节点文献
Collision-Free Path Planning with Kinematic Constraints in Urban Scenarios
【摘要】 In urban driving scenarios,owing to the presence of multiple static obstacles such as parked cars and roadblocks,planning a collision-free and smooth path remains a challenging problem.In addition,the pathplanning problem is mostly non-convex,and contains multiple local minima.Therefore,a method for combining a sampling-based method and an optimization-based method is proposed in this paper to generate a collision-free path with kinematic constraints for urban scenarios.The sampling-based method constructs a search graph to search for a seeding path for exploring a safe driving corridor,and the optimization-based method constructs a quadratic programming problem considering the desired state constraints,continuity constraints,driving corridor constraints,and kinematic constraints to perform path optimization.The experimental results show that the proposed method is able to plan a collision-free and smooth path in real time when managing typical urban scenarios.
【Abstract】 In urban driving scenarios,owing to the presence of multiple static obstacles such as parked cars and roadblocks,planning a collision-free and smooth path remains a challenging problem.In addition,the pathplanning problem is mostly non-convex,and contains multiple local minima.Therefore,a method for combining a sampling-based method and an optimization-based method is proposed in this paper to generate a collision-free path with kinematic constraints for urban scenarios.The sampling-based method constructs a search graph to search for a seeding path for exploring a safe driving corridor,and the optimization-based method constructs a quadratic programming problem considering the desired state constraints,continuity constraints,driving corridor constraints,and kinematic constraints to perform path optimization.The experimental results show that the proposed method is able to plan a collision-free and smooth path in real time when managing typical urban scenarios.
【Key words】 autonomous vehicle; path planning; dynamic searching; quadratic programming;
- 【文献出处】 Journal of Shanghai Jiao Tong University(Science) ,上海交通大学学报(英文版) , 编辑部邮箱 ,2021年05期
- 【分类号】U463.6