Abstract: As a core of intelligent transportation systems, the efficiency and reliability routing algorithms in Vehicular Ad Hoc Networks (VANETs) directly impact critical applications like traffic safety warning, autonomous driving, and intelligent traffic management. In complex traffic, vehicle nodes' interaction makes VANETs' topology change complex and link stability fragile, challenging routing algorithms. Given this, constructing adaptable routing algorithms is crucial for the communication of VANET. To solve this, we propose a novel Neighborhood Potential-based Link-aware Routing (NPLAR) algorithm. The NPLAR innovatively constructs a neighborhood potential energy model, comprehensively quantifying the impact of the static and dynamic features of the neighborhood environment on link stability, offering a more accurate basis for routing decisions. By integrating complex network theory and graph neural networks, it effectively captures the multi-hop neighborhood propagation mechanism of neighborhood potential energy, better predicting network changes and enabling efficient routing path selection. Moreover, NPLAR integrates link stability indices with network link QoS metrics, building a multi-dimensional routing decision framework. This framework achieves adaptive decision optimization in highly dynamic environments, significantly enhancing the routing algorithm's overall performance. Experimental results show that compared with existing VANET routing algorithms, NPLAR increases the average throughput by 8.3%-35.7%. In terms of the packet loss rate, NPLAR reduces it by 6%-50.4%, and the communication delay is reduced by 11.3%-39%. These data clearly demonstrate NPLAR's superiority in enhancing network performance.

摘要： 车联网作为智能交通系统的核心组成部分，其路由算法的高效性与可靠性直接关系到交通安全预警、自动驾驶、智能交通管理等关键应用的实施效果。然而，在复杂的车辆交通环境中，车辆节点间的交互效应导致车联网的拓扑变化更加复杂，链路稳定性更加脆弱，进一步加剧了车联网场景中路由算法的挑战。在此背景下，如何构建适应高动态变化的复杂交通环境的路由算法，成为提升车联网通信效能的关键挑战。对此，本文提出一种基于邻域势能模型的车联网链路感知路由算法（NPLAR）。该算法通过构建邻域势能模型，量化反映邻域环境的静态和动态特征对链路稳定性的影响，并结合复杂网络理论和图神经网络，捕捉邻域势能在多跳邻域的传播机制。进一步地，算法融合链路稳定性指数与网络链路QoS指标，通过多维路由决策实现在高动态环境下的自适应决策优化。实验结果表明，相较于已有的基于拓扑、基于地理位置、基于传输策略以及融合交通信息的车联网路由算法，NPLAR的吞吐量平均提升8.3%~35.7%，丢包率和通信时延平均降低6%~50.4%和11.3%~39%，具有较优的性能表现。