AI Based System for Emergency Aware Traffic Control

DOI

10.2991/978-94-6239-723-1_18How to use a DOI?

Keywords

Artificial Intelligence; Deep learning; Emergency Vehicle; ESP32 microcontroller; IoT; Performance metrics; Precision; Recall; traffic control; YOLOv8

Abstract

The delays and congestions that are witnessed in the urban intersections are becoming a serious impediment to the prompt movement of emergency vehicles. On the one hand, the traditional traffic lights are founded on the recipes of time tables that also limit their flexibility to dynamism in the traffic or responding to an emergency. The present paper gives a hybrid model that applies artificial intelligence to permit adjustable signal control and priority of emergency vehicles in real-time. The solution proposed will utilize camera monitored solutions which are applied to capture real time traffic around the intersection. The video feed is processed with the help of a vision model that is based on deep learning to determine the degree of traffic density and emergency vehicles. According to the analysis findings, control commands are transmitted wirelessly to an ESP32-based controller, which controls the phases of signals and in the case of emergency vehicles, transmits them instantly by priority. The emergency event does not disrupt the system and the process of adaptive regulation of traffic is reestablished. The system is economical and the fact that it is in a modular form, it can be effectively implemented with time in the smart urban traffic infrastructures.

Copyright

© 2026 The Author(s)

Open Access

Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

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TY  - CONF
AU  - Tanaya Chavan
AU  - Sheetal S. Patil
AU  - Gauri R. Rao
PY  - 2026
DA  - 2026/07/14
TI  - AI Based System for Emergency Aware Traffic Control
BT  - Proceedings of the International Conference on Responsible, Risk-aware, and Regulated AI (RRRAI 2026)
PB  - Atlantis Press
SP  - 197
EP  - 206
SN  - 1951-6851
UR  - https://doi.org/10.2991/978-94-6239-723-1_18
DO  - 10.2991/978-94-6239-723-1_18
ID  - Chavan2026
ER  -