# QDCNN: Quantum Deep Learning for Enhancing Safety and Reliability in Autonomous Transportation Systems

arXiv: https://arxiv.org/abs/2503.01916 · HTML: https://mlsanity.com/paper/2503.01916/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 8 of its 39 references that are in the map.
- First posted on arXiv 1 Mar 2025.

## Builds on (most cited first, 8 of 8)

- [Survey of Deep Reinforcement Learning for Motion Planning of Autonomous Vehicles](https://mlsanity.com/paper/2001.11231/) (2020)
- [Universal expressiveness of variational quantum classifiers and quantum kernels for support vector machines](https://mlsanity.com/paper/2207.05865/) (2022)
- [Deep Learning for Inertial Positioning: A Survey](https://mlsanity.com/paper/2303.03757/) (2023)
- [Fundamental Limits of Spectral Clustering in Stochastic Block Models](https://mlsanity.com/paper/2301.09289/) (2023)
- [Dynamic Conditional Imitation Learning for Autonomous Driving](https://mlsanity.com/paper/2211.11579/) (2022)
- [Quantum Computing in Intelligent Transportation Systems: A Survey](https://mlsanity.com/paper/2406.00862/) (2024)
- [6G Enabled Advanced Transportation Systems](https://mlsanity.com/paper/2305.15184/) (2023)
- [Clothoid Curve-based Emergency-Stopping Path Planning with Adaptive Potential Field for Autonomous Vehicles](https://mlsanity.com/paper/2308.10049/) (2023)

## Built on by (most cited first, 1 of 1)

- [Quantum Machine Learning in Transportation: A Case Study of Pedestrian Stress Modelling](https://mlsanity.com/paper/2507.01235/) (2025)

## Abstract (arXiv)

In transportation cyber-physical systems (CPS), ensuring safety and reliability in real-time decision-making is essential for successfully deploying autonomous vehicles and intelligent transportation networks. However, these systems face significant challenges, such as computational complexity and the ability to handle ambiguous inputs like shadows in complex environments. This paper introduces a Quantum Deep Convolutional Neural Network (QDCNN) designed to enhance the safety and reliability of CPS in transportation by leveraging quantum algorithms. At the core of QDCNN is the UUdag method, which is utilized to improve shadow detection through a propagation algorithm that trains the centroid value with preprocessing and postprocessing operations to classify shadow regions in images accurately. The proposed QDCNN is evaluated on three datasets on normal conditions and one road affected by rain to test its robustness. It outperforms existing methods in terms of computational efficiency, achieving a shadow detection time of just 0.0049352 seconds, faster than classical algorithms like intensity-based thresholding (0.03 seconds), chromaticity-based shadow detection (1.47 seconds), and local binary pattern techniques (2.05 seconds). This remarkable speed, superior accuracy, and noise resilience demonstrate the key factors for safe navigation in autonomous transportation in real-time. This research demonstrates the potential of quantum-enhanced models in addressing critical limitations of classical methods, contributing to more dependable and robust autonomous transportation systems within the CPS framework.