An improved vehicle-pedestrian near-crash identification method with a roadside LiDAR sensor

Copyright © 2020 National Safety Council and Elsevier Ltd. All rights reserved..

PROBLEM: Potential conflicts between pedestrians and vehicles represent a challenge to pedestrian safety. Near-crash is used as a surrogate metric for pedestrian safety evaluations when historical vehicle-pedestrian crash data are not available. One challenge of using near-crash data for pedestrian safety evaluation is the identification of near-crash events.

METHOD: This paper introduces a novel method for pedestrian-vehicle near-crash identification that uses a roadside LiDAR sensor. The trajectory of each road user can be extracted from roadside LiDAR data via several data processing algorithms: background filtering, lane identification, object clustering, object classification, and object tracking. Three indicators, namely, the post encroachment time (PET), the proportion of the stopping distance (PSD), and the crash potential index (CPI) are applied for conflict risk classification.

RESULTS: The performance of the developed method was evaluated with field-collected data at four sites in Reno, Nevada, United States. The results of case studies demonstrate that pedestrian-vehicle near-crash events could be identified successfully via the proposed method. Practical applications: The proposed method is especially suitable for pedestrian-vehicle near-crash identification at individual sites. The extracted near-crash events can serve as supplementary material to naturalistic driving study (NDS) data for safety evaluation.

Medienart:

E-Artikel

Erscheinungsjahr:

2020

Erschienen:

2020

Enthalten in:

Zur Gesamtaufnahme - volume:73

Enthalten in:

Journal of safety research - 73(2020) vom: 20. Juni, Seite 211-224

Sprache:

Englisch

Beteiligte Personen:

Wu, Jianqing [VerfasserIn]
Xu, Hao [VerfasserIn]
Zhang, Yongsheng [VerfasserIn]
Sun, Renjuan [VerfasserIn]

Links:

Volltext

Themen:

Journal Article
Near-crash identification
Pedestrian safety
Research Support, Non-U.S. Gov't
Roadside LiDAR

Anmerkungen:

Date Completed 04.03.2021

Date Revised 04.03.2021

published: Print-Electronic

Citation Status MEDLINE

doi:

10.1016/j.jsr.2020.03.006

funding:

Förderinstitution / Projekttitel:

PPN (Katalog-ID):

NLM31142032X