Traffic signal control plays an important role in alleviating traffic congestion and improving urban commuting efficiency. In recent years, breakthroughs have been made in traffic signal control algorithms based on deep reinforcement learning using real-time traffic data as input. However, traffic data in real-world scenarios often involve data distortion. Traditional solutions use reinforcement learning algorithms to control signal lights after repairing distorted data. However, on the one hand, the dynamic phases of traffic signal introduces additional uncertainty to distortion repair, and on the other hand, distortion repair is difficult to combine with deep reinforcement learning frameworks to improve performance. To address these issues, a distorted traffic signal control model based on hidden state prediction, HCRL, is proposed. The HCRL model comprises encoding, control, and encoding prediction sub-models. By introducing a hidden state representation mechanism for signalized intersections, the HCRL model can adapt better to deep reinforcement learning frameworks and effectively express the control state of signalized intersections. In addition, the HCRL model uses a special transfer training method to avoid data distortion interference in the control sub-model. Two real datasets are used to verify the impact of data distortion on the intelligent signal light control algorithms. The experimental results show that the HCRL model outperforms the distortion-completion-based traffic signal control models in all distortion scenarios and distortion rates; further, it demonstrates strong robustness against data distortion when compared with other baseline models.

To optimize Quality of Service (QoS), Mobile Edge Computing (MEC) has been deeply integrated into the Internet of Vehicle (IoV) to provide geographically proximal computing resources for vehicles, thereby reducing task processing latency and energy consumption. However, traditional MEC server deployment relies primarily on terrestrial Base Stations (BSs), resulting in high deployment costs and limited coverage, making it difficult to ensure uninterrupted services for all vehicles. Air-ground collaborative IoV technology has emerged as a solution to these challenges. Unmanned Aerial Vehicles (UAVs) can dynamically assist Road-Side Units (RSUs) using their flexibility in line-of-sight links, providing more flexible computing resources for vehicular users, thereby ensuring the continuity and efficiency of in-vehicle services. Therefore, this study proposes a Dynamic Vehicular Edge Task Offloading Method (DVETOM) based on air-ground collaboration. This method adopts a vehicle-road-air architecture, establishing Vehicle-to-RSU (V2R) and Vehicle-to-UAV (V2U) links. Transmission and computation models are constructed for three modes: local execution of vehicular tasks, offloading tasks to the RSU, and offloading tasks to the UAV. An objective function is established with the joint optimization goal of minimizing system latency and energy consumption. DVETOM transforms the task offloading problem into a Markov Decision Process (MDP) and optimizes the task offloading strategy by using the Distributed Deep Deterministic Policy Gradient (D4PG) algorithm based on Deep Reinforcement Learning (DRL). Compared with 5 benchmark methods, experimental results show that DVETOM outperforms existing methods by 3.45%—23.7% in terms of reducing system latency and 5.8%—23.47% in terms of reducing system energy consumption while improving QoS for vehicular users. In conclusion, DVETOM enhances the offloading of vehicular edge computing tasks within the IoV effectively. It offers IoV users a more efficient and energy-conserving solution, showcasing its extensive potential for application in intelligent transportation systems.

Detecting defects on unstructured roads is important for road traffic safety; however, annotated datasets required for detection is limited. This study proposes the Multi-Augmentation with Memory (MAM) semi-supervised object detection algorithm to address the lack of annotated datasets for unstructured roads and the inability of existing models to learn from unlabeled data. First, a cache mechanism is introduced to store the positions of the bounding box regression information for unannotated images and images with pseudo annotations, avoiding computational resource wastage caused by subsequent matching. Second, the study proposes a hybrid data augmentation strategy that mixes the cached pseudo-labeled images with unlabeled images inputted into the student model, to enhance the model′s generalizability to new data and balance the scale distribution of images. The MAM semi-supervised object detection algorithm is not limited by the object detection model and better maintains the consistency of object bounding boxes, thus avoiding the need to compute consistency loss. Experimental results show that the MAM algorithm is superior to other fully supervised and semi-supervised learning algorithms. On a self-built unstructured road defect dataset, called Defect, the MAM algorithm achieves improvements of 6.8, 11.1, and 6.0 percentage points in terms of mean Average Precision (mAP) compared to those of the Soft Teacher algorithm in scenarios with annotation ratios of 10%, 20%, and 30%, respectively. On a self-built unstructured road pothole dataset, called Pothole, the MAM algorithm achieves mAP improvements of 5.8 and 4.3 percentage points compared to those of the Soft Teacher algorithm in scenarios with annotation ratios of 15% and 30%, respectively.

This study proposes a traffic strategy to improve the efficiency of vehicles passing through unsignalized intersections under vehicle-infrastructure cooperation, with the objectives of reducing vehicle acceleration change rate and target vehicle travel time. The study establishes a vehicle road collaboration scenario, divides dynamic conflict areas and static conflict areas, defines model input parameters, constructs a vehicle traffic sequence model and vehicle motion state control model, and verifies the effectiveness of the models through SIMULINK simulation. In common and special traffic scenarios, the strategy reduces the average maximum acceleration change rate during the vehicle deceleration phase by 17.27% and 45.95%, average amplitude of vehicle acceleration change by 37.06% and 38.89%, average maximum acceleration by 37.53% and 48.2%, and average travel time by 41.33% and 44.31%, respectively. In addition, compared to similar algorithms in the literature, this strategy optimizes the average travel time by 42.82% and average vehicle speed by 45.8%. The optimization effect is significant, and both indicators are more balanced. Simultaneously, the vehicle speed does not fluctuate frequently, and the ride comfort improves. Therefore, this strategy significantly improves overall traffic efficiency without much sacrifice to the comfort performance of partial vehicles.

The dynamic nature of tasks in Internet of Vehicles (IoV) environments increases the complexity of real-time computational offloading. To address the difficulty of completing real-time tasks in a timely manner owing to limited terrestrial network coverage in IoV scenarios, this study proposes a collaborative computational offloading approach for Satellite Vehicular Mobile Edge Computing Networks (SVMECN). First, a geometric relationship model between satellites and the ground is constructed to calculate the transmission rates between devices and satellites, as well as between terrestrial gateways and satellites. The task processing delay is computed based on this model. The model fully considers the real-time nature of tasks and dynamically adjusts for the impact of satellite movement on terrestrial data transmission. Through collaborative processing between satellites and terrestrial gateways, the latency requirements of in-vehicle applications are met. Second, the study proposes a collaborative computational offloading algorithm based on Pointer Attention Mechanism and Actor-Critic (ST-PART). This algorithm dynamically adjusts task priorities according to their real-time nature, offloads tasks for computation in order of priority, and dynamically selects and collaboratively processes tasks among different computing nodes to minimize task processing delays. The proposed algorithm is simulated in an SVMECN environment. Compared with traditional heuristic algorithms, the proposed algorithm improves operational efficiency. Experimental and analytical results indicate that the proposed algorithm can significantly reduce task processing delays while meeting the real-time requirements of tasks. Compared with algorithms without collaboration between terrestrial and satellite components, the proposed algorithm can reduce latency costs by 2.35%-68.68%.