AutoMatch: Leveraging Traffic Camera to Improve Perception and Localization of Autonomous Vehicles

Traffic camera is one of the most ubiquitous traffic facilities, providing high coverage of complex, accident-prone road sections such as intersections. This work leverages traffic cameras to improve the perception and localization performance of autonomous vehicles at intersections. In particular, vehicles can expand their range of perception by matching the images captured by both the traffic cameras and on-vehicle cameras. Moreover, a traffic camera can match its images to an existing high-definition map (HD map) to derive centimeter-level location of the vehicles in its field of view. To this end, we propose AutoMatch - a novel system for real-time image registration, which is a key enabling technology for traffic camera-assisted perception and localization of autonomous vehicles. Our key idea is to leverage landmark keypoints of distinctive structures such as ground signs at intersections to facilitate image registration between traffic cameras and HD maps or vehicles. By leveraging the strong structural characteristics of ground signs, AutoMatch can extract very few but precise landmark keypoints for registration, which effectively reduces the communication/compute overhead. We implement AutoMatch on a testbed consisting of a self-built autonomous car, drones for surveying and mapping, and real traffic cameras. In addition, we collect two new multi-view traffic image datasets at intersections, which contain images from 220 real operational traffic cameras in 22 cities. Experimental results show that AutoMatch achieves pixel-level image registration accuracy within 88 milliseconds, and delivers an 11.7x improvement in accuracy, 1.4x speedup in compute time, and 17.1x data transmission saving over existing approaches.