Correlation between driver visual characteristics and lane change parameters in urban long-term work zone
To investigate the correlation between drivers' visual characteristics and lane change parameters in urban long-term work zone, firstly, this paper obtains the oculomotor parameters of different drivers in urban long-term operation areas through real vehicle experiments and concludes that the pupil area and saccade angel of drivers in warning area and upstream transition area road sections have significant differences within 95% confidence interval by one-way ANOVA method. Next, scatter plots of lane change behavior parameters and eye movement parameters were plotted and validated by the Person correlation statistics method to find that: In the warning zone section, the driver's pupil area was negatively correlated with running speed, distance from the latest lane change point, and lateral displacement acceleration to varying degrees, with the strongest correlation with distance from the latest lane change point, |г|=0.816; the saccade angel was correlated with running speed and lateral displacement acceleration, with the strongest correlation with running speed, |г|=0.667. In the upstream transition zone section, the driver's pupil area was negatively correlated with all three lane change parameters to varying degrees, with the strongest correlation with the distance from the latest lane change point, |г|=0.512; the saccade angel was correlated with the distance from the latest lane change point, |г|=0.538, with no significant correlation with the running speed and lateral displacement acceleration.
Distracted E-Bike Riding among Delivery Workers in China: Prevalence, Analysis, and Mitigation
With the fast development of the online-to-offline market, there are over 13 million delivery workers in China, of which the primary transportation tool is electric bicycles (i.e., e-bikes). Because of the difficulty in executing enforced regulations, delivery workers usually engage in non-riding-related tasks (NRRTs) while riding e-bikes, leading to distracted riding, which can threaten traffic safety. Although distraction engagement in vehicle driving and motorcycle/bicycle riding has been extensively investigated, the prevalence of and the factors leading to distracted riding among delivery workers have not yet been explored. In this study, a survey has been designed to explore the social-psychological factors leading to NRRT engagement among delivery workers. The factors were chosen based on the theory of planned behavior (TPB) framework. We have also assessed workers’ awareness of the regulation in the survey. A total of 150 delivery workers (146 males and 4 females, mean age: 27.25) participated in the study. Results show that delivery workers tended to engage more in technology-based NRRTs, and “manually operating the phone” was the most prevalent. Compared to older peers, younger workers held more positive attitudes toward engaging in NRRTs and perceived engaging in NRRTs as more prevalent among their colleagues. TPB-related factors (i.e., attitudes, descriptive norms, injunctive norms, and perceived behavior control) were found to be correlated with self-reported NRRT engagement. The awareness of the regulations, however, had limited effects in explaining NRRT engagement. This research can provide insights into the design of countermeasures aiming at reducing the prevalence of NRRT engagement among delivery workers.
How do drivers allocate visual attention to vulnerable road users when turning at urban intersections?
Drivers turning at urban intersections pose a high risk to Vulnerable Road Users (VRUs), such as cyclists and pedestrians. In vehicle collisions with VRUs, driver attention misallocation is considered a leading contributor. While previous naturalistic studies have examined driver gaze behaviors at intersections, findings are limited to general gaze directions obtained through video analysis, meaning specific areas to which drivers attend cannot be determined. We present a secondary analysis of an on-road instrumented vehicle dataset collected in 2019 which offers eye-tracking and video data from 26 experienced drivers (13 cyclists and 13 non-cyclists). Three coders jointly examined eye-tracking footage from four right-signalized turns (n=96) to quantify drivers’ glance distributions to various areas of interest, including those most relevant to VRU safety when drivers turn. Individual temporal glance patterns and general attention allocation trends are presented and described. (1) Relevant pedestrians were the top objects of glance irrespective of signal status, and (2) at red light turns, driver attention was heavily skewed toward leftward traffic. This analysis provides a detailed report of driver glance distributions toward scene-specific areas (as opposed to general directions) at urban intersections and discusses how these patterns may influence VRU safety.
Improving Driving Automation Training Through Scaffolding of Roles and Responsibilities Information: A Comparison between Older and Younger Drivers
Adaptive Cruise Control (ACC) is an Advanced Driver Assistance System (ADAS) commonly found in new vehicles that shares the responsibilities of maintaining headway and speed. However, drivers often have a limited understanding of their roles and responsibilities and how they should modify their behaviors when driving with ACC. This study investigates the effect of scaffolding teaching technique by providing additional background knowledge about ACC and highlighting drivers’ new roles and responsibilities for both older and younger adults during text-based ACC training programs. The study also initiates a new approach to evaluate drivers’ learning outcomes at different stages of driving automation training (i.e., reading behavior during training, post-training knowledge test, gaze monitoring behavior, and driving performance during simulated driving). Thirty-nine participants (20 younger + 19 older) received one of the two ACC training protocols: basic (system functionality, operational procedures, and limitations) and comprehensive (basic training + ACC background information and driving roles and responsibilities). The results showed that the comprehensive training led to reduced reading page revisits and adjusted workload during training, better performance in post-training knowledge tests, and more ACC engagement during simulated driving. The findings also suggested the feasibilities and connections within the new training evaluation approach that can provide insights into understanding ACC training outcomes through different stages. Future research is needed to further explore the effect of scaffolding teaching method on trainees’ learning-behavioral translation and the application of the new training evaluation approach to support experimental design or other in-vehicle technologies.
Look right! The influence of bicycle crossing design on drivers’ approaching behavior
One of the most frequent crashes between cyclists and motor vehicles is the so-called “turning into” accident, where a motor vehicle turns into the main road and collides with a cyclist riding on the main road and crossing the vehicle’s course. Previous studies mainly examined this type of crash and its causes with accident analyses or observational approaches. This study uses a driving simulator to examine the effect of possible countermeasures like the drivers’ expectancy towards crossing cyclists, the view into the junction, and various bicycle crossing designs. N = 66 participants passed T-junctions that differ in the mentioned measures. Gaze and driving data were collected to assess the criticality of each approach. Results indicate that drivers approach marked bicycle crossings at a lower speed than unmarked crossings. Furthermore, crossings with pronounced designs showed more uncritical approaches. However, an alarming percentage of all approaches were critical because drivers showed no appropriate gaze behavior. This was even increased when the view into the junction was limited. The findings suggest that especially the view at junctions must not be obstructed to provide sufficient fields of view. Pronounced bicycle crossing, however, can enhance drivers’ approaching behavior and might help to reduce the frequency of “turning into” accidents.
Machine Learning in Driver Drowsiness Detection: A Focus on HRV, EDA, and Eye Tracking
Drowsy driving continues to be a significant cause of road traffic accidents, necessitating the development of robust drowsiness detection systems. This research enhances our understanding of driver drowsiness by analyzing physiological indicators – heart rate variability (HRV), the percentage of eyelid closure over the pupil over time (PERCLOS), blink rate, blink percentage, and electrodermal activity (EDA) signals. Data was collected from 40 participants in a controlled scenario, with half of the group driving in a non-monotonous scenario and the other half in a monotonous scenario. Participant fatigue was assessed twice using the Fatigue Assessment Scale (FAS).The research developed three machine learning models: HRV-Based Model, EDA-Based Model, and Eye-Based Model, achieving accuracy rates of 98.28%, 96.32%, and 90% respectively. These models were trained on the aforementioned physiological data, and their effectiveness was evaluated against a range of advanced machine learning models including GRU, Transformers, Mogrifier LSTM, Momentum LSTM, Difference Target Propagation, and Decoupled Neural Interfaces Using Synthetic Gradients. The HRV-Based Model and EDA-Based Model demonstrated robust performance in classifying driver drowsiness. However, the Eye-Based Model had some difficulty accurately identifying instances of drowsiness, likely due to the imbalanced dataset and underrepresentation of certain fatigue states. The study duration, which was confined to 45 minutes, could have contributed to this imbalance, suggesting that longer data collection periods might yield more balanced datasets. The average fatigue scores obtained from the FAS before and after the experiment showed a relatively consistent level of reported fatigue among participants, highlighting the potential impact of external factors on fatigue levels. By integrating the outcomes of these individual models, each demonstrating strong performance, this research establishes a comprehensive and robust drowsiness detection system. The HRV-Based Model displayed remarkable accuracy, while the EDA-Based Model and the Eye-Based Model contributed valuable insights despite some limitations. The research highlights the necessity of further optimization, including more balanced data collection and investigation of individual and external factors impacting drowsiness. Despite the challenges, this work significantly contributes to the ongoing efforts to improve road safety by laying the foundation for effective real-time drowsiness detection systems and intervention methods.
On the relationship between occlusion times and in-car glance durations in simulated driving
Drivers have spare visual capacity in driving, and often this capacity is used for engaging in secondary in-car tasks. Previous research has suggested that the spare visual capacity could be estimated with the occlusion method. However, the relationship between drivers’ occlusion times and in-car glance duration preferences has not been sufficiently investigated for granting occlusion times the role of an estimate of spare visual capacity. We conducted a driving simulator experiment (N = 30) and investigated if there is an association between drivers’ occlusion times and in-car glance durations in a given driving scenario. Furthermore, we explored which factors and variables could explain the strength of the association. The findings suggest an association between occlusion time preferences and in-car glance durations in visually and cognitively low demanding unstructured tasks but that this association is lost if the in-car task is more demanding. The findings might be explained by the inability to utilize peripheral vision for lane-keeping when conducting in-car tasks and/or by in-car task structures that override drivers’ preferences for the in-car glance durations. It seems that the occlusion technique could be utilized as an estimate of drivers’ spare visual capacity in research – but with caution. It is strongly recommended to use occlusion times in combination with driving performance metrics. There is less spare visual capacity if this capacity is used for secondary tasks that interfere with the driver’s ability to utilize peripheral vision for driving or preferences for the in-car glance durations. However, we suggest that the occlusion method can be a valid method to control for inter-individual differences in in-car glance duration preferences when investigating the visual distraction potential of, for instance, in-vehicle infotainment systems.
Pilot study: Effect of roles and responsibility training on driver’s use of adaptive cruise control between younger and older adults
With the development of driver support systems (SAE Levels 1 – 2), drivers must take on new monitoring and supervision tasks in additional to manual driving. Training is necessary to clarify drivers' new roles and promote safe usage and trust in these systems. Providing training for lower-levels of automation may also benefit drivers’ acceptance of future Fully Automated Vehicles (FAVs, SAE Level 5). However, younger and older drivers differ in training preferences (e.g., owner's manual vs on-road trial and error) and hold different attitudes towards automation. This study investigates the effects of additional training on drivers' roles and responsibilities when using Adaptive Cruise Control (ACC, SAE Level 1) for younger and older drivers. Thirty-nine adults (20 younger + 19 older) were trained on one of two ACC training protocols: basic (system functionality, operational procedures, and limitations) and comprehensive (basic training + ACC background and roles of responsibilities). Participants’ situational trust and ACC usage was evaluated before, during, and after experiencing an emergency event while using ACC in a driving simulator study. Results showed that the comprehensive training promoted drivers' situational trust in ACC, ACC usage, and the acceptance of FAVs. Compared to younger drivers, older drivers used ACC less, reported less dynamic situational trust, higher levels of workload, and lower acceptance. Overall, comprehensive training resulted in older drivers behaving similarly to younger drivers. The comprehensive training also promoted the acceptance of FAVs for both younger and older drivers. In conclusion, training of drivers’ roles and responsibilities has an impact on drivers’ usage of ACC and may be particularly useful for older drivers.
Study on the gaze characteristics of urban roads in cold areas under congestion
Abstract: In order to analyze the gaze characteristics of urban road drivers in cold area congestion, the German Dikablis eye tracker and its supportingD-Lab software were used to carry out real vehicle tests on urban roads in Changchun City, and the influence of cold area congestion road characteristics on driver gaze characteristics was quantified by statistical analysis. The results show that drivers mainly obtain traffic information through gaze when driving, and the fixation points are mostly concentrated in road conditions and road traffic flow, especially in the state of ice and snow thawing pavement. The one-way variance result of driver fixation duration was less than 0.05, which was significantly different when driving on urban roads in cold areas under congestion.
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.
