Improving Lane Level Dynamics for EV Traversal: A Reinforcement Learning Approach

Akanksha Tyagi, Meghna Lowalekar, Praveen Paruchuri

2024

Abstract

Emergency vehicles (EVs) perform a critical task of attending medical emergencies and delay in their operations can result in loss of lives to long term or permanent health implications. Therefore, it is very important to design strategies that can reduce the delay of EVs caused by slow moving traffic. Most of the existing work on this topic focuses on assignment and dispatch of EVs from different base stations to hospitals or finding the appropriate routes from dispatch location to hospital. However, these works ignore the effect of lane changes when EV is travelling on a stretch of a road. In this work, we focus on lane level dynamics for EV traversal and showcase that a pro-active picking of lanes can result in significant reductions in traversal time. In particular, we design a Reinforcement Learning (RL) model to compute the most optimal lane for an EV to travel at each timestep. We propose RLLS (Reinforcement Learning based Lane Search) algorithm for a general purposes EV traversal problem and perform a series of experiments using the well-known traffic simulator SUMO. Our experimentation demonstrates that our model outperforms the default SUMO algorithm and is also significantly better than the existing state-of-the-art heuristic approach BLS (Best Lane Search) strategy in normal traffic conditions. We also simulate worst case scenarios by introducing slowed down vehicles at regular time intervals into the traffic and observe that our model generalizes well to different traffic scenarios.

Paper Citation

in Harvard Style

Tyagi A., Lowalekar M. and Paruchuri P. (2024). Improving Lane Level Dynamics for EV Traversal: A Reinforcement Learning Approach. In Proceedings of the 10th International Conference on Vehicle Technology and Intelligent Transport Systems - Volume 1: VEHITS; ISBN 978-989-758-703-0, SciTePress, pages 134-143. DOI: 10.5220/0012637200003702

in Bibtex Style

@conference{vehits24,
author={Akanksha Tyagi and Meghna Lowalekar and Praveen Paruchuri},
title={Improving Lane Level Dynamics for EV Traversal: A Reinforcement Learning Approach},
booktitle={Proceedings of the 10th International Conference on Vehicle Technology and Intelligent Transport Systems - Volume 1: VEHITS},
year={2024},
pages={134-143},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0012637200003702},
isbn={978-989-758-703-0},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 10th International Conference on Vehicle Technology and Intelligent Transport Systems - Volume 1: VEHITS
TI - Improving Lane Level Dynamics for EV Traversal: A Reinforcement Learning Approach
SN - 978-989-758-703-0
AU - Tyagi A.
AU - Lowalekar M.
AU - Paruchuri P.
PY - 2024
SP - 134
EP - 143
DO - 10.5220/0012637200003702
PB - SciTePress