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arXiv · 2307.09466

Optimal Vehicle Trajectory Planning for Static Obstacle Avoidance using Nonlinear Optimization

Yajia Zhang·Hongyi Sun·Ruizhi Chai·Daike Kang·Shan Li·Liyun Li
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Abstract

Vehicle trajectory planning is a key component for an autonomous driving system. A practical system not only requires the component to compute a feasible trajectory, but also a comfortable one given certain comfort metrics. Nevertheless, computation efficiency is critical for the system to be deployed as a commercial product. In this paper, we present a novel trajectory planning algorithm based on nonlinear optimization. The algorithm computes a kinematically feasible and comfort-optimal trajectory that achieves collision avoidance with static obstacles. Furthermore, the algorithm is time efficient. It generates an 6-second trajectory within 10 milliseconds on an Intel i7 machine or 20 milliseconds on an Nvidia Drive Orin platform.

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Yajia Zhang, Hongyi Sun, Ruizhi Chai, Daike Kang, Shan Li, Liyun Li. 2023-07-18. Optimal Vehicle Trajectory Planning for Static Obstacle Avoidance using Nonlinear Optimization. https://arxiv.org/abs/2307.09466

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