Eco-driving for Intelligent Electric Vehicles at Signalized Intersection: A Proximal Policy Optimization Approach

The increasing number of motor vehicles promotes the consumption of transportation energy, which brings about a new research field named eco-driving. As a complex problem, the eco-driving operation near intersection is hard to be implemented. This paper utilizes the advanced reinforcement learning (RL) technique to solve the problem. A reward function that integrated safety, efficiency and energy conservation is designed, while a proximal policy optimization (PPO) algorithm is given to solve the continuous action space problem. Microscopic traffic simulations show that the smart electric vehicle can learn a speed profile that avoids unnecessary stopping near the intersection. Meanwhile, the proposed framework can significantly reduce the energy consumption of the electric vehicles by more than 66% with less increase of vehicle travel time. Moreover, sensitivity analysis shows that the trade-off between energy consumption and travel time can be flexibly adjusted by setting the parameters.