RL for Emergency Vehicle Traffic Optimization
Lane-level reinforcement learning with V2V communication that speeds emergency vehicle traversal in simulated traffic. Best Student Paper nominee, VEHITS 2024.
- Role
- Research Intern · IIIT Hyderabad
- Timeline
- Jun 2022 – Dec 2023
- Stack
- PythonStable-Baselines3SUMOReinforcement LearningV2V
- Links
- Paper
Problem
When an ambulance is stuck in traffic, the seconds lost to surrounding vehicles reacting late can cost lives. Lane-level dynamics are the bottleneck: which vehicle should yield, into which lane, and at what moment. Fixed traffic rules handle this poorly, so we set out to learn the behavior instead.
Approach
We modeled lane selection as a reinforcement learning problem with vehicle-to-vehicle (V2V) communication in the loop. Vehicles share state, which allows the policy to coordinate yielding behavior ahead of the emergency vehicle rather than reacting to it. Policies were trained with Stable-Baselines3 and evaluated in SUMO across multi-modal traffic environments, ranging from grid-world abstractions to road networks inspired by real layouts.
Architecture & Tech
A SUMO simulation stack drives the environments, and a Gym style wrapper exposes lane-level state, including V2V shared observations, to the Stable-Baselines3 agents. The evaluation suite compares learned policies against baseline and human-level driving behavior on traversal time and safety metrics.
Results & Impact
- The learned policies reduced emergency vehicle traversal times significantly and surpassed human-level performance on the evaluated networks.
- The work was published as "Improving Lane Level Dynamics for EV Traversal: A Reinforcement Learning Approach" at VEHITS 2024, where it was nominated for the Best Student Paper Award.
What I'd do next
I would stress the policies under partial V2V adoption, where only some vehicles communicate, add sensor noise to the observations, and test sim-to-real transfer on scaled testbeds.