Patterns of near-crash events in a naturalistic driving dataset: applying rules mining

Driver Behavior Extraction from Videos in Naturalistic Driving Datasets with 3D ConvNets

Naturalistic driving data (NDD) is an important source of information to understand crash causation and human factors and to further develop crash avoidance countermeasures. Videos recorded while driving are often included in such datasets. While there is often a large amount of video data in NDD, only a small portion of them can be annotated by human coders and used for research, which underuses all video data. In this paper, we explored a computer vision method to automatically extract the information we need from videos. More specifically, we developed a 3D ConvNet algorithm to automaticall…

评估交叉口 proximity 在行人碰撞中的作用：数据挖掘方法的洞察

Assessing the Role of Intersection Proximity in Pedestrian Crashes: Insights from Data Mining Approach

研究通过数据挖掘方法分析非交叉口行人碰撞风险，发现50%的碰撞发生在198英尺内，提出基于距离的分区策略及干预措施。

基于K均值聚类和关联规则挖掘的自动化车辆碰撞模式数据驱动分析

Data-Driven Analysis of Crash Patterns in SAE Level 2 and Level 4 Automated Vehicles Using K-means Clustering and Association Rule Mining

本研究利用K均值聚类和关联规则挖掘分析SAE 2级和4级自动驾驶车辆碰撞模式，揭示碰撞动态以指导安全策略制定。

Identifying roadway departure crash patterns on rural two-lane highways under different lighting conditions: association knowledge using data mining approach

More than half of all fatalities on U.S. highways occur due to roadway departure (RwD) each year. Previous research has explored various risk factors that contribute to RwD crashes, however, a comprehensive investigation considering the effect of lighting conditions has been insufficiently addressed. Using the Louisiana Department of Transportation and Development crash database, fatal and injury RwD crashes occurring on rural two-lane (R2L) highways between 2008-2017 were analyzed based on daylight and dark (with/without streetlight). This research employed a safe system approach to explore m…

Applying Association Rules Mining to Investigate Pedestrian Fatal and Injury Crash Patterns Under Different Lighting Conditions

The pattern of pedestrian crashes varies greatly depending on lighting circumstances, emphasizing the need of examining pedestrian crashes in various lighting conditions. Using Louisiana pedestrian fatal and injury crash data (2010-2019), this study applied Association Rules Mining (ARM) to identify the hidden pattern of crash risk factors according to three different lighting conditions (daylight, dark-with-streetlight, and dark-no-streetlight). Based on the generated rules, the results show that daylight pedestrian crashes are associated with children (less than 15 years), senior pedestrians…

从大规模自然驾驶数据中主动学习碰撞风险

Learning collision risk proactively from naturalistic driving data at scale

本文提出GSSM方法，通过自然驾驶数据主动学习碰撞风险，无需碰撞或风险标签，实现高精度和及时性预警，提升自动驾驶安全。