Model predictive control

Robot

Control (management)

Vehicle dynamics

Engineering

Transport engineering

Motion planning

Control engineering

Computer security

Theoretical computer science

Obstacle avoidance

Computer science

Collision

Collision avoidance

Reachability

Physics

Trajectory

Overtaking

Simulation

Astronomy

Mobile robot

Automotive engineering

Artificial intelligence

Control theory (sociology)

Workspace

IEEE Transactions on Intelligent Transportation Systems

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.

Trajectory Planning for Autonomous High-Speed Overtaking in Structured Environments Using Robust MPC