A Decision-Making Model for Self-Driving Vehicles Based on Overtaking Frequency

Abstract

The driving state of a self-driving vehicle represents an important component in the self-driving decision system. To ensure the safe and efficient driving state of a self-driving vehicle, the driving state of the self-driving vehicle needs to be evaluated quantitatively. In this paper, a driving state assessment method for the decision system of self-driving vehicles is proposed. First, a self-driving vehicle and surrounding vehicles are compared in terms of the overtaking frequency (OTF), and an OTF-based driving state evaluation algorithm is proposed considering the future driving efficiency. Next, a decision model based on the deep deterministic policy gradient (DDPG) algorithm and the proposed method is designed, and the driving state assessment method is integrated with the existing time-to-collision (TTC) and minimum safe distance. In addition, the reward function and multiple driving scenarios are designed so that the most efficient driving strategy at the current moment can be determined by optimal search under the condition of ensuring safety. Finally, the proposed decision model is verified by simulations in four three-lane highway scenarios. The simulation results show that the proposed decision model that integrates the self-driving vehicle driving state assessment method can help self-driving vehicles to drive safely and to maintain good maneuverability.