Hybrid Electric Vehicle Energy Management With Computer Vision and Deep Reinforcement Learning

Modern automotive systems have been equipped with a highly increasing number of onboard computer vision hardware and software, which are considered to be beneficial for achieving eco-driving. This article combines computer vision and deep reinforcement learning (DRL) to improve the fuel economy of hybrid electric vehicles. The proposed method is capable of autonomously learning the optimal control policy from visual inputs. The state-of-the-art convolutional neural networks-based object detection method is utilized to extract available visual information from onboard cameras. The detected visual information is used as a state input for a continuous DRL model to output energy management strategies. To evaluate the proposed method, we construct 100 km real city and highway driving cycles, in which visual information is incorporated. The results show that the DRL-based system with visual information consumes 4.3-8.8% less fuel compared with the one without visual information, and the proposed method achieves 96.5% fuel economy of the global optimum-dynamic programming.

Automated summary

This study combines computer vision and deep reinforcement learning to improve the fuel economy of hybrid electric vehicles, achieving significant reduction in fuel consumption and 96.5% fuel economy of the global optimum-dynamic programming in real driving scenarios.