Intelligent vehicle driving decision-making model based on variational AutoEncoder network and deep reinforcement learning - Wang, Shufeng; Wang, Zhengli; Wang, Xinkai; Liang, Qingwei; Meng, Lingyi (Expert systems with applications, 2025)
In this paper, an end-to-end driving decision-making model is proposed for intelligent vehicle, utilizing a Variational AutoEncoder (VAE) network and Deep Reinforcement Learning to address the challenges in complex and dynamical driving environments. Firstly, the traffic environment image features are extracted by VAE network, which can effectively reduce the amount of data input and improve the learning efficiency. Secondly, the Soft Actor-Critic (SAC) algorithm is improved through the application of TD error value constraints, N-step learning, etc. Then driving risk field and rule constraints are introduced into the improve SAC algorithm. Based on the real-time driving risk field, the skipping frame method can enhance learning efficiency, and the rule constraints can reduce the dangerous actions in the output of the algorithm. In order to verify the effectiveness of the model, in the CARLA simulation platform the models of scenario and algorithm are established, and the simulations are carried out. The results show that using decision-making model built by the proposed algorithm, the average driving distance by the intelligent vehicle has been improved by 91.37%, the average reward value of the task has been increased by 132.04%, the average success rate of the task has been improved by 46.56%, the training time is also significantly reduced. It demonstrated that the proposed decision-making model provides a significant improvement in driving safety and learning efficiency.