Quantum AI for road safety and beyond: A scoping review of applications in intelligent transportation systems

Background Intelligent Transportation Systems (ITS) increasingly draw on artificial intelligence for prediction, classification, and control tasks. Quantum computing and quantum machine learning (QML) have emerged as candidate tools, though current hardware remains in the noisy intermediate-scale quantum (NISQ) era. Objective This review maps the literature applying quantum or quantum-inspired AI to ITS, with attention to road-safety applications. Methods A PRISMA-ScR-adapted scoping review searched five databases (Web of Science, ScienceDirect, arXiv, Scopus, IEEE Xplore). Screening was conducted jointly by the review author and a co-author against structured eligibility criteria; an independent, criteria-blind AI-assisted second pass assessed reliability (substantial agreement, kappa = 0.77). From 271 records identified, 50 were synthesized: 41 primary studies and 9 background reviews. A five-item quality checklist was applied to the 15 primary studies with confirmed full-text access. Results The 41 primary studies span five application themes, led by collision/accident detection and avoidance (22 studies), ahead of traffic-state forecasting, ITS operations, electric-vehicle systems, and network security. Reported methods include quantum neural networks, support vector machines, variational circuits, game theory, annealing/QAOA, and federated learning. Quantum and hybrid approaches outperformed classical baselines on structured, interaction-heavy tasks using markedly fewer parameters, though fixed quantum-kernel methods showed recurring overfitting; most studies ran on simulators rather than physical hardware. Conclusion Quantum AI for ITS is a young, rapidly growing field (mostly 2024–2026) in which road safety is the largest application cluster. Reported advantage centers on parameter efficiency, not raw accuracy. Key gaps include near-absent physical-hardware validation, standardized benchmarks, and fully independent dual screening.