A Real-Time Motion Planner with Trajectory Optimization for Autonomous Vehicles

被引:0
|
作者
Xu, Wenda [1 ]
Wei, Junqing
Dolan, John M.
Zhao, Huijing [1 ]
Zha, Hongbin [1 ]
机构
[1] Peking Univ, Sch Elect Engn & Comp Sci, Beijing 100871, Peoples R China
关键词
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暂无
中图分类号
TP [自动化技术、计算机技术];
学科分类号
0812 ;
摘要
In this paper, an efficient real-time autonomous driving motion planner with trajectory optimization is proposed. The planner first discretizes the plan space and searches for the best trajectory based on a set of cost functions. Then an iterative optimization is applied to both the path and speed of the resultant trajectory. The post-optimization is of low computational complexity and is able to converge to a higher-quality solution within a few iterations. Compared with the planner without optimization, this framework can reduce the planning time by 52% and improve the trajectory quality. The proposed motion planner is implemented and tested both in simulation and on a real autonomous vehicle in three different scenarios. Experiments show that the planner outputs high-quality trajectories and performs intelligent driving behaviors.
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收藏
页码:2061 / 2067
页数:7
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