期刊
IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS
卷 21, 期 6, 页码 2310-2323出版社
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TITS.2019.2916354
关键词
Trajectory; Planning; Roads; Vehicle dynamics; Collision avoidance; Lead; Autonomous vehicles; Trajectory planning; autonomous overtaking; MPC; robust MPC; autonomous vehicles
资金
- Jaguar Land Rover [EP/N01300X/1]
- United Kingdom-Engineering and Physical Sciences Research Council (UK-EPSRC) [EP/N01300X/1]
- EPSRC [EP/N01300X/1, EP/N01300X/2] Funding Source: UKRI
Automated vehicles are increasingly getting main-streamed and this has pushed development of systems for autonomous manoeuvring (e.g., lane-change, merge, and overtake) to the forefront. A novel framework for situational awareness and trajectory planning to perform autonomous overtaking in high-speed structured environments (e.g., highway and motorway) is presented in this paper. A combination of a potential field like function and reachability sets of a vehicle are used to identify safe zones on a road that the vehicle can navigate towards. These safe zones are provided to a tube-based robust model predictive controller as reference to generate feasible trajectories for combined lateral and longitudinal motion of a vehicle. The strengths of the proposed framework are: 1) it is free from non-convex collision avoidance constraints; 2) it ensures feasibility of trajectory even if decelerating or accelerating while performing lateral motion; and 3) it is real-time implementable. The ability of the proposed framework to plan feasible trajectories for high-speed overtaking is validated in a high-fidelity IPG CarMaker and Simulink co-simulation environment.
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