Collaborative Physics-Informed Neural Dynamics Approach for Autonomous Vehicles With Nonconvex Safety-Critical Constraints

Increasing complex driving scenarios inevitably expose autonomous vehicles to intricate nonconvex challenges, which are usually simplified or even ignored by current research, leading to accuracy compromises. In this regard, this paper newly constructs a collaborative physics-informed neural dynamics (CPIND) approach to simultaneously tackle the trajectory tracking and safety-critical control problems in autonomous vehicles. Specifically, the proposed CPIND approach consists of a linear quadratic regulator to provide global control inputs for the trajectory tracking task and a safety-critical optimization control scheme with newly established control barrier functions. The constructed optimization control scheme is essentially nonconvex with respect to decision variables to update the local control inputs for the obstacle avoidance task with the highest priority. A theorem is provided to substantiate the convergence of the proposed CPIND approach. At last, a comparative example, an example on the CarSim-Simulink co-simulation platform, and an experiment on the AgileX autonomous driving platform are conducted to verify the effectiveness and preponderance of the proposed CPIND approach. Overall, the proposed CPIND approach enables autonomous vehicles to follow a collision-free trajectory effectively and accurately, offering a promising solution for enhancing the safety and reliability of autonomous vehicles.