A Literature Review of Performance Metrics of Automated Driving Systems for On-Road Vehicles

The article presents a review of recent literature on the performance metrics of Automated Driving Systems (ADS). More specifically, performance indicators of environment perception and motion planning modules are reviewed as they are the most complicated ADS modules. The need for the incorporation...

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Autores principales:	Mysore Narasimhamurthy Sharath, Babak Mehran
Formato:	article
Lenguaje:	EN
Publicado:	Frontiers Media S.A. 2021
Materias:	safety metrics of ADS human-like perception human-like driving behavior ADS safety regulation obstacle threat level Transportation engineering TA1001-1280
Acceso en línea:	https://doaj.org/article/04f9f86b43e84ec8afcea417e2ca6ba8
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id	oai:doaj.org-article:04f9f86b43e84ec8afcea417e2ca6ba8
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spelling	oai:doaj.org-article:04f9f86b43e84ec8afcea417e2ca6ba82021-12-01T13:42:47ZA Literature Review of Performance Metrics of Automated Driving Systems for On-Road Vehicles2673-521010.3389/ffutr.2021.759125https://doaj.org/article/04f9f86b43e84ec8afcea417e2ca6ba82021-11-01T00:00:00Zhttps://www.frontiersin.org/articles/10.3389/ffutr.2021.759125/fullhttps://doaj.org/toc/2673-5210The article presents a review of recent literature on the performance metrics of Automated Driving Systems (ADS). More specifically, performance indicators of environment perception and motion planning modules are reviewed as they are the most complicated ADS modules. The need for the incorporation of the level of threat an obstacle poses in the performance metrics is described. A methodology to quantify the level of threat of an obstacle is presented in this regard. The approach involves simultaneously considering multiple stimulus parameters (that elicit responses from drivers), thereby not ignoring multivariate interactions. Human-likeness of ADS is a desirable characteristic as ADS share road infrastructure with humans. The described method can be used to develop human-like perception and motion planning modules of ADS. In this regard, performance metrics capable of quantifying human-likeness of ADS are also presented. A comparison of different performance metrics is then summarized. ADS operators have an obligation to report any incident (crash/disengagement) to safety regulating authorities. However, precrash events/states are not being reported. The need for the collection of the precrash scenario is described. A desirable modification to the data reporting/collecting is suggested as a framework. The framework describes the precrash sequences to be reported along with the possible ways of utilizing such a valuable dataset (by the safety regulating authorities) to comprehensively assess (and consequently improve) the safety of ADS. The framework proposes to collect and maintain a repository of precrash sequences. Such a repository can be used to 1) comprehensively learn and model the precrash scenarios, 2) learn the characteristics of precrash scenarios and eventually anticipate them, 3) assess the appropriateness of the different performance metrics in precrash scenarios, 4) synthesize a diverse dataset of precrash scenarios, 5) identify the ideal configuration of sensors and algorithms to enhance safety, and 6) monitor the performance of perception and motion planning modules.Mysore Narasimhamurthy SharathBabak MehranFrontiers Media S.A.articlesafety metrics of ADShuman-like perceptionhuman-like driving behaviorADS safety regulationobstacle threat levelTransportation engineeringTA1001-1280ENFrontiers in Future Transportation, Vol 2 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	safety metrics of ADS human-like perception human-like driving behavior ADS safety regulation obstacle threat level Transportation engineering TA1001-1280
spellingShingle	safety metrics of ADS human-like perception human-like driving behavior ADS safety regulation obstacle threat level Transportation engineering TA1001-1280 Mysore Narasimhamurthy Sharath Babak Mehran A Literature Review of Performance Metrics of Automated Driving Systems for On-Road Vehicles
description	The article presents a review of recent literature on the performance metrics of Automated Driving Systems (ADS). More specifically, performance indicators of environment perception and motion planning modules are reviewed as they are the most complicated ADS modules. The need for the incorporation of the level of threat an obstacle poses in the performance metrics is described. A methodology to quantify the level of threat of an obstacle is presented in this regard. The approach involves simultaneously considering multiple stimulus parameters (that elicit responses from drivers), thereby not ignoring multivariate interactions. Human-likeness of ADS is a desirable characteristic as ADS share road infrastructure with humans. The described method can be used to develop human-like perception and motion planning modules of ADS. In this regard, performance metrics capable of quantifying human-likeness of ADS are also presented. A comparison of different performance metrics is then summarized. ADS operators have an obligation to report any incident (crash/disengagement) to safety regulating authorities. However, precrash events/states are not being reported. The need for the collection of the precrash scenario is described. A desirable modification to the data reporting/collecting is suggested as a framework. The framework describes the precrash sequences to be reported along with the possible ways of utilizing such a valuable dataset (by the safety regulating authorities) to comprehensively assess (and consequently improve) the safety of ADS. The framework proposes to collect and maintain a repository of precrash sequences. Such a repository can be used to 1) comprehensively learn and model the precrash scenarios, 2) learn the characteristics of precrash scenarios and eventually anticipate them, 3) assess the appropriateness of the different performance metrics in precrash scenarios, 4) synthesize a diverse dataset of precrash scenarios, 5) identify the ideal configuration of sensors and algorithms to enhance safety, and 6) monitor the performance of perception and motion planning modules.
format	article
author	Mysore Narasimhamurthy Sharath Babak Mehran
author_facet	Mysore Narasimhamurthy Sharath Babak Mehran
author_sort	Mysore Narasimhamurthy Sharath
title	A Literature Review of Performance Metrics of Automated Driving Systems for On-Road Vehicles
title_short	A Literature Review of Performance Metrics of Automated Driving Systems for On-Road Vehicles
title_full	A Literature Review of Performance Metrics of Automated Driving Systems for On-Road Vehicles
title_fullStr	A Literature Review of Performance Metrics of Automated Driving Systems for On-Road Vehicles
title_full_unstemmed	A Literature Review of Performance Metrics of Automated Driving Systems for On-Road Vehicles
title_sort	literature review of performance metrics of automated driving systems for on-road vehicles
publisher	Frontiers Media S.A.
publishDate	2021
url	https://doaj.org/article/04f9f86b43e84ec8afcea417e2ca6ba8
work_keys_str_mv	AT mysorenarasimhamurthysharath aliteraturereviewofperformancemetricsofautomateddrivingsystemsforonroadvehicles AT babakmehran aliteraturereviewofperformancemetricsofautomateddrivingsystemsforonroadvehicles AT mysorenarasimhamurthysharath literaturereviewofperformancemetricsofautomateddrivingsystemsforonroadvehicles AT babakmehran literaturereviewofperformancemetricsofautomateddrivingsystemsforonroadvehicles
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A Literature Review of Performance Metrics of Automated Driving Systems for On-Road Vehicles

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