Validation of a Driving Simulator Study on Driver Behavior at Passive Rail Level Crossings

Larue, Grégoire S.; Wullems, Christian; Sheldrake, Michelle; Rakotonirainy, Andry

doi:10.1177/0018720818783507

Cited by 25 publications

(5 citation statements)

References 30 publications

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“…Overall, the evidence suggests that of all the driver-related factors, unintentional errors are far more commonplace than deliberate violations ( 11 –14 ). The main factors leading to such errors are: insufficient sighting distances, or limited conspicuity of the warning signs and/or approaching train; drivers’ lack of awareness of the required response to passive crossing warning signs; drivers’ inability to correctly assess if sufficient time is available to safely complete the crossing ( 15 ), particularly on account of poor ability to estimate the speed of trains as they approach ( 16 ); inattention as a result of complacency caused by low expectations of encountering a train ( 17 ), or distraction ( 18 ); and the so-called “looked-but-failed-to-see” error ( 10 ), in which drivers fail to identify hazards despite looking at the hazard’s source, largely resulting from limitations in human information processing.…”

mentioning

confidence: 99%

Getting the Attention of Drivers Back on Passive Railway Level Crossings: Evaluation of Advanced Flashing Lights

Larue

Watling

Black

et al. 2019

Transportation Research Record: Journal of the Transportation R

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Improving safety at railway level crossings remains a priority for the rail industry internationally, as they remain a significant hazard. A high proportion of collisions occur at passive level crossings, because of their high prevalence and their lower effectiveness at mitigating the risks that road users encounter at such crossings. The unreasonable cost required to upgrade them to incorporate active warnings implies that such crossings will remain on the road, and that alternative approaches are required. Drivers tend to make errors at such crossings, and this can be related to approaching such level crossings at speeds that are too high, exhibiting reduced scanning behaviors to look for trains, and not complying with stop signs at the crossing. An alternative approach is to upgrade the advanced signage with active flashing lights activated by road vehicles, aimed at reducing looked-but-failed-to-see errors and reinforcing the behavior expected from road users at such crossings. A field trial was conducted in New Zealand that evaluated how approach speeds and the visual scanning behavior of 27 drivers, recorded with an eye tracker, changed with such treatments. It was found that the presence of road vehicle-activated advanced signage provided a range of benefits for drivers unaware of the presence of a passive crossings, such as increasing drivers’ attention to road signs through drivers fixating on signage for longer durations and reduced (slower) approach speeds. Further research is needed to evaluate whether these benefits are sustained over time, and whether this can minimize complacency resulting from familiarity.

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mentioning

confidence: 99%

Getting the Attention of Drivers Back on Passive Railway Level Crossings: Evaluation of Advanced Flashing Lights

Larue

Watling

Black

et al. 2019

Transportation Research Record: Journal of the Transportation R

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“…Several limitations of our study warrant discussion. First, although driving simulation systems are considered an efficient and safe means of allowing the simulation of high-risk collisions that occur in real-world driving environments, the question of how to elicit similar driving behaviors when using driving simulation systems as a research tool as in the real-world driving environments is an ongoing research topic [ 22 , 30 , 31 ]. Thus, the results we obtained might not fully reflect the situation of a true driving environment.…”

Section: Discussionmentioning

confidence: 99%

A Simulation-Based Study of the Effect of Brake Light Flashing Frequency on Driver Brake Behavior from the Perspective of Response Time

Hsieh

Chen

Lee

et al. 2022

Behavioral Sciences

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To prevent vehicle crashes, studies have proposed the use of flashing signals (brake lights or other light indicators) to improve the driver’s response time when the leading vehicle is braking. However, there are no consistent results on the ideal flashing frequency of the brake lights. This study aimed to investigate different brake light flashing frequencies to assess their impact on braking response time. Twenty-four participants aged 25 to 30 were recruited. Two driving speed environments (50 and 80 km/h), three deceleration rates (0.25, 0.6, and 1 g), and four brake light flashing frequencies (0, 2, 4, and 7 Hz) were examined. Braking response time, average braking force, and braking response time ratio were used to evaluate the driving behavior. The results showed that the braking response time and average braking force were affected by the deceleration rate in the 50 km/h driving environment. In the 50 and 80 km/h driving environments, although there were no significant differences among the three deceleration rates, the braking response time decreased by 3–7% under the flashing brake light condition. These findings can be used as a reference for safety designs as well as future studies on driving behavior.

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“…Field driving studies (FDS) monitor driver behavior using instrumented vehicles; even with the use of monitoring equipment, instructors are frequently present in the vehicle to record measurements and code driving performance [129], [130]. The driver behavior data under the precise control of the experiment could also be obtained in the driving simulator test (driving simulator study) [131].…”

Section: B Driver Behavior Studies Types (Rq2)mentioning

confidence: 99%

Driver Behavior Classification: A Systematic Literature Review

2023

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Driver behavior is receiving increasing attention as a result of the staggering number of road accidents. Many road safety reports regard human behavior as the most important factor in the likelihood of accidents. The detection and classification of aggressive or abnormal driver behavior is an essential requirement in the real world to avoid deadly road accidents and to protect road users. The automatic detection of the driver's behavior aids in the prevention of dangerous situations for the driver and all other participants in the driving environment, as well as the implementation of corrective measures. This paper presents a systematic literature review (SLR) of the classification of driver behavior. The study aim is to highlight and analyze the different types of driver behavior, data sources, datasets, features, and artificial intelligence techniques used to classify driver behavior and its performance. Based on the results obtained from the analysis of the selected works, we aim to identify the key contributions and challenges of studying driver behavior classification and propose potential avenues for further directions for practitioners and researchers.INDEX TERMS Driver behavior, intelligent transport system, systematic literature review, machine learning, deep learning.

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Validation of a Driving Simulator Study on Driver Behavior at Passive Rail Level Crossings

Abstract: This study also provides support for the findings of previous driving simulator studies conducted to evaluate compliance and approach speeds of passive level crossings.

Cited by 25 publications

References 30 publications

Getting the Attention of Drivers Back on Passive Railway Level Crossings: Evaluation of Advanced Flashing Lights

Getting the Attention of Drivers Back on Passive Railway Level Crossings: Evaluation of Advanced Flashing Lights

A Simulation-Based Study of the Effect of Brake Light Flashing Frequency on Driver Brake Behavior from the Perspective of Response Time

Driver Behavior Classification: A Systematic Literature Review

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