Pedestrian Moving Patterns during Potential Conflicts with 110 On-Road Driving Vehicles. (November 2019)

• Record Type: Journal Article
• Authors: Tian, Renran; Chien, Stanley; Chen, Yaobin; Sherony, Rini
• Is Part Of: Proceedings of the Human Factors and Ergonomics Society ... Annual Meeting. Volume 63:Part 1(2019)
• Page Start: 2036
• Page End: 2040
• Publication Date: 2019-11
• Subjects: Human engineering -- Congresses; 620.8205
• DOI: 10.1177/1071181319631434
• Languages: English
• ISSNs: 1541-9312

Abstract

As the most commonly seen vulnerable road users, protection and interaction with pedestrians are key functionalities in vehicle active safety and self-driving research areas. Development and evaluation of such systems require deeper understanding of pedestrian behaviors, especially motion patterns, in different driving environments. Traditionally, most of the pedestrian movement studies rely on fixed roadside cameras in specific road locations with higher pedestrian density, like intersections and junctions. Although these studies can provide information to describe pedestrian walking behavior and vehicle-pedestrian interactions in micro and macro levels, there are two main limitations. Firstly, pedestrian movement data are rarely collected from the vehicle's point of view, which makes some critical variables difficult to be collected related to pedestrian initial appearance situation. Secondly, insufficient data are acquired to cover low- pedestrian-density road environments like mid-block, rural areas, and small un-controlled intersections. In this study, we focus on three important pedestrian movement variables including appearance distances, initial time-to-collision, and crossing speed under different driving and road scenarios. Based on a large-scale naturalistic driving study, crossing pedestrians were randomly captured in the scene videos from 110 passenger cars when potential ego-vehicle-to-pedestrian conflicts appeared during a one-year period. Motion data of these pedestrians were then analyzed to calculate the targeted behavior measurements, with the empirical results reported.