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Autonomous braking through vehicle precise decision-making and control to reduce accidents is a key issue, especially in the early diffusion phase of autonomous vehicle development. This paper proposes a deep reinforcement learning (DRL)-based autonomous braking decision-making strategy in an emergency situation. Three key influencing factors, including efficiency, accuracy and passengers' comfort, are fully considered and satisfied by the proposed strategy. First, the vehicle lane-changing process and the braking process are analyzed in detail, which include the critical factors in the design of the autonomous braking strategy. Second, we propose a DRL process that determines the optimal strategy for autonomous braking. Particularly, a multi-objective reward function is designed, which can compromise the rewards achieved of different brake moments, the degree of the accident, and the comfort of the …