Collision Avoidance of Autonomous Vehicles Using the Control Lyapunov Function - Control Barrier Function - Quadratic Programming Approach with Deep Reinforcement Learning Decision Making

Collision avoidance and path planning are critical topics in autonomous vehicle development. This paper presents the progressive development of an optimization-based controller for autonomous vehicles using the Control Lyapunov Function - Control Barrier Function - Quadratic Programming (CLF – CBF - QP) approach. This framework enables the vehicle to navigate to its destination while avoiding obstacles. A unicycle model is utilized to incorporate vehicle dynamics. A series of simulations were conducted, starting with basic model-in-the-loop (MIL) non-real-time simulations, followed by real-time simulations. Multiple scenarios with different controller configurations and obstacle setups were tested, demonstrating the effectiveness of the proposed controllers in avoiding collisions. Real-time simulations in Simulink were used to demonstrate that the proposed controller could compute control actions for each state within a very short timestep, highlighting its computational efficiency. This efficiency underscores the potential for deploying the controller in real-world vehicle autonomous driving systems. Furthermore, we explored the feasibility of a hierarchical control framework comprising a Deep Reinforcement Learning (DRL) specifically Deep Q-Network (DQN) based high-level controller and a CLF-CBF-QP-based low-level controller. Simulation results show that the vehicle can effectively respond to obstacles and generate a successful trajectory towards its goal.