In this paper, we present an approach to enhance the intelligence of autonomous vehicles by focusing on real-time collaborative route planning. This aspect is critical not only for individual vehicles but also for ensuring effective interaction among multiple autonomous systems. The challenge is to optimize route planning algorithms for real-time operation without compromising accuracy. To address this, we leverage quantum computing techniques, integrating them with CNN architectures to develop Quantum Convolutional Neural Networks (QCNNs) and the Optimized QCNN. Neural networks play a key role in tasks such as predicting paths, estimating traffic flow, and selecting optimal routes, ensuring efficient and adaptive planning. The integration of quantum computing with neural networks significantly enhances real-time performance and the Optimized QCNN, developed using the Circuit Depth Reduction strategy, further improves accuracy and convergence rate. Our evaluation, conducted on the Cityscapes Dataset, demonstrates that QCNNs comparing with traditional CNNs achieve faster convergence rate and lower loss value. Furthermore, Optimized QCNNs surpass standard QCNNs in both convergence rate and loss, while also achieving higher accuracy even than traditional CNNs, making them a powerful model for real-time collaborative route planning in autonomous vehicles.
In this paper, we present an approach to enhance the intelligence of autonomous vehicles by focusing on real-time collaborative route planning. This aspect is critical not only for individual vehicles but also for ensuring effective interaction among multiple autonomous systems. The challenge is to optimize route planning algorithms for real-time operation without compromising accuracy. To address this, we leverage quantum computing techniques, integrating them with CNN architectures to develop Quantum Convolutional Neural Networks (QCNNs) and the Optimized QCNN. Neural networks play a key role in tasks such as predicting paths, estimating traffic flow, and selecting optimal routes, ensuring efficient and adaptive planning. The integration of quantum computing with neural networks significantly enhances real-time performance and the Optimized QCNN, developed using the Circuit Depth Reduction strategy, further improves accuracy and convergence rate. Our evaluation, conducted on the Cityscapes Dataset, demonstrates that QCNNs comparing with traditional CNNs achieve faster convergence rate and lower loss value. Furthermore, Optimized QCNNs surpass standard QCNNs in both convergence rate and loss, while also achieving higher accuracy even than traditional CNNs, making them a powerful model for real-time collaborative route planning in autonomous vehicles.
This paper presents a novel quantum artificial intelligence model for efficient and accurate pothole detection on road surfaces, specifically tailored for autonomous vehicle (AV) environments. Potholes are widely recognized in the literature as critical risk factors that can cause significant vehicle damage and contribute to traffic accidents. Accordingly, timely and precise detection of potholes is essential to ensure the safety of both vehicles and their occupants. To meet the stringent real-time constraints of AV navigation, the proposed approach employs quantum convolutional neural networks (QCNNs), which provide enhanced expressiveness, low-latency inference, and fewer parameters while achieving performance comparable to classical convolutional neural networks (CNNs). To ensure algorithmic stability under various road and traffic dynamics, a Lyapunov optimization strategy is integrated into the QCNN architecture. This enables the design of a stabilized learning framework, which dynamically regulates the detection model to maintain robustness and performance even in fluctuating environments. Experiments show that the proposed QCNN outperforms classical CNNs in both accuracy and efficiency. In addition, the proposed Lyapunov optimization-based QCNN architecture control further enhances real-time performance, considering inference delay.
Abstract: The integration of Quantum Artificial Intelligence (Quantum AI) into smart cities represents a transformative leap in traffic management and urban planning. Traditional AI-driven urban systems, while effective, struggle with real-time adaptability, large-scale data processing, and complex optimization challenges. Quantum AI, leveraging quantum superposition, entanglement, and parallel computation, enables cities to optimize traffic flow, infrastructure planning, energy management, and public safety with unparalleled speed and accuracy. This chapter explores how Quantum AI enhances predictive traffic modeling, real-time traffic light synchronization, and autonomous vehicle coordination, reducing congestion and improving mobility efficiency. Additionally, Quantum AI is reshaping urban planning by enabling smart infrastructure development, sustainable resource management, and proactive disaster response systems. Despite challenges such as high computational costs, data privacy concerns, and ethical implications, Quantum AI holds the potential to revolutionize city management, paving the way for resilient, sustainable, and highly adaptive urban environments. The future of smart cities will be defined by AI-powered quantum ecosystems, ensuring seamless urban mobility, enhanced sustainability, and intelligent decision-making for next-generation urban development. Keywords: Quantum AI, smart cities, traffic optimization, urban planning, quantum computing, real-time traffic management, predictive analytics, sustainable urban development, artificial intelligence, autonomous transportation, disaster response, smart infrastructure, quantum-enhanced mobility.
This paper proposes a computation-efficient quantum convolutional neural network (CE-QCNN) architecture designed for autonomous driving applications. A key contribution of this work lies in the formulation of a tri-value qubit encoding (TQE) scheme, which compactly embeds three-channel RGB image data into single-qubit states via a sequence of quantum rotations. This strategy enables significant qubit resource reduction while preserving the representational richness of multi-channel visual inputs. The encoded quantum states are subsequently processed through parameterized quantum circuits for convolutional feature extraction, forming the core of the proposed CE-QCNN framework. To further improve learning stability and early-stage performance, a knowledge distillation (KD) strategy is employed, transferring supervision from a pretrained classical CNN model to the quantum network. The proposed model is evaluated on the KITTI dataset, a standard benchmark for autonomous driving, where it demonstrates both competitive detection accuracy and reduced computational complexity. These results substantiate the scalability and practical applicability of CE-QCNNs for future quantum-enhanced perception systems in real-time autonomous driving scenarios.
Autonomous driving systems demand real-time processing of high-dimensional sensor data, large-scale decision making, NP-hard routing optimization under strict latency and computational constraints. Classical approaches using convolutional neural networks face scalability issues and performance limitations in dynamic environments. IBM’s Qiskit framework provides a comprehensive solution with a modular architecture, enabling the construction of quantum circuits that encode multi-sensor data into exponentially large Hilbert spaces. The core components like Terra and Aer support circuit design and noise-aware simulation. Additional libraries extend these capabilities to support quantum machine learning and optimization algorithms. This paper presents a unified Qiskit-based framework incorporating quantum neural networks for trajectory prediction, quantum convolutional neural networks for safety classification in poor conditions, and quantum approximate optimization algorithm for efficient vehicle routing. We showcase how quantum superposition explores multiple routing paths in parallel and how quantum circuits represent grid locations to improve path finding. Our survey reveals measurable performance advantages of quantum-enhanced models over classical methods, forming the basis of quantum-accelerated autonomous driving systems.
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$\dagger $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 QDCNN’s —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.
Real-time processing with high classification accuracy is a fundamental requirement in autonomous driving systems. However, existing neural network models for classification often face a tradeoff between computational efficiency and accuracy, necessitating the development of advanced optimization methods to address this limitation. Additionally, dynamic driving environments offer opportunities to enhance classification performance by leveraging the principles of quantum computing, particularly the properties of superposition and entanglement. In response to these challenges, a multi-stage quantum convolutional neural network (MS-QCNN) approach is proposed, designed to improve image analysis performance by effectively utilizing the multi-stage structure of QCNN. A Lyapunov optimization framework is applied to achieve optimal performance, which maximizes time-averaged efficiency while ensuring system stability. This framework dynamically adjusts the MS-QCNN model in response to environmental variations, promoting enhanced queue stability and achieving optimal time-averaged performance.