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Abstract

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.

Keywords

autonomous driving, motion planning, trajectory optimization, lattice planner

Notes

Associated Project(s): Autonomous Driving Motion Planning

Number of pages: 7

Text Reference

Wenda Xu and John M. Dolan, "A Real-Time Motion Planner with Trajectory Optimization for Autonomous Vehicles," Proceedings of the International Conference on Robotics and Automation, May, 2012, pp. 2061-2067.

BibTeX Reference

@inproceedings{Xu_2012_7199,
   author = "Wenda Xu and John M Dolan",
   title = "A Real-Time Motion Planner with Trajectory Optimization for Autonomous Vehicles",
   booktitle = "Proceedings of the International Conference on Robotics and Automation",
   pages = "2061-2067",
   month = "May",
   year = "2012",
}

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