Harnessing the Automotive Infoverse
Like today’s autonomous vehicle prototypes, vehicles in the future will have rich sensors to map and identify objects in the environment. For example, many autonomous vehicle prototypes today come with lineofsight depth perception sensors like 3D cameras. These 3D sensors are used for improving vehicular safety in autonomous driving, but have fundamentally limited visibility due to occlusions, sensing range, and extreme weather and lighting conditions. To improve visibility and performance, not just for autonomous vehicles but for other Advanced Driving Assistance Systems (ADAS), we explore a capability called Augmented Vehicular Reality (AVR). AVR broadens the vehicle’s visual horizon by enabling it to share visual information with other nearby vehicles, but requires the design of novel relative positioning techniques, new perspective transformation methods, and approaches to isolate and predict the motion of dynamic objects in order to reduce bandwidth requirements and to hide latency.
Our AVR prototype achieves submeter reconstruction accuracy of dynamic objects while reducing bandwidth requirements by over two orders of magnitude relative to an approach that exchanges full raw sensor information.