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

• URL: http://arxiv.org/abs/2503.01916v1
• Date: Sat, 01 Mar 2025 19:04:44 GMT
• Title: QDCNN: Quantum Deep Learning for Enhancing Safety and Reliability in Autonomous Transportation Systems
• Abstract summary: This paper introduces a Quantum Deep Convolutional Neural Network (QDCNN) to enhance the safety and reliability of CPS in transportation.<n>The proposed QDCNN is evaluated on three datasets on normal conditions and one road affected by rain to test its robustness.<n>It outperforms existing methods in terms of computational efficiency, achieving a shadow detection time of just 0.0049352 seconds.
• Score: 5.062798070922488
• License: http://creativecommons.org/licenses/by/4.0/
• 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.

Related papers

• QDS-SNN: Energy-efficient Quantum Deeply-Supervised Spiking Neural Network Algorithm for Traffic Sign Recognition [16.96141578287489]
  Spiking Neural Networks (SNNs) offer a biologically inspired energy-efficient alternative due to their information processing capabilities.<n>This study proposes a Quantum Deep-supervised Spiking Neural Network (QDSSNN) for efficient, low-power deep supervision.<n>Using quantum superposition and entanglement, QNNs enable expressive representations and parallel computation.
  arXiv  Detail & Related papers  (2026-06-03T11:07:12Z)
• Multi-Resolution End-to-End Deep Neural Network for Optimizing Latency-Accuracy Tradeoff in Autonomous Driving [2.134211474877041]
  In autonomous driving, safety depends on both prediction quality and the end-to-end delay from sensing to actuation.<n>We present a multi-resolution, end-to-end deep neural network for the CARLA urban driving challenge using monocular camera input.<n>Results show consistent improvements in per-route safety metrics.
  arXiv  Detail & Related papers  (2026-05-27T22:06:23Z)
• A Latency Coding Framework for Deep Spiking Neural Networks with Ultra-Low Latency [51.75891259753546]
  Spiking neural networks (SNNs) offer a biologically inspired computing paradigm with significant potential for energy-efficient neural processing.<n>Time-To-First-Spike (TTFS) coding encodes information through the precise timing of a neuron's first spike.<n>TTFS models lack efficient training methods, suffering from high inference latency and limited performance.<n>We present a comprehensive framework, which enables the efficient training of deep TTFS-coded SNNs by employing backpropagation throuh time (BPTT) algorithm.
  arXiv  Detail & Related papers  (2026-03-24T13:52:03Z)
• Mini Autonomous Car Driving based on 3D Convolutional Neural Networks [0.6349729476511409]
  Development of reliable and trustworthy autonomous systems poses challenges such as high complexity, prolonged training periods, and intrinsic levels of uncertainty.<n>Mini Autonomous Cars (MACs) are used as a practical testbed, enabling validation of autonomous control methodologies on small-scale setups.<n>This work presents a methodology based on RGB-D information and three-dimensional convolutional neural networks (3D CNNs) for MAC autonomous driving in simulated environments.
  arXiv  Detail & Related papers  (2025-08-29T00:21:34Z)
• Road Traffic Sign Recognition method using Siamese network Combining Efficient-CNN based Encoder [5.597437966490453]
  Traffic signs recognition (TSR) plays an essential role in assistant driving and intelligent transportation system.<n>In this article, we propose IECES-network which with improved encoders and Siamese net.<n>The proposed method achieves competitive performance precision-recall and accuracy metric average is 88.1%, 86.43% and 86.1% with a 2.9M lightweight scale.
  arXiv  Detail & Related papers  (2025-02-21T09:03:05Z)
• QNN-VRCS: A Quantum Neural Network for Vehicle Road Cooperation Systems [2.7985570786346745]
  This research integrates quantum computing techniques to enhance Vehicle Road Cooperation Systems (VRCS)<n>We propose an optimized Quantum Neural Network (QNN) to better handle the complexities of traffic data processing.<n> Empirical evaluations on two traffic datasets show that our model achieves superior classification accuracies of 97.42% and 84.08%.
  arXiv  Detail & Related papers  (2024-12-17T09:20:08Z)
• PNAS-MOT: Multi-Modal Object Tracking with Pareto Neural Architecture Search [64.28335667655129]
  Multiple object tracking is a critical task in autonomous driving. As tracking accuracy improves, neural networks become increasingly complex, posing challenges for their practical application in real driving scenarios due to the high level of latency. In this paper, we explore the use of the neural architecture search (NAS) methods to search for efficient architectures for tracking, aiming for low real-time latency while maintaining relatively high accuracy.
  arXiv  Detail & Related papers  (2024-03-23T04:18:49Z)
• Fast Quantum Convolutional Neural Networks for Low-Complexity Object Detection in Autonomous Driving Applications [18.34157974553066]
  A quantum convolution-based object detection (QCOD) is proposed to perform object detection at high speed. The QCOD utilizes our proposed fast quantum convolution that uploads input channel information and re-constructs output channels. Our experiments with KITTI autonomous driving object detection dataset verify that the proposed fast quantum convolution and QCOD are successfully operated in real object detection applications.
  arXiv  Detail & Related papers  (2023-12-28T00:38:10Z)
• MonoPIC -- A Monocular Low-Latency Pedestrian Intention Classification Framework for IoT Edges Using ID3 Modelled Decision Trees [0.0]
  We propose an algorithm that classifies the intent of a single arbitrarily chosen pedestrian in a two dimensional frame into logic states. This bypasses the need to employ any relatively high latency deep-learning algorithms. The model was able to achieve an average testing accuracy of 83.56% with a reliable variance of 0.0042 while operating with an average latency of 48 milliseconds.
  arXiv  Detail & Related papers  (2023-04-01T02:42:24Z)
• Quantization-aware Interval Bound Propagation for Training Certifiably Robust Quantized Neural Networks [58.195261590442406]
  We study the problem of training and certifying adversarially robust quantized neural networks (QNNs) Recent work has shown that floating-point neural networks that have been verified to be robust can become vulnerable to adversarial attacks after quantization. We present quantization-aware interval bound propagation (QA-IBP), a novel method for training robust QNNs.
  arXiv  Detail & Related papers  (2022-11-29T13:32:38Z)
• Correlating sparse sensing for large-scale traffic speed estimation: A Laplacian-enhanced low-rank tensor kriging approach [76.45949280328838]
  We propose a Laplacian enhanced low-rank tensor (LETC) framework featuring both lowrankness and multi-temporal correlations for large-scale traffic speed kriging. We then design an efficient solution algorithm via several effective numeric techniques to scale up the proposed model to network-wide kriging.
  arXiv  Detail & Related papers  (2022-10-21T07:25:57Z)
• Self-Configurable Stabilized Real-Time Detection Learning for Autonomous Driving Applications [15.689145350449737]
  We improve the performance of an object detection neural network utilizing optical flow estimation. It adaptively determines whether to use optical flow to suit the dynamic vehicle environment. In the demonstration, our proposed framework improves the accuracy by 3.02%, the number of detected objects by 59.6%, and the queue stability for computing.
  arXiv  Detail & Related papers  (2022-09-29T03:11:33Z)
• Efficient Federated Learning with Spike Neural Networks for Traffic Sign Recognition [70.306089187104]
  We introduce powerful Spike Neural Networks (SNNs) into traffic sign recognition for energy-efficient and fast model training. Numerical results indicate that the proposed federated SNN outperforms traditional federated convolutional neural networks in terms of accuracy, noise immunity, and energy efficiency as well.
  arXiv  Detail & Related papers  (2022-05-28T03:11:48Z)
• Efficient and Robust LiDAR-Based End-to-End Navigation [132.52661670308606]
  We present an efficient and robust LiDAR-based end-to-end navigation framework. We propose Fast-LiDARNet that is based on sparse convolution kernel optimization and hardware-aware model design. We then propose Hybrid Evidential Fusion that directly estimates the uncertainty of the prediction from only a single forward pass.
  arXiv  Detail & Related papers  (2021-05-20T17:52:37Z)
• Temporal Pulses Driven Spiking Neural Network for Fast Object Recognition in Autonomous Driving [65.36115045035903]
  We propose an approach to address the object recognition problem directly with raw temporal pulses utilizing the spiking neural network (SNN) Being evaluated on various datasets, our proposed method has shown comparable performance as the state-of-the-art methods, while achieving remarkable time efficiency.
  arXiv  Detail & Related papers  (2020-01-24T22:58:55Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.