The growing complexity and volume of traffic and sensory data in Connected and Autonomous Vehicles (CAVs) make it crucial to develop more effective methods for selecting and extracting features from high-dimensional images and videos. This paper proposes a novel quantum-accelerated feature selection framework utilising the Variational Quantum Eigensolver (VQE) to enhance perception tasks in CAV environments. By decomposing high-dimensional traffic images into compact patches encoded as quantum Hamiltonians, the proposed design aims to support Intelligent Transportation Systems (ITS) tasks such as object detection, vehicle classification, and traffic monitoring in CAVs.The quantum hybrid method serves as a pipeline for extracting selective features that have been optimised using gradient-aware parameter pruning and conditional principal component analysis (PCA). The implementation of this quantum hybrid approach facilitates addressing computational challenges at the edge in embedded vehicular systems, reducing data dimensionality while preserving key discriminative spatial patterns necessary for object detection and traffic monitoring. Although the quantum method currently underperforms in certain clustering and separability metrics, it demonstrates potential as a viable alternative for advanced perception in CAVs.This work lays a foundation for integrating quantum computing into edge-based autonomous vehicle perception, highlighting both opportunities and challenges with near-term quantum hardware.
The growing complexity and volume of traffic and sensory data in Connected and Autonomous Vehicles (CAVs) make it crucial to develop more effective methods for selecting and extracting features from high-dimensional images and videos. This paper proposes a novel quantum-accelerated feature selection framework utilising the Variational Quantum Eigensolver (VQE) to enhance perception tasks in CAV environments. By decomposing high-dimensional traffic images into compact patches encoded as quantum Hamiltonians, the proposed design aims to support Intelligent Transportation Systems (ITS) tasks such as object detection, vehicle classification, and traffic monitoring in CAVs.The quantum hybrid method serves as a pipeline for extracting selective features that have been optimised using gradient-aware parameter pruning and conditional principal component analysis (PCA). The implementation of this quantum hybrid approach facilitates addressing computational challenges at the edge in embedded vehicular systems, reducing data dimensionality while preserving key discriminative spatial patterns necessary for object detection and traffic monitoring. Although the quantum method currently underperforms in certain clustering and separability metrics, it demonstrates potential as a viable alternative for advanced perception in CAVs.This work lays a foundation for integrating quantum computing into edge-based autonomous vehicle perception, highlighting both opportunities and challenges with near-term quantum hardware.
Connected autonomous vehicles (CAVs) require extensive data processing to make real-time decisions such as obstacle detection, collision avoidance, and route optimization. To enable fast and accurate decision-making in CAVs, services traditionally executed in cloud data centers are being moved to the edge to minimize data transfer from vehicles to the cloud. We present a framework called Q-Edge that utilizes quantum computing at the network edge to improve the speed and effectiveness of data analysis for CAVs. By integrating quantum computing principles such as superposition, entanglement, and teleportation with edge computing, we propose a solution to address the latency and bandwidth issues inherent in traditional cloud computing methods. This approach, leveraging quantum software engineering practices, enables realtime decision-making, optimizing CAV performance and enhancing traffic management, urban mobility, and road safety.
Integrating quantum computing with edge computing presents an approach to managing the vast data generated by Connected Autonomous Vehicles (CAVs). This article introduces a novel framework that leverages quantum algorithms at the network edge to significantly enhance the efficiency and speed of data analysis for CAVs. By combining the principles of quantum computing with the distributed nature of edge computing, we propose a solution that not only addresses the latency and bandwidth challenges of conventional cloud computing models but also paves the way for real-time decision-making processes essential for autonomous vehicle operations. Our findings reveal the potential for quantum edge computing to transform the processing capabilities at the edge, offering a scalable, efficient, and faster method for data analysis that could significantly improve traffic management, urban mobility, and overall safety on the roads.
Quantum based vehicle detection is the innovative integration of Quantum Machine Learning (QML) techniques with classical computer vision methods to enhance vehicle detection and speed tracking systems using OpenCV. Traditional approaches in vehicle detection and speed tracking are often hindered by computational inefficiencies and accuracy limitations in complex traffic scenarios. Our proposed hybrid quantum-classical framework aims to overcome these complications by exploiting the computational advantages of quantum computing. In this study, we employ Quantum Principal Component Analysis (QPCA) for feature extraction and data dimensionality reduction, optimizing the input data for improved processing efficiency. The core detection mechanism integrates Quantum Neural Networks (QNNs) with classical Convolutional Neural Networks (CNNs), enhancing the model’s accuracy in identifying vehicles within video frames. For speed tracking, we apply quantum optimization techniques to refine the tracking of vehicle positions across consecutive frames, ensuring precise speed calculation. Using OpenCV for real-time video processing and Qiskit for quantum computations, the findings from the experiments indicate significant improvements in both detection a precision, accuracy and computational efficiency. The hybrid quantum-classical model showcases the potential of quantum computing in advancing computer vision applications, particularly in intelligent transportation systems. This research highlights the transformative impact of QML techniques on practical applications and paves the way for further advancements in quantum-enhanced vehicle detection and speed tracking.
Complex optimization problems, such as traffic routing and electric vehicle (EV) charging scheduling, are becoming increasingly challenging for intelligent transportation systems (ITSs), in particular as computational resources are limited and network conditions evolve frequently. This paper explores a quantum computing approach to address these issues by proposing a hybrid quantum-classical (HQC) workflow that leverages the variational quantum eigensolver (VQE), an algorithm particularly well suited for execution on noisy intermediate-scale quantum (NISQ) hardware. To this end, the EV charging scheduling and traffic routing problems are both reformulated as binary optimization problems and then encoded into Ising Hamiltonians. Within each VQE iteration, a parametrized quantum circuit (PQC) is prepared and measured on the quantum processor to evaluate the Hamiltonian’s expectation value, while a classical optimizer—such as COBYLA, SPSA, Adam, or RMSProp—updates the circuit parameters until convergence. In order to find optimal or nearly optimal solutions, VQE uses PQCs in combination with classical optimization algorithms to iteratively minimize the problem Hamiltonian. Simulation results exhibit that the VQE-based method increases the efficiency of EV charging coordination and improves route selection performance. These results demonstrate how quantum computing will potentially advance optimization algorithms for next-generation ITSs, representing a practical step toward quantum-assisted mobility solutions.
Variational Quantum Eigensolver (VQE) has emerged as a prominent hybrid quantum-classical algorithm for near-term quantum computing, particularly in applications like quantum machine learning. However, the performance of VQE is significantly constrained by the limitations of classical optimization methods employed to minimize the expectation value of the Hamiltonian. Traditional VQE approaches often suffer from slow convergence and sensitivity to initial conditions, particularly when dealing with complex, high-dimensional problems encountered in machine learning scenarios. This work introduces a novel adaptive quantum computing framework for VQE optimization, designed to dynamically adjust the quantum circuit parameters during the optimization process. By continuously monitoring the optimization progress and intelligently modifying the parameters, we aim to accelerate convergence and improve the robustness of VQE algorithms for machine learning tasks. The core idea is to shift from a static, fixed-parameter VQE to a dynamic, adaptive one. We explore various adaptive control strategies and demonstrate their potential to significantly reduce the number of iterations required to reach a solution, leading to improved computational efficiency and enabling VQE to tackle more complex machine learning problems within the constraints of current quantum hardware. The research focuses on the theoretical underpinnings and the design principles of this adaptive approach, laying the foundation for future implementations and experimental validation.
Cooperative platooning can reduce the energy use of Connected and Autonomous Vehicle (CAV) fleets, but the routing problem becomes difficult when vehicles must meet on the same road segments at compatible times while moving through unstable urban traffic. This paper develops an edge-assisted, closed-loop evaluation pipeline for platooning-aware vehicle routing. Roadside Units estimate local traffic kinematics from video, classify segment-level flow stability, and activate platooning rewards only on road segments where close-gap coordination is physically appropriate. The resulting multi-vehicle routing problem is written directly as a Quadratic Unconstrained Binary Optimization (QUBO) model, so pairwise platooning interactions are represented as native quadratic Ising terms instead of requiring auxiliary MILP linearization variables. We evaluate the framework using a 24-hour microscopic SUMO simulation of Troy, NY, together with localized IBM Quantum hardware benchmarks. The SUMO study shows an 18.5% reduction in fleet tractive-energy demand relative to a non-cooperative baseline. On 25-active-qubit benchmark instances executed on $\texttt{ibm_boston}$, Linear-Chain QAOA reduces two-qubit CNOT depth by 66.7% compared with dense QAOA and samples the exact classical ground state with Pfeas=38.6% and Popt=14.2% at p=2. These results suggest that edge perception and shallow quantum optimization can work together as a useful component of closed-loop CAV platoon dispatching.