SafePass: Efficient emergency vehicle passage with minimal disruption to traffic flow

Abstract

Emergency vehicle passage in congested urban networks poses a dual challenge: ensuring rapid response while minimizing disruption to surrounding traffic. This study addresses this challenge in the context of Connected Autonomous Emergency Vehicles (CA-EVs), proposing SafePass, a lightweight distributed framework for seamless CA-EV passage through decentralized, cooperative maneuvering of surrounding Connected Autonomous Non-Emergency Vehicles (CA-NEVs). At its core, SafePass employs the Target Lane Potential (TLP), a novel utility-based metric combining lane-choice utility with probabilistic gap acceptance, augmented by a cascade-aware penalty that suppresses upstream shockwaves triggered by gap-creation maneuvers. Evaluated in Simulation of Urban Mobility (SUMO) using synthetic traffic and real-world trajectory data from the Next Generation Simulation (NGSIM) US-101, Wuhan University Next Generation Simulation (WUT-NGSIM), and modified Waymo Open datasets, SafePass consistently clears lanes well before the CA-EV’s Estimated Time of Arrival (ETA), reducing CA-EV travel time by up to 30% compared to baselines while lowering surrounding vehicle travel time by 8%–10%, demonstrating that safety and efficiency need not be traded off.