Enhancing Situational Awareness and Performance of Adaptive Cruise Control through Model Predictive Control and Deep Reinforcement Learning

Abstract

The main objective of this paper is to develop a novel reinforcement learning framework for automated weight tuning for model predictive control (MPC) based Adaptive Cruise Control (ACC) systems. Although MPC based ACC design has been extensively studied over the years, adapting the weights of the controller to a wide variety of driving scenarios remain a challenging task. The main contribution of this work is automating the MPC weight tuning process by formulating this objective as a deep reinforcement learning (RL) problem and training the RL agent on a wide range of simulated traffic scenarios. The trained agent observes state trajectories of both the host and surrounding vehicles and learns to map the driving environment to a suitable set of MPC weights. This approach significantly shortens the exhausting manual weight tuning process and also enhances the situational awareness of the system by anticipating the actions of surrounding drivers. Simulation results show that the developed algorithm outperforms standard ACC approaches in terms of tracking accuracy and passenger comfort.