Deep Reinforcement Learning-Based Integrated Control of Hybrid Electric Vehicles Driven by Lane-Level High-Definition Map

Abstract

For automobiles, the innovation of autopilot, connected communication, and new energy have been treated as the trend. This paper proposed a deep reinforcement learning-based integrated control strategy driven by the lane-level high-definition map for hybrid electric vehicles. Firstly, an optimal route with a length of 1277km from Shanghai to Beijing was determined from Google Maps. Based on the longitude, latitude, and elevation data from Google Earth, a lane-level driving environment was built and the global speed was preplanned based on the limited and safe speed. Secondly, a multi-policy integrated control framework and training method were proposed, adopting two types of deep reinforcement learning algorithms to control the velocity and steering angle of the vehicle layer and power distribution of the powertrain. Finally, processor-in-the-loop tests were completed by adopting NVIDIA Jetson Nano and Jetson AGX Xavier. Results show that the integrated control strategy achieves stable control effects on velocity and steering angle based on the environment model, gets a fuel economy of 6.77L/100km, and presents ideal efficiency on embedded devices. Moreover, the results are compared with three other typical powertrains in long-distance driving, and the comparison of driving costs and infrastructure emphasizes the necessity of the development of hybrid electric vehicles.