Existing research on merging behavior generally prioritize the application of various algorithms, but often overlooks the fine-grained process and analysis of trajectories. This leads to the neglect of surrounding vehicle matching, the opaqueness of indicators definition, and reproducible crisis. To address these gaps, this paper presents a reproducible approach to merging behavior analysis. Specifically, we outline the causes of subjectivity and irreproducibility in existing studies. Thereafter, we employ lanelet2 High Definition (HD) map to construct a reproducible framework, that minimizes subjectivities, defines standardized indicators, identifies alongside vehicles, and divides scenarios. A comparative macroscopic and microscopic analysis is subsequently conducted. More importantly, this paper adheres to the Reproducible Research concept, providing all the source codes and reproduction instructions. Our results demonstrate that although scenarios with alongside vehicles occur in less than 6% of cases, their characteristics are significantly different from others, and these scenarios are often accompanied by high risk. This paper refines the understanding of merging behavior, raises awareness of reproducible studies, and serves as a watershed moment.
翻译:现有的合并行为研究通常优先采用各种算法,但常常忽略轨迹的细致过程和分析,导致周围车辆匹配的忽视,指标定义的不透明性和危机的不可重复性。为解决这些问题,本文提出了一种可重复的合并行为分析方法。具体而言,我们概述了现有研究中主观性和不可重复性的原因。随后,我们使用Lanelet2高清(HD)地图构建了一个可重复的框架,最小化了主观性,定义了标准化指标,识别了旁边的车辆并分割了场景。随后进行了比较的宏观和微观分析。更重要的是,本文坚持可重复研究的概念,提供所有源代码和再现说明。结果表明,虽然旁边车辆出现的情况少于6%的情况,但它们的特征与其他情况显着不同,并且这些情况通常伴随着高风险。本文完善了对合并行为的理解,提高了可重复研究意识,并成为了一个分水岭时刻。