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 passenger. Third, a typical actor-critic (AC) algorithm named deep deterministic policy gradient (DDPG) is adopted for solving the autonomous braking problem, which can improve the efficiency of the optimal strategy and be stable in continuous control tasks. Once the strategy is well trained, the vehicle can automatically take optimal braking behavior in an emergency to improve driving safety. Extensive simulations validate the effectiveness and efficiency of our proposal in terms of learning effectiveness, decision-making accuracy and driving safety.

中文翻译：

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基于深度强化学习的车辆紧急自动制动决策策略

通过车辆精确决策和控制来减少事故的自动制动是一个关键问题，尤其是在自动驾驶汽车发展的早期扩散阶段。本文提出了一种基于深度强化学习（DRL）的紧急情况下的自主制动决策策略。所提出的策略充分考虑并满足了三个关键影响因素，包括效率、准确性和乘客舒适度。首先，详细分析了车辆换道过程和制动过程，其中包括自动制动策略设计中的关键因素。其次，我们提出了一个 DRL 过程，以确定自动制动的最佳策略。特别是，设计了一个多目标奖励函数，这可能会影响不同制动力矩所获得的回报、事故程度和乘客的舒适度。第三，采用典型的actor-critic（AC）算法，称为深度确定性策略梯度（DDPG）来解决自主制动问题，可以提高最优策略的效率，并在连续控制任务中保持稳定。一旦策略得到很好的训练，车辆可以在紧急情况下自动采取最佳制动行为，以提高驾驶安全性。广泛的模拟验证了我们的提议在学习有效性、决策准确性和驾驶安全方面的有效性和效率。采用深度确定性策略梯度（DDPG）的典型actor-critic（AC）算法解决自主制动问题，可以提高最优策略的效率，并在连续控制任务中保持稳定。一旦策略得到很好的训练，车辆可以在紧急情况下自动采取最佳制动行为，以提高驾驶安全性。广泛的模拟验证了我们的提议在学习有效性、决策准确性和驾驶安全方面的有效性和效率。采用深度确定性策略梯度（DDPG）的典型actor-critic（AC）算法解决自主制动问题，可以提高最优策略的效率，并在连续控制任务中保持稳定。一旦策略得到很好的训练，车辆可以在紧急情况下自动采取最佳制动行为，以提高驾驶安全性。广泛的模拟验证了我们的提议在学习有效性、决策准确性和驾驶安全方面的有效性和效率。