An Efficient Fusion-Tree-Based Perception Data Dissemination Scheme in MmWave Vehicular Networks

It is important to cooperatively share perception data among vehicles for the safety of autonomous driving systems. However, since vehicles may experience long data transmission delays, especially in case that the number of vehicles participating in cooperative perception is large, the dissemination of large-sized perception data remains a challenging problem. Considering the high transmission cost of perception data in the overlapping perception area of nearby vehicles, we propose a novel fusion-tree-based perception data dissemination scheme in millimeter-wave (mmWave) vehicular networks. Vehicles first aggregate their perception data at a preselected leader along a dynamically constructed fusion tree, and a merge-and- forward policy is adopted to reduce the amount of redundant data. After the aggregation stage ends, the leader broadcasts the merged data back to all vehicles through multi-hop broadcast links. The performance evaluation results show that the proposed scheme yields the best transmission performance among the three comparison schemes in terms of dissemination delay.