201524th International Technical Conference on the Enhanced Safety of Vehicles (ESV)National Highway Traffic Safety AdministrationRequires access

Target Crash Population of Automated Vehicles

Paul Rau, Mikio Yanagisawa, Wassim G Najm

Open publisher page 16 citations

Abstract

This paper describes a method to determine the target crash population that could be addressed by automated vehicles. The method maps specific automated vehicle functions to five layers of crash information including crash location, pre-crash scenario, driving conditions, travel speed, and driver condition. The focus of this paper is on automated vehicle functions at automation levels 2 through 4 as defined by the National Highway Traffic Safety Administration. This paper identifies the automated vehicle functions and their automation levels, operational characteristics and conditions, and applicable pre-crash scenarios through literature review and relevant research programs. This paper also identifies the approach to query the crash data and account for level 0 and level 1 automated vehicle functions when estimating target crash populations for automated vehicle functions at levels 2 through 4. The application of this method, using the General Estimates System and Fatality Analysis Reporting system crash databases, would express the target crash populations in terms of the annual frequency of all crashes, fatal-only crashes, and involved persons at different injury levels.

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What this paper is about

This paper describes a method to determine the target crash population that could be addressed by automated vehicles. The method maps specific automated vehicle functions to five layers of crash information including crash location, pre-crash scenario, driving conditions, travel speed, and driver condition. The focus of this paper is on automated vehicle functions at automation levels 2 through 4 as defined by the National Highway Traffic Safety Administration. This paper identifies the automated vehicle functions and their automation levels, operational characteristics and conditions, and applicable pre-crash scenarios through literature review and relevant research programs. This paper also identifies the approach to query the crash data and account for level 0 and level 1 automated vehicle functions when estimating target crash populations for automated vehicle functions at levels 2 through 4. The application of this method, using the General Estimates System and Fatality Analysis Reporting system crash databases, would express the target crash populations in terms of the annual frequency of all crashes, fatal-only crashes, and involved persons at different injury levels.

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OpenAlex reports 16 citations for this work. Citation counts describe recorded attention and do not establish research quality.

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Available abstract

This paper describes a method to determine the target crash population that could be addressed by automated vehicles. The method maps specific automated vehicle functions to five layers of crash information including crash location, pre-crash scenario, driving conditions, travel speed, and driver condition. The focus of this paper is on automated vehicle functions at automation levels 2 through 4 as defined by the National Highway Traffic Safety Administration. This paper identifies the automated vehicle functions and their automation levels, operational characteristics and conditions, and applicable pre-crash scenarios through literature review and relevant research programs. This paper also identifies the approach to query the crash data and account for level 0 and level 1 automated vehicle functions when estimating target crash populations for automated vehicle functions at levels 2 through 4. The application of this method, using the General Estimates System and Fatality Analysis Reporting system crash databases, would express the target crash populations in terms of the annual frequency of all crashes, fatal-only crashes, and involved persons at different injury levels.

Key concepts: Crash, Automation, Population, Transport engineering, Computer science, Engineering, Computer security, Medicine

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