An Intelligent Lane Changing Decision Method for Connected Vehicles

Aiming at the problem of driving scenarios redundancy in the lane change decision-making, this paper proposes a deep reinforcement learning method (DRL) for lane change decision based on embedded attention mechanism (CADQN). The algorithm introduces the Convolutional Attention Mechanism Module (CBAM) into the DQN network to optimize the scenarios in time and space dimensions, and assist connected vehicles in making lane changing decisions. The algorithm is verified by the traffic simulation platform under the highway environment, and the results show that CADQN is helpful to improve the global traffic efficiency, and with the increase of traffic flow density, the benefit is more significant. Moreover, the visualization results of the attention layer in the CADQN can guide the optimization of the driving scenario.