Learning-Based Safety-Critical Motion Planning with Input-to-State Barrier Certificate
Xinze Jin, Qing‐Shan Jia, Tao Zhang, Huaxia Xia · 2021
Motion planning in an effective and safe manner is a critical yet challenging task for autonomous driving. Learning-based framework as a new fashion in simulation and optimization has great potentials to develop a time-efficient navigation policy. To design a controller that addresses safety with explicit formulation, we incorporate control barrier function approach to generate a constrained optimization problem. The proposed method with uncertainty analysis helps to deal with disturbance towards motion planning task. Simulation results show that the algorithm produces improvement in the learning process and the adaptation to safety and performance.