HDM-RRT: A Fast HD-Map-Guided Motion Planning Algorithm for Autonomous Driving in the Campus Environment

On campus, the complexity of the environment and the lack of regulatory constraints make it difficult to model the environment, resulting in less efficient motion planning algorithms. To solve this problem, HD-Map-guided sampling-based motion planning is a feasible research direction. We proposed a...

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Vydáno v:	Remote sensing (Basel, Switzerland) Ročník 15; číslo 2; s. 487
Hlavní autoři:	Guo, Xiaomin, Cao, Yongxing, Zhou, Jian, Huang, Yuanxian, Li, Bijun
Médium:	Journal Article
Jazyk:	angličtina
Vydáno:	Basel MDPI AG 01.01.2023
Témata:	algorithm efficiency Algorithms autonomous driving Autonomous vehicles College campuses Constraint modelling Convergence Efficiency HD-Map Heuristic Motion planning Normal distribution Optimization risk risk assessment Roads & highways Sampling sampling-based algorithm Stability
ISSN:	2072-4292, 2072-4292
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Abstract	On campus, the complexity of the environment and the lack of regulatory constraints make it difficult to model the environment, resulting in less efficient motion planning algorithms. To solve this problem, HD-Map-guided sampling-based motion planning is a feasible research direction. We proposed a motion planning algorithm for autonomous vehicles on campus, called HD-Map-guided rapidly-exploring random tree (HDM-RRT). In our algorithm, A collision risk map (CR-Map) that quantifies the collision risk coefficient on the road is combined with the Gaussian distribution for sampling to improve the efficiency of algorithm. Then, the node optimization strategy of the algorithm is deeply optimized through the prior information of the CR-Map to improve the convergence rate and solve the problem of poor stability in campus environments. Three experiments were designed to verify the efficiency and stability of our approach. The results show that the sampling efficiency of our algorithm is four times higher than that of the Gaussian distribution method. The average convergence rate of the proposed algorithm outperforms the RRT* algorithm and DT-RRT* algorithm. In terms of algorithm efficiency, the average computation time of the proposed algorithm is only 15.98 ms, which is much better than that of the three compared algorithms.
AbstractList	On campus, the complexity of the environment and the lack of regulatory constraints make it difficult to model the environment, resulting in less efficient motion planning algorithms. To solve this problem, HD-Map-guided sampling-based motion planning is a feasible research direction. We proposed a motion planning algorithm for autonomous vehicles on campus, called HD-Map-guided rapidly-exploring random tree (HDM-RRT). In our algorithm, A collision risk map (CR-Map) that quantifies the collision risk coefficient on the road is combined with the Gaussian distribution for sampling to improve the efficiency of algorithm. Then, the node optimization strategy of the algorithm is deeply optimized through the prior information of the CR-Map to improve the convergence rate and solve the problem of poor stability in campus environments. Three experiments were designed to verify the efficiency and stability of our approach. The results show that the sampling efficiency of our algorithm is four times higher than that of the Gaussian distribution method. The average convergence rate of the proposed algorithm outperforms the RRT* algorithm and DT-RRT* algorithm. In terms of algorithm efficiency, the average computation time of the proposed algorithm is only 15.98 ms, which is much better than that of the three compared algorithms.
Author	Cao, Yongxing Zhou, Jian Guo, Xiaomin Huang, Yuanxian Li, Bijun
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SubjectTerms	algorithm efficiency Algorithms autonomous driving Autonomous vehicles College campuses Constraint modelling Convergence Efficiency HD-Map Heuristic Motion planning Normal distribution Optimization risk risk assessment Roads & highways Sampling sampling-based algorithm Stability
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Title	HDM-RRT: A Fast HD-Map-Guided Motion Planning Algorithm for Autonomous Driving in the Campus Environment
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