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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. ...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. ...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).

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 conditions—a 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 i.e. Post Encroachment Time (PET) and Minimum Distance Safety Envelope (MDSE) formulations in identifying potentially unsafe scenarios for VRUs at the Tempe intersection.

Extending upon earlier works of the Institute of Automated Mobility (IAM) that have explored the Operational Safety Assessment (OSA) metrics and infrastructure-based safety monitoring systems, in this work, we compare the performance of an infrastructure-based Light Detection And Ranging (LIDAR) system to an onboard vehicle-based LIDAR system in testing at the Maricopa County Department of Transportation SMARTDrive testbed in Anthem, Arizona. ...The sensor modalities are located in infrastructure and onboard the test vehicles, including LIDAR, cameras, a real-time differential GPS, and a drone with a camera. Bespoke localization and tracking algorithms are created for the LIDAR and cameras. ...Bespoke localization and tracking algorithms are created for the LIDAR and cameras. In total, there are 26 different scenarios of the test vehicles navigating the testbed intersection; for this work, we are only considering car following scenarios.