As more users seek generative AI (GAI) models to enhance work efficiency, GAI and Model-as-a-Service will drive transformative changes and upgrades across all industries. However, when users utilize GAI models provided by the service provider, they cannot be certain that the model’s quality matches the provider’s claims. Considering the need to protect intellectual property, the service provider will not disclose model details for user verification. To this end, we take the Internet of Vehicles as research background, proposing a zero knowledge model proof architecture based on UAVs. We also introduce a multiagent reinforcement learning algorithm to optimize the verification process. In specific, we first propose a verification scheme for the key operations of generative adversarial networks based on noninteractive zero knowledge proof. The zero knowledge proof architecture ensures that model parameters cannot be stolen during the verification process. After that, we propose an Age of Verification (AoV) metric to ensure the timeliness and freshness of zero knowledge proof. We also construct a tradeoff optimization problem between the energy consumption of UAV as a verifier and the AoV of edge servers as service providers, and transform the problem based on Lyapunov optimization theory. Following that, we propose an enhanced multiagent proximal policy optimization algorithm to enable the collaborative verification of edge servers by multiple UAVs. The algorithm simulation results demonstrate that the reward value of our proposed algorithm is over 10% higher than that of the standard algorithm, with a faster and more stable overall convergence speed. Additionally, the zero knowledge proof performance test results indicate that the verification delay in our proposed architecture is less than 500 ms during the verification phase, meeting practical requirements.

With the development of intelligent transportation systems (ITSs), digital twin (DT) technology is becoming increasingly widespread in the application of connected automated vehicles (CAVs) to enhance driving safety. However, when DT systems are used for driving safety decisions through virtual control of reality and virtual reflection of reality, decision errors may occur, which can be fatal for the driving safety of CAVs. The main reasons are attributed to three aspects: 1) the accuracy; 2) the communication delay; and 3) the safety control of the DT system. In this article, we study to improve the accuracy and safety of the DT system decisions with communication delay. First, we considered powertrain factors to construct a high-precision and high-fidelity DT system. We use the Goodness-of-Fit Functions (GoFs) and Measure-of-Performances (MoPs) to fit the vehicle’s model and carry out error measurements in the DT system. Second, we analyze the stability of the DT system using plant stability and string stability under time delay. The effective range of time delay ensures the accuracy and stability of the DT system, and provides a safety constraint for the design of the CAV’s controller. Finally, we propose a DT-assisted robust safety-critical traffic control (RSTC) strategy based on the control barrier functions (CBFs). This strategy ensures the driving safety of CAVs with preceding and following vehicles while maintaining traffic stability. The theoretical analysis and experimental results present that the proposed scheme can effectively avoid conflicts and crash risks to ensure driving safety.

Blockchain has been considered as a critical solution to handle the privacy and security concerns for data sharing in vehicular networks. However, deploying vehicular blockchain onboard vehicles is constrained by the sophisticated communication environments of vehicular networks, the restricted resources of vehicles, and self-interested property of vehicles. In this paper, a Cellular Vehicle-to-Everything (C-V2X) based vehicular blockchain sharding framework is presented in vehicular edge computing. To motivate vehicles to assist in validating block data in vehicular shard, a contract-based incentive mechanism is presented to efficiently solve the joint moral hazard and adverse selection problem. Considering packet sensing ratio, half-duplex effect, and successful sensing probability, a dual PC5/Uu interface based block consensus delay model is formulated during the consensus process. To achieve two objectives of capability-discrimination and effort-motivation, we aim at enhancing the saved delay utility of blockchain service requester (BSR) while ensuring complex conditions of vehicles. Simulation outcomes demonstrate that the proposed mechanism successfully fulfills capability-discrimination and effort-motivation, and offers a 52% and 7% increase in BSR’s utility compared to the linear pricing scheme and uniform scheme, respectively.

Collaborative Vehicular Edge Computing (CVEC) employs an edge server on the roadside unit and volunteer vehicles as processors to provide vehicle-to-infrastructure (V2I) offloading and vehicle-to-vehicle (V2V) offloading for requester vehicles in computation offloading. Since the processors have heterogeneous computing capabilities, we study a hybrid task scheduling problem to minimize the total service cost of all requester vehicles subject to feasible constraints. More specifically, the service cost of a requester vehicle is formulated as the product of the priority value and weighted sum of the delay and energy consumption of processing the task. We derive delay constraints of the V2V and V2I offloading according to the mobility of the vehicles. Furthermore, we present a deep reinforcement learning approach to solve the above problem in the dynamic vehicular environment. Particularly, we adopt the state-of-the-art Rainbow algorithm to accelerate the convergence and achieve better performance. Finally, we provide numerical results to demonstrate that our approach outperforms the baseline approaches in achieving the faster and more accurate learning.

Vehicle trajectory prediction (VTP) is important for ensuring safe decision-making and planning in Internet of Vehicles (IoV). In complex traffic scenarios, accurate and reliable trajectory prediction requires comprehensive understanding of the interaction behaviors among vehicles. However, existing methods fail to effectively capture vehicle interaction features and fully explore their potential dependencies, limiting improvements in prediction accuracy. To this end, we propose a social attention network fused multipatch temporal–variable dependency (SAN-FTVD) model to tackle the above problems. In specific, we first design a variable token embedding module (VTEM) to extract the motion state information of vehicles, which independently embeds each variable of vehicle historical data into a variable token. After that, we propose a physical informed vehicle interaction encoder (PI-VIE) to capture vehicle interaction features over continuous time. The encoder is combined with physical priors to encode vehicle interaction features based on the correlations between the variable tokens. Following that, a temporal–variable dependency fusion module (TVDFM) is proposed to extract and fuse the multipatch temporal and variable dependencies, fully exploring potential dependencies in vehicle interaction features. Numerical results demonstrate that compared with the state-of-the-art model, the proposed model reduces the average prediction root mean square error over 5-s time range by 8% and 7% on two public data sets with 75% less inference cost. Furthermore, extensive ablation experiments validate the effectiveness of the above modules in the model.

The 5th generation (5G) mobile network technology is accelerating the development of autonomous vehicles by significantly shortening the communication latency and improving the reliability of network connection and transmission. However, as the number of vehicles increases, neither cloud servers nor multi-access edge computing (MEC) servers alone could sufficiently meet the Quality-of-Service (QoS) requirements for computing-intensive vehicle tasks. In this paper, we consider a hierarchical offloading scenario, where vehicle tasks are allowed to execute in MEC servers, convergence servers or cloud servers. To reduce the cost of latency and energy, we optimize the communication and computation resource allocation problem. The optimization problem is converted to a Markov decision process, and deep reinforcement learning is used to tackle the resource slicing and scheduling problem. Simulation results show that the proposed scheme is more resilient and efficient than that of single cloud server offloading or single MEC server offloading.