Cooperative Perception (CP) has been a promising paradigm to enhance single-vehicle awareness by enabling perception sharing among connected vehicles. However, existing studies often overlook the impact of constrained and heterogeneous edge resources, leading to synchronization bottlenecks and limited deployment efficiency. To address these challenges, this paper proposes EI-Cooper, an Edge Intelligence (EI)-enhanced cooperative framework for adaptive and efficient CP in heterogeneous vehicular networks. The novelty of EI-Cooper is fourfold. First, we leverage key EI techniques including selective cooperation, model pruning and bandwidth allocation to jointly coordinate the perception, computation, communication within the CP pipeline. To the best of our knowledge, EI-Cooper represents the first attempt to extend CP with EI capabilities. Secondly, we formulate aSynchronization-EfficientCooperativePerception (SECP) problem, which jointly determines edge selection, pruning ratios and bandwidths to balance end-to-end synchronization efficiency and perception accuracy. Thirdly, to tackle the closed-box nature and computational NP-hardness of SECP, we decompose it into two interpretable subproblems, respectively capturing macro-level spatial completeness and micro-level semantic retention. Finally, we develop aTwo-StageHierarchicalOptimization (TSHO) algorithm, where the first stage maximizes coverage via submodular node selection with a$(1-1/e)$approximation, and the second stage performs alternating optimization of pruning and bandwidth allocation under convergence guarantees. Extensive experiments on public datasets and a real-world prototype demonstrate the superiority of EI-Cooper.

Recent years have witnessed significant advancements in collaborative perception, particularly in enhancing accuracy and reducing communication overhead. However, due to the limited computation capacity of vehicles and dynamic bandwidth environment, traditional methods are constrained by fixed computational strategies, posing a challenge to providing low delay services in heterogeneous Vehicular Edge Computing (VEC). Considering this, we present a framework to accelerate collaborative perception, where vehicles adaptively partition the inference models and jointly offload them to the edge node. Further, we model the total delay by considering task arrival order, as well as the heterogeneous capacities of each node. Then, we formulate the Model Partitioning and Offloading (MPO) problem aiming to minimize the total delay of collaborative perception tasks. On this basis, we propose the Partitioning and Offloading Points Selection (POPS) algorithm, leveraging dynamic Thompson Sampling to select the optimal offloading points for each vehicle. By actively adjusting exploration intensity and passive parameter update rules, the POPS algorithm is highly adaptable to dynamic bandwidth environments. Finally, we conduct extensive performance evaluations, and the results demonstrate the superiority of our algorithm.

This work investigates the truthful auction for dependent task offloading in vehicular edge computing by considering the selfishness and rationality of participating nodes. Specifically, we first illustrate a truthfulness-guaranteed dependent task offloading architecture. Then, we formulate the Truthfulness-Guaranteed Dependent Task Offloading problem, aiming at maximizing the system utility (SU) while ensuring truthfulness and individual rationality in dynamic environments. Further, we design both centralized and distributed auction mechanisms to derive the optimal and approximate solutions, respectively. For centralized auction mechanism, we adopt the branch-and-price algorithm to determine the offloaded nodes, which yields maximum SU. Then, we adopt VCG mechanism to determine the payment of buyers. For distributed auction mechanism, each seller independently chooses the winning bid, and the buyer greedily chooses the offloaded node with maximum utility. Then, a novel payment mechanism regarding the cost of failed buyers is designed to guarantee the truthfulness and individual rationality. Finally, we build the simulation model and conduct the performance evaluation based on realistic vehicular trajectories. The results demonstrate that the proposed distributed auction mechanism achieves performance within approximately 4% of the optimal method, while significantly reducing computational complexity. Additionally, it significantly outperforms other methods in terms of system utility across various task requirements.

With rapid development of vehicular sensing and mobile communication technologies, cooperative sensing becomes an emerging paradigm of future intelligent transportation systems (ITSs). This paper investigates a LiDAR based cooperative sensing scenario in Vehicular Edge Computing (VEC). Specifically, we present the system architecture, in which vehicles with on-board LiDAR are able to detect objects via local processing of the sensed point-cloud data, and the outputs can be further shared via vehicle-to-vehicle (V2V) /vehicle-to-infrastructure (V2I) communications and fused in edge nodes. Then, we formulate the Edge Assisted Task Offloading (EATO) problem by considering the heterogeneous computation and communication capacities of vehicles and edge nodes, aiming at minimizing the average delay of the cooperative sensing tasks. Further, we propose a Multi-Armed Bandit (MAB)-based algorithm to make task offloading decisions adaptively. Finally, we implement the system prototype and give a comprehensive performance evaluation, which demonstrates the effectiveness of the proposed algorithm.

This paper explores on accelerating Deep Neural Network (DNN) inference with reliability guarantee in Vehicular Edge Computing (VEC) by considering the synergistic impacts of vehicle mobility and Vehicle-to-Vehicle/Infrastructure (V2V/V2I) communications. First, we show the necessity of striking a balance between DNN inference acceleration and reliability in VEC, and give insights into the design rationale by analyzing the features of overlapped DNN partitioning and mobility-aware task offloading. Second, we formulate the Cooperative Partitioning and Offloading (CPO) problem by presenting a cooperative DNN partitioning and offloading scenario, followed by deriving an offloading reliability model and a DNN inference delay model. The CPO is proved as NP-hard. Third, we propose two approximation algorithms, i.e., Submodular Approximation Allocation Algorithm (SA3) and Feed Me the Rest algorithm (FMtR). In particular, SA3 determines the edge allocation in a centralized way, which achieves 1/3-optimal approximation on maximizing the inference reliability. On this basis, FMtR partitions the DNN models and offloads the tasks to the allocated edge nodes in a distributed way, which achieves 1/2-optimal approximation on maximizing the inference reliability. Finally, we build the simulation model and give a comprehensive performance evaluation, which demonstrates the superiority of the proposed solutions.