Although in their early stages, autonomous vehicles, have demonstrated huge potential in shaping future lifestyles. However, to be accepted by ordinary users, autonomous vehicles have a critical issue to solve – being trustworthy at collision detection. Autonomous vehicles that experience accidents once every few months or years would be unacceptable to the general public. In the real world, human driven vehicles collide at every second. More than 1.3 million people are killed by road accidents every single year. The current approaches for vehicle collision detection such as vehicle to vehicle communication, radar, laser-based Lidar, and GPS are far from acceptable in terms of reliability, cost, energy consumption, and size. For example, radar is too sensitive to metallic material, Lidar is too expensive and does not work well on absorbing/reflective surfaces, GPS based methods are difficult in cities with tall buildings, vehicle to vehicle communication cannot detect pedestrians or any objects unconnected, segmentation based vision methods are too computing power-thirsty to be miniaturized, and normal vision sensors cannot cope with fog, rain and dim environment at night. To save people’s lives and make autonomous vehicles safe to serve human society, a new type of trustworthy, robust, low-cost, and low energy consumption vehicle collision detection and avoidance systems are needed.
This ULTRACEPT consortium proposes an innovative solution with brain-inspired multiple layered and multiple modalities information processing for trustworthy vehicle collision detection. Connecting multidisciplinary teams from different countries together via staff exchange and collaboration, it takes the advantages of low-cost spatial-temporal and parallel computing capacity of bio-inspired visual neural systems and multiple modalities data inputs in extracting potential collision cues at complex weather and lighting conditions.
