A behavior decision-making model for autonomous vehicles was proposed based on an ensemble deep reinforcement learning method. The decision model was constructed based on the Markov decision process (MDP) theory. Three basic network models were integrated, including the Deep Q-learning Network (DQN), the Double DQN (DDQN), and the Dueling double DDQN (Dueling DDQN), by using the Standard Voting Method. Some tests and the generalization validation tests were done, for 5 vehicle driving behaviors, including the lane changing to the left, the lane keeping, the lane changing to the right, the accelerating in the same lane, and the decelerating in the same lane, in highway simulation environments under the scenarios of 3-lane, 4-lane, and 5-lane in one direction. The results show that the decision success rate of the proposed model increase 6%, 3% and 6%, respectively, compare with the other three network models. The average vehicle speed has also been improved; And the 100-round tests take less than 1 ms, which meets the requirement for real-time decision-making. Therefore, the decision-making model improves driving safety and decision-making efficiency.
