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.
Highlights
The autonomous vehicle driving control is a promising solution for increased road traffic accidents [1]
Traditional Adaptive Cruise Control (ACC) decisionmaking strategies are designed based on rules, which define the behavior mode of vehicles for each scenario and uses some characteristic variables as the basis for judgment in condition switching [5]
In order to screen out the redundant information in the action set, accelerate the convergence of the training process and simplify the model, this paper selects various drivers with different styles to carry out multiple pre-training to achieve the driver action collection
Summary
The autonomous vehicle driving control is a promising solution for increased road traffic accidents [1]. In order to screen out the redundant information in the action set, accelerate the convergence of the training process and simplify the model, this paper selects various drivers with different styles to carry out multiple pre-training to achieve the driver action collection It determines the range of the final action set under the ACC condition. This state reflects the deviation of the lateral offset displacement and angle of the vehicle heading relative to the lane line at the current time In this algorithm, the driving convention for the model is considered to be on the right side of the road.
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