TY - JOUR
T1 - Infrastructure-Based LiDAR Monitoring for Assessing Automated Driving Safety
AU - Srinivasan, Anshuman
AU - Mahartayasa, Yoga
AU - Jammula, Varun Chandra
AU - Lu, Duo
AU - Como, Steven
AU - Wishart, Jeffrey
AU - Yang, Yezhou
AU - Yu, Hongbin
N1 - Funding Information:
Authors are grateful to the Institute of Automated Mobility (IAM) for generously funding this work and Maricopa County Department of Transportation (MCDOT) for enabling infrastructural resources required for this work. HY, AS and YM also acknowledge the support from National Science Foundation (grant nos. 1624842, 1839485, 1933742) Industry-University Cooperative Research Center (IUCRC) for Efficient Vehicles and Sustainable Transportation Systems (EVSTS) at Arizona State University. This work is partially done when Duo Lu was a PhD student at Arizona State University.
Publisher Copyright:
© 2022 SAE International. All Rights Reserved.
PY - 2022/3/29
Y1 - 2022/3/29
N2 - The successful deployment of automated vehicles (AVs) has recently coincided with the use of off-board sensors for assessments of operational safety. Many intersections and roadways have monocular cameras used primarily for traffic monitoring; however, monocular cameras may not be sufficient to allow for useful AV operational safety assessments to be made in all operational design domains (ODDs) such as low ambient light and inclement weather conditions. Additional sensor modalities such as Light Detecting and Ranging (LiDAR) sensors allow for a wider range of scenarios to be accommodated and may also provide improved measurements of the Operational Safety Assessment (OSA) metrics previously introduced by the Institute of Automated Mobility (IAM). Building on earlier work from the IAM in creating an infrastructure- based sensor system to evaluate OSA metrics in real- world scenarios, this paper presents an approach for real-time localization and velocity estimation for AVs using a network of LiDAR sensors. The LiDAR data are captured by a network of three Luminar LiDAR sensors at an intersection in Anthem, AZ, while camera data are collected from the same intersection. Using the collected LiDAR data, the proposed method uses a distance-based clustering algorithm to detect 3D bounding boxes for each vehicle passing through the intersection. Subsequently, the positions and velocities of each detected bounding box are tracked over time using a combination of two filters. The accuracy of both the localization and velocity estimation using LiDAR is assessed by comparing the LiDAR estimated state vectors against the differential GPS position and velocity measurements from a test vehicle passing through the intersection, as well as against a camera-based algorithm applied on drone video footage It is shown that the proposed method, taking advantage of simultaneous data capture from multiple LiDAR sensors, offers great potential for fast, accurate operational safety assessment of AV's with an average localization error of only 10 cm observed between LiDAR and real-time differential GPS position data, when tracking a vehicle over 170 meters of roadway.
AB - The successful deployment of automated vehicles (AVs) has recently coincided with the use of off-board sensors for assessments of operational safety. Many intersections and roadways have monocular cameras used primarily for traffic monitoring; however, monocular cameras may not be sufficient to allow for useful AV operational safety assessments to be made in all operational design domains (ODDs) such as low ambient light and inclement weather conditions. Additional sensor modalities such as Light Detecting and Ranging (LiDAR) sensors allow for a wider range of scenarios to be accommodated and may also provide improved measurements of the Operational Safety Assessment (OSA) metrics previously introduced by the Institute of Automated Mobility (IAM). Building on earlier work from the IAM in creating an infrastructure- based sensor system to evaluate OSA metrics in real- world scenarios, this paper presents an approach for real-time localization and velocity estimation for AVs using a network of LiDAR sensors. The LiDAR data are captured by a network of three Luminar LiDAR sensors at an intersection in Anthem, AZ, while camera data are collected from the same intersection. Using the collected LiDAR data, the proposed method uses a distance-based clustering algorithm to detect 3D bounding boxes for each vehicle passing through the intersection. Subsequently, the positions and velocities of each detected bounding box are tracked over time using a combination of two filters. The accuracy of both the localization and velocity estimation using LiDAR is assessed by comparing the LiDAR estimated state vectors against the differential GPS position and velocity measurements from a test vehicle passing through the intersection, as well as against a camera-based algorithm applied on drone video footage It is shown that the proposed method, taking advantage of simultaneous data capture from multiple LiDAR sensors, offers great potential for fast, accurate operational safety assessment of AV's with an average localization error of only 10 cm observed between LiDAR and real-time differential GPS position data, when tracking a vehicle over 170 meters of roadway.
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U2 - 10.4271/2022-01-0081
DO - 10.4271/2022-01-0081
M3 - Conference article
AN - SCOPUS:85128110655
SN - 0148-7191
JO - SAE Technical Papers
JF - SAE Technical Papers
IS - 2022
T2 - SAE 2022 Annual World Congress Experience, WCX 2022
Y2 - 5 April 2022 through 7 April 2022
ER -