论文标题
基于组合学习方法的紧急车辆的战术决策
Tactical Decision Making for Emergency Vehicles Based on A Combinational Learning Method
论文作者
论文摘要
增加紧急车辆（EV）的响应时间可能会导致财产和生命的损失。因此，对电动汽车的微观控制的战术决策仍然是必不可少的问题。在本文中，制定了基于规则的避免策略（AS），即EV之前的优先区域中的CVS应加速或更改其车道以避免它。此外，提出了一种具有速度自适应紧凑状态空间（SC-DQN）的新型DQN方法，以适合EVS的高速功能，并在各种道路拓扑中进行概括。之后，执行对SC-DQN输入的反馈，以便它们作为组合方法有机地结合。以下方法表明，DRL可以补充基于规则的避免概括的策略，相反，基于规则的避免策略可以补充DRL的稳定性，并且它们的组合可能导致较小的响应时间，较低的碰撞率和较低的轨迹。
Increasing the response time of emergency vehicles(EVs) could lead to an immeasurable loss of property and life. On this account, tactical decision making for EVs' microscopic control remains an indispensable issue to be improved. In this paper, a rule-based avoiding strategy(AS) is devised, that CVs in the prioritized zone ahead of EV should accelerate or change their lane to avoid it. Besides, a novel DQN method with speed-adaptive compact state space (SC-DQN) is put forward to fit in EVs' high-speed feature and generalize in various road topologies. Afterward, the execution of AS feedback to the input of SC-DQN so that they joint organically as a combinational method. The following approach reveals that DRL could complement rule-based avoiding strategy in generalization, and on the contrary, the rule-based avoiding strategy could complement DRL in stability, and their combination could lead to less response time, lower collision rate and smoother trajectory.
