Praveen Prasanth KV

This research highlights the integration of Deep Reinforcement Learning (DRL) with Deep Deterministic Policy Gradient (DDPG) in Adaptive Cruise Control (ACC), a key part of Advanced Driver Assistance Systems (ADAS). Using a simulation environment featuring a lead car and a Battery Electric Vehicle (BEV), the DRL controller manages the BEV's velocity, addressing multi-objective ACC goals. Driving objectives like tracking accuracy, ride comfort, and safety are translated into reward functions for the DRL. Comparing it with Model Predictive Control (MPC) in highway scenarios, the DRL excels in ride comfort while matching MPC in accuracy and safety. Moreover, the DRL exhibits robustness and efficiency across various driving scenarios, showcasing superior performance and strong adaptability.