Visual Detection and Deep Reinforcement Learning-Based Car Following and Energy Management for Hybrid Electric Vehicles

Abstract

Practical vision-based technology is essential for the autonomous driving of intelligent hybrid electric vehicles. In this article, a hierarchical control structure is proposed, which combines you only look once-based object detection and learning-based intelligent control by deep reinforcement learning. After modeling a typical car-following scene, the leading car is detected in the driving image, and the real-time distance between two cars is evaluated by vision-based distance measurement. Then, a deep Q-network is adopted to learn the car-following control strategy and energy management strategy, which achieves multiobjective control of the hybrid powertrain while maintaining a reasonable distance for the following car. When completing off-line training, the online processor-in-the-loop test by the edge computing device NVIDIA Jetson AGX Xavier is performed. Results show that the hierarchical control strategy gets a fuel economy of 5.76 L/100 km while keeping a safe following distance. Moreover, the time consumed to run a driving cycle of 1797 s in the embedded device is 476.87 s, which means that a control loop, including target recognition, distance measurement, car-following control, and energy management, takes 0.26 s. This study proves that vehicle vision can lay the technical foundation for intelligent driving, and the results illustrate that the hierarchical control structure is capable of achieving considerable computing efficiency on embedded devices and has the potential for real vehicle control.