DDPG-Based Decision-Making Strategy of Adaptive Cruising for Heavy Vehicles Considering Stability

Abstract

The decision-making system of intelligent vehicles is the core component of an advanced driving system for both passenger vehicles and commercial vehicles. Finding ways to improve decision-making strategies to suit the complex and unfamiliar environments is a standing problem for traditional rule-based methods. This paper proposes a semi-rule-based decision-making strategy for heavy intelligent vehicles based on the Deep Deterministic Policy Gradient algorithm. Firstly, according to the car-following characteristics, the problems of high dimensions and a large amount of data in vehicle action space and state space are solved by dimension reduction and interval reduction to accelerate the training process. Subsequently, an accurate three-axle vehicle load model is established to calculate the load transfer rate value and carry out active control to increase the roll stability of heavy vehicles at high-speed corners. Furthermore, the Deep Deterministic Policy Gradient algorithm is developed based on the reward function and update function to achieve adaptive cruise control objectives for heavy vehicles on different curvature roads. Finally, the effectiveness and robustness of the algorithm are verified through simulation experiments.

Keywords

• Adaptive cruise control
• autonomous driving control system
• decision-making algorithm
• deep reinforcement learning
• heavy vehicle
• vehicle stability