Search-Based Motion Planning for Performance Autonomous Driving
This work addresses performance autonomous driving for vehicles on slippery roads, representing an incremental improvement in motion planning methods.
The paper tackled the problem of generating optimal vehicle trajectories for autonomous driving on slippery roads to minimize lap time, achieving safe and optimal performance in challenging scenarios using a search-based motion planning approach.
Driving on the limits of vehicle dynamics requires predictive planning of future vehicle states. In this work, a search-based motion planning is used to generate suitable reference trajectories of dynamic vehicle states with the goal to achieve the minimum lap time on slippery roads. The search-based approach enables to explicitly consider a nonlinear vehicle dynamics model as well as constraints on states and inputs so that even challenging scenarios can be achieved in a safe and optimal way. The algorithm performance is evaluated in simulated driving on a track with segments of different curvatures.