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

2025-03-01

AI Summary

To address the insufficient real-time perception robustness of autonomous driving in complex environments (e.g., rain, shadows) within transportation cyber-physical systems (CPS), this paper proposes a quantum-enhanced end-to-end visual perception framework. We innovatively design the UU† quantum propagation algorithm—first integrating quantum computation deeply into the forward pass of convolutional neural networks—and combine it with a pre-/post-processing-driven dynamic centroid training strategy to achieve high-accuracy shadow-region classification. The proposed method maintains high accuracy and strong noise robustness on both clear-weather and rainy-road datasets. Shadow detection latency is merely 0.0049 seconds—3–400× faster than classical approaches—significantly enhancing real-time navigation safety and decision reliability.

Technology Category

• Computer Vision: Vision for Robotics & Autonomous Driving
• Machine Learning: Quantum Machine Learning
• Intelligent Robots: Multimodal Perception & Sensor Fusion

Application Category

• Responsible Web: Human-perceived consequences of algorithmic deployment on the web
• Security and Privacy: Data transparency and provenance
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking

Abstract

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 UU{dag} 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.

Problem

• Enhances safety and reliability in autonomous transportation systems.
• Improves shadow detection in complex environments using quantum algorithms.
• Reduces computational complexity and enhances real-time decision-making efficiency.

Innovation

• QDCNN uses quantum algorithms for enhanced safety.
• UU† method improves shadow detection accuracy.
• Achieves faster shadow detection than classical methods.