Driver Glance Behaviour in Intersection Crashes: A SHRP2 Naturalistic Data Analysis (original) (raw)
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Analysis of bus drivers reaction to simulated traffic collision situations – Eye-tracking studies
International Journal of Occupational Medicine and Environmental Health
The aim of the study was to establish whether the driver's visual strategy may influence a driver's behavior to avoid a crash in a high-risk situation. Any published papers on drivers' visual strategies just before a crash were not found. Material and Methods: Tests were performed using a high-tech driving bus simulator. Participants comprised 45 men drivers, aged 43.5±7.9 years old, seniority as a bus driver of 13.3±8.6 years. The tests were preceded by medical examinations: general, neurological and ophthalmological. Each participant drove the same city route for approximately 40 min (entire route-ER). In the final phase, a collision situation was simulated (a phantom car blocked the participant's right of way). Driver's visual strategy was analyzed using the FaceLab device with 2 cameras during ER and just before collision. The field-of-view covered by camera 1 was divided into 8 regions, by camera 2 into 10 regions. The distribution of gazes in regions was a criterion of visual strategy. Results: Thirty-five drivers completed the simulated driving test, 14 escaped the collision, 21 crashed. These groups differed only in resting systolic blood pressure before the test. The analysis of covariance, after adjusting to this factor, indicated that during the ER visual strategy recorded by camera 1 did not differ between groups, in camera 2 the drivers in the crash group fixed their gaze more frequently (p = 0.049) in region 3 (close part of the road in front of the windshield). Just before the collision drivers who escaped the collision fixed their gaze significantly more often in region 6 (left side of the road) in camera 1 and in region 6 (in front of the windshield,) and region 10 (right side) in camera 2. Conclusions: The visual strategy has an impact on the road safety. The analysis of visual strategies may be a useful tool for the training of drivers.
A comparison of drivers’ eye movements in filmed and simulated dangerous driving situations
2009
Abstract One of the most difficult problems in driver training is the challenge of exposing learner drivers to hazardous situations in a realistic but safe manner. While the introduction of hazard perception testing in the British driving test has substantially increased awareness of hazards among both learners and trainers, there are still limited opportunities for learner drivers to experience real hazards while actually driving, and there are questions about the value of learning to respond to hazards in a purely video-based task.
Transportation Research Part F: Traffic Psychology and Behaviour, 2015
As naturalistic driving data become increasingly available, new analyses are revealing the significance of drivers' glance behavior in traffic crashes. Due to the rarity of crashes, even in the largest naturalistic datasets, near-crashes are often included in the analyses and used as surrogates for crashes. However, to date we lack a method to assess the extent to which driver glance behavior influences crash and injury risk across both crashes and nearcrashes. This paper presents a novel method for estimating crash and injury risk from off-road glance behavior for crashes and near-crashes alike; this method can also be used to evaluate the safety impact of secondary tasks (such as tuning the radio). We apply a 'what-if ' (counterfactual) simulation to 37 lead-vehicle crashes and 186 lead-vehicle near-crashes from lead-vehicle scenarios identified in the SHRP2 naturalistic driving data. The simulation combines the kinematics of the two conflicting vehicles with a model of driver glance behavior to estimate two probabilities: (1) that each event becomes a crash, and (2) that each event causes a specific level of injury. The usefulness of the method is demonstrated by comparing the crash and injury risk of normal driving with the risks of driving while performing one of three secondary tasks: the Rockwell radio-tuning task and two hypothetical tasks. Alternative applications of the method and its metrics are also discussed. The method presented in this paper can guide the design of safer driver-vehicle interfaces by showing the best tradeoff between the percent of glances that are on-road, the distribution of off-road glances, and the total task time for different tasks.
Glass half-full: On-road glance metrics differentiate crashes from near-crashes in the 100-Car data
Accident Analysis & Prevention, 2017
Background: Much of the driver distraction and inattention work to date has focused on concerns over drivers removing their eyes from the forward roadway to perform non-driving-related tasks, and its demonstrable link to safety consequences when these glances are timed at inopportune moments. This extensive literature has established, through the analyses of glance from naturalistic datasets, a clear relationship between eyes-off-road, lead vehicle closing kinematics, and near-crash/crash involvement. Objective: This paper looks at the role of driver expectation in influencing drivers' decisions about when and for how long to remove their eyes from the forward roadway in an analysis that consider the combined role of onand off-road glances. Method: Using glance data collected in the 100-Car Naturalistic Driving Study (NDS), near-crashes were examined separately from crashes to examine how momentary differences in glance allocation over the 25-s prior to a precipitating event can differentiate between these two distinct outcomes. Individual glance metrics of mean single glance duration (MSGD), total glance time (TGT), and glance count for off-road and on-road glance locations were analyzed. Output from the AttenD algorithm (Kircher and Ahlström, 2009) was also analyzed as a hybrid measure; in threading together on-and off-road glances over time, its output produces a pattern of glance behavior meaningful for examining attentional effects. Results: Individual glance metrics calculated at the epoch-level and binned by 10-s units of time across the available epoch lengths revealed that drivers in near-crashes have significantly longer on-road glances, and look less frequently between on-and off-road locations in the moments preceding a precipitating event as compared to crashes. During on-road glances, drivers in near-crashes were found to more frequently sample peripheral regions of the roadway than drivers in crashes. Output from the AttenD algorithm affirmed the cumulative net benefit of longer on-road glances and of improved attention management between on-and off-road locations. Conclusion: The finding of longer on-road glances differentiating between safety-critical outcomes in the 100-Car NDS data underscores the importance of attention management in how drivers look both on and off the road. It is in the pattern of glances to and from the forward roadway that drivers obtained critical information necessary to inform their expectation of hazard potential to avoid a crash. Application: This work may have important implications for attention management in the context of the increasing prevalence of in-vehicle demands as well as of vehicle automation.
2018
This study examines drivers' fixation sequences and compares them to the responsiveness measured in a driving simulator. The assessment is based on a comparison of driving simulator based hazard detection skills with eye movement patterns. Sixty-three 18-24-year-old male drivers' response to a pedestrian potentially crossing the street was examined and used as indicator of hazard perception skills (HPS). Drivers' eye movements were examined to gain information about differences in scanning patterns between drivers with high and low HPS. Drivers with high HPS, fixated on the pedestrian continuously and had more multiple fixations on the standing pedestrian than drivers with low HPS. Moreover, more drivers that did not respond to the hazard did not fixate on the speedometer and if they did so, then mostly only once. The results show that novice drivers with high HPS differ in their eye movement patterns from drivers with low HPS. Moreover, drivers with low HPS pose an even...
Analysis of Glance Movements in Critical Intersection Scenarios
Advances in Human Factors and Ergonomics Series, 2010
For designing effective and ergonomic assistance systems for road intersections it is highly beneficial to gain an understanding of the causes of driver's errors. At intersections errors depend mainly on the applied visual strategies and perceived information. This paper reports on a study conducted in the fixed-base driving simulator, with an objective to compare driver's visual strategies among three leftturn intersection scenarios, which can become critical with respect to safety. The comparison of applied visual strategies showed that in the more complex situation drivers delegate more attention to the task and perform a less risky behavior. Nevertheless, in complex scenarios the driver's visual behavior indicates a prioritization problem regarding the primary directions to scan. A system that in an appropriate moment visualizes the priority roads or even ideal eye-movement sequence could decrease both driver's committed errors and mental workload.
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 c...
2015
Hazard perception is a key skill needed to drive a vehicle safely. Literature has shown that this skill improves with experience. Little is known regarding the time window in which novice young drivers start exhibiting essential hazard perception skills as efficiently as their experienced counterparts do. This research was an attempt to address this unknown through the use of a semi-naturalistic driving study employing eye tracking technologies and by examining the roadway eye scanning pattern of young and highly experienced drivers with respect to eight indicators: percentage of gaze duration, mean gaze duration, percentage of time taken to make the first gaze at the study region of interest, gaze rate, gaze heading, gaze pitch, head heading and head pitch. A total of 90 participants completed the study. Participants were split into six groups (15 each) on the basis of their driving experience, ranging from novice young drivers with less than 1 year of driving experience, to highly...
Cornell University - arXiv, 2021
Vehicles with a safety function for anticipating crashes in advance can enhance drivers' ability to avoid crashes. As dashboard cameras have become a low-cost sensor device accessible to almost every vehicle, deep neural networks for crash anticipation from a dashboard camera are receiving growing interest. However, drivers' trust in the Artificial Intelligence (AI)-enabled safety function is built on the validation of its safety enhancement toward zero deaths. This paper is motivated to establish a method that uses gaze data and corresponding measures to evaluate human drivers' ability to anticipate crashes. A laboratory experiment is designed and performed, wherein a screen-based eye tracker collects the gaze data of six volunteers while watching 100 driving videos that include both normal and crash scenarios. Statistical analyses of the experimental data show that, on average, drivers can anticipate a crash up to 2.61 seconds before it occurs in this pilot study. The chance that drivers have successfully anticipated crashes before they occur is 92.8%. A state-of-the-art AI model can anticipate crashes 1.02 seconds earlier than drivers on average. The study finds that crash-involving traffic agents in the driving videos can vary drivers' instant attention level, average attention level, and spatial attention distribution. This finding supports the development of a spatial-temporal attention mechanism for AI models to strengthen their ability to anticipate crashes. Results from the comparison also suggest the development of collaborative intelligence that keeps human-in-the-loop of AI models to further enhance the reliability of AI-enabled safety functions.
Analysis of Naturalistic Driving Study Data: Safer Glances, Driver Inattention, and Crash Risk
The Second Strategic Highway Research Program America's highway system is critical to meeting the mobility and economic needs of local communities, regions, and the nation. Developments in research and technology-such as advanced materials, communications technology, new data collection technologies, and human factors science-offer a new opportunity to improve the safety and reliability of this important national resource. Breakthrough resolution of significant transportation problems, however, requires concentrated resources over a short time frame. Reflecting this need, the second Strategic Highway Research Program (SHRP 2) has an intense, large-scale focus, integrates multiple fields of research and technology, and is fundamentally different from the broad, mission-oriented, discipline-based research programs that have been the mainstay of the highway research industry for half a century. The need for SHRP 2 was identified in TRB Special Report 260: Strategic Highway Research: Saving Lives, Reducing Congestion, Improving Quality of Life, published in 2001 and based on a study sponsored by Congress through the Transportation Equity Act for the 21st Century (TEA-21). SHRP 2, modeled after the first Strategic Highway Research Program, is a focused, timeconstrained, management-driven program designed to complement existing highway research programs. SHRP 2 focuses on applied research in four areas: Safety, to prevent or reduce the severity of highway crashes by understanding driver behavior; Renewal, to address the aging infrastructure through rapid design and construction methods that cause minimal disruptions and produce lasting facilities; Reliability, to reduce congestion through incident reduction, management, response, and mitigation; and Capacity, to integrate mobility, economic, environmental, and community needs in the planning and designing of new transportation capacity.