Drivers’ assessment and analysis of developing potential hazards require mindful attention and plan since
human error is overwhelmingly to blame for most automobile accidents. Today’s automobile is nearly
autonomous due to Advanced Driver Assistance System (ADAS) and the role of driver could be taken over by
automation, but the vehicle also requires delivering performance identical to or better than that of a human
driver if it is to be trusted. Thus, the next generation ADAS should focus on imitating the human driver’s
analysis and assessment of hazard perception.
This workshop is focused on to provide a unique platform for contributions and discussions on challenges in the analysis and assessment of potential developing hazards to advance the research and development of hazard perception in autonomous driving. This is increasingly a prime subject in computer vision, AI, and robotics with application to intelligent transportation systems. Accurate detection and anticipation of positions, movements and actions performed by multiple road users (pedestrians, vehicles, cyclists and so on) is the key to address the anticipation of a developing potential hazard. This is necessary for empowering autonomous vehicles with the capability to support reliable and safe autonomous decision making. It requires solving the situation awareness by considering multi-modal data (audio, visual, depth, GPS, etc.) and representing road scenarios in terms of objects, actions, and scene configurations to a suitable decision-making strategy. As a result, this will have the advantage to allow the analysis and assessment of hazards involving scene objects using a theory-of-mind approach, encouraged by the behaviour of a driver’s mind in similar contexts.
Full Paper Submission:
17th October 2021 31st October 2021 (23:59 PST)
Supplementary: 31st October 2021 (23:59 PST)
Acceptance Notice: 9th November 2021 (23:59 PST)
Camera-Ready Paper: 15th November 2021 (23:59 PST)