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Publication . Conference object . 2020

Common Methodology for Data-Driven Scenario-Based Safety Assurance in the HEADSTART Project

Wagener, Nicolas;
Open Access  
Published: 09 Nov 2020
Publisher: Zenodo
Abstract
One objective of introducing Connected and Automated Driving (CAD) functions on the roads is to reduce the number of accidents due to human errors by reducing the tasks of the driver (partial automation) or removing it completely from the driving system. Building safe and reliable automated vehicles require specific testing methods that are adapted to higher levels of automation. Harmonised European Solutions for Testing Automated Road Transport (HEADSTART) is a research project funded by the European Union that aims to define testing and validation procedures for CAD functions. HEADSTART brings a methodology for testing and validating these functions with Data-Driven Scenario-Based Safety Assurance. The goal of this paper is first to present the overall HEADSTART methodology for validating CAD safety, and then further explain three aspects of it, such as: The database mechanics to extract and parametrize logical scenarios, the relevance metrics for the selection of scenarios, and the allocation of scenarios for test execution.
Subjects

Automated Driving, Safety Assurance

Funded by
EC| HEADSTART
Project
HEADSTART
HARMONISED EUROPEAN SOLUTIONS FOR TESTING AUTOMATED ROAD TRANSPORT
  • Funder: European Commission (EC)
  • Project Code: 824309
  • Funding stream: H2020 | RIA
Validated by funder
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Article . Conference object . 2020