Enhancing Traffic Safety Through AI-Driven, Privacy-Preserving, And Secure Impaired Driving Detection Systems

Author

• Razan Alsulieman, University of Southern MississippiFollow

Date of Award

5-2026

Degree Type

Dissertation

Degree Name

Doctor of Philosophy (PhD)

School

Computing Sciences and Computer Engineering

Committee Chair

Ahmed Sherif

Committee Chair School

Computing Sciences and Computer Engineering

Committee Member 2

Sarah Lee

Committee Member 2 School

Computing Sciences and Computer Engineering

Committee Member 3

Chaoyang Zhang

Committee Member 3 School

Computing Sciences and Computer Engineering

Committee Member 4

Amer Dawoud

Committee Member 4 School

Computing Sciences and Computer Engineering

Committee Member 5

Rabab Abdelfattah

Committee Member 5 School

Computing Sciences and Computer Engineering

Abstract

Drunk driving remains a major threat to road safety worldwide, contributing significantly to traffic injuries and fatalities each year. Traditional detection approaches are largely reactive and vehicle-centric, relying on in-vehicle sensors, breathalyzers, or post-incident enforcement. These methods often depend on driver cooperation, intrusive hardware installations, or limited monitoring environments, restricting their scalability and effectiveness in large transportation systems. At the same time, modern cities increasingly deploy roadside cameras, surveillance networks, and drone- based monitoring systems, creating new opportunities for proactive intoxication detection at the infrastructure level. However, leveraging such external monitoring introduces challenges related to secure data collection, reliable AI-based analysis, privacy protection, and real-world deployment.

This dissertation proposes a secure, privacy-preserving Artificial Intelligence framework for proactive drunk driving detection using out-of-vehicle surveillance data. The framework addresses three key aspects required for reliable infrastructure-level monitoring. First, a lightweight authentication scheme is developed to ensure secure data collection from distributed monitoring platforms such as drones and surveillance devices. The proposed design employs physically unclonable functions and symmetric cryptographic primitives to provide protection against impersonation, replay attacks, and device cloning while maintaining low computational overhead for resource-constrained environments.

Second, AI-based intoxication detection models are developed using Machine Learning and Deep Learning techniques to analyze facial imagery captured under real-world surveillance conditions. Extensive experiments evaluate multiple models under varying noise and disruption scenarios to ensure robustness across both low- and high-resource computational environments. The framework also incorporates explainable AI methods to improve transparency and verify that model decisions rely on meaningful facial features.

Finally, the framework integrates privacy-preserving learning mechanisms through federated learning, enabling distributed model training without transferring sensitive facial images to centralized servers. This approach protects user privacy while maintaining strong detection performance across distributed monitoring nodes. These contributions establish a secure, scalable, and privacy-aware infrastructure-level system for proactive intoxication detection, supporting intelligent transportation systems aimed at improving traffic safety.

Copyright

Razan Alsulieman, 2026

Recommended Citation

Alsulieman, Razan, "Enhancing Traffic Safety Through AI-Driven, Privacy-Preserving, And Secure Impaired Driving Detection Systems" (2026). Dissertations. 2447.
https://aquila.usm.edu/dissertations/2447