Vehicle Edge Computing (VEC) leverages compact cloud computing at the mobile network edge to meet the processing and latency needs of vehicles. By bringing computation closer to the vehicles, VEC reduces data transmission, minimizes latency, and boosts performance for compute-intensive applications. However, during peak hours of urban road traffic, the scarce computational resources available at edge servers could pose challenges in fulfilling the processing needs of vehicles. Introducing Unmanned Aerial Vehicles (UAVs) as supplementary edge computing nodes could significantly mitigate the aforementioned issue. In this paper, we propose a flexible edge computing framework in which a fleet of UAVs function as mobile computational service providers, offering computation offloading services to multiple vehicles. We design and optimize a computation offloading model for the UAV-enabled vehicle edge computing environment. The proposed model tackles the task offloading challenge, aiming to optimize UAV revenue and task processing efficiency while considering the constraints of UAVs’ restricted computational power and energy resources. Towards this end, our model jointly considers two key factors: task partitioning and computational resource allocation. To tackle the challenges posed by the aforementioned non-convex optimization problem, we construct a Markov Decision Process (MDP) model for the multi-UAV-enabled mobile edge computing system and introduce an innovative Multi-Agent Deep Reinforcement Learning (MADRL) framework addressing the decision-making challenge represented by MDP model. Comprehensive simulation outcomes illustrate that our devised task offloading technique outperforms other optimization methods.

Traffic object detection based on computer vision (CV) can usually be deployed on the embedded computing platform of autonomous vehicles or unmanned aerial vehicles (UAVs), to provide critical information about traffic scenes for autonomous driving or traffic management. However, due to limited computing resources, there is a need for small, lightweight, and reliable object detectors. As an emerging technology, spatial pyramid pooling methods have great potential in improving the detection performance of real-time object detectors. Most of the existing works focus on the development of more complex spatial pyramid pooling methods for higher accuracy, but real-time performance is also important in the everchanging traffic scene. Thus, to balance the tradeoff between real-time detection and accuracy, we design a solution for real-time traffic object detection: a novel real-time object detector, named ESPPNet. Specifically, we propose an efficient plug-and-play spatial pyramid pooling method (ESPP). The method consists of a progressive spatial pyramid pool structure (PSPP) and a multi-scale feature enhancement module (MFEM). We first use PSPP to capture multi-scale feature maps with richer nonlinear features. Then, MFEM is used to establish effective long-range dependencies for multi-scale features. Experimental results on the VisDrone and SODA10M public datasets demonstrate that our method can achieve better real-time performance, less resource utilization, and higher accuracy, compared with other state-of-the-art methods.

Real-time object detection is an essential part of various Internet of Things (IoT) applications. Unmanned aerial vehicles (UAVs) employ visual sensors to capture high-definition images to detect objects of interest. However, current research on UAV detectors mainly focuses on developing more sophisticated network architectures, with little attention paid to the limitations of UAV computing resources. In this work, we present an end-to-end split-and-shuffle detector, named SCSDet. Unlike the mainstream detector designs that heavily rely on bottleneck structures, our method is based on inexpensive split-and-shuffle operations. It encourages the detector to avoid unnecessary transformation layers for channel down-sampling, thereby minimizing memory and computation cost. This is rarely studied in detector architecture design. Specifically, we first design a lightweight backbone structure (SCSNet) based on split-and-shuffle, which allows frequent interaction between different gradient information to capture more useful non-linear features for small-scale objects at a low cost. Next, we construct an efficient receptive field module (ERFM) to generate richer multi-receptive field expressions for the initial feature space. It significantly alleviates the adverse effects of single receptive field size on the capability of the detectors to detect small-scale objects. Finally, we propose a grouped local attention convolution (GLAConv), which utilizes local sliding windows with different coverage rates to adaptively learn channel and spatial attention. This makes the detector to focus on the foreground. Experimental results show that our method achieves high accuracy with low complexity in UAV object detection.

Digital twin (DT)-empowered platoon can improve platoon management efficiency and driving safety. However, the resource allocation scheme of low-latency platoon DT (PDT) and the interactions with platoon control strategy are important issues in the study of PDTs. In this article, we study the resource allocation in the PDT network and the interaction mechanism between PDT and platoon control for an unmanned aerial vehicle (UAV)-hosted PDT. We introduce the Age of Information (AoI) metrics to characterize the freshness of the DTs. To explore the impact of the PDT resource allocation scheme on the platoon control strategy, we propose a joint optimization model for power resource allocation and platoon control. Specifically, the allocation of power resources affects the PDT’s AoI, and the high-latency PDT in turn affects the platoon control strategy. Our objective is minimize the weighted sum of the system’s average energy consumption and the PDT’s average peak AoI. To solve the problem, we first reformulate the power resource allocation problem over a period of time as a Markov decision process (MDP) model, and then propose the Dirichlet deep deterministic policy gradient (DDPG)-based power allocation (D3PGPA) method based on Dirichlet distribution and DDPG algorithm. The method can not only effectively explores the state space while satisfying the constraints of limited resources but also improve the stability of the algorithm. Numerical results show that the D3PGPA method can host a PDT with low AoI and improve the stability of the platoon. Besides, our proposed method performs stably and outperforms other benchmark methods.

With the development of Internet of Vehicles (IoV) technology and the application of artificial intelligence-based algorithms, platoon driving based on connected autonomous vehicles (CAVs) has become one of the effective solutions to reduce environmental pollution and improve traffic safety. However, the connectivity, autonomy, and passenger comfort in platooning vehicles cannot be realized without the support of advanced communication technologies and auxiliary computing. In this work, we research the problem of computation offloading and resource allocation within a platoon network. Considering the comprehensive effects of vehicle mobility, co-channel interference, and multivehicle cooperation, we propose a system optimization model for joint computation offloading and power allocation (COPA). Our objective is to minimize the weighted sum of the system average energy consumption and task data processing delay. In the dynamic platoon network, we design a multiagent deep deterministic policy gradient (DDPG)-based joint COPA scheme, which can learn the temporal correlation of environment states and make more accurate power allocation actions. Moreover, we conduct extensive computer simulations to demonstrate the robustness and effectiveness of the DDPG-based COPA scheme. Numerical results demonstrate that the proposed scheme has a better performance compared with other benchmark schemes.