Abstract:

Autonomous vehicles often use multiple sensors to detect and track surrounding targets. However， accurate multi-target detection and tracking remains a major challenge and difficulty in achieving autonomous driving due to the heterogeneous characteristics of sensors and complex driving environments. For the task of multi-target detection and tracking in Advanced Driver Assistance System （ADAS）， a sensor configuration scheme based on a visual sensor and five millimeter-wave radars （1V5R） is used in this paper and a multi-sensor information fusion algorithm based on distributed robust Kalman filters is designed to realize accurate perception of surrounding targets. Firstly， considering different data characteristics of sensors， various Kalman filters such as linear Kalman filters and extended Kalman filters are adopted for data fusion and a 1V5R information fusion framework is built based on distributed Kalman filtering algorithm. Then， to reduce the impact of sensor dynamic error on fusion accuracy， the robust estimation theory is introduced into the Kalman-weighted fusion， enabling real-time estimation and correction of dynamic error. Finally， the proposed multi-sensor information fusion algorithm is validated through simulation and vehicle tests. The results show that compared to measurements from a single sensor， the proposed algorithm can robustly perform the task of information fusion of multiple sensors and improve the accuracy of detection and tracking， with good robustness.

Key words: multi-target detection and tracking, sensor information fusion, distributed Kalman filter, robust estimation