitigation Braking Systems)-equipped vehicle. Applying the crash probability estimation in this database, we estimate the potential decrease in crash probability owing to use of CMBS. A comparison of the results with CMBS on and off shows a 13.7% reduction of crash probability in a typical rear-end near-crash scenario with a one-second delay of driver's braking response. These results indicate that CMBS is positive in collision prevention, especially in the case of inattentive drivers or ole drivers. The proposed crash probability estimation offers a practical way for evaluating the safety benefits in the design and testing of FCATs.

Relationship Between Kinematic and Physiological Indices During Braking Events of Different Intensities

Objective To study the relationship between physiological indices and kinematic indices during braking events of different intensities. Background Based on mental workload theory, driving and other task demands may generate changes in physiological indices, such as the driver's heart rate and skin conductance. However, no attempts were made to associate changes in physiological indices with changes in vehicle kinematics that result from the driver attempts to meet task demands. Method Twenty-five drivers participated in a field experiment. We manipulated braking demands using roadside signs to communicate the speed (km/h) before braking (50 or 60) and the target speed for braking (30 or to a complete stop). In an additional session, we asked drivers to brake as if they were responding to an impending collision. We analyzed the relationship between the intensities of braking events as measured by deceleration values (g) and changes in heart rate, heart rate variability, and skin conductance. Results All physiological indices were associated with deceleration intensity. Especially salient were the differences in physiological indices between the intensive (|g| > 0.5) and nonintensive braking events. The strongest relationship was between braking intensity and skin conductance. Conclusions Skin conductance, heart rate, and heart rate variability can mirror the mental workload elicited by varying braking intensities. Application Associating vehicle kinematics with physiological indices related to short-term driving events may help improve the performance of driver assistance systems.

The effect of varying levels of vehicle automation on drivers' lane changing behaviour

Much of the Human Factors research into vehicle automation has focused on driver responses to critical scenarios where a crash might occur. However, there is less knowledge about the effects of vehicle automation on drivers' behaviour during non-critical take-over situations, such as driver-initiated lane-changing or overtaking. The current driving simulator study, conducted as part of the EC-funded AdaptIVe project, addresses this issue. It uses a within-subjects design to compare drivers' lane-changing behaviour in conventional manual driving, partially automated driving (PAD) and conditionally automated driving (CAD). In PAD, drivers were required to re-take control from an automated driving system in order to overtake a slow moving vehicle, while in CAD, the driver used the indicator lever to initiate a system-performed overtaking manoeuvre. Results showed that while drivers' acceptance of both the PAD and CAD systems was high, they generally preferred CAD. A comparison of overtaking positions showed that drivers initiated overtaking manoeuvres slightly later in PAD than in manual driving or CAD. In addition, when compared to conventional driving, drivers had higher deviations in lane positioning and speed, along with higher lateral accelerations during lane changes following PAD. These results indicate that even in situations which are not time-critical, drivers' vehicle control after automation is degraded compared to conventional driving.

Modelling how drivers respond to a bicyclist crossing their path at an intersection: How do test track and driving simulator compare?

Bicyclist fatalities are a great concern in the European Union. Most of them are due to crashes between motorized vehicles and bicyclists at unsignalised intersections. Different countermeasures are currently being developed and implemented in order to save lives. One type of countermeasure, active safety systems, requires a deep understanding of driver behaviour to be effective without being annoying. The current study provides new knowledge about driver behaviour which can inform assessment programmes for active safety systems such as Euro NCAP. This study investigated how drivers responded to bicyclists crossing their path at an intersection. The influences of car speed and cyclist speed on the driver response process were assessed for three different crossing configurations. The same experimental protocol was tested in a fixed-base driving simulator and on a test track. A virtual model of the test track was used in the driving simulator to keep the protocol as consistent as possible across testing environments. Results show that neither car speed nor bicycle speed directly influenced the response process. The crossing configuration did not directly influence the braking response process either, but it did influence the strategy chosen by the drivers to approach the intersection. The point in time when the bicycle became visible (which depended on the car speed, the bicycle speed, and the crossing configuration) and the crossing configuration alone had the largest effects on the driver response process. Dissimilarities between test-track and driving-simulator studies were found; however, there were also interesting similarities, especially in relation to the driver braking behaviour. Drivers followed the same strategy to initiate braking, independent of the test environment. On the other hand, the test environment affected participants' strategies for releasing the gas pedal and regulating deceleration. Finally, a mathematical model, based on both experiments, is proposed to characterize driver braking behaviour in response to bicyclists crossing at intersections. This model has direct implications on what variables an in-vehicle safety system should consider and how tests in evaluation programs should be designed.

How safe is tuning a radio?: using the radio tuning task as a benchmark for distracted driving

Drivers engage in non-driving tasks while driving, such as interactions entertainment systems. Studies have identified glance patterns related to such interactions, and manual radio tuning has been used as a reference task to set an upper bound on the acceptable demand of interactions. Consequently, some view the risk associated with radio tuning as defining the upper limit of glance measures associated with visual-manual in-vehicle activities. However, we have little knowledge about the actual degree of crash risk that radio tuning poses and, by extension, the risk of tasks that have similar glance patterns as the radio tuning task. In the current study, we use counterfactual simulation to take the glance patterns for manual radio tuning tasks from an on-road experiment and apply these patterns to lead-vehicle events observed in naturalistic driving studies. We then quantify how often the glance patterns from radio tuning are associated with rear-end crashes, compared to driving only situations. We used the pre-crash kinematics from 34 crash events from the SHRP2 naturalistic driving study to investigate the effect of radio tuning in crash-imminent situations, and we also investigated the effect of radio tuning on 2,475 routine braking events from the Safety Pilot project. The counterfactual simulation showed that off-road glances transform some near-crashes that could have been avoided into crashes, and glance patterns observed in on-road radio tuning experiment produced 2.85-5.00 times more crashes than baseline driving.

How to quantitatively evaluate safety of driver behavior upon accident? A biomechanical methodology

How to evaluate driver spontaneous reactions in various collision patterns in a quantitative way is one of the most important topics in vehicle safety. Firstly, this paper constructs representative numerical crash scenarios described by impact velocity, impact angle and contact position based on finite element (FE) computation platform. Secondly, a driver cabin model is extracted and described in the well validated multi-rigid body (MB) model to compute the value of weighted injury criterion to quantitatively assess drivers’ overall injury under certain circumstances. Furthermore, based on the coupling of FE and MB, parametric studies on various crash scenarios are conducted. It is revealed that the WIC (Weighted Injury Criteria) value variation law under high impact velocities is quite distinct comparing with the one in low impact velocities. In addition, the coupling effect can be elucidated by the fact that the difference of WIC value among three impact velocities under smaller impact angles tends to be distinctly higher than that under larger impact angles. Meanwhile, high impact velocity also increases the sensitivity of WIC under different collision positions and impact angles. Results may provide a new methodology to quantitatively evaluate driving behaviors and serve as a significant guiding step towards collision avoidance for autonomous driving vehicles.

Using naturalistic driving study data to investigate the impact of driver distraction on driver's brake reaction time in freeway rear-end events in car-following situation

INTRODUCTION

The rear-end crash is one of the most common freeway crash types, and driver distraction is often cited as a leading cause of rear-end crashes. Previous research indicates that driver distraction could have negative effects on driving performance, but the specific association between driver distraction and crash risk is still not fully revealed. This study sought to understand the mechanism by which driver distraction, defined as secondary task distraction, could influence crash risk, as indicated by a driver's reaction time, in freeway car-following situations.

METHOD

A statistical analysis, exploring the causal model structure regarding drivers' distraction impacts on reaction times, was conducted. Distraction duration, distraction scenario, and secondary task type were chosen as distraction-related factors. Besides, exogenous factors including weather, visual obstruction, lighting condition, traffic density, and intersection presence and endogenous factors including driver age and gender were considered.

RESULTS

There was an association between driver distraction and reaction time in the sample freeway rear-end events from SHRP 2 NDS database. Distraction duration, the distracted status when a leader braked, and secondary task type were related to reaction time, while all other factors showed no significant effect on reaction time.

CONCLUSIONS

The analysis showed that driver distraction duration is the primary direct cause of the increase in reaction time, with other factors having indirect effects mediated by distraction duration. Longer distraction duration, the distracted status when a leader braked, and engaging in auditory-visual-manual secondary task tended to result in longer reaction times.

PRACTICAL APPLICATIONS

Given drivers will be distracted occasionally, countermeasures which shorten distraction duration or avoid distraction presence while a leader vehicle brakes are worth considering. This study helps better understand the mechanism of freeway rear-end events in car-following situations, and provides a methodology that can be adopted to study the association between driver behavior and driving features.

Coming back into the loop: Drivers' perceptual-motor performance in critical events after automated driving

This driving simulator study, conducted as part of the EU AdaptIVe project, investigated drivers' performance in critical traffic events, during the resumption of control from an automated driving system. Prior to the critical events, using a between-participant design, 75 drivers were exposed to various screen manipulations that varied the amount of available visual information from the road environment and automation state, which aimed to take them progressively further 'out-of-the-loop' (OoTL). The current paper presents an analysis of the timing, type, and rate of drivers' collision avoidance response, also investigating how these were influenced by the criticality of the unfolding situation. Results showed that the amount of visual information available to drivers during automation impacted on how quickly they resumed manual control, with less information associated with slower take-over times, however, this did not influence the timing of when drivers began a collision avoidance manoeuvre. Instead, the observed behaviour is in line with recent accounts emphasising the role of scenario kinematics in the timing of driver avoidance response. When considering collision incidents in particular, avoidance manoeuvres were initiated when the situation criticality exceeded an Inverse Time To Collision value of ≈0.3s^-1. Our results suggest that take-over time and timing and quality of avoidance response appear to be largely independent, and while long take-over time did not predict collision outcome, kinematically late initiation of avoidance did. Hence, system design should focus on achieving kinematically early avoidance initiation, rather than short take-over times.

Influence of Taillight Width on the Ability to Recognize Closing Speed, Closing Distance, and Closing versus Separating

Front-to-rear crashes account for a large number of fatal collisions in the United States. While many of these crashes might be related to driver error (i.e. following too closely, inattention to traffic ahead, etc.) a cluster of these crashes are likely to be related to the human visual system limitations of depth perception during motion. Observers with valid CDL and non-commercial licenses were shown two 4-second video clips showing a slower moving vehicle ahead, referred to as the lead vehicle. The lead vehicle was depicted at distances of 91 m (300 ft) to 457 m (1500 ft) while closing at 72 km/h (45 mph). The lead vehicle was depicted on an unilluminated two-lane highway at night to allow the taillights to be the salient stimulus. The lead vehicle had either the standard taillights with a width of 1.7 m (5.4 ft) or narrowed taillights that were 0.4 m (1.43 ft) apart. The order in which each clip was viewed was counterbalanced. Observers consistently believed the narrower taillight configuration was farther away despite the vehicles’ headlights being on, allowing the entire vehicle width to be seen at distances closer than 128 m (420 ft). Also, observers perceived the wider taillight vehicle to be closing faster when viewing at distances closer than 128 m (420 ft). Drivers with CDL licenses performed no better or worse than non-commercial drivers which supports the hypothesis that crashes involving a high-speed vehicle closing on a slow moving or stopped vehicle might be related to human limitations, rather than driving experience, inattention or careless behavior.

Glass half-full: On-road glance metrics differentiate crashes from near-crashes in the 100-Car data

BACKGROUND

Much of the driver distraction and inattention work to date has focused on concerns over drivers removing their eyes from the forward roadway to perform non-driving-related tasks, and its demonstrable link to safety consequences when these glances are timed at inopportune moments. This extensive literature has established, through the analyses of glance from naturalistic datasets, a clear relationship between eyes-off-road, lead vehicle closing kinematics, and near-crash/crash involvement.

OBJECTIVE

This paper looks at the role of driver expectation in influencing drivers' decisions about when and for how long to remove their eyes from the forward roadway in an analysis that consider the combined role of on- and off-road glances.

METHOD

Using glance data collected in the 100-Car Naturalistic Driving Study (NDS), near-crashes were examined separately from crashes to examine how momentary differences in glance allocation over the 25-s prior to a precipitating event can differentiate between these two distinct outcomes. Individual glance metrics of mean single glance duration (MSGD), total glance time (TGT), and glance count for off-road and on-road glance locations were analyzed. Output from the AttenD algorithm (Kircher and Ahlström, 2009) was also analyzed as a hybrid measure; in threading together on- and off-road glances over time, its output produces a pattern of glance behavior meaningful for examining attentional effects.

RESULTS

Individual glance metrics calculated at the epoch-level and binned by 10-s units of time across the available epoch lengths revealed that drivers in near-crashes have significantly longer on-road glances, and look less frequently between on- and off- road locations in the moments preceding a precipitating event as compared to crashes. During on-road glances, drivers in near-crashes were found to more frequently sample peripheral regions of the roadway than drivers in crashes. Output from the AttenD algorithm affirmed the cumulative net benefit of longer on-road glances and of improved attention management between on- and off-road locations.

CONCLUSION

The finding of longer on-road glances differentiating between safety-critical outcomes in the 100-Car NDS data underscores the importance of attention management in how drivers look both on and off the road. It is in the pattern of glances to and from the forward roadway that drivers obtained critical information necessary to inform their expectation of hazard potential to avoid a crash.

APPLICATION

This work may have important implications for attention management in the context of the increasing prevalence of in-vehicle demands as well as of vehicle automation.

A quantitative driver model of pre-crash brake onset and control

An existing modelling framework is leveraged to create a driver braking model for use in simulations of critical longitudinal scenarios with a slower or braking lead vehicle. The model applies intermittent brake adjustments to minimize accumulated looming prediction error. It is here applied to the simulation of a set of lead vehicle scenarios. The simulation results in terms of brake initiation timing and brake jerk are demonstrated to capture well the specific types of kinematics-dependencies that have been recently reported from naturalistic near-crashes and crashes.

Effects of Cognitive Load on Driving Performance: The Cognitive Control Hypothesis

OBJECTIVE

The objective of this paper was to outline an explanatory framework for understanding effects of cognitive load on driving performance and to review the existing experimental literature in the light of this framework.

BACKGROUND

Although there is general consensus that taking the eyes off the forward roadway significantly impairs most aspects of driving, the effects of primarily cognitively loading tasks on driving performance are not well understood.

METHOD

Based on existing models of driver attention, an explanatory framework was outlined. This framework can be summarized in terms of the cognitive control hypothesis: Cognitive load selectively impairs driving subtasks that rely on cognitive control but leaves automatic performance unaffected. An extensive literature review was conducted wherein existing results were reinterpreted based on the proposed framework.

RESULTS

It was demonstrated that the general pattern of experimental results reported in the literature aligns well with the cognitive control hypothesis and that several apparent discrepancies between studies can be reconciled based on the proposed framework. More specifically, performance on nonpracticed or inherently variable tasks, relying on cognitive control, is consistently impaired by cognitive load, whereas the performance on automatized (well-practiced and consistently mapped) tasks is unaffected and sometimes even improved.

CONCLUSION

Effects of cognitive load on driving are strongly selective and task dependent.

APPLICATION

The present results have important implications for the generalization of results obtained from experimental studies to real-world driving. The proposed framework can also serve to guide future research on the potential causal role of cognitive load in real-world crashes.

Counterfactual simulations applied to SHRP2 crashes: The effect of driver behavior models on safety benefit estimations of intelligent safety systems

As the development and deployment of in-vehicle intelligent safety systems (ISS) for crash avoidance and mitigation have rapidly increased in the last decades, the need to evaluate their prospective safety benefits before introduction has never been higher. Counterfactual simulations using relevant mathematical models (for vehicle dynamics, sensors, the environment, ISS algorithms, and models of driver behavior) have been identified as having high potential. However, although most of these models are relatively mature, models of driver behavior in the critical seconds before a crash are still relatively immature. There are also large conceptual differences between different driver models. The objective of this paper is, firstly, to demonstrate the importance of the choice of driver model when counterfactual simulations are used to evaluate two ISS: Forward collision warning (FCW), and autonomous emergency braking (AEB). Secondly, the paper demonstrates how counterfactual simulations can be used to perform sensitivity analyses on parameter settings, both for driver behavior and ISS algorithms. Finally, the paper evaluates the effect of the choice of glance distribution in the driver behavior model on the safety benefit estimation. The paper uses pre-crash kinematics and driver behavior from 34 rear-end crashes from the SHRP2 naturalistic driving study for the demonstrations. The results for FCW show a large difference in the percent of avoided crashes between conceptually different models of driver behavior, while differences were small for conceptually similar models. As expected, the choice of model of driver behavior did not affect AEB benefit much. Based on our results, researchers and others who aim to evaluate ISS with the driver in the loop through counterfactual simulations should be sure to make deliberate and well-grounded choices of driver models: the choice of model matters.

Safety evaluation of intersections with dynamic use of exit-lanes for left-turn using field data

As a newly proposed unconventional intersection design, the exit-lanes for left-turn (EFL) intersection is found to be effective in increasing the intersection capacity with high level of application flexibility, especially under heavy left-turn traffic conditions. However, the operational safety of EFL is of most concern to the authority prior to its implementation. This paper evaluates the safety of the EFL intersections by studying the behavior of left-turn maneuvers using field data collected at 7 locations in China. A total of 22830 left-turn vehicles were captured, in which 9793 vehicles turned left using the mixed-usage area. Four potential safety problems, including the red-light violations, head-on collision risks, trapped vehicles, and rear-end crash risks, were discussed. Statistical analyses were carried out to compare the safety risk between the EFL intersection and the conventional one. Results indicate that the safety problems of EFL intersections mainly lie in higher percentages in red-light violations at the pre-signal (1.83% higher), wrong-way violation problems during the peak hours (the violation rate reaches up to 11.07%), and the lower travel speeds in the mixed-usage area (18.75% lower). Such risks can be counteracted, however, by providing more guiding information, installing cameras to investigate and punish violation maneuvers, and adjusting design parameter values for layout design and signal timing, respectively.