Dynamic Path Planning for Autonomous Driving on Branch Streets With Crossing Pedestrian Avoidance Guidance

• Published in: IEEE access Vol. 7; pp. 144720 - 144731
• Main Authors: Wu, Wenjing, Jia, Hongfei, Luo, Qingyu, Wang, Zhanzhong
• Format: Journal Article
• Language: English
• Published: Piscataway IEEE 2019; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Algorithms; Autonomous vehicle; Autonomous vehicles; Barriers; Collision avoidance; Driving; Dynamics; Heuristic algorithms; optimal control; Path planning; Pedestrian crossings; Pedestrians; Roads; Streets; Throttles; Traffic safety; Vehicle dynamics; Vehicles; velocity obstacle
• ISSN: 2169-3536; 2169-3536
• DOI: 10.1109/ACCESS.2019.2938232

This paper presents a real-time dynamic path planning method for autonomous driving to avoid collision with crossing pedestrian on branch streets. The velocity obstacle algorithms are introduced to pick up the collision-free velocities for vehicles. In this method, the curvilinear lane edges are considered as static obstacle while crossing pedestrians and approaching vehicles are considered as velocity obstacles. The paths planning of vehicles are optimized by considering the delay minimum and comfort of drivers under the constraints of appropriate parameters for veer, throttle, or brake systems. A single vehicle's path planning and multi-vehicles 'coordinated or uncoordinated paths planning with crossing pedestrian collision avoidance are experimentally simulated including the longitudinal and lateral motions planning of vehicles. The simulation results demonstrate the effectiveness of the proposed method and indicate its wide practical application on autonomous driving to improve the traffic safety of branch streets.