# Design of an Intelligent Vehicle Behavior Decision Algorithm Based on DGAIL
###### Abstract  
With the development of AI, the intelligence level of vehicles is increasing. Structured roads, as common and important traffic scenes, are the most typical application scenarios for realizing autonomous driving. The driving behavior decision-making of intelligent vehicles has always been a controversial and difficult research topic. Currently, the mainstream decision-making methods, which are mainly based on rules, lack adaptability and generalization to the environment. Aimed at the particularity of intelligent vehicle behavior decisions and the complexity of the environment, this thesis proposes an intelligent vehicle driving behavior decision method based on DQN generative adversarial imitation learning (DGAIL) in the structured road traffic environment, in which the DQN algorithm is utilized as a GAIL generator. The results show that the DGAIL method can preserve the design of the reward value function, ensure the effectiveness of training, and achieve safe and efficient driving on structured roads. The experimental results show that, compared with A3C, DQN and GAIL, the model based on DGAIL spends less average training time to achieve a 95% success rate in the straight road scene and merging road scene, respectively. Apparently, this algorithm can effectively accelerate the selection of actions, reduce the randomness of actions during the exploration, and improve the effect of the decision-making model. intelligent driving; driving decision; imitation learning; generative adversarial imitation learning +
Footnote †: journal: Applied  
Presently, there are three main decision-making methods for intelligent vehicles, namely, rule-based decision-making methods, \"end-to-end\" decision-making methods, and decision-making methods based on deep reinforcement learning. Deep reinforcement learning is similar to the process of humans learning new knowledge. In continuous interaction with the environment, the agent uses the rewards or punishments that are obtained to continuously optimize the strategy until it learns the optimal strategy. In 2017, [PERSON] proposed a vehicle braking system based on the DQN algorithm and applied the algorithm to the control of the vehicle braking system [3]. In 2018, [PERSON] proposed an intelligent vehicle decision-making system based on the A3C algorithm. The input layer is the image information obtained by a convolutional neural network (CNN). The A3C algorithm is used to train the vehicle in the simulation environment, and good results have been achieved [4]. In 2018, [PERSON] of Wayve, a self-driving car company, proposed a DDPG-based lane-keeping method. The image information obtained by the monocular camera is input into the CNN, and the DDPG algorithm is used to output the vehicle's decision-making actions and carry them out; this method has been verified by real vehicles and achieved a better lane tracking effect [5]. In 2018, [PERSON] proposed the DDPG algorithm, which added a teaching data part to the loss function of DDPG and achieved better results than the DDPG algorithm in the lane-keeping test in the CARLA environment [6]. In 2018, [PERSON] proposed a self-vehicle overtaking control method based on deep reinforcement learning in dynamic uncertain environments, which can realize lane keeping and autonomous overtaking behaviors in high-speed dynamic scenarios [7]. In 2020, [PERSON] proposed a DQN-based decision-making method in high-speed scenarios, combining the DQN algorithm with expert knowledge, which greatly shortened the training time [8]. Although deep reinforcement learning has performed well in some application scenarios, it still faces the problem of low learning efficiency, and it is difficult to achieve the efficiency shown by humans when solving problems.