Quantum–Classical Hybrid System for Real-Time Accident Detection

DOI

10.2991/978-94-6239-628-9_6How to use a DOI?

Keywords

Quantum Computing; Quantum Machine Learning (QML); Hybrid Quantum–Classical Model; Vehicular Impact Detection; Smartphone Sensors; Accident Prediction; Intelligent Transportation Systems (ITS); Internet of Things (IoT); Logistic Regression; Sensor Fusion

Abstract

Traffic accidents still are prominently among global fatalities and the need for real-time and accurate impact detection presents a big challenge to advanced protection systems. A vehicular impact estimation hybrid quantum-classical model is proposed in this research based on data provided by smartphone-integrated sensors including accelerometers, gyroscopes, barometers, and microphones. Six fundamental motion and environment metrics are extracted and described in a six-qubit quantum circuit where quantum superposition and entanglement truly encapsulate complex nonlinearities between sensor outputs. Results of quantum measurement characteristics are combined with classical descriptors and investigated with a logistic regression model to distinguish between several types of impacts, including sudden braking, airbag deployment, barrier collision, and minor crashes. Experiments with synthetic sensor data show that the hybrid approach increases the accuracy of detection, interpretability, and robustness compared to state-of-the-art machine learning methods. This research highlights the possibility of using quantum-inspired accident detection on everyday smartphones in a scalable and cost-effective manner, offering an attractive solution to developing future intelligent vehicle protection systems. The hybrid model achieves over 93% accuracy with lower error variance than classical and quantum-only methods, confirming its superior stability and reliability.

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  - Rajnish Tiwari
AU  - Prashant Kumar
AU  - Mridul Manas
AU  - Rishi Singh Rana
AU  - Chirag Taneja
AU  - Vijendra Singh Rawat
PY  - 2026
DA  - 2026/03/31
TI  - Quantum–Classical Hybrid System for Real-Time Accident Detection
BT  - Proceedings of the International Conference on Recent Trends in Intelligent Computing, Manufacturing, and Electronics (rTIME 2025)
PB  - Atlantis Press
SP  - 47
EP  - 59
SN  - 2352-5401
UR  - https://doi.org/10.2991/978-94-6239-628-9_6
DO  - 10.2991/978-94-6239-628-9_6
ID  - Tiwari2026
ER  -