Title | ||
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A Novel Direct Trajectory Planning Approach Based on Generative Adversarial Networks and Rapidly-Exploring Random Tree |
Abstract | ||
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Trajectory planning is essential for self-driving vehicles and has stringent requirements for accuracy and efficiency. The existing trajectory planning methods have limitations in the feasibility of planned trajectories and computational efficiency. This paper proposes a life-long learning framework to achieve effective and high-accuracy direct trajectory planning (DTP) tasks. Based on generative adversarial networks (GANs), this study develops a lightweight GDTP model to map the initial/final states and the control action sequence. Additionally, by embedding the GDTP into the rapidly-exploring random tree (RRT), a GDTP-RRT algorithm is further designed for long-distance and multi-stage planning tasks. Taking the tractor-trailer as an application case, we test the proposed method in multiple scenarios with varying characteristics. The experimental results show that the method can plan highly feasible trajectories in a short time, compared with the most applied algorithm – the cubic curve RRT* (CCRRT*). It is found that the tracking errors of our method are 29.1% and 44.1% lower than the CCRRT* in terms of position and heading angle. This paper provides an effective and stable vehicle trajectory planning method for complex self-driving tasks. |
Year | DOI | Venue |
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2022 | 10.1109/TITS.2022.3164391 | IEEE Transactions on Intelligent Transportation Systems |
Keywords | DocType | Volume |
Self-driving vehicles,trajectory planning,generative adversarial networks,rapidly-exploring random tree,tractor-trailer | Journal | 23 |
Issue | ISSN | Citations |
10 | 1524-9050 | 0 |
PageRank | References | Authors |
0.34 | 15 | 5 |
Name | Order | Citations | PageRank |
---|---|---|---|
Cong Zhao | 1 | 0 | 0.34 |
Yifan Zhu | 2 | 0 | 0.34 |
Yuchuan Du | 3 | 4 | 3.21 |
Feixiong Liao | 4 | 0 | 0.34 |
Ching-Yao Chan | 5 | 79 | 23.48 |