Abstract:

An obstacle avoidance path planning algorithm based on tracking error observation and target measurement error observation was proposed to enhance the obstacle avoidance safety of autonomous buses, aiming at the risk of collision caused by the uncertainty of tracking control and target measurement. The mean-variance model was utilized to describe the tracking error, and the Gaussian process regression was introduced to quantify target measurement error. A probability distribution model for the lateral spatial position was established, providing theoretical support for solving the safe lateral offset value. An obstacle avoidance path planning was implemented through the Bézier path model, and a comprehensive evaluation system was established to assess the performance of the obstacle avoidance planning. In the simulation environment, the influence of uncertain factors such as tracking error and measurement error on obstacle avoidance quality was explored, and the real vehicle data simulation verification was carried out on the test road of unmanned intelligent bus in Tianjin University, and the effect of the three algorithms was evaluated. The results show that the obstacle avoidance safety is improved by 17.61% in actual driving, and it has good robustness. The proposed average time of the algorithm is 7.43 ms, which has good real-time performance.

Key words: autonomous buses, avoidance path planning, tracking error observation, target measurement error observation