Autonomous vehicle navigation requires finely calibrated environmental awareness, resilient onboard deliberation, and continual, near-instantaneous adaptation to evolving roadway behaviours. Current deep learning frameworks achieve strong scores on perception benchmarks, yet their propensity to overlook nonlinear spatial–temporal couplings—coupled with mandated low-latency constraints—remains a fundamental safety liability in actual driving contexts. To mitigate this gap, the present work introduces a hybrid architecture that overlays a Quantum Simplicial Classical Attention Convolutional Network (QSCACN2Net) with a Brown Bear Optimization Algorithm (BBOA), thereby achieving dual improvements in navigational efficacy and reliability. The QSCACN2Net component leverages quantum-classical hybrid propagation and simplicial attention blocks to isolate multidimensional environmental couplings. By combining sub-millisecond feature resolution with coherent ingestion of panoramic vehicular and atmospheric context, the architecture assimilates relevant information across disparate magnitudes of environmental structure. Simultaneously, the BBOA mimics seasonal hibernation metabolic patterns to design hyperparameter search; this biologically inspired mechanism secures accelerated convergence and pronounced robustness with minimal expansion of the hyperparameter search space. Validate the proposed architecture on the benchmarking KITTI dataset, reporting statistically relevant improvements in absolute error, quantifiable reductions in mean pixel perturbation measurements, and tightened end-to-end latency regimes that exceed existing state-of-the-art solutions. Combinatorial analysis of the experimental results substantiates scalable deployment, illustrating that the architecture satisfies the stringent maintainability, safety, and resource operability criteria demanded by future production-grade autonomous driving frameworks.
Autonomous vehicle navigation requires finely calibrated environmental awareness, resilient onboard deliberation, and continual, near-instantaneous adaptation to evolving roadway behaviours. Current deep learning frameworks achieve strong scores on perception benchmarks, yet their propensity to overlook nonlinear spatial–temporal couplings—coupled with mandated low-latency constraints—remains a fundamental safety liability in actual driving contexts. To mitigate this gap, the present work introduces a hybrid architecture that overlays a Quantum Simplicial Classical Attention Convolutional Network (QSCACN2Net) with a Brown Bear Optimization Algorithm (BBOA), thereby achieving dual improvements in navigational efficacy and reliability. The QSCACN2Net component leverages quantum-classical hybrid propagation and simplicial attention blocks to isolate multidimensional environmental couplings. By combining sub-millisecond feature resolution with coherent ingestion of panoramic vehicular and atmospheric context, the architecture assimilates relevant information across disparate magnitudes of environmental structure. Simultaneously, the BBOA mimics seasonal hibernation metabolic patterns to design hyperparameter search; this biologically inspired mechanism secures accelerated convergence and pronounced robustness with minimal expansion of the hyperparameter search space. Validate the proposed architecture on the benchmarking KITTI dataset, reporting statistically relevant improvements in absolute error, quantifiable reductions in mean pixel perturbation measurements, and tightened end-to-end latency regimes that exceed existing state-of-the-art solutions. Combinatorial analysis of the experimental results substantiates scalable deployment, illustrating that the architecture satisfies the stringent maintainability, safety, and resource operability criteria demanded by future production-grade autonomous driving frameworks.
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.
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.
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.
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.
The rapid integration of Internet of Things (IoT) technologies in Autonomous Vehicles (AVs) has significantly expanded the attack surface of vehicle-to-vehicle (V2V) and vehicle-to-infrastructure (V2I) communication systems.These networks exchange large volume of safety-critical data in realtime, which increases their exposure to attacks like message tampering, identity spoofing, and coordinated injection.Traditional cryptographic systems become more suspectable to emerging quantum computing threats, whereas the existing intrusion detection systems suffer from high latency, static encryption, and increased energy consumption due to frequent vehicular movement.These challenges restrict the ability of current systems to guarantee secure, real-time, energy-efficient, and sustainable communication for next-generation AVs.To address these challenges, this study introduced a novel Quantum-Enhanced Lightweight Protocol (QELP) that combines hybrid quantum-classical security mechanisms with adaptive encryption orchestration to improve security, real-time performance, and sustainable operation in IoT-enabled autonomous vehicle sensor networks.QELP combines BB84based Quantum Key Distribution (QKD) and lattice-based post-quantum cryptography (Kyber+) to establish quantum-resilient session keys.The context-aware encryption method dynamically adjusts the encryption intensity and key refresh rate based on residual energy, traffic density, anomaly score, and vehicular mobility to balance security strength and computational overhead.The model includes multi-layer error and attack detection techniques, including quantum-based intrusion alerts, digital signature validation, and behavioural deviation checks.The VeReMi and CARLA-NS3 datasets are employed for message-based V2X attack validation and to assess the networklevel behavior under real-time traffic scenarios.The performance is evaluated by simulations using Python (Qiskit) for quantum layers and NS3 for AV networks.The simulation results outperform with 98.76% attack detection rate, 0.015 false alarm rate, 13.7ms latency, 12.2% energy depletion score, 94.6% security risk score reduction towards a stronger quantumresilient defense and an encryption throughput improvement to 96.8%, handling encrypted data at high speed.The findings confirm that QELP provides a scalable, quantum-resilient, and energy-aware security architecture for next-generation autonomous vehicular communication systems.
Traffic sign recognition is essential for autonomous driving, enhancing driving efficiency and ensuring road safety. Traffic signs utilize colors and patterns to relay critical information, with color accuracy being especially significant. Traditional models, reliant on extensive data and computing resources, struggle to meet the efficiency demands of electric vehicles with carbon-intelligent computing. Leveraging the physical properties of quantum superposition states, quantum computing offers a solution with its unique parallel computing capabilities, potentially enhancing recognition efficiency and facilitating real-time processing. Quantum convolutional neural networks (QCNNs) show promise in processing large-scale image data with improved efficiency and accuracy. However, most of QCNN researches focus on grayscale image classification, with limited studies on multichannel data. This article introduces the quantum channel attention convolutional neural network (QCACNN), which incorporates a quantum channel attention layer (QCAL) to enhance multichannel data classification. The experimental results demonstrate that QCACNNs surpasses traditional QCNNs in traffic sign recognition, performing comparably to conventional CNNs and SENet models with fewer computing resources. Detailed performance analysis and ablation studies validate the effectiveness of each component within this architecture. Quantum noise resistance tests confirm the robustness of QCACNN, making it a viable solution for electric vehicles with enhanced scalability.