Track Compensation Algorithm using Free Space Information with Occupancy Grid Map

다국어 초록 (Multilingual Abstract)

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 perceptionof surrounding environments are the essential foundations to overcome them. Consequently, many sensor fusionalgorithms 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 generallyoutperforms 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. Theaim of this paper was to utilize the free space information to improve track information for vehicles. We employthe probability model with two occupancy grid map (OGM) types, which are Bayesian theory and Dempster-Shafertheory based OGMs, to classify free space information states and to efficiently handle free space information. Finaloutput from the proposed algorithm is the target vehicle’s compensated track. Experimental results verify superiorperformance compared with non-compensated algorithms.

참고문헌 (Reference)

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