A cooperative vehicle-infrastructure based urban driving environment perception method using a D-S theory-based credibility map

Abstract

Driving environment perception technology is a key issue for driving assistance systems and autonomous vehicles. Driving environment information collection and information fusion and representation are two important contents of this technology. On-board sensors such as camera, radar, lidar, etc., are popularly used to collect relevant information. However, when using these on-board sensors, uncertainty arises from ignorance and errors, where ignorance is usually caused by vehicle occlusion, and errors is caused by imprecise pose estimation and noisy measurements. Increasing sensor number would decrease such uncertainty, but complexity and cost is also increased in this way. In this paper, we present a V2I (vehicle-infrastructure) based urban driving environment collection method achieved only by two sensors, i.e., a Road Side Unit camera (RSU camera) and vehicle GPS. RSU camera is used to extract lane marking information, including position and type, and vehicle position information. Vehicle GPS provide position information of vehicle. A credibility map is proposed as the compact representation of urban environment, and accept those information from RSU camera and vehicle GPS. In order to hand uncertainty caused by sensors, Dempster-Shafer theory is employed to estimate the credibility of each cell, fuse the credibility root in the information provided by RSU camera and vehicle GPS, and map onto the credibility map. The performance of this method has been validated with real-world urban traffic data.