With the advancement of artificial intelligence, automobiles are progressively transitioning from traditional mechanization to Connected Autonomous Vehicles (CAVs), significantly enhancing driving comfort and safety. As the standard communication protocol in CAVs, the Controller Area Network (CAN) remains vulnerable to attacks due to the lack of robust security mechanisms. While existing deep learning-based vehicle network intrusion detection systems can effectively identify known attacks, their ability to detect unknown attacks is limited due to the same data distribution in the source and target domain. To address this issue, we propose a domain adaptation and time-series imaging-based intrusion detection system (DATI-IDS) to detect known and unknown attacks, where the deep domain adaptation method is used to solve the source and target domain data distribution difference problem by optimizing the multiple kernel maximum mean discrepancy (MK-MMD) between the source domain and target domain images and the classification loss, and the time-series imaging method is used to capture temporal dependencies and improve efficiency by transforming the CAN ID sequence into a two-dimensional gramian angular summation field (GASF) image. The effectiveness of the proposed model is evaluated across nine distinct unknown attack scenarios using the Car-Hacking dataset and the survival analysis dataset. Comparative analysis with previous studies demonstrates superior performance, faster inference times, and reduced model complexity.

In Vehicular Edge Computing (VEC), a reasonable and efficient user association and resource allocation approach is a worthwhile research issue. However, most studies in Internet of Vehicles (IoV) only consider vehicle mobility and IoV communication. Therefore, we propose a user association and resource allocation strategy in Integrated Sensing and Communication (ISAC)-aided VEC. Compared with existing solutions, we consider constraints such as sensing and communication interference, vehicle mobility, Road Side Unit (RSU) sensing performance, and vehicle user quality of service (QoS). By quantifying the sensing and communication performance of RSUs, we construct a user association and resource allocation model with the optimisation objective of maximising the average sensing performance and communication performance of the system. Then, combining double dueling deep Q-network (D3QN) algorithm and twin delayed deep deterministic policy gradient (TD3) algorithm, we propose a DRL algorithm based on D3QN-TD3. We represent the user association and resource allocation problem as a Markov Decision Process (MDP) and solve it using the proposed algorithm to obtain the optimal user association, channel allocation, and power allocation strategies. Experimental results show that the proposed algorithm has better performance in terms of downlink transmission rate, radar sensing mutual information, system utility, and task completion rate.

UAV-assisted VEC can provide content caching services for vehicles by flying close to the vehicles for vehicle's QoS. However, in real-world scenarios with traffic congestion, due to the battery capacity and cache space limitations of UAVs, low content response speed and high response latency may occur. Based on this, we proposed a dynamic energy consumption-based content caching strategy in UAV-assisted VEC. We use the PSO algorithm to solve the problem and obtain the optimal UAV deployment location. For content caching, we construct a content caching model by considering UAV deployment, vehicle user preference, UAV cache capacity, and UAV energy consumption with the goal of minimizing content request latency. In addition, we propose an IAFSA-based content caching strategy. We reduce the solution space of the fish swarm algorithm, decrease the number of caching decisions, and improve the convergence performance of AFSA by employing dynamic horizons and step sizes. Experimental results show that the proposed IAFSA effectively reduces the average content request latency of the vehicle, improves the cache hit rate, and reduces the number of content return trips. Particularly, the proposed strategy reduces the average content request latency by more than 9.84% compared to the baseline algorithm.

As traditional automobiles evolve into the Internet of Vehicles (IoV), the increasingly frequent interactions between intelligent vehicles and external environments make cybersecurity a critical issue. While existing machine learning-based automotive intrusion detection methods demonstrate strong detection performance, most rely on supervised learning frameworks. These approaches not only incur high manual data labeling costs but also show significant limitations when handling real-world, unlabeled attack samples. In this paper, we propose an efficient intrusion detection system for in-vehicle networks based on self-supervised contrastive learning. By leveraging data augmentation, we construct positive sample pairs and learn robust feature representations through joint training using both reconstruction loss and contrastive loss. Additionally, we fuse the features extracted from the backbone and core networks into global representations for downstream classification tasks. Experiments on real in-vehicle intrusion datasets show that SSC-IDS achieves strong performance in anomaly detection. Furthermore, we test the model’s robustness under varying rates of anomaly contamination.

Cooperative Adaptive Cruise Control (CACC) systems can significantly improve traffic safety and roadway capacity utilizing short following gaps of vehicles enabled by inter-vehicle communications. However, due to merging processes occurring at freeway merging areas, existing CACC operation approaches are generally not applicable and the operation will have to revert back to Adaptive Cruise Control (ACC) or human-driven mode, which in turn will result in a capacity drop. This paper proposes an optimal trajectory optimization strategy for Connected and Automated Vehicles (CAVs) to cooperatively carry out mainline platooning and on-ramp merging. Firstly, a control framework of the CACC is adopted for a longitudinal control of CAVs, which helps individual CAVs to join platoons and to maintain platoon operations. Secondly, to ensure smooth lane-changing executions while achieving stable platoons, an optimal controller that considers lane-changing motivation of merging vehicles and impact of merging on platoons, is proposed. Third, a Legendre pseudo-spectral algorithm is applied to transform the controller into a simpler nonlinear programming problem and to efficiently solve it. Simulation assessments of the proposed method are conducted at both individual vehicle level and traffic-flow level. At the individual vehicle level, the proposed method has the potential to improve the traffic safety without compromising fuel consumption and emissions compared with unoptimized feasible schemes. At a traffic-flow level, an online evaluation platform is implemented, and a typical freeway on-ramp area is studied. The simulation results have demonstrated that the proposed controller provides significant improvements in terms of efficiencies in traffic operations.

This paper comprehensively discusses the cooperative communication and computation of vehicular system. Based on the cooperative transmission, an stochastic model of vehicle-to-vehicle (V2V) communication reliability is established using probability theory. Furthermore, the computation reliability is defined as a new metric for computation offloading, and a vehicle computational performance evaluation model is also established. In order to effectively compute the required data, we combine V2V communication and vehicle computing to further characterize the coupling reliability of cooperative communications and computation systems. In addition, we propose a virtual queue model that combines queue length and vehicle privacy entropy to optimize partitioning. Finally, considering the amount of processing data and cut-off time of vehicle applications, we establish the optimal partition model of vehicle computing with the goal of maximizing the coupling reliability, and propose the coupling-oriented reliability calculation for vehicle collaboration using dynamic programming methods. Simulations show that the proposed scheme outperforms traditional approaches in terms of coupling reliability and completion rate. In addition, the allocation between local computing and data offloading is controlled by the server’s privacy perception of collaboration events.