同济大学学报（自然科学版）2025，Vol.53Issue(10)：1544-1552,9.DOI:10.11908/j.issn.0253-374x.24151
高速公路匝道汇入场景识别与分析
Recognition and Analysis of Highway Entrance Ramp Merging Scenarios
摘要
Abstract
The current classification methods for highway entrance ramp merging behavior are limited to single and isolated driving scenarios,without considering the dynamic changes in the spatial and temporal positions of merging vehicles and surrounding vehicles in the target lane,neglecting the study of bidirectional and multi-interaction merging scenarios,which poses significant challenges for researchers in understanding real traffic flow characteristics and developing automated driving systems with lane-changing algorithms.To reveal the dynamic interactive behavior among vehicles during actual merging processes,this paper,combining the ExiD dataset with high-definition map data to extract natural driving merging trajectory data,proposes a merging scenario classification method that fully considers the spatiotemporal dynamic changes.Additionally,it utilizes the Jensen-Shannon divergence to construct a quantitative model and index for measuring scenario similarity,representing the differences and similarities among different merging scenarios.The results show that,after considering the spatiotemporal distribution of surrounding vehicles in the target lane,the merging behavior on the ramp can be divided into eight distinct scenarios with significant differences,including scenarios with higher risks,such as parallel vehicles(average time-to-collision<2 s).These findings are of great importance in analyzing high-risk merging scenarios and designing automated merging strategies that align with the interaction characteristics of human drivers.关键词
汇入行为/场景划分/高精地图/时空动态变化/相似性量化评估/詹森‒香农散度Key words
merging behavior/scenario classification/high-definition maps/spatiotemporal dynamic changes/similarity quantitative assessment/Jensen-Shannon divergence分类
交通工程引用本文复制引用
李林波,王一琦,李杨,罗文泽,吴兵..高速公路匝道汇入场景识别与分析[J].同济大学学报（自然科学版）,2025,53(10):1544-1552,9.基金项目
国家自然科学面上基金(52172331) （52172331）
