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

Topics: Analytics for Intelligent Transportation; Autonomous Vehicles and Automated Driving Control; Autonomous Vehicles and Automated Driving Perception; Decision Support Systems; Information Systems and Technologies; Intelligent Infrastructure and Guidance Systems; Systems Modeling and Simulation

Authors: Akanksha Tyagi ; Meghna Lowalekar and Praveen Paruchuri

Affiliation: International Institute of Information Technology Hyderabad (IIIT-H), Hyderabad, India

Keyword(s): Reinforcement Learning, Emergency Vehicles, Lane Level Dynamics.

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 trave rsal 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. (More)

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Paper citation in several formats:
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 - VEHITS; ISBN 978-989-758-703-0; ISSN 2184-495X, SciTePress, pages 134-143. DOI: 10.5220/0012637200003702

@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 - VEHITS},
year={2024},
pages={134-143},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0012637200003702},
isbn={978-989-758-703-0},
issn={2184-495X},
}

TY - CONF

JO - Proceedings of the 10th International Conference on Vehicle Technology and Intelligent Transport Systems - VEHITS
TI - Improving Lane Level Dynamics for EV Traversal: A Reinforcement Learning Approach
SN - 978-989-758-703-0
IS - 2184-495X
AU - Tyagi, A.
AU - Lowalekar, M.
AU - Paruchuri, P.
PY - 2024
SP - 134
EP - 143
DO - 10.5220/0012637200003702
PB - SciTePress

- Science and Technology Publications, Lda.
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