Vehicle-based studies of driving in the real world: the hard truth?

Development of sleepiness in professional truck drivers: Real-road testing for driver drowsiness and attention warning (DDAW) system evaluation

All new vehicle types within the European Union must now be equipped with a driver drowsiness and attention warning system starting from 2022. The specific requirements for the test procedure necessary for type approval are defined in the Annex of EU Regulation C/2021/2639. The objectives of this study were to: (i) investigate how sleepiness develops in professional truck drivers under real-road driving conditions; and (ii) assess the feasibility of a test procedure for validating driver drowsiness and attention warning systems according to the EU regulation. Twenty-four professional truck drivers participated in the test. They drove for 180 km on a dual-lane motorway, first during daytime after a normal night's sleep and then at nighttime after being awake since early morning. The results showed higher sleepiness levels during nighttime driving compared with daytime, with a faster increase in sleepiness with distance driven, especially during the night. Psychomotor vigilance task results corroborated these findings. From a driver drowsiness and attention warning testing perspective, the study design with sleep-deprived drivers at night was successful in inducing the targeted sleepiness level of a Karolinska Sleepiness Scale score of ≥ 8. Many drivers who reported a Karolinska Sleepiness Scale ≥ 8 during the drives also acknowledged feeling sleepy in the post-drive questionnaire. Reaching high levels of sleepiness on real roads during daytime is more problematic, not the least from legal and ethical perspectives as higher traffic densities during the daytime lead to increased risks.

Multimodal driver state modeling through unsupervised learning

Naturalistic driving data (NDD) can help understand drivers' reactions to each driving scenario and provide personalized context to driving behavior. However, NDD requires a high amount of manual labor to label certain driver's state and behavioral patterns. Unsupervised analysis of NDD can be used to automatically detect different patterns from the driver and vehicle data. In this paper, we propose a methodology to understand changes in driver's physiological responses within different driving patterns. Our methodology first decomposes a driving scenario by using a Bayesian Change Point detection model. We then apply the Latent Dirichlet Allocation method on both driver state and behavior data to detect patterns. We present two case studies in which vehicles were equipped to collect exterior, interior, and driver behavioral data. Four patterns of driving behaviors (i.e., harsh brake, normal brake, curved driving, and highway driving), as well as two patterns of driver's heart rate (HR) (i.e., normal vs. abnormal high HR), and gaze entropy (i.e., low versus high), were detected in these two case studies. The findings of these case studies indicated that among our participants, the drivers' HR had a higher fraction of abnormal patterns during harsh brakes, accelerating and curved driving. Additionally, free-flow driving with close to zero accelerations on the highway was accompanied by more fraction of normal HR as well as a lower gaze entropy pattern. With the proposed methodology we can better understand variations in driver's psychophysiological states within different driving scenarios. The findings of this work, has the potential to guide future autonomous vehicles to take actions that are fit to each specific driver.

Global lessons learned from naturalistic driving studies to advance traffic safety and operation research: A systematic review

The state of practice of investigating traffic safety and operation is primarily based on traditional data sources, such as spot sensors, loop detectors, and historical crash data. Recently, researchers have utilized transportation data from instrumented vehicles, driving simulators, and microsimulation modeling. However, these data sources might not represent the actual driving environment at a trajectory level and might introduce bias due to their experimental control. The shortcomings of these data sources can be overcome via Naturalistic Driving Studies (NDSs) considering the fact that NDS provides detailed real-time driving data that would help investigate the safety and operational impacts of human behavior along with other factors related to weather, traffic, and roadway geometry in a naturalistic setting. With the enormous potential of the NDS data, this study leveraged the Preferred Reporting Items for Systematic Reviews and Meta-Analyzes (PRISMA) approach to shortlist the most relevant naturalistic studies out of 2304 initial studies around the world with a focus on traffic safety and operation over the past fifteen years (2005-2020). A total of 117 studies were systematically reviewed, which were grouped into seven relevant topics, including driver behavior and performance, crash/near-crash causation, driver distraction, pedestrian/bicycle safety, intersection/traffic signal related studies, detection and prediction using NDSs data, based on their frequency of appearance in the keywords of these studies. The proper deployment of Connected and Autonomous Vehicles (CAV) require an appropriate level of human behavior integration, especially at the intimal stages where both CAV and human-driven vehicles will interact and share the same roadways in a mixed traffic environment. In order to integrate the heterogeneous nature of human behavior through behavior cloning approach, real-time trajectory-level NDS data is essential. The insights from this study revealed that NDSs could be effectively leveraged to perfect the behavior cloning to facilitate rapid and safe implementation of CAV.

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.

Profiling drivers to assess safe and eco-driving behavior - A systematic review of naturalistic driving studies

Road accidents and vehicular emissions are two significant issues related to road transportation, affecting both human life and the environment. Prior research suggests that driver behavior is a crucial factor in the majority of road crashes and is a significant factor influencing fuel consumption and vehicle emission. Significant improvement in driving behavior can be achieved by providing feedback to drivers about their driving behavior. An increasing interest among researchers to identify risky and non-economical driving maneuvers has led to the development of driver behavior profiling, i.e., rating/categorizing drivers into different categories based on how they drive. To get an insight into different parameters and methodology adopted by researchers for categorizing drivers into different categories, this paper presents a systematic review of studies on driver behavior profiling. In the present paper, PRISMA approach was adopted to shortlist the most relevant studies for systematic review out of 1231 initial studies, which were extracted using the relevant keywords. The findings from our study suggest that the selection of parameters for profiling the driver will depend on the application of the profiling scheme, type of device used for extracting data, and importance of parameter in rating criteria. Further, the findings suggest that significant improvement in driving behavior can be achieved by providing feedback to the drivers about their driving behavior and by implementing usage-based insurance schemes. It is also suggested that future studies shall focus on using smartphone devices for the collection of driver data as smartphones are nowadays easily accessible to everyone.

Investigating On-Road Lane Maintenance and Speed Regulation in Post-Stroke Driving: A Pilot Case-Control Study

Stroke can adversely affect the coordination and judgement of drivers due to executive dysfunction, which is relatively common in the post-stroke population but often undetected. Quantitatively examining vehicle control performance in post-stroke driving becomes essential to inspect whether and where post-stroke older drivers are risky. To date, it is unclear as to which indicators, such as lane keeping or speed control, can differentiate the driving performance of post-stroke older drivers from that of normal (neurotypical) older drivers. By employing a case-control design using advanced vehicle movement tracking and analysis technology, this pilot study aimed to compare the variations in driving trajectory, lane keeping and speed control between the two groups of older drivers using spatial and statistical techniques. The results showed that the mean standard deviation of lane deviation (SDLD) in post-stroke participants was higher than that of normal participants in complex driving tasks (U-turn and left turn) but almost the same in simple driving tasks (straight line sections). No statistically significant differences were found in the speed control performance. The findings indicate that, although older drivers can still drive as they need to after a stroke, the decline in cognitive abilities still imposes a higher cognitive workload and more effort for post-stroke older drivers. Future studies can investigate post-stroke adults' driving behaviour at more challenging driving scenarios or design driving intervention programs to improve their executive function in driving.

Driver speed compliance following automatic incident detection: Insights from a naturalistic driving study

Automatic incident detection (AID) systems and variable speed limits (VSLs) can reduce crash probability and traffic congestion. Studies based on loop detector data have shown that AID systems decrease the variation in speeds between drivers. Despite the impact on driver behaviour characteristics, most mathematical models evaluating the effect of AID systems on traffic operations do not capture driver response realistically. This study examines the main factors related to driver speed compliance with a sequence of three VSLs triggered by an AID system. For this purpose, the variable speed limit database of the executive agency of the Dutch Ministry of Infrastructure and Water Management (Rijkswaterstaat) was integrated into the UDRIVE naturalistic driving database for passenger car data collected in the Netherlands. The video data were annotated to analyse driver glance behaviour and secondary task engagement. A logistic regression model was estimated to predict driver speed compliance after each VSL in the sequence. The results reveal that the factors predicting compliance to the VSLs differ based on which of the three VSLs the driver is subjected to. Low speeds and accelerations before the gantry, approaching a slower leader, high proportion of time with eyes-on-road and close consecutive gantries were associated with high compliance with the first VSL in the sequence (i.e., indicating a speed limit of 70 km/h with flashing attention lights). Low speeds and accelerations before the gantry, close consecutive gantries and a small number of lanes resulted in high compliance with the second VSL (i.e., a speed limit of 50 km/h with flashing attention lights). Low speeds before the gantry and close consecutive gantries were linked to high compliance with the third VSL (i.e., indicating a speed limit of 50 km/h). Although further investigations based on a larger sample are needed, these findings are relevant to the development of human-like driving assistance systems and of traffic simulations that assess the impact of AID systems on traffic operations realistically.

Finding manoeuvre motifs in vehicle telematics

Driving behaviour has a great impact on road safety. A popular way of analysing driving behaviour is to move the focus to the manoeuvres as they give useful information about the driver who is performing them. In this paper, we investigate a new way of identifying manoeuvres from vehicle telematics data, through motif detection in time-series. We implement a modified version of the Extended Motif Discovery (EMD) algorithm, a classical variable-length motif detection algorithm for time-series and we applied it to the UAH-DriveSet, a publicly available naturalistic driving dataset. After a systematic exploration of the extracted motifs, we were able to conclude that the EMD algorithm was not only capable of extracting simple manoeuvres such as accelerations, brakes and curves, but also more complex manoeuvres, such as lane changes and overtaking manoeuvres, which validates motif discovery as a worthwhile line for future research.

A critical overview of driver recording tools

INTRODUCTION

Technological advancements during recent decades have led to the development of a wide array of tools and methods in order to record driving behavior and measure various aspects of driving performance. The aim of the present study is to present and comparatively assess the various driver recording tools that researchers have at their disposal.

METHOD

In order to achieve this aim, a multitude of published studies from the international literature have been examined based on the driver recording methodologies that have been implemented. An examination of more traditional survey methods (questionnaires, police reports, and direct observer methods) is initially conducted, followed by investigating issues pertinent to the use of driving simulators. Afterwards, an extensive section is provided for naturalistic driving data tools, including the utilization of on-board diagnostics (OBD) and in-vehicle data recorders (IVDRs). Lastly, in-depth incident analysis and the exploitation of smartphone data are discussed.

RESULTS

A critical synthesis of the results is conducted, providing the advantages and disadvantages of utilizing each tool and including additional knowledge regarding ease of experimental implementation, data handling issues, impacts on subsequent analyses, as well as the respective cost parameters.

CONCLUSIONS

New technologies provide undeniably powerful tools that allow for seamless data handling, storage, and analysis, such as smartphones and in-vehicle data recorders. However, this sometimes comes at considerable costs (which may or may not pay off at a later stage), while legacy driver recording methods still have their own niches to fill in research. Practical Applications: The present research supports researchers when designing driver behavior monitoring studies. The present work enables better scheduling and pacing of research activities, but can also provide insights for the distribution of research funds.

Computer-based hazard perception test scores are associated with the frequency of heavy braking in everyday driving

Computer-based hazard perception tests are used in a number of countries as part of the driver licensing processes, and hence evaluating the validity of such tests is crucial. One strategy for assessing the validity of the scores generated by a hazard perception test is to determine whether they can predict on-road driving performance. Only a few prior studies have attempted this, all relying on the subjective ratings of an examiner who was present during a single brief drive and was not blind to the driver's demographic characteristics, potentially contaminating the outcomes. Additionally, only one such study focused on the most relevant participant group with respect to the validity of tests used in licencing processes, namely young drivers. We sought to remedy this situation in the present project by measuring young drivers' performance over an extended period of everyday driving via g-force triggered video cameras ("dashcams") installed in their own vehicles. As a precursor to the dashcam study itself, we developed a new computerized hazard perception test and assessed the validity of its scores by more traditional means (Study 1). As expected, test scores distinguished between high-risk and lower-risk driver groups, and correlated with scores on an established hazard perception test previously shown to predict crash risk. In the subsequent dashcam study (Study 2), the frequency of heavy-braking events (controlling for distance driven) was used as a more objective measure of driving performance. Results indicated that drivers with higher rates of heavy braking had slower hazard perception response times, further supporting the use of these scores as a valid measure of drivers' ability to exercise hazard perception skill during real driving. More generally, this study also demonstrates the viability of using low-cost off-the-shelf dashcams to measure real-world driving behaviour.

Creating the environment for driver distraction: A thematic framework of sociotechnical factors

As modern society becomes more reliant on technology, its use within the vehicle is becoming a concern for road safety due to both portable and built-in devices offering sources of distraction. While the effects of distracting technologies are well documented, little is known about the causal factors that lead to the drivers' engagement with technological devices. The relevance of the sociotechnical system within which the behaviour occurs requires further research. This paper presents two experiments, the first aims to assess the drivers self-reported decision to engage with technological tasks while driving and their reasoning for doing so with respect to the wider sociotechnical system. This utilised a semi-structured interview method, conducted with 30 drivers to initiate a discussion on their likelihood of engaging with 22 different tasks across 7 different road types. Inductive thematic analysis provided a hierarchical thematic framework that detailed the self-reported causal factors that influence the drivers' use of technology whilst driving. The second experiment assessed the relevance of the hierarchical framework to a model of distraction that was established from within the literature on the drivers use of distracting technologies while driving. The findings provide validation for some relationships studied in the literature, as well as providing insights into relationships that require further study. The role of the sociotechnical system in the engagement of distractions while driving is highlighted, with the causal factors reported by drivers suggesting the importance of considering the wider system within which the behaviour is occurring and how it may be creating the conditions for distraction to occur. This supports previous claims made within the literature based model. Recommendations are proposed that encourage a movement away from individual focused countermeasures towards systemic actors.

Mind the gap: Drivers underestimate the impact of the behaviour of other traffic on their workload

This study examines the effect of traffic demand on driver workload by varying a range of characteristics of traffic behaviour, in particular focusing on the influence of a lane change performed by a neighbouring vehicle. To examine drivers' ability to manage their own workload in these traffic situations, a self-initiated, surrogate mobile phone task was presented to them, to coincide with changes in traffic demand. Results showed that whilst participants delayed the initiation of the task when the lane change was performed in close proximity to them, the delay was insufficient to mitigate the effects of the increased workload, leading to task errors. This was attributed to driver's willingness to engage in secondary tasks, even though their (self-reported) workload had not returned to baseline levels. The minimum workload recovery period was calculated as being 12 s after the onset of the adjacent vehicle's manoeuvre, and this has implications for the design of workload managers.

Crashing Left vs. Right: Examining Navigation Asymmetries Using the SHRP2 Naturalistic Driving Study Data

The magnitude of leftward bias demonstrated in pseudoneglect has been found to differ between younger and older adults in laboratory settings. The objective of this study was to examine the association between age and asymmetries in navigation in a naturalistic setting by examining the frequency of the location of impact on participants' vehicles during crashes and near crashes. The location of impact following crashes and near crashes, and participant's age and gender were retrieved from the SHRP2 NDS database, a large scale naturalistic driving study. Over the course of the study, data were collected from 3,546 participants driving in the United States of America (right-side traffic directionality), which included 1,465 crashes and 2,722 near crashes. During crashes and near crashes, irrespective of age, the location impact was most often on the front side of the participant vehicle. In contrast with results from laboratory environments, age was not associated with the location of impact during crashes and near crashes, and overall, crashes were over-represented on the left side of the vehicle compared to the right. Specifically, crashes were 1.41 times as likely to occur on the left compared to the right side of participants' vehicles. Overall, these findings inform future research that attempts to apply laboratory research, regarding asymmetry in navigation, to naturalistic settings.

Is it really good to talk? Testing the impact of providing concurrent verbal protocols on driving performance

Questions have been raised regarding the impact that providing concurrent verbal protocols has on task performance in various settings; however, there has been little empirical testing of this in road transport. The aim of this study was to examine the impact of providing concurrent verbal protocols on driving performance. Participants drove an instrumented vehicle around a set route, twice whilst providing a concurrent verbal protocol, and twice without. A comparison revealed no differences in behaviour related to speed, braking and steering wheel angle when driving mid-block, but a significant difference in aspects of braking and acceleration at roundabouts. When not providing a verbal protocol, participants were found to brake harder on approach to a roundabout and accelerate more heavily coming out of roundabouts. It is concluded that providing verbal protocols may have a positive effect on braking and accelerating. Practical implications related to driver training and future research are discussed. Practitioner Summary: Verbal protocol analysis is used by ergonomists to understand aspects of cognition and decision-making during complex tasks such as driving and control room operation. This study examines the impact that it has on driving performance, providing evidence to support its continued use in ergonomics applications.

Use of adaptive cruise control functions on motorways and urban roads: Changes over time in an on-road study

The study aimed at investigating how drivers use Adaptive Cruise Control and its functions in distinct road environments and to verify if changes occur over time. Fifteen participants were invited to drive a vehicle equipped with a Stop & Go Adaptive Cruise Control system on nine occasions. The course remained the same for each test run and included roads on urban and motorway environments. Results showed significant effect of experience for ACC usage percentage, and selection of the shortest time headway value in the urban road environment. This indicates that getting to know a system is not a homogenous process, as mastering the use of all the system's functions can take differing lengths of time in distinct road environments. Results can be used not only for the development of the new generation of systems that integrate ACC functionalities but also for determining the length of training required to operate an ACC system.