Abstract: In order to improve the performance of lane-changing decision-making for intelligent network-connected mining
trucks in surface coal mines,this paper proposes a lane-changing decision-making method that integrates deep reinforcement
learning and finite state machines. First,a two-layer decision-making framework is constructed,where the upper layer utilizes
deep Q-networks to generate preliminary lane-changing decisions,and the lower layer performs security constraints through finite
state machines. Second,the dual network and competitive network structure are introduced to optimize the DQN performance,
which effectively alleviates the Q-value over-estimation problem. Then,a state transfer rule is designed based on the Gipps
security model to dynamically evaluate the security of the lane-changing gap. Finally,a multi-objective reward function is
designed to comprehensively evaluate and guide the lane changing behavior. Experiments are conducted on the Highway-env
platform,and the results show that the success rate of the fusion method for lane changing reaches 81. 36% in high traffic density
scenarios,which is significantly improved compared to a single DuDQN(50. 84%),with a reduced number of collisions
and enhanced driving stability. This framework can effectively improve the safety and efficiency of decision-making,and has
certain reference significance for the decision-making of open-pit mine transportation lane-changing.

Key words: smart grid-connected mining truck,deep reinforcement learning,finite state machine,lane change decision,
multi-objective reward function