Hybrid PPO–DQN for Multi-Objective Adaptive Cruise Control in Eco-Driving: Reward Shaping Toward Safety and Sustainability (Student Abstract)

摘要

In adaptive cruise control (ACC), balancing safety, comfort, and sustainability still remains challenging. Accordingly, we propose a hybrid reinforcement learning framework combining proximal policy optimization (PPO) and deep Q-network (DQN) with a multi-objective reward for autonomous carbon-neutral eco-driving. Experimental results revealed the contrasts between eco and non-eco modes, underscoring how reward design shapes driving behaviors.