A Motion Planning Method for Autonomous Vehicles

Xi-jun ZHAO, Jin LIU, Sen ZHU, Lan ZHU, Hong-ming WANG

Abstract


A model based predictive motion planning with combining Bayesian Methodology for autonomous driving is proposed. The planning algorithm firstly generates candidate obstacle free trajectories among the current state and sampled desired states using trajectory generation techniques. Then Bayesian Methodology is applied to select the optimal trajectory among candidate trajectories. The preferred trajectory selected in the previous planning cycle is regarded as the prior knowledge, while the trajectory cost in current cycle is transformed into likelihood function. Both the distribution follows the normal distribution and the Bayesian theorem is adopted to calculate the posterior knowledge to determine the preferred trajectory. Then the speed profile is calculated with the preferred trajectory to produce the real trajectory. The proposed motion planner is implemented and tested in simulations. Experiments results show that the planner has good performance in autonomous driving and especially reduces indecision behavior in uncertain environments, and improves stability of autonomous driving.

Keywords


Motion planning, Bayesian method, Indecision, Predictive approach


DOI
10.12783/dtetr/icca2016/5999

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