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Road-Condition-Aware Dynamic Path Planning for Intelligent Vehicles

Procedia Computer Science 2020年 174卷 419-423页

作者： Yan Chen Anming Dong Yi Sun School of Information Science and Engineering Qufu Normal University Rizhao 276826 China School of Computer Science and Technology Qilu University of Technology (Shandong Academy of Sciences) Jinan 250353 China Computer Science Center (National Supercomputer Center in Jinan) Shandong Provincial Key Laboratory of Computer Networks Qilu University of Technology (Shandong Academy of Sciences) Jinan 250014 China

Intelligent vehicles face a more complex driving environment, which requires their path planning algorithms to have the ability to respond quickly to complex environmental changes. Therefore, it is very necessary to plan the optimal path for smart vehicles in real-time. Recently, the convergence of graph dynamics and its equivalence with the shortest path solution have been proved, and the biased minimum consensus algorithm has been successfully applied to the shortest path planning problem. In this paper, considering the road conditions (degree of congestion, whether an accident occurred, etc.) and the Euclidean distance of the route, we improve biased minimum consensus algorithm to achieve optimal path planning in the complex situation of real-time updating of road conditions. In the simulation, we used the algorithm for path planning in the road topological maps of Beijing City, and compared with the RRT (Rapidly-exploring Random Trees algorithm) algorithm and the BRRT (Bidirectional Rapidly-exploring Random Trees algorithm) algorithm. The improved biased minimum consensus algorithm has better performance in real-time path planning problems. The research results shed new light on the real-time dynamic path planning in real traffic, which has practical significance.

关键词： Intelligent Vehicle Path Planning improved biased minimum consensus algorithm Optimal Path

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