Track Compensation Algorithm Using Free Space Information with Occupancy Grid Map

Over the past few years, numerous technologies have emerged to enable safe and convenient driving. However, there still exist various problems autonomous vehicles should overcome. Precise detection and perception of surrounding environments are the essential foundations to overcome them. Consequently, many sensor fusion algorithms have been developed to handle more complex situations, with sensor manufacturers also making strenuous efforts to enhance sensor performance. Although Light Detection And Ranging(LiDAR) sensor generally outperforms other sensor types, they remain prohibitively expensive from car manufacturing companies perspective. Therefore, camera and radar sensors have been enhanced, and are starting to provide free space information, similar to LiDAR sensor data and somewhat different from target information they have previously provided. The aim of this paper was to utilize the free space information to improve track information for vehicles. We employ the probability model with two occupancy grid map (OGM) types, which are Bayesian theory and Dempster-Shafer theory based OGMs, to classify free space information states and to efficiently handle free space information. Final output from the proposed algorithm is the target vehicle’s compensated track. Experimental results verify superior performance compared with non-compensated algorithms.