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• W2904303363 startingPage "24" @default.
• W2904303363 abstract "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." @default.
• W2904303363 created "2018-12-22" @default.
• W2904303363 creator A5027164541 @default.
• W2904303363 creator A5070901859 @default.
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• W2904303363 creator A5091574711 @default.
• W2904303363 date "2019-03-01" @default.
• W2904303363 modified "2023-09-26" @default.
• W2904303363 title "Model Predictive Trajectory Planning for Automated Driving" @default.
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• W2904303363 doi "https://doi.org/10.1109/tiv.2018.2886683" @default.
• W2904303363 hasPublicationYear "2019" @default.
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