期刊
IEEE TRANSACTIONS ON INTELLIGENT VEHICLES
卷 4, 期 1, 页码 24-38出版社
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TIV.2018.2886683
关键词
Trajectory planning; maneuver decision; model predictive control; maneuver decoupling; homotopic classes
类别
资金
- German Federal Ministry for Economic Affairs and Energy (BMWi)
- German Science Foundation DFG within the priority program Cooperatively Interacting Automobiles
In order to enable automated driving systems on the road, several key challenges need to be solved. One of these issues is real-time maneuver decision and trajectory planning. This paper introduces a general framework for maneuver and trajectory planning with model predictive methods. It discusses several representations of this framework in distinct complexity levels. In general, sophisticated models require to nonquadratic objective functions with nonlinear constraints, leading to increased computational complexities during calculation. Yet, oversimplified models can neither cope with vehicle dynamics in critical maneuvers nor do they represent complex traffic scenes with several maneuver options appropriately. A scheme to partition the trajectory space into homotopy regions is proposed. In each homotopy class, linearization about a trajectory from this class is applied. Demonstrations by simulation and with experimental vehicles show the capability of the proposed method in selecting optimal maneuvers and trajectories. This is even valid during extreme maneuvers, such as in last moment collision avoidance.
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