A rear-end collision risk assessment model based on drivers' collision avoidance process under influences of cell phone use and gender-A driving simulator based study

One-pedal or two-pedal: Does the regenerative braking system improve driving safety?

Electric vehicles equipped with regenerative braking systems provide drivers a new driving mode, the one-pedal mode, which enables drivers to accelerate and decelerate with the throttle alone. However, there is a lack of systematic research on driving behavior in one-pedal mode, and whether it actually enhances or reduces safety remains to be validated. A driving simulator was used to analyze driving behavior and safety in the one-pedal mode in situations with different urgency level, with the two-pedal mode (the traditional driving mode in internal combustion engine vehicles) serving as a comparative group. The driver's perception times, initial and final throttle release times, throttle to brake transition times, maximum brake pedal forces, collision ratios, and time-to-collision (TTC) were measured under the lead vehicle decelerating at 0.1 g, 0.2 g, 0.5 g, 0.75 g, as well as uncertainty (decelerating at 0.2 g to 25 km/h, then decelerating at 0.75 g to 0), and under headways of 1.5 s and 2.5 s. Results showed: 1) The regenerative braking system did not affect driver perception and reaction of the lead vehicle braking event and drivers extended throttle release to avoid rapid speed drops when the lead vehicle braked slowly; 2) the one-pedal mode exhibited a longer throttle to brake transition time and increased uncertainty in timing of brake pedal application; 3) the one-pedal mode was safer than the two-pedal mode in low urgency situations but became unsafe in high urgency or uncertain situations due to delayed braking. The implications of this research include enhancing regenerative braking systems and developing forward collision warning systems.

Analysis of speed reductions and crash risk of aggressive drivers during emergent pre-crash scenarios at unsignalized intersections

Aggressive driver behavior (ADB) is often linked with road crashes, especially during crash imminent situations. Previous studies demonstrated that ADB was positively correlated with collision risk; however, this relationship has not quantified evidently. This study aimed to analyze drivers' collision risk and speed reduction behavior during an emergent pre-crash scenario (such as a conflict encroaching into an unsignalized intersection at different critical time gaps) using a driving simulator. The effect of ADB on crash risk is investigated using the time to collision (TTC). Further, drivers' collision evasive behavior is analyzed using speed reduction time (SRT) survival probabilities. Fifty-eight Indian drivers are identified as aggressive, moderately aggressive, and, non-aggressive based on aggressive indicators such as vehicle kinematics (percentage of the time spent in speeding and rapid accelerations, maximum brake pressure, etc.). Two separate models are built to analyze ADB effects on TTC and SRT using a Generalized Linear Mixed Model (GLMM) and a Weibull Accelerated Failure Time (AFT) model, respectively. From the results, it can be observed that aggressive drivers' TTC and SRT are reduced by 82% and 38%, respectively. Compared to a 7 sec conflict approaching time gap, TTC is reduced by 18%, 39%, 51%, and 58% for 6 sec, 5 sec, 4 sec, and 3 sec conflict approaching time gaps, respectively. The estimated SRT survival probabilities for aggressive, moderately aggressive and non-aggressive drivers are 0%, 3% and 68% at 3 sec of conflict approaching time gap, respectively. SRT survival probability increased by 25% for matured drivers and decreased by 48% for drivers who tend to engage in frequent speeding. Important implications of the study findings are discussed.

Vehicle crash simulations for safety: Introduction of connected and automated vehicles on the roadways

Traffic accidents are one main cause of human fatalities in modern society. With the fast development of connected and autonomous vehicles (CAVs), there comes both challenges and opportunities in improving traffic safety on the roads. While on-road tests are limited due to their high cost and hardware requirements, simulation has been widely used to study traffic safety. To make the simulation as realistic as possible, real-world crash data such as crash reports could be leveraged in the creation of the simulation. In addition, to enable such simulations to capture the complexity of traffic, especially when both CAVs and human-driven vehicles co-exist on the road, careful consideration needs to be given to the depiction of human behaviors and control algorithms of CAVs and their interactions. In this paper, the authors reviewed literature that is closely related to crash analysis based on crash reports and to simulation of mixed traffic when CAVs and human-driven vehicles co-exist, for studying traffic safety. Three main aspects are examined based on our literature review: data source, simulation methods, and human factors. It was found that there is an abundance of research in the respective areas, namely, crash report analysis, crash simulation studies (including vehicle simulation, traffic simulation, and driving simulation), and human factors. However, there is a lack of integration between them. Future research is recommended to integrate and leverage different state-of-the-art transportation-related technologies to contribute to road safety by developing an all-in-one-step crash analysis system.

The effect of different mobile uses on crash frequency among young drivers: application of statistical models and clustering analysis

This study focuses on investigating the use of mobile phones among young drivers by employing an online questionnaire survey data. Ordinal logistic regression model was used for modelling the probabilities of crashes due to different uses of mobile phone while driving. Moreover, binary logistic regression models were used for predicting the probabilities of different uses of mobile phone. Logistic regression models revealed that texting and internet use have the same likelihood of causing crashes. Drivers having prior experience of being fined for mobile phone use, also showed a higher tendency to be involved in 2 crashes. Moreover, these drivers had a higher likelihood of being involved in texting, as compared to other uses of mobile phones. Drivers with more education had a higher tendency for internet use during driving. Drivers who use mobile phone for long periods during driving have a lesser tendency to get involved in texting, internet use or GPS navigation. Moreover, drivers with a previous crash record have less likelihood of being involved in texting. The models of this study can be useful in developing effective road safety measures. Clustering was also applied in this study which reinforced the findings of the statistical analysis and models.

Texting While Driving: A Literature Review on Driving Simulator Studies

Road safety is increasingly threatened by distracted driving. Studies have shown that there is a significantly increased risk for a driver of being involved in a car crash due to visual distractions (not watching the road), manual distractions (hands are off the wheel for other non-driving activities), and cognitive and acoustic distractions (the driver is not focused on the driving task). Driving simulators (DSs) are powerful tools for identifying drivers' responses to different distracting factors in a safe manner. This paper aims to systematically review simulator-based studies to investigate what types of distractions are introduced when using the phone for texting while driving (TWD), what hardware and measures are used to analyze distraction, and what the impact of using mobile devices to read and write messages while driving is on driving performance. The review followed the Preferred Reporting Items for Systematic Reviews and Meta-Analysis extension for Scoping Reviews (PRISMA-ScR) guidelines. A total of 7151 studies were identified in the database search, of which 67 were included in the review, and they were analyzed in order to respond to four research questions. The main findings revealed that TWD distraction has negative effects on driving performance, affecting drivers' divided attention and concentration, which can lead to potentially life-threatening traffic events. We also provide several recommendations for driving simulators that can ensure high reliability and validity for experiments. This review can serve as a basis for regulators and interested parties to propose restrictions related to using mobile phones in a vehicle and improve road safety.

An exploratory study of drivers' EEG response during emergent collision avoidance

INTRODUCTION

EEG (electroencephalogram) has been applied as a valuable measure to estimate drivers' mental status and cognitive workload during driving tasks. However, most previous studies have focused on the EEG features at particular driver status, such as fatigue or distraction, with less attention paid to EEG response in emergent and safety-critical situations. This study aims to investigate the underlying patterns of different EEG components during an emergent collision avoidance process.

METHOD

A driving simulator experiment was conducted with 38 participants (19 females and 19 males). The scenario included a roadside pedestrian who suddenly crossed the road when the driver approached. The participants' EEG data were collected during the pedestrian-collision avoidance process. The log-transformed power and power ratio of four typical EEG components (i.e., delta, theta, alpha and beta) were extracted from four collision avoidance stages: Stage 1-normal driving stage, Stage 2-hazard perception stage, Stage 3-evasive action stage, and Stage 4-post-hazard stage.

RESULTS

The activities of all four EEG bands changed consistently during the collision avoidance process, with the power increased significantly from Stage 1 to Stage 4. Drivers who collided with the pedestrian and drivers who avoided the collision successfully did not show a significant difference in EEG activity across the stages. Male drivers had a higher delta power ratio and lower alpha power ratio than females in both hazard perception and evasive action stages.

CONCLUSIONS

Enhanced activities of different EEG bands could be concurrent at emergent and safety-critical situations. Female drivers were more mentally aroused than male drivers during the collision avoidance process.

PRACTICAL APPLICATIONS

The study generates more understanding of drivers' neurophysiological response in an emergent and safety-critical collision avoidance event. Driver state monitoring and warning systems that aim to assist drivers in impending collisions may utilize the patterns of EEG activity identified in the collision avoidance process.

Talking on the Phone While Driving: A Literature Review on Driving Simulator Studies

Distracted driving is a growing concern around the world and has been the focus of many naturalistic and simulator-based studies. Driving simulators provide excellent practical and theoretical help in studying the driving process, and considerable efforts have been made to prove their validity. This research aimed to review relevant simulator-based studies focused on investigating the effects of the talking-on-the-phone-while-driving distraction on drivers' behavior. This work is a scoping review which followed the Preferred Reporting Items for Systematic Reviews and Meta-Analysis Extension for Scoping Reviews (PRISMA-ScR) guidelines. The search was performed on five databases, covering twenty years of research results. It was focused on finding answers to three research questions that could offer an overview of the main sources of distraction, the research infrastructure, and the measures that were used to analyze and predict the effects of distractions. A number of 4332 studies were identified in the database search, from which 83 were included in the review. The main findings revealed that TPWD distraction negatively affects driving performance, exposing drivers to dangerous traffic situations. Moreover, there is a general understanding that the driver's cognitive, manual, visual, and auditory resources are all involved, to a certain degree, when executing a secondary task while driving.

Driver operational level identification of driving risk and graded time-based alarm under near-crash conditions: A driving simulator study

Rear-end collision and side collision are two types of accidents with the highest accident rate in the world. Numerous studies have focused on rear-end accident research, but only a few constructive countermeasures are put forward. Driving risk evolution at the driver operational level before an accident is critical to collision avoidance. This paper puts forward a driver operational level identification of driving risk and graded alarm under near-crash conditions. Firstly, driving simulation is utilized to acquire the operation data of SV (subjective vehicle) under the condition of emergent deceleration of LV (leading vehicle). The kinematic model is built to characterize the law of the risk discrimination indices of SV including THW (time headway), SHW (space headway) and TTCi (the reciprocal of time to collision). The predicted results are consistent with the naturalistic driving data. Secondly, the three-dimensional distribution 'speed-spacing-TTCi' is applied to classify the risky driving state of SV. The precarious distribution is concentrated at the area where relative velocity increased to 23-40 km/h and spacing decreased to 18-30 m. Finally, based on the reaction time and braking distance reduction, the optimal external intervention is determined to be the acousto-optic way by driving simulation. For moderate drivers, a three-level alarm of 2.94 s, 1.94 s and 1.1 s is calibrated considering different driving styles and cumulative frequency curve of reaction time.

An improved automated braking system for rear-end collisions: A study based on a driving simulator experiment

INTRODUCTION

To assist drivers in avoiding rear-end collisions, many early warning systems have been developed up to date. Autonomous braking technology is also used as the last defense to ensure driver's safety.

METHOD

By taking the accuracy and timeliness of automatic system control into account, this paper proposes a rear-end Real-Time Autonomous Emergency Braking (RTAEB) system. The system inserts brake intervention based on drivers' real-time conflict identification and collision avoidance performance. A driving simulator-based experiment under different traffic conditions and deceleration scenarios were conducted to test the different thresholds to trigger intervention and the intervention outcomes. The system effectiveness is verified by four evaluation indexes, including collision avoidance rate, accuracy rate, sensitivity rate, and precision rate.

RESULTS

The results showed that the system could help avoid all collision events successfully and enlarge the final headway distance, and a TTC threshold of 1.5 s and a maximum deceleration threshold of -7.5 m/s² could achieve the best collision avoidance effect. The paper demonstrates the situations that are more inclined to trigger the RTAEB (i.e., a sudden brake of the leading vehicle and a small car-following distance). Moreover, the study shows that driver characteristics (i.e., gender and profession) have no significant association with system trigger. Practical Applications: The study suggests that development of collision avoidance systems design should pay attention to both the real-time traffic situation and drivers' collision avoidance capability under the present situation.

Effect of daily car-following behaviors on urban roadway rear-end crashes and near-crashes: A naturalistic driving study

The rear-end crash is one of the most common types of crashes, and key risk factors have been broadly identified in the car-following behaviors preceding a crash. However, the relationships between rear-end crash risk and daily car-following behaviors, or habits, have not been well examined. This study aims to identify the daily car-following behaviors on urban surface roads and urban expressways that have the most influence on rear-end crashes and near-crashes (CNC). Two months of naturalistic driving study data were used to investigate the daily car-following behavior of 54 drivers. A paired t-test and a Wilcoxon matched-pairs signed rank test were conducted to find the differences in behaviors on the two road types, and basic Poisson regression and Poisson hurdle regression models were used to explore significant risk factors. Results revealed that (1) drivers' longitudinal vehicle control, time control, and emergency behaviors are significantly different on urban surface roads and urban expressways; (2) for surface roads, three key influencing factors were ranked, in descending order, as the standard deviation of relative speed, percentage of time gap less than 1 s, and maximum acceleration; (3) for expressways, four key factors were ranked: minimum time gap, maximum deceleration, percentage of TTC less than 5 s, and the percentage of large positive jerk. The knowledge achieved on risky daily driving behaviors can be applied to training drivers to improve safe practices, assist insurance companies in creating usage-based insurance strategies, and support driver assistant systems design.

Effect of warning message on driver's stop/go decision and red-light-running behaviors under fog condition

The red-light-running (RLR) warning system has substantial potentials in helping drivers make proper stop/go decisions and reducing the RLR violations. Adverse foggy weather degrades drivers' performances and may also affect the effectiveness of the RLR warning system. However, limited research has been conducted regarding the impact of the RLR warning on driving performances under foggy weather. Thus, this study aims to explore drivers' decision-making process and RLR behaviors at intersection dilemma zones and evaluate the effectiveness of the RLR auditory-warning (RLR-AW) system in both fog and clear weather conditions. A concept of the RLR-AW system was proposed and designed in a driving simulator experiment. The simulated driving with the RLR-AW system was conducted in both clear and foggy weather conditions. The results show that drivers took compensation actions in fog while approaching the intersection, such as driving at lower speeds and using harder maximum brakes. The RLR-AW was able to reduce RLR rates in both clear and fog conditions, and drivers tended to respond more quickly and take smoother brake reactions with the RLR-AW provided. Moreover, the RLR-AW showed more remarkable influences on drivers' behaviors in fog with higher decrement in brake reaction time and maximum deceleration rate. Overall, findings of the study shed light on the design of in-vehicle RLR-AW system and highlight the necessity of drivers applying the system in adverse weather conditions.

Reaction time and brake pedal force after total knee replacement: timeframe for return to car driving

PURPOSE

This prospective cohort study aimed to examine objective and subjective parameters in patients who underwent total knee replacement (TKR) to assess from when on driving a car can be deemed safe again.

METHODS

Thirty patients (16 women, 14 men, age 66 ± 11 years) who received TKR of the right knee and 45 healthy controls (26 women, 19 men, age 32 ± 9 years) were asked to perform an emergency braking manoeuvre using a driving simulator. Brake pedal force (BPF), neuronal reaction time (NRT), brake reaction time (BRT), and subjective parameters (pain, subjective driving ability) were measured preoperatively as well as 5 days, 3-4, and 6 weeks after TKR.

RESULTS

Preoperative NRT was 506 ± 162 ms, BRT 985 ± 356 ms, and BPF 614 ± 292 N. NRT increased to 561 ± 218 ms, BRT to 1091 ± 404 ms and BPF decreased to 411 ± 191 N 5 days after TKR. Three weeks after surgery, NRT was 581 ± 164 ms and BRT 1013 ± 260 ms, while BPF increased to 555 ± 200 N. Only BPF showed significant differences (p < 0.01). In week 6, all parameters were restored to baseline levels; patients showed significant pain decrease and evaluated their driving ability as "good" again.

CONCLUSION

BPF was the only parameter displaying a significant postoperative decrease. However, preoperative patients' baseline levels and subjective confidence in driving ability were only reached 6 weeks after the operation. These results indicate that a minimum waiting period of 6 weeks should be considered before patients can safely participate in road traffic at their individual preoperative safety level again.

LEVEL OF EVIDENCE

II.

Assessing the impact of traffic signal performance on crash frequency for signalized intersections along urban arterials: A random parameter modeling approach

The recent development of Automated Traffic Signal Performance Measures (ATSPMs), has provided new opportunities and insights into traffic signal operations. As agencies begin to make decisions regarding investment in infrastructure and operation systems, it is imperative to understand the impacts these systems may have on safety. Past research has thoroughly investigated the impact of geometry and signal timing parameters on the safety of intersections, but little is understood on the relationship between improved signal performance and safety. This study uses vehicle trajectory data to create performance metrics for 121 signalized intersections on ten corridors near Columbus, Ohio. These metrics are used to understand the relationship between signal performance and safety. Two performance metrics, percent arrivals on green (POG) and level of travel time reliability (LOTTR), were used along with other volume and geometric data to model the total crash frequency on signalized mainline approaches. The crash data were modeled using a random parameters negative binomial approach. In consideration of potential unobserved heterogeneity between intersections, a correlated random parameters specification was tested alongside the traditional uncorrelated random parameters and fixed parameters model. Based on goodness of fit measures, the correlated random parameter model was chosen to interpret results because this model explains the complex cross-correlation among the estimates of random parameters. The elasticity values revealed a one percent increase in percent arrivals on green is associated with a reduction in total crashes by 1.12 %. The results of this study show the investment in signal operations and optimization result in an improvement in safety at signalized intersections. Further research should be explored to expand this study to additional intersections over a larger time period.

Driving impairments and duration of distractions: Assessing crash risk by harnessing microscopic naturalistic driving data

Distracted and impaired driving is a key contributing factor in crashes, leading to about 35% of all transportation-related deaths in recent years. Along these lines, cognitive issues like inattentiveness can further increase the chances of crash involvement. Despite its prevalence and importance, little is known about how the duration of these distractions is associated with critical events, such as crashes or near-crashes. With new sensors and increasing computational resources, it is possible to monitor drivers, vehicle performance, and roadway features to extract useful information, e.g., eyes off the road, indicating distraction and inattention. Using high-resolution microscopic SHRP2 naturalistic driving data, this study conducts in-depth analysis of both impairments and distractions. The data has more than 2 million seconds of observations in 7394 baselines (no event), 1228 near-crashes, and 617 crashes. The event data was processed and linked with driver behavior and roadway factors. The intervals of distracted driving during the period of observation (15 seconds) were extracted; next, rigorous fixed and random parameter logistic regression models of crash/near-crash risk were estimated. The results reveal that alcohol and drug impairment is associated with a substantial increase in crash/near-crash event involvement of 34%, and the highest correlations with crash risk include duration of distraction through dialing on a cellphone, texting while driving, and reaching for an object. Using detailed pre-crash data from instrumented vehicles, the study contributes by quantifying crash risk vis-à-vis detailed driving impairment and information on secondary task involvement, and discusses the implications of the results.

A Comparative Study of Accident Risk Related to Speech-Based and Handheld Texting during a Sudden Braking Event in Urban Road Environments

The use of mobile phones while driving is a very common phenomenon that has become one of the main causes of traffic accidents. Many studies on the effects of mobile phone use on accident risk have focused on conversation and texting; however, few studies have directly compared the impacts of speech-based texting and handheld texting on accident risk, especially during sudden braking events. This study aims to statistically model and quantify the effects of potential factors on accident risk associated with a sudden braking event in terms of the driving behavior characteristics of young drivers, the behavior of the lead vehicle (LV), and mobile phone distraction tasks (i.e., both speech-based and handheld texting). For this purpose, a total of fifty-five licensed young drivers completed a driving simulator experiment in a Chinese urban road environment under five driving conditions: baseline (no phone use), simple speech-based texting, complex speech-based texting, simple handheld texting, and complex handheld texting. Generalized linear mixed models were developed for the brake reaction time and rear-end accident probability during the sudden braking events. The results showed that handheld texting tasks led to a delayed response to the sudden braking events as compared to the baseline. However, speech-based texting tasks did not slow down the response. Moreover, drivers responded faster when the initial time headway was shorter, when the initial speed was higher, or when the LV deceleration rate was greater. The rear-end accident probability respectively increased by 2.41 and 2.77 times in the presence of simple and complex handheld texting while driving. Surprisingly, the effects of speech-based texting tasks were not significant, but the accident risk increased if drivers drove the vehicle with a shorter initial time headway or a higher LV deceleration rate. In summary, these findings suggest that the effects of mobile phone distraction tasks, driving behavior characteristics, and the behavior of the LV should be taken into consideration when developing algorithms for forward collision warning systems.

Evaluating the causal effects of cellphone distraction on crash risk using propensity score methods

INTRODUCTION/OBJECTIVE

This paper evaluates the causal effects of cellphone distraction on traffic crashes using propensity score weighting approaches and naturalistic driving study (NDS) data.

METHODS

We adopt three propensity score weighting approaches to estimate the causal odds ratio (OR) of cellphone use on three different event-populations, including average treatment effect (ATE) on the whole population, average treatment effect on the treated population (ATT), and average treatment effect on the overlapping population (ATO). Three types of cellphone distractions are evaluated: overall cellphone use, talking, and visual-manual tasks. The propensity scores are estimated based on driver, roadway, and environmental characteristics. The Second Strategic Highway Research Program NDS data used in this study include 3400 participant drivers with 1047 severe crashes and 19,798 random case-cohort control driving segments.

RESULTS

The study reveals several highly imbalanced potential confounding factors among cellphone use groups, e.g., income, age, and time of day, which could lead to biased risk estimation. All three propensity score approaches improve the balance of the baseline characteristics. The propensity score adjusted ORs differ from unweighted ORs substantially, ranging from -44.25% to 54.88%. Specifically, the adjusted ORs for young drivers are higher than unweighted ORs and these for middle-age drivers are lower. Among different cellphone related distractions, the ORs associated with visual-manual tasks (OR range: 3.47-6.63) are uniformly higher than overall cellphone distraction and cellphone talking (OR range: 0.63-4.15). Cellphone talking increases the risk for young drivers but has no significant impact on middle-age drivers.

CONCLUSION

Propensity score approaches effectively mitigate potential confounding effect caused by imbalanced driver environmental characteristics in the observational NDS data. The ATT and ATO estimands are recommended for NDS case-cohort studies. ATT reflects the effect among exposed events, i.e. crashes or controls with cellphone exposure and ATO reflects the effect among events with similar characteristics. The study confirms the significant causal effect of overall cellphone distraction on crash risk and the heterogeneity in safety impact by age group.

Cognitive anticipation cellular automata model: An attempt to understand the relation between the traffic states and rear-end collisions

We have investigated the accident's statistics of Europe and North America that are provided by the UN. This investigation has shown that accidents due to the traffic represent around 50 % of the total number of accidents every year. Among them, rear-end collisions hold a 20 % share. These numbers display the fact that the interaction between drivers can be held responsible of those incidents. In this respect, we have explored the reasons behind the conflict situations that may be responsible of the occurrence of rear-end collisions by the mean of a cognitive psychology based cellular automata model. Indeed, through field experiments performed by an embedded camera, we have extricated a psychological cognitive process of anticipation. We have defined the latter as the tendency of drivers to accelerate based on the history of their predecessor. Then, we have exploited the tools of the physics of traffic by which we have developed a CA-model that take into consideration this process. As a result, we were able to generate those incidents' situations. By considering two types of drivers: conservative who respect the learned information about the safe manoeuvres but make mistakes or aggressive who violate those secure processes, we have proved the complexity of the relationship between the states of the traffic flow and the drivers' behaviours. In fact, we have shown that rear-end collisions are a result of the anticipation as a response of the drivers to the traffic conditions: the congestion. Moreover, we have also highlighted an improvement of the flow in the congested state up to 11 % due to the anticipation, but that can only be achieved through vehicle-to-vehicle communication. Finally, we have investigated the hot spots. We have found that the traffic perturbations, that generate those hot spots and can be responsible of collisions, are more likely to be located away in the downstream direction. The distance between the two locations depends on the traffic density. This difference between the positions of the traffic perturbation and the hot spot has showcased the complexity, in time and space, of the transmission and the reception of deceleration information by the drivers.

What is the difference between perceived and actual risk of distracted driving? A field study on a real highway

Distracted driving is a leading cause of traffic accidents. It is influenced by driver attitude toward secondary tasks; however, field-based studies on the effects of low-perceived-risk tasks on lateral driving have rarely been reported. A total of 17 experienced non-professional drivers were recruited to participate in two secondary tasks: a cognitive experiment (conversation) and a visual distraction experiment (observation of following vehicles), each representing low-perceived-risk secondary tasks. One-way analysis of variance (ANOVA) was conducted to evaluate the effects of low-perceived-risk tasks on lateral driving performance. ANOVA results indicated that compared with baseline (no task) lateral performance, lane-keeping ability was enhanced during cognitive distractions. In the visual distraction experiment, more than 50% of the distractions required 1–2 s. Lane deviation and its growth rate increased with the duration of distraction. Compared with cognitive distraction, lane deviation increased significantly with visual distraction, and lane-keeping performance was seriously impaired. For low-perceived-risk tasks, visual distractions impaired driving safety more seriously, compared with cognitive distractions, suggesting that drivers misjudge the risks associated with visual tasks. These results can contribute to the design of advanced driving-assistance systems and improve professional driver programs, potentially reducing the frequency of traffic accidents caused by distracted driving.

Drivers' gap acceptance behaviours at intersections: A driving simulator study to understand the impact of mobile phone visual-manual interactions

Mobile phone use is often considered to be the main source of distraction on the road. Gap acceptance at intersections is a frequent and complex driving task that requires high visual attention from drivers. This study aims to investigate the effect of mobile phone use on the gap acceptance manoeuvre at intersections. Different mobile phone use positions, intersection type, gap size and driver characteristics were considered in the study. A total of 41 licenced drivers drove in an advanced driving simulator in three phone use conditions: baseline (no phone use), using the phone under the steering wheel (covert) and using the phone above the steering wheel (overt). Drivers drove the simulator three times and experienced two intersection types (straight-forward vs. left-turn) and two gap sizes (4 s vs. 7 s) during each drive. A parametric accelerated failure time (AFT) duration model was developed to evaluate the intersection crossing completion time of drivers. The results showed no significant difference of gap acceptance behaviours between the two phone use positions. The distraction task did not affect drivers' gap acceptance decision, but it increased the crossing completion time by over 10 % compared to baseline. Besides, drivers behaved conservatively at intersections while using a mobile phone, such as adopting a larger deceleration, waiting a longer time, and mainting a larger distance to the front vehicle, etc. However, these compensational behaviours were not helpful in improving the intersection traffic situation regarding both safety and efficiency. Intersection type and gap size were both significant factors of gap acceptance decision and crossing completion time. Additionally, younger drivers were more likely to accept a gap than older drivers, and female drivers spent longer time to cross the intersection than males.

Mobile Phone Use in a Car-Following Situation: Impact on Time Headway and Effectiveness of Driver's Rear-End Risk Compensation Behavior via a Driving Simulator Study

Mobile phone use while driving has become one of the leading causes of traffic accidents and poses a significant threat to public health. This study investigated the impact of speech-based texting and handheld texting (two difficulty levels in each task) on car-following performance in terms of time headway and collision avoidance capability; and further examined the relationship between time headway increase strategy and the corresponding accident frequency. Fifty-three participants completed the car-following experiment in a driving simulator. A Generalized Estimating Equation method was applied to develop the linear regression model for time headway and the binary logistic regression model for accident probability. The results of the model for time headway indicated that drivers adopted compensation behavior to offset the increased workload by increasing their time headway by 0.41 and 0.59 s while conducting speech-based texting and handheld texting, respectively. The model results for the rear-end accident probability showed that the accident probability increased by 2.34 and 3.56 times, respectively, during the use of speech-based texting and handheld texting tasks. Additionally, the greater the deceleration of the lead vehicle, the higher the probability of a rear-end accident. Further, the relationship between time headway increase patterns and the corresponding accident frequencies showed that all drivers’ compensation behaviors were different, and only a few drivers increased their time headway by 60% or more, which could completely offset the increased accident risk associated with mobile phone distraction. The findings provide a theoretical reference for the formulation of traffic regulations related to mobile phone use, driver safety education programs, and road safety public awareness campaigns. Moreover, the developed accident risk models may contribute to the development of a driving safety warning system.

How do drivers avoid collisions? A driving simulator-based study

INTRODUCTION

Drivers' collision avoidance performance in an impending collision situation plays a decisive role for safety outcomes. This study explored drivers' collision avoidance performances in three typical collision scenarios that were right-angle collision, head-on collision, and collision with pedestrian.

METHOD

A high-fidelity driving simulator was used to design the scenarios and conduct the experiment. 45 participants took part in the simulator experiment. Drivers' longitudinal/lateral collision avoidance performances and collision result were recorded.

RESULTS

Experimental results showed that brake only was the most common response among the three collision scenarios, followed by brake combining swerve in head-on and pedestrian collision scenarios. In right-angle collision scenario with TTC (time to collision) largest among three scenarios, no driver swerved, and meanwhile drivers who showed slow brake reaction tended to compensate the collision risk by taking a larger maximum deceleration rate within a shorter time. Swerve-toward-conflict was a prevalent phenomenon in head-on and pedestrian collision scenarios and significantly associated with collision risk. Drivers that swerved toward the conflict object had a shorter swerve reaction time than drivers that swerved away from conflict.

CONCLUSIONS

Long brake reaction time and wrong swerve direction were the main factors leading to a high collision likelihood. The swerve-toward-conflict maneuver caused a delay in brake action and degraded subsequent braking performances. The prevalent phenomenon indicated that drivers tended to use an intuitive (heuristic) way to make decisions in critical traffic situations. Practical applications: The study generated a better understanding of collision development and shed lights on the design of future advanced collision avoidance systems for semi-automated vehicles. Manufactures should also engage more efforts in developing active steering assistance systems to assist drivers in collision avoidance.

Determining the initial impact of rear-end collisions by trace evidence left on the vehicle from tires: A case report

If an automobile happens to crash into the back of another vehicle while travelling at high speeds, both vehicles will be seriously damaged. Consequently, it is not easy to reconstruct the initial collision state between the two vehicles or determine whether or not the risk perception of the driver is normal. The entire picture of the accident cannot be fully understood and thus clarifying the relevant legal responsibility is difficult. The trace evidence of tires, such as pattern, direction, and impression examination as well as other characteristics, can be carefully observed and used as evidence in accident reconstruction. A case report of a fatal collision involving a bus crashing into the frame of a full trailer on a freeway is examined in this study. The police agency used the characteristics of the trace evidence of the bus tires to reconstruct the initial collision state of the two vehicles to clarify the cause of the accident, and these determination guidelines can be used by police while handling similar cases in the future. This case uses new information regarding the initial collision state of road traffic accidents for reconstruction and provides knowledge and interest for the forensic community.

How does intersection field of view influence driving safety in an emergent situation?

Restricted intersection field of view (IFOV) can influence drivers' hazard detection abilities and driving safety in an emergent traffic event. However, no field studies or crash-data analyses have been conducted to prove the adequateness of the current intersection sight-distance design standards, which are adopted to ensure that the approaching-intersection drivers have a sufficient field of view to detect traffic hazards and travel safely at intersections. In this study, we conducted a driving simulator experiment to compare drivers' behavioral and eye-movement measures between different IFOV conditions that met the current intersection sight distance design standards. We examined the influencing mechanism of IFOV on the drivers' collision avoidance process being composed of three consequential stages, respectively in terms of search stage, decision stage and action stage. Our experiment results showed that restricted IFOV impacts the three-stage driving performance interlockingly. Enlarging IFOV can significantly improve drivers' performance in detecting a conflicting vehicle more timely, having a longer perception-reaction time in monitoring the hazard, spending more time on observing intersection surroundings, and taking brake actions earlier and more smoothly so that drivers were more likely to successfully avoid colliding with the conflicting vehicle. In addition, we found that compared with female drivers, male drivers were less likely to take brake actions to avoid a potential collision and had a lower deceleration rate in the braking stage of collision avoidance while there was no significant gender difference in crash involvement rates. The findings indicated that male drivers were more skillful in vehicle control than female drivers. Nevertheless, male drivers had less traffic-crash expectation, which degraded their overall crash avoidance effect. Considering the traffic safety that more than five million intersection-related crashes occur in American each year, these experimental findings have implications for public safety and health.

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.

Driving behavior recognition using EEG data from a simulated car-following experiment

Driving behavior recognition is the foundation of driver assistance systems, with potential applications in automated driving systems. Most prevailing studies have used subjective questionnaire data and objective driving data to classify driving behaviors, while few studies have used physiological signals such as electroencephalography (EEG) to gather data. To bridge this gap, this paper proposes a two-layer learning method for driving behavior recognition using EEG data. A simulated car-following driving experiment was designed and conducted to simultaneously collect data on the driving behaviors and EEG data of drivers. The proposed learning method consists of two layers. In Layer I, two-dimensional driving behavior features representing driving style and stability were selected and extracted from raw driving behavior data using K-means and support vector machine recursive feature elimination. Five groups of driving behaviors were classified based on these two-dimensional driving behavior features. In Layer II, the classification results from Layer I were utilized as inputs to generate a k-Nearest-Neighbor classifier identifying driving behavior groups using EEG data. Using independent component analysis, a fast Fourier transformation, and linear discriminant analysis sequentially, the raw EEG signals were processed to extract two core EEG features. Classifier performance was enhanced using the adaptive synthetic sampling approach. A leave-one-subject-out cross validation was conducted. The results showed that the average classification accuracy for all tested traffic states was 69.5% and the highest accuracy reached 83.5%, suggesting a significant correlation between EEG patterns and car-following behavior.

Red-Light-Running Crashes' Classification, Comparison, and Risk Analysis Based on General Estimates System (GES) Crash Database

Red-light running (RLR) has been identified as one of the prominent contributing factors involved in signalized intersection crashes. In order to reduce RLR crashes, primarily, a better understanding of RLR behavior and crashes is needed. In this study, three RLR crash types were extracted from the general estimates system (GES), including go-straight (GS) RLR vehicle colliding with go-straight non-RLR vehicle, go-straight RLR vehicle colliding with left-turn (LT) non-RLR vehicle, and left-turn RLR vehicle colliding with go-straight non-RLR vehicle. Then, crash features within each crash type scenario were compared, and risk analyses of GS RLR and LT RLR were also conducted. The results indicated that for the GS RLR driver, the speed limit displayed the highest effects on the percentages of GS RLR collision scenarios. For the LT RLR driver, the number of lanes displayed the highest effects on the percentages of LT RLR collision scenarios. Additionally, the drivers who were older than 50 years, distracted, and had a limited view were more likely to be involved in LT RLR accidents. Furthermore, the speeding drivers were more likely to be involved in GS RLR accidents. These findings could give a comprehensive understanding of RLR crash features and propensities for each RLR crash type.

Autonomous emergency braking systems adapted to snowy road conditions improve drivers' perceived safety and trust

OBJECTIVE

This study investigated drivers' evaluation of a conventional autonomous emergency braking (AEB) system on high and reduced tire-road friction and compared these results to those of an AEB system adaptive to the reduced tire-road friction by earlier braking. Current automated systems such as the AEB do not adapt the vehicle control strategy to the road friction; for example, on snowy roads. Because winter precipitation is associated with a 19% increase in traffic crashes and a 13% increase in injuries compared to dry conditions, the potential of conventional AEB to prevent collisions could be significantly improved by including friction in the control algorithm. Whereas adaption is not legally required for a conventional AEB system, higher automated functions will have to adapt to the current tire-road friction because human drivers will not be required to monitor the driving environment at all times. For automated driving functions to be used, high levels of perceived safety and trust of occupants have to be reached with new systems. The application case of an AEB is used to investigate drivers' evaluation depending on the road condition in order to gain knowledge for the design of future driving functions.

METHODS

In a driving simulator, the conventional, nonadaptive AEB was evaluated on dry roads with high friction (μ = 1) and on snowy roads with reduced friction (μ = 0.3). In addition, an AEB system adapted to road friction was designed for this study and compared with the conventional AEB on snowy roads with reduced friction. Ninety-six drivers (48 males, 48 females) assigned to 5 age groups (20-29, 30-39, 40-49, 50-59, and 60-75 years) drove with AEB in the simulator. The drivers observed and evaluated the AEB's braking actions in response to an imminent rear-end collision at an intersection.

RESULTS

The results show that drivers' safety and trust in the conventional AEB were significantly lower on snowy roads, and the nonadaptive autonomous braking strategy was considered less appropriate on snowy roads compared to dry roads. As expected, the adaptive AEB braking strategy was considered more appropriate for snowy roads than the nonadaptive strategy. In conditions of reduced friction, drivers' subjective safety and trust were significantly improved when driving with the adaptive AEB compared to the conventional AEB. Women felt less safe than men when AEB was braking. Differences between age groups were not of statistical significance.

CONCLUSIONS

Drivers notice the adaptation of the autonomous braking strategy on snowy roads with reduced friction. On snowy roads, they feel safer and trust the adaptive system more than the nonadaptive automation.