Unmanned aerial vehicles (UAVs) have increasingly become integral to logistics and distribution due to their flexibility and mobility. However, the existing studies often overlook the dynamic nature of customer demands and wind conditions, limiting the practical applicability of their proposed strategies. To tackle this challenge, we firstly construct a time-slicing-based UAV path planning model that incorporates dynamic customer demands and wind impacts. Based on this model, a two-stage logistics UAV path planning framework is developed according to the analysis of the customer pool updates and dynamic attitudes. Secondly, a dynamic demand and wind-aware logistics UAV path planning problem is formulated to minimize the weighted average of the energy consumption and the customer satisfaction penalty cost, which comprehensively takes the energy consumption constraints, load weight constraints, and hybrid time window constraints into consideration. To solve this problem, an improved particle swarm optimization (PSO)-based multiple logistics UAV path planning algorithm is developed, which has good performance with fast convergence and better solutions. Finally, extensive simulation results verify that the proposed algorithm can not only adhere to the UAV’s maximum load and battery power constraints but also significantly enhance the loading efficiency and battery utilization rate. Particularly, compared to the genetic algorithm (GA), simulated annealing (SA), and traditional PSO strategies, our proposed algorithm achieves satisfactory solutions within a reasonable time frame and reduces the distribution costs by up to $9.82\%$.

Assessing risk in traffic scenarios is crucial for advancing autonomous vehicle safety. As the number of vehicles and their interactions increase, there is a heightened need for prompt and precise risk evaluation. Traditional collision warning systems, which rely on conflict points, are limited to specific traffic scenarios and are inadequate for the dynamic demands of complex and diverse traffic environments. Additionally, current field-based risk assessment methods evaluate collision risks based only on instantaneous vehicle motion states, lacking in both timeliness and accuracy. To address these challenges, this paper proposed a novel risk assessment method. It involves constructing a vehicle-road cooperative environment using a distributed digital twins system and employs a multi-agent multi-modal trajectory prediction algorithm to forecast future vehicle motion states. By leveraging predictive multi-dimensional kinematic features, the proposed method dynamically generates future driving risk fields, effectively assesses collision risks in complex traffic scenarios by modeling multi-source collision risks within future traffic scenes. Extensive simulation results and analyses reveal that DPDRF significantly improves the average Pre-Collision Warning Time (PCWT) by 2.53 times compared to DDRPF and by 1.81 times compared to DSF. Furthermore, DPDRF reduces the average Pre-Collision Warning Error (PCWE) by 79% relative to PRF.

Vehicular edge computing (VEC) systems face critical challenges in balancing computational efficiency, task delay, and data privacy. This paper presents a Federated MultiAgent Deep Reinforcement Learning (FMADRL) framework to achieve privacy-preserving task offloading in dynamic vehicular networks. The proposed method leverages federated learning to collaboratively train task offloading policies across vehicles, Mobile Edge Computing (MEC) servers, and the cloud, ensuring that sensitive data remains localized while enabling global optimization. A novel reward function is designed to balance task completion, delay, energy consumption, and privacy constraints, while a federated actor-critic model ensures robust decision-making under dynamic network conditions. Simulation results demonstrate that the FMADRL framework significantly reduces average task delay and energy consumption by 30% and 25%, respectively, compared to traditional methods, while maintaining data privacy. These findings underscore the potential of FMADRL to enhance the scalability, efficiency, and security of VEC systems in intelligent transportation networks.