Tactical Decision Making for Emergency Vehicles Based on A Combinational Learning Method

TL;DR

This paper introduces a combined rule-based and deep reinforcement learning approach to improve emergency vehicle response times and safety through tactical decision making in complex traffic scenarios.

Contribution

It proposes a novel combinational method integrating rule-based strategies with a specialized DQN for emergency vehicle control, enhancing generalization and stability.

Findings

Reduced response time for emergency vehicles

Lower collision rates in simulations

Smoother trajectory planning

Abstract

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.