Emergency Preemption Without Online Exploration: A Decision Transformer Approach
arXiv:2603.22315v1 Announce Type: new 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, including online RL baselines that require environmen
arXiv:2603.22315v1 Announce Type: new 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, including online RL baselines that require environment interaction. MADT further improves on larger grids, overtaking DT with 45.2% reduction on 8x8 via graph-attention coordination. Return conditioning produces a smooth dispatch interface: varying the target return from 100 to -400 trades EV travel time (72.4-138.2 s) against civilian delay (16.8-5.4 s/veh), requiring no retraining. A Constrained DT extension adds explicit civilian disruption budgets as a second control knob.
Executive Summary
This article proposes a novel approach to emergency vehicle response optimization using a Decision Transformer (DT), a type of sequence modeling framework. By casting corridor optimization as offline, return-conditioned sequence modeling, the approach eliminates online environment interaction during policy learning and enables dispatch-level urgency control. The proposed Multi-Agent Decision Transformer (MADT) extends to multi-agent settings via graph attention for spatial coordination. The article reports significant reductions in emergency vehicle travel time, civilian delay, and stops, outperforming online RL baselines and other methods. The approach also offers a smooth dispatch interface with varying target return values and an extension for explicit civilian disruption budgets. This study has the potential to improve emergency response times and outcomes, but its practical and policy implications require further exploration.
Key Points
- ▸ The Decision Transformer (DT) approach eliminates online environment interaction during policy learning.
- ▸ The Multi-Agent Decision Transformer (MADT) extends to multi-agent settings via graph attention for spatial coordination.
- ▸ The approach offers significant reductions in emergency vehicle travel time, civilian delay, and stops.
Merits
Strength in Optimizing Emergency Response
The proposed approach demonstrates significant improvements in emergency vehicle response times, civilian delay, and stops, which are critical determinants of survival outcomes.
Flexibility and Scalability
The approach can be adapted to different scenarios, including multi-agent settings, and offers a smooth dispatch interface with varying target return values.
Demerits
Limited Real-World Deployment
The study's results are based on a simulator, and it is unclear how the approach would perform in real-world settings with complex and dynamic traffic patterns.
Potential for Over-Optimization
The approach's focus on minimizing emergency vehicle travel time and civilian delay may lead to over-optimization, potentially compromising other important factors, such as public safety and fairness.
Expert Commentary
This study demonstrates a promising approach to optimizing emergency vehicle response times, but its practical and policy implications require further exploration. The use of artificial intelligence and machine learning in emergency response systems is a growing area of research, and this study contributes to this field. However, the study's results are based on a simulator, and it is unclear how the approach would perform in real-world settings with complex and dynamic traffic patterns. Additionally, the approach's focus on minimizing emergency vehicle travel time and civilian delay may lead to over-optimization, potentially compromising other important factors, such as public safety and fairness.
Recommendations
- ✓ Future studies should investigate the performance of the proposed approach in real-world settings and explore ways to mitigate potential over-optimization.
- ✓ Policy makers should consider the use of artificial intelligence and machine learning in emergency response systems to optimize outcomes and improve public safety.
Sources
Original: arXiv - cs.LG
