Road Hazard Stimuli: Annotated naturalistic road videos for studying hazard detection and scene perception.

Driving requires vision, yet there is little empirical data about how vision and cognition support safe driving. It is difficult to study perception during natural driving because the experimental rigor required would be dangerous and unethical to implement on the road. The driving environment is complex, dynamic, and immensely variable, making it extremely challenging to accurately replicate in simulation. Our proposed solution is to study vision using stimuli which reflect this inherent complexity by using footage of real driving situations. To this end, we curated a set of 750 crowd-sourced video clips (434 hazard and 316 no-hazard clips), which have been spatially, temporally, and categorically annotated. These annotations describe where the hazard appears, what it is, and when it occurs. In addition, perceived dangerousness changes from moment to moment and is not a simple binary detection judgement. To capture this more granular aspect of our stimuli, we asked 48 observers to rate the perceived hazardousness of 1356 brief video clips taken from these 750 source clips on a continuous scale. These ratings span the entire scale, have high interrater agreement, and are robust to driving history. This novel stimulus set is not only useful for understanding drivers' ability to detect hazards, but is also a tool for studying dynamic scene perception and other aspects of visual function. While this stimulus set was originally designed for behavioral studies, researchers interested in other areas such as traffic safety or computer vision may also find this dataset a useful resource.

Taking prevalence effects on the road: Rare hazards are often missed.

Previous work has shown that, in many visual search and detection tasks, observers frequently miss rare but important targets, like weapons in bags or abnormalities in radiological images. These prior studies of the low-prevalence effect (LPE) use static stimuli and typically permitted observers to search at will. In contrast, many real-world tasks, like looking for dangerous behavior on the road, only afford observers a brief glimpse of a complex, changing scene before they must make a decision. Can the LPE be a factor in in dynamic, time-limited moments of real driving? To test this, we developed a novel hazard-detection task that preserves much of the perceptual richness and complexity of hazard detection in the real world, while allowing for experimental control over event prevalence. Observers viewed brief video clips of road scenes recorded from dashboard cameras and reported whether they saw a hazardous event. In separate sessions, the prevalence of these events was either high (50% of videos) or low (4%). Under low prevalence, observers missed hazards at more than twice the rate observed in the high-prevalence condition. Follow-up experiments demonstrate that this elevation of miss rate at low prevalence persists when participants were allowed to correct their responses, increases as hazards become increasingly rare (down to 1% prevalence) and is resistant to simple cognitive intervention (participant prebriefing). Together, our results demonstrate that the LPE generalizes to complex perceptual decisions in dynamic natural scenes, such as driving, where observers must monitor and respond to rare hazards.

Primary or secondary tasks? Dual-task interference between cyclist hazard perception and cadence control using cross-modal sensory aids with rider assistance bike computers.

This research investigated the risks involved in bicycle riding while using various sensory modalities to deliver training information. To understand the risks associated with using bike computers, this study evaluated hazard perception performance through lab-based simulations of authentic riding conditions. Analysing hazard sensitivity (d') of signal detection theory, the rider's response time, and eye glances provided insights into the risks of using bike computers. In this study, 30 participants were tested with eight hazard perception tasks while they maintained a cadence of 60 ± 5 RPM and used bike computers with different sensory displays, namely visual, auditory, and tactile feedback signals. The results indicated that synchronously using different sense organs to receive cadence feedback significantly affects hazard perception performance; direct visual information leads to the worst rider distraction, with a mean sensitivity to hazards (d') of -1.03. For systems with multiple interacting sensory aids, auditory aids were found to result in the greatest reduction in sensitivity to hazards (d' mean = -0.57), whereas tactile sensory aids reduced the degree of rider distraction (d' mean = -0.23). Our work complements existing work in this domain by advancing the understanding of how to design devices that deliver information subtly, thereby preventing disruption of a rider's perception of road hazards.

Direction based Hazard Routing Protocol (DHRP) for disseminating road hazard information using road side infrastructures in VANETs.

This paper presents Direction based Hazard Routing Protocol (DHRP) for disseminating information about fixed road hazards such as road blocks, tree fall, boulders on road, snow pile up, landslide, road maintenance work and other obstacles to the vehicles approaching the hazardous location. The proposed work focuses on dissemination of hazard messages on highways with sparse traffic. The vehicle coming across the hazard would report the presence of the hazard. It is proposed to use Road Side fixed infrastructure Units for reliable and timely delivery of hazard messages to vehicles. The vehicles can then take appropriate safety action to avoid the hazardous location. The proposed protocol has been implemented and tested using SUMO simulator to generate road traffic and NS 2.33 network simulator to analyze the performance of DHRP. The performance of the proposed protocol was also compared with simple flooding protocol and the results are presented.