Detection and response to critical lead vehicle deceleration events with peripheral vision: Glance response times are independent of visual eccentricity

The effect of an occlusion-induced delay on braking behavior in critical situations: A driving simulator study

OBJECTIVE

To share results of an experiment that used visual occlusion for a new purpose: inducing a waiting time.

BACKGROUND

Senders was a leading figure in human factors. In his research on the visual demands of driving, he used occlusion techniques.

METHODS

In a simulator experiment, we examined how drivers brake for different levels of urgency and different visual conditions. In three blocks (1 = brake lights, 2 = no brake lights, 3 = occlusion), drivers followed a vehicle at 13.4 or 33.4 m distance. At certain moments, the lead vehicle decelerated moderately (1.7 m/s2) or strongly (6.5 m/s2). In the occlusion condition, the screens blanked for 0.4 s (if 6.5 m/s2) or 2.0 s (if 1.7 m/s2) when the lead vehicle started to decelerate. Participants were instructed to brake only after the occlusion ended.

RESULTS

The lack of brake lights caused a delayed response. In the occlusion condition, drivers adapted to the instructed late braking by braking harder. However, adaptation was not always possible: In the most urgent condition, most participants collided with the lead vehicle because the ego-vehicle's deceleration limits were reached. In non-urgent conditions, some drivers braked unnecessarily hard. Furthermore, while waiting until the occlusion cleared, some drivers lightly touched the brake pedal.

CONCLUSION

This experimental design demonstrates how drivers (sometimes fail to) adjust their braking behavior to the criticality of the situation.

APPLICATION

The phenomena of biomechanical readiness and (inappropriate) dosing of the brake pedal may be relevant to safety, traffic flow, and ADAS design.

The role of peripheral vision during decision-making in dynamic viewing sequences

Decision-making in team sports necessitates monitoring multiple performers located at different distances (i.e., viewing eccentricities) from a critical information source. The processing of peripheral information is generally impaired under anxiety and when responding to stimuli located at larger eccentricities. These hypotheses have not been sufficiently tested in dynamic performance environments. We examined how pressure and eccentricities affect decision-making and visual behaviour in 4v4 basketball defensive scenarios using a head mounted display. Experienced players monitored plays from the first-person perspective (centre position) and made defensive steps towards opponents threatening the basket from different eccentricities under low- and high-pressure. To tax working memory, participants simultaneously performed a backward counting task. Players responded slower and with lower accuracy to opponents at larger eccentricities. Players mostly fixated on the ball-carrier, but over 50% of fixations were located on peripheral players, indicating that information in the periphery must be frequently updated with foveal vision (i.e., pivot strategy). When pressured, participants increased mental effort and improved counting performance; however, gaze behaviour and decision-making were relatively unaffected. Findings suggest that basketball players respond more quickly to opponents positioned at lower compared to higher eccentricities at the cost of impaired responses to opponents in the periphery.

Effect of peripheral refractive errors on driving performance

The effect of peripheral refractive errors on driving while performing secondary tasks at 40° of eccentricity was studied in thirty-one young drivers. They drove a driving simulator under 7 different induced peripheral refractive errors (baseline (0D), spherical lenses of +/- 2D, +/- 4D and cylindrical lenses of +2D and +4D). Peripheral visual acuity and contrast sensitivity were also evaluated at 40°. Driving performance was significantly impaired by the addition of myopic defocus (4D) and astigmatism (4D). Worse driving significantly correlated with worse contrast sensitivity for the route in general, but also with worse visual acuity when participants interacted with the secondary task. Induced peripheral refractive errors may negatively impact driving when performing secondary tasks.

Beyond gaze fixation: Modeling peripheral vision in relation to speed, Tesla Autopilot, cognitive load, and age in highway driving

OBJECTIVE

The study aims to model driver perception across the visual field in dynamic, real-world highway driving.

BACKGROUND

Peripheral vision acquires information across the visual field and guides a driver's information search. Studies in naturalistic settings are lacking however, with most research having been conducted in controlled simulation environments with limited eccentricities and driving dynamics.

METHODS

We analyzed data from 24 participants who drove a Tesla Model S with Autopilot on the highway. While driving, participants completed the peripheral detection task (PDT) using LEDs and the N-back task to generate cognitive load. The I-DT (identification by dispersion threshold) algorithm sampled naturalistic gaze fixations during PDTs to cover a broader and continuous spectrum of eccentricity. A generalized Bayesian regression model predicted LED detection probability during the PDT-as a surrogate for peripheral vision-in relation to eccentricity, vehicle speed, driving mode, cognitive load, and age.

RESULTS

The model predicted that LED detection probability was high and stable through near-peripheral vision but it declined rapidly beyond 20°-30° eccentricity, showing a narrower useful field over a broader visual field (maximum 70°) during highway driving. Reduced speed (while following another vehicle), cognitive load, and older age were the main factors that degraded the mid-peripheral vision (20°-50°), while using Autopilot had little effect.

CONCLUSIONS

Drivers can reliably detect objects through near-peripheral vision, but their peripheral detection degrades gradually due to further eccentricity, foveal demand during low-speed vehicle following, cognitive load, and age.

APPLICATIONS

The findings encourage the development of further multivariate computational models to estimate peripheral vision and assess driver situation awareness for crash prevention.

On the importance of driver models for the development and assessment of active safety: A new collision warning system to make overtaking cyclists safer

The total number of road crashes in Europe is decreasing, but the number of crashes involving cyclists is not decreasing at the same rate. When cars and bicycles share the same lane, cars typically need to overtake them, creating dangerous conflicts-especially on rural roads, where cars travel much faster than cyclists. In order to protect cyclists, advanced driver assistance systems (ADAS) are being developed and introduced to the market. One of them is a forward collision warning (FCW) system that helps prevent rear-end crashes by identifying and alerting drivers of threats ahead. The objective of this study is to assess the relative safety benefit of a behaviour-based (BB) FCW system that protects cyclists in a car-to-cyclist overtaking scenario. Virtual safety assessments were performed on crashes derived from naturalistic driving data. A series of driver response models was used to simulate different driver reactions to the warning. Crash frequency in conjunction with an injury risk model was used to estimate the risk of cyclist injury and fatality. The virtual safety assessment estimated that, compared to no FCW, the BB FCW could reduce cyclists' fatalities by 53-96% and serious injuries by 43-94%, depending on the driver response model. The shorter the driver's reaction time and the larger the driver's deceleration, the greater the benefits of the FCW. The BB FCW also proved to be more effective than a reference FCW based on the Euro NCAP standard test protocol. The findings of this study demonstrate the BB FCW's great potential to avoid crashes and reduce injuries in car-to-cyclist overtaking scenarios, even when the driver response model did not exceed a comfortable rate of deceleration. The results suggest that a driver behaviour model integrated into ADAS collision threat algorithms can provide substantial safety benefits.

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.