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The operational safety of automated driving system (ADS)-equipped vehicles (AVs) must be quantified using well-defined metrics in order to gain an unambiguous understanding of the level of risk associated with AV deployment on public roads. In this research, efforts to evaluate the operational safety assessment (OSA) metrics introduced in prior work by the Institute of Automated Mobility (IAM) are described. An initial validation of the proposed set of OSA metrics involved using the open-source simulation software Car Learning to Act (CARLA) and Scenario Runner, which are used to place a subject vehicle in selected scenarios and obtain measurements for the various relevant OSA metrics. Car following scenarios were selected from the list of 37 pre-crash scenarios identified by the National Highway Traffic Safety Administration (NHTSA) as the most common driving situations that lead to crash events involving two light vehicles.

Assurance of the operational safety of automated vehicles (AVs) is crucial to enable commercialization and deployment on public roads. The operational safety must be quantified without ambiguity using well-defined metrics. Several efforts are in place to establish an appropriate set of metrics that can quantify the operational safety of AVs in a technology-neutral way, including the Operational Safety Assessment (OSA) metrics proposed by the Institute of Automated Mobility (IAM). The focus of this work is to compute real-world measurements of the relevant safety envelope OSA metrics in car-following scenarios. This allows for an analysis of the impact of different parameters and thresholds and for an evaluation of the individual usefulness of the safety envelope OSA metrics. The current work complements prior IAM work involving evaluating the safety envelope OSA metrics in car-following scenarios in simulation.

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).

As the automated vehicle (AV) industry continues to progress, it is important to establish the level of operational safety of these vehicles prior to and throughout their deployment on public roads. The Institute of Automated Mobility (IAM) has previously proposed a set of operational safety assessment (OSA) metrics which can be used to quantify the operational safety of vehicles. The OSA metrics provide a starting point to consistently quantify performance, but a framework to interpret the metrics measurements is needed to objectively quantify the overall operational safety for a vehicle in a given scenario. This work aims to present an approach to applying a calculation of the safety envelope component of the OSA metrics to rear-world collisions for use in such an assessment. In this paper, the OSA methodology concept is introduced as a means for quantifying the operational safety of a vehicle.

The operational safety of Automated Driving System-equipped vehicles (AVs) is a critical issue with AVs being deployed on public roads. Methodologies for evaluating the operational safety are therefore necessary to maintain public safety. One possible approach is a safety case established by the AV developer that uses evidence to support a structured argument that the AV exhibits a given level of operational safety. One of the key components of a safety case for AVs is a set of testing results showing behavioral competency in a variety of scenarios within the AV’s operational design domain (ODD). The Institute of Automated Mobility (IAM) has previously published operational safety assessment (OSA) metrics along with a means to evaluate the severity of violations of the safety envelope-type OSA metrics for navigation of individual scenarios in the proposed OSA Methodology.

Data-driven driving safety assessment is crucial in understanding the insights of traffic accidents caused by dangerous driving behaviors. Meanwhile, quantifying driving safety through well-defined metrics in real-world naturalistic driving data is also an important step for the operational safety assessment of automated vehicles (AV). However, the lack of flexible data acquisition methods and fine-grained datasets has hindered progress in this critical area. In response to this challenge, we propose a novel dataset for driving safety metrics analysis specifically tailored to car-following situations. Leveraging state-of-the-art Artificial Intelligence (AI) technology, we employ drones to capture high-resolution video data at 12 traffic scenes in the Phoenix metropolitan area. After that, we developed advanced computer vision algorithms and semantically annotated maps to extract precise vehicle trajectories and leader-follower relations among vehicles.