Paper Info
Title
Automatic Vehicle-Pedestrian Conflict Identification With Trajectories Of Road Users Extracted From Roadside Lidar Sensors Using A Rule-Based Method
Abstract
Vehicle-pedestrian conflicts have been the major concern for traffic safety. Surrogate safety measures are widely applied for pedestrian safety evaluation. However, how to quickly identify the vehicle-pedestrian surrogate safety measures at the individual site is challenging due to the difficulty of obtaining the high-resolution trajectories of road users. This paper presented an effective method to generate the high-resolution traffic trajectories from the roadside deployed Light Detection and Ranging (LiDAR) sensor. The vehicle-pedestrian conflicts can then be identified from the trajectories simply using the speed-distance profile (SDP) of the vehicles. The SDP can be used to develop a rule-based method for vehicle-pedestrian identification. The events can be divided into different risk levels based on the spatial distribution of the SDP. The case study shows that the rule-based method can detect vehicle-pedestrian near-crash events effectively. The other indicators, such as widely used time-to-collision (TTC) or deceleration rate to avoid a crash (DRAC), can be also obtained from the SDP. The engineers can also adjust the thresholds in the rule-based method to meet the specific requirements at different sites. The proposed method can be extended to identify vehicle-vehicle conflicts or vehicle-bicycle conflicts in future studies.
Year
DOI
Venue
2019
10.1109/ACCESS.2019.2951763
IEEE ACCESS
Keywords
DocType
Volume
Laser radar, Safety, Trajectory, Accidents, Roads, Sensors, Data mining, Roadside LiDAR, vehicle-pedestrian conflicts, surrogate safety measures, high-resolution trajectories, pedestrian safety
Journal
7
ISSN
Citations 
PageRank 
2169-3536
0
0.34
References 
Authors
0
5
Name
Order
Citations
PageRank
Bin Lv100.68
Renjuan Sun200.34
Hongbo Zhang377.73
Hao Xu41212.74
Rui Yue512.38