Experimental Validation of Online Motion Planning for Semi-Autonomous Vehicles

This work describes the optimization of paths and velocity profiles for the usage in semi-autonomous vehicles. The focus of the optimization lies on an energy efficient motion while considering the driver’s longitudinal demands. The trade-off between travel time and energy consumption of the vehicle can be directly chosen by the driver. With help of a nonlinear model predictive control framework the global optimum of the velocity optimization with nonlinear constraints based on the vehicle’s physical limits is found. The online path planner together with the online velocity planner provide the input to the underlying vehicle’s control modules, as a basis for automated driving. The effectiveness of the proposed approach is shown via hardware-in-the-loop tests and real-world driving tests.