Evaluation of Operational Safety Assessment (OSA) Metrics for Automated Vehicles in Simulation

Elli, Maria Soledad; Wishart, Jeffrey; Como, Steven; Dhakshinamoorthy, Siddhaarthan; Weast, Jack

doi:10.4271/2021-01-0868

Cited by 19 publications

(8 citation statements)

References 9 publications

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“…Temporal-based indicators: Time to collision (TTC), extended time to collision (time exposed time-to-collision (TET), time integrated time-to-collision(TIT) [527]), modified TTC (MTTC), crash index (CI), time-to-accident (TA), time headway (H), and post-encroachment time (PET). Reviewing different safety metric is out of the scope of this paper and we refer the interested readers to recent papers on safety metrics [528][529][530]. As part of the Institute of Automated Mobility (IAM) project, the authors of this paper are working toward extending safety metrics into network-level metrics and developing a taxonomy of metrics for safety metrics for AVs based on the level of access required of ADS data [370].…”

Section: Safety Metricsmentioning

confidence: 99%

“…Reviewing different safety metric is out of the scope of this paper and we refer the interested readers to recent papers on safety metrics [528–530]. As part of the Institute of Automated Mobility (IAM) project, the authors of this paper are working toward extending safety metrics into network‐level metrics and developing a taxonomy of metrics for safety metrics for AVs based on the level of access required of ADS data [370].…”

Section: Safety Metricsmentioning

confidence: 99%

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Deep learning serves traffic safety analysis: A forward‐looking review

Razi

Chen

et al. 2022

IET Intelligent Trans Sys

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This paper explores deep learning (DL) methods that are used or have the potential to be used for traffic video analysis, emphasising driving safety for both autonomous vehicles and human‐operated vehicles. A typical processing pipeline is presented, which can be used to understand and interpret traffic videos by extracting operational safety metrics and providing general hints and guidelines to improve traffic safety. This processing framework includes several steps, including video enhancement, video stabilisation, semantic and incident segmentation, object detection and classification, trajectory extraction, speed estimation, event analysis, modelling, and anomaly detection. The main goal is to guide traffic analysts to develop their own custom‐built processing frameworks by selecting the best choices for each step and offering new designs for the lacking modules by providing a comparative analysis of the most successful conventional and DL‐based algorithms proposed for each step. Existing open‐source tools and public datasets that can help train DL models are also reviewed. To be more specific, exemplary traffic problems are reviewed and required steps are mentioned for each problem. Besides, connections to the closely related research areas of drivers' cognition evaluation, crowd‐sourcing‐based monitoring systems, edge computing in roadside infrastructures, automated driving systems‐equipped vehicles are investigated, and the missing gaps are highlighted. Finally, commercial implementations of traffic monitoring systems, their future outlook, and open problems and remaining challenges for widespread use of such systems are reviewed.

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Section: Safety Metricsmentioning

confidence: 99%

Section: Safety Metricsmentioning

confidence: 99%

Deep learning serves traffic safety analysis: A forward‐looking review

Razi

Chen

et al. 2022

IET Intelligent Trans Sys

View full text Add to dashboard Cite

show abstract

“…In a study undertaken by the National Highway Traffic Safety Administration (NHTSA) involving two light vehicles, it was determined that vehicle following behaviours were on a list of 37 pre-crash scenarios identified as the most common driving situations that lead to crash events. The operational safety of autonomous vehicles has to be quantified using defined metrics to provide a clear understanding of the level of risk associated with AV deployment on public highways [57].…”

Section: Current Gaps In Researchmentioning

confidence: 99%

Potential of Connected Fully Autonomous Vehicles in Reducing Congestion and Associated Carbon Emissions

2022

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Congestion is an ongoing problem for many urban centres worldwide (such as London), leading to excessive delays, noise and air pollution, frustrated drivers, and high energy consumption. The carbon footprint of conventional transport systems can be high as a result and transport is among the highest contributors of greenhouse gas emissions. Therefore, with the growing interest in developing connected fully autonomous vehicles (ConFAVs), there is a pressing need to consider their effects within the congested urban setting. To address this, the current research study was designed to investigate the potential for ConFAVs in providing a sustainable transport solution. During this research, a simulation model was developed, calibrated, and validated using field data collected from several sites in East London, using the graphical user interface (GUI) simulation software PTV VISSIM to simulate the proposed driving and car following behaviour, which included the platooning of these ConFAVs, to assess how they could improve the level of service of the roads. Using the new model, this research addresses the shortcomings of two other adaptations of the Wiedemann 99 car-following models by changing the ConFAV’s behaviour to be more cautious when travelling behind a human driven vehicle, and less cautious when behind another ConFAV. As little is known about the transitional period from zero autonomy to full autonomy on the already congested road network, due to the fact that these vehicles are typically tested in small numbers (often one at a time in a controlled environment), the present research study introduced ConFAVs to the simulated network gradually and in large numbers at 20% intervals (namely 0% where there are no ConFAVs, 20%, 40%, 60%, 80%, and finally 100% where all vehicles within the network were ConFAVs). The average delays and subsequent level of service for the roads within the networks were then assessed against each ConFAV penetration level. This helped understand how the network’s efficiency changes when the number of ConFAVs increases, and the potential benefits for these self-driving vehicles on congestion and the ensuing greenhouse gas emissions. The model showed that a reduction in delay of up to 100% can be achieved by introducing ConFAVs, which translates to a significant reduction in greenhouse gas emissions. This, coupled with the fact that ConFAVs are predominantly electric, points to a future sustainable road transport system. The primary purpose of this research would be to investigate the potential of ConFAVs in reducing traffic congestion and, as a result, greenhouse gas emissions.

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“…An essential objective of driving safety analysis is extracting operational safety metrics, which are quantifiable measures extracted from traffic videos (or other data sources) that determine the relative risk of an event that may lead to a crash. Some important safety metrics that are used to analyze car crashes include: Reviewing different safety metric is out of the scope of this paper and we refer the interested readers to recent papers on safety metrics [429][430][431]. As part of the Institute of Automated Mobility (IAM) project, the authors of this paper are working toward extending safety metrics into network-level metrics and developing a taxonomy of metrics for safety metrics for AVs based on the level of access required of ADS data [432].…”

Section: Safety Metricsmentioning

confidence: 99%

Deep Learning Serves Traffic Safety Analysis: A Forward-looking Review

Razi¹,

Chen²,

Li³

et al. 2022

Preprint

View full text Add to dashboard Cite

This paper explores Deep Learning (DL) methods that are used or have the potential to be used for traffic video analysis, emphasizing driving safety for both Autonomous Vehicles (AVs) and human-operated vehicles. We present a typical processing pipeline, which can be used to understand and interpret traffic videos by extracting operational safety metrics and providing general hints and guidelines to improve traffic safety. This processing framework includes several steps, including video enhancement, video stabilization, semantic and incident segmentation, object detection and classification, trajectory extraction, speed estimation, event analysis, modeling and anomaly detection. Our main goal is to guide traffic analysts to develop their own custom-built processing frameworks by selecting the best choices for each step and offering new designs for the lacking modules by providing a comparative analysis of the most successful conventional and DL-based algorithms proposed for each step. We also review existing open-source tools and public datasets that can help train DL models. To be more specific, we review exemplary traffic problems and mentioned requires steps for each problem. Besides, we investigate connections to the closely related research areas of drivers' cognition evaluation, Crowd-sourcing-based monitoring systems, Edge Computing in roadside infrastructures, ADS-equipped AVs, and highlight the missing gaps. Finally, we review commercial implementations of traffic monitoring systems, their future outlook, and open problems and remaining challenges for widespread use of such systems ‡ .

show abstract

Evaluation of Operational Safety Assessment (OSA) Metrics for Automated Vehicles in Simulation

Cited by 19 publications

References 9 publications

Deep learning serves traffic safety analysis: A forward‐looking review

Deep learning serves traffic safety analysis: A forward‐looking review

Potential of Connected Fully Autonomous Vehicles in Reducing Congestion and Associated Carbon Emissions

Deep Learning Serves Traffic Safety Analysis: A Forward-looking Review

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