A Multi-Objective Adaptive Car-Following Framework for Autonomous Connected Vehicles with Deep Reinforcement Learning

Autonomous connected vehicles (ACV) involve advanced control strategies to effectively balance safety, efficiency, energy consumption, and passenger comfort. This research introduces a deep reinforcement learning (DRL)-based car-following (CF) framework employing the Deep Deterministic Policy Gradient (DDPG) algorithm, which integrates a multi-objective reward function that balances the four goals while maintaining safe policy learning. Utilizing real-world driving data from the highD dataset, the proposed model learns adaptive speed control policies suitable for dynamic traffic scenarios. The performance of the DRL-based model is evaluated against a traditional model predictive control-adaptive cruise control (MPC-ACC) controller. Results show that the DRL model significantly enhances safety, achieving zero collisions and a higher average time-to-collision (TTC) of 8.45 s, compared to 5.67 s for MPC and 6.12 s for human drivers. For efficiency, the model demonstrates 89.2% headway compliance and maintains speed tracking errors below 1.2 m/s in 90% of cases. In terms of energy optimization, the proposed approach reduces fuel consumption by 5.4% relative to MPC. Additionally, it enhances passenger comfort by lowering jerk values by 65%, achieving 0.12 m/s³ vs. 0.34 m/s³ for human drivers. A multi-objective reward function is integrated to ensure stable policy convergence while simultaneously balancing the four key performance metrics. Moreover, the findings underscore the potential of DRL in advancing autonomous vehicle control, offering a robust and sustainable solution for safer, more efficient, and more comfortable transportation systems.