Graph Neural Network-based Clustering Enhancement in VANET for Cooperative Driving

Engineering notes

The significantly increasing number of vehicles brings convenience to daily life while also introducing significant challenges to the transportation network and air pollution. It has been proved that platooning/clustering-based driving can significantly reduce road congestion and exhaust emissions and improve road capacity and energy efficiency.

Chinese explanation / 中文解读

中文解读待补充：本站会优先为端到端自动驾驶、BEV感知、3D目标检测、轨迹预测、路径规划、LiDAR感知等高价值论文补充中文说明。

Original abstract

The significantly increasing number of vehicles brings convenience to daily life while also introducing significant challenges to the transportation network and air pollution. It has been proved that platooning/clustering-based driving can significantly reduce road congestion and exhaust emissions and improve road capacity and energy efficiency. This paper aims to improve the stability of vehicle clustering to enhance the lifetime of cooperative driving. Specifically, we use a Graph Neural Network (GNN) model to learn effective node representations, which can help aggregate vehicles with similar patterns into stable clusters. To the best of our knowledge, this is the first generalized learnable GNN-based model for vehicular ad hoc network clustering. In addition, our centralized approach makes full use of the ubiquitous presence of the base stations and edge clouds. It is noted that a base station has a vantage view of the vehicle distribution within the coverage area as compared to distributed clustering approaches. Specifically, eNodeB-assisted clustering can greatly reduce the control message overhead during the cluster formation and offload to eNodeB the complex computations required for machine learning algorithms. We evaluated the performance of the proposed clustering algorithms on the open-source highD dataset. The experiment results demonstrate that the average cluster lifetime and cluster efficiency of our GNN-based clustering algorithm outperforms state-of-the-art baselines.

Links and sources

• Official / arXiv page

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