Data underlying the publication: Minimising missed and false alarms: a vehicle spacing based approach to conflict detection

This dataset includes the resulting data of the research: Minimising missed and false alarms: a vehicle spacing based approach to conflict detection. It contains processed data from the 100Car NDS, organised data from the CitySim FreewayB subset, as well as output files generated by conflict detection analyses. The research objective is to minimise missed and false alarms in vehicle conflict detection by optimising critical spacing thresholds. This study combines simulated traffic scenarios and real-world near-crashes to evaluate conflict detection strategies. Systematic data collection methods are used to compile vehicle trajectories, conflict events, and spacing distributions for comprehensive analysis. The scripts that produced these data are open-sourced at https://github.com/Yiru-Jiao/Conflict-detection-MFaM

本数据集涵盖了题为《最小化漏检与误报：基于车辆间距的冲突检测方法》的研究产出数据。其包含来自100车自然驾驶数据集（100Car NDS）的预处理数据、CitySim高速公路B子集（CitySim FreewayB subset）的整理后数据，以及冲突检测分析生成的输出文件。本研究的核心目标为通过优化临界间距阈值，降低车辆冲突检测中的漏检与误报率。本研究结合模拟交通场景与真实世界临界碰撞事件，对冲突检测策略开展评估。研究采用系统化数据采集方法，整理车辆轨迹、冲突事件与间距分布数据以支撑全面分析。生成上述数据的代码脚本已在https://github.com/Yiru-Jiao/Conflict-detection-MFaM 开源。