Computation of Solution Spaces for Optimization-Based Trajectory Planning

被引:25
作者
Schaefer, Lukas [1 ]
Manzinger, Stefanie [1 ]
Althoff, Matthias [1 ]
机构
[1] Tech Univ Munich, Dept Informat, D-85748 Garching, Germany
来源
IEEE TRANSACTIONS ON INTELLIGENT VEHICLES | 2023年 / 8卷 / 01期
关键词
Collision avoidance; Vehicle dynamics; Planning; Trajectory; Trajectory planning; Intelligent vehicles; Computational modeling; Automated vehicles; optimization; reachability analysis; trajectory planning; MODEL-PREDICTIVE CONTROL; COLLISION-AVOIDANCE; AUTONOMOUS VEHICLES; ONLINE VERIFICATION; CONVEX-OPTIMIZATION; ROAD VEHICLES; MOTION; MANEUVERS; GENERATION; ALGORITHM;
D O I
10.1109/TIV.2021.3077702
中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
The nonlinear vehicle dynamics and the non-convexity of collision avoidance constraints pose major challenges for optimization-based trajectory planning of automated vehicles. Current solutions are either tailored to specific traffic scenarios, simplify the vehicle dynamics, are computationally demanding, or may get stuck in local minima. This work presents a novel approach to address the aforementioned shortcomings by identifying collision-free driving corridors that represent spatio-temporal constraints for motion planning using set-based reachability analysis. We derive a suitable formulation of collision avoidance constraints from driving corridors that can be integrated into arbitrary nonlinear programs as well as (successive) convexification procedures. When combining our approach with existing motion planning methods based on continuous optimization, trajectories can be planned in arbitrary traffic situations in a computationally efficient way. We demonstrate the efficacy of our approach using scenarios from the CommonRoad benchmark suite.
引用
收藏
页码:216 / 231
页数:16
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