Evaluating Safety Metrics for Vulnerable Road Users at Urban Traffic Intersections Using High-Density Infrastructure LiDAR System Arizona State University

Format:

Book

Conference/Event

Author/Creator:

Rath, Prabin Kumar, author.

Contributor:

Harrison, Blake

Lu, Duo

Wishart, Jeffrey

Yang, Yezhou

Yu, Hongbin

Conference Name:

WCX SAE World Congress Experience (2024-04-16 : Detroit, Michigan, United States)

Language:

English

Physical Description:

1 online resource cm

Place of Publication:

Warrendale, PA SAE International 2024

Summary:

Ensuring the safety of vulnerable road users (VRUs) such as pedestrians, users of micro-mobility vehicles, and cyclists is imperative for the commercialization of automated vehicles (AVs) in urban traffic scenarios. City traffic intersections are of particular concern due to the precarious situations VRUs often encounter when navigating these locations, primarily because of the unpredictable nature of urban traffic. Earlier work from the Institute of Automated Vehicles (IAM) has developed and evaluated Driving Assessment (DA) metrics for analyzing car following scenarios. In this work, we extend those evaluations to an urban traffic intersection testbed located in downtown Tempe, Arizona. A multimodal infrastructure sensor setup, comprising a high-density, 128-channel LiDAR and a 720p RGB camera, was employed to collect data during the dusk period, with the objective of capturing data during the transition from daylight to night. In this study, we present and empirically assess the benefits of high-density LiDAR in low-light and dark conditionsa persistent challenge in VRU detection when compared to traditional RGB traffic cameras. Robust detection and tracking algorithms were utilized for analyzing VRU-to-vehicle and vehicle-to-vehicle interactions using the LiDAR data. The analysis explores the effectiveness of two DA metrics based on the id est Post Encroachment Time (PET) and Minimum Distance Safety Envelope (MDSE) formulations in identifying potentially unsafe scenarios for VRUs at the Tempe intersection. The codebase for the data pipeline, along with the high-density LiDAR dataset, has been open-sourced with the goal of benefiting the AV research community in the development of new methods for ensuring safety at urban traffic intersections

Notes:

Vendor supplied data

Publisher Number:

2024-01-2641

Access Restriction:

Restricted for use by site license