"The number of road traffic accident fatalities world wide has recently reached 1.3 million each year, with between 20 and 50 million injuries being caused by road accidents. In theory, all accidents can be avoided. Studies showed that more than 90% road accidents are caused by or related to human error. Developing an efficient system that can detect hazardous situations robustly is the key to reduce road accidents.This HAZCEPT consortium will focus on automatic hazard scene recognition for safe driving. The primary objective of this project is to build international capacity and cooperation in the fields of nature inspired visual computation, pattern recognition and human behaviour analysis, to explore effective methodologies for hazard perception.Vision plays a critical role in hazard perception and escape behaviour for most animal species in a dynamic world. Many animal species, such as dung beetles and locusts, have also developed efficient night vision systems that work well in low light conditions. At a higher level, human vision combines cognitive cues such as road markings and signposts to judge hazard situations robustly. The naturally evolved biological vision systems provide ideal models to develop tireless artificial vision systems for hazard perception. On the other hand, a driver plays a key role in the safe driving loop, the status and behaviour of a driver should be constantly monitored and automatically analysed.Taking the inspiration from biological vision systems, the consortium will bring together neurobiologists, neural system modellers, pattern recognition experts, an autonomous system integrator and a human/robot movement analyst to develop a hazard perception system, whilst also building strong connections between the European institutions and partner institutions in the East of Asia via staff secondments, and jointly organised seminars and workshops."