V2X and Deep Reinforcement Learning-Aided Mobility-Aware Lane Changing for Emergency Vehicle Preemption in Connected Autonomous Transport Systems

Emergency vehicle preemption (EVP) aims to provide the right-of-way to emergency vehicles (EVs) so that they can travel to the incident location efficiently. The travel time of EVs is the most important indicator of EVP efficiency, which should be minimized by distinct methods or algorithms. However, conventional EVP methods using strobe emitters, light emitters or sirens performs poorly in high-density vehicular traffic. Vehicle-to-everything (V2X) communication plays a pivotal role in intelligent transportation systems (ITS), which can assist EVs to travel safely and efficiently in connected autonomous transport systems (CATS). Enabled by V2X, this paper proposes a deep reinforcement learning-aided mobility-aware lane change algorithm (DRL-MLC) to enhance the efficiency of EVP. In the first stage, the EV learns to change lane based on a policy-based deep reinforcement learning (DRL) algorithm to find the shortest trajectory. In the second stage, autonomous vehicles (AVs) perform mobility-aware lane changing (MLC) to make way for the EV based on the emergency messages (EM) they received. Note that the performance of DRL-MLC strongly relies on the quality of service (QoS) of V2X, and improper network parameters of the on-board units (OBUs) that do not match with the vehicular density will significantly degrade the QoS. Therefore, in the third stage, the proposed algorithm fine-tunes specific parameters including communication range, carrier sensing range, packet rate, and contention window according to the real-time vehicular density based on a curve-fitting optimization method. Our results indicate that at medium-to-high density (e.g., 0.15 veh/m), the average speed of DRL-MLC has more than 49% average improvement than traditional lane changing models, and the ten-minute target travel time for EVs can be achieved by 95% with the proposed algorithm.