Research on the Decision-making Model and Algorithm of Following and Lane-Changing Behavior of Connected Autonomous Vehicles on Freeway

In order to achieve the widespread application of autonomous driving technology in basic freeway segments, especially in the automated decision-making of following and lane changing behaviors, Connected Autonomous Vehicles (CAVs) must be able to reliably complete driving tasks in complex traffic environments. Our study introduces a novel behavior decision-making architecture for connected autonomous vehicles, which employs the Dueling Double Deep Q-Network (D3QN) algorithm as its core methodology. The model optimizes the decision-making ability in complex traffic scenarios by separating action selection and value assessment and implementing them by different neural networks. The multi-dimensional reward function, which comprehensively considers safety, comfort and efficiency, is introduced into the reinforcement learning training of the model. The simulation scenario of the basic freeway segment is established and the model is trained in the mixed traffic flow environment, compared with the traditional DQN and DDQN, the D3QN model can not only ensure traffic safety in the task of following and changing lanes on the expressway, but also ensure traffic safety. It also improves the smoothness of the ride.