Emergency Preemption Without Online Exploration: A Decision Transformer Approach
Haoran Su, Hanxiao Deng, Yandong Sun

TL;DR
This paper introduces a return-conditioned decision transformer framework for emergency vehicle preemption that eliminates online interaction, improves response times, and extends to multi-agent coordination, outperforming traditional methods in simulations.
Contribution
It presents a novel offline, return-conditioned approach for emergency corridor optimization using Decision Transformers, including multi-agent extensions with graph attention.
Findings
Reduces EV travel time by 37.7% on a 4x4 grid.
Achieves lowest civilian delay and fewest EV stops among all tested methods.
Further improves performance with multi-agent MADT on larger grids.
Abstract
Emergency vehicle (EV) response time is a critical determinant of survival outcomes, yet deployed signal preemption strategies remain reactive and uncontrollable. We propose a return-conditioned framework for emergency corridor optimization based on the Decision Transformer (DT). By casting corridor optimization as offline, return-conditioned sequence modeling, our approach (1) eliminates online environment interaction during policy learning, (2) enables dispatch-level urgency control through a single target-return scalar, and (3) extends to multi-agent settings via a Multi-Agent Decision Transformer (MADT) with graph attention for spatial coordination. On the LightSim simulator, DT reduces average EV travel time by 37.7% relative to fixed-timing preemption on a 4x4 grid (88.6 s vs. 142.3 s), achieving the lowest civilian delay (11.3 s/veh) and fewest EV stops (1.2) among all methods,…
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Taxonomy
TopicsTransportation and Mobility Innovations · Traffic control and management · Vehicle Routing Optimization Methods
