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Research paper | Open Access

Accelerated testing for automated vehicles safety evaluation in cut-in scenarios based on importance sampling, genetic algorithm and simulation applications

Yiming Xu, Yajie Zou, Jian Sun( )
Tongji University, Shanghai, China
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Abstract

Purpose

It would take billions of miles’ field road testing to demonstrate that the safety of automated vehicle is statistically significantly higher than the safety of human driving because that the accident of vehicle is rare event.

Design/methodology/approach

This paper proposes an accelerated testing method for automated vehicles safety evaluation based on improved importance sampling (IS) techniques. Taking the typical cut-in scenario as example, the proposed method extracts the critical variables of the scenario. Then, the distributions of critical variables are statistically fitted. The genetic algorithm is used to calculate the optimal IS parameters by solving an optimization problem. Considering the error of distribution fitting, the result is modified so that it can accurately reveal the safety benefits of automated vehicles in the real world.

Findings

Based on the naturalistic driving data in Shanghai, the proposed method is validated by simulation. The result shows that compared with the existing methods, the proposed method improves the test efficiency by 35 per cent, and the accuracy of accelerated test result is increased by 23 per cent.

Originality/value

This paper has three contributions. First, the genetic algorithm is used to calculate IS parameters, which improves the efficiency of test. Second, the result of test is modified by the error correction parameter, which improves the accuracy of test result. Third, typical high-risk cut-in scenarios in China are analyzed, and the proposed method is validated by simulation.

Keywords

SimulationGenetic algorithmAutomated vehiclesSafety evaluationImportance samplingLane changingHigh-risk scenarios

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Journal of Intelligent and Connected Vehicles
Volume 1 Issue 1,
October 2018
Pages 28-38
DOI: 10.1108/JICV-01-2018-0002
Cite this article:
Xu Y, Zou Y, Sun J. Accelerated testing for automated vehicles safety evaluation in cut-in scenarios based on importance sampling, genetic algorithm and simulation applications. Journal of Intelligent and Connected Vehicles, 2018, 1(1): 28-38. https://doi.org/10.1108/JICV-01-2018-0002

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Received: 25 January 2018
Revised: 02 May 2018
Accepted: 09 May 2018
Published: 14 August 2018
© 2018 Yiming Xu, Yajie Zou and Jian Sun. Published in Journal of Intelligent and Connected Vehicles. Published by Emerald Publishing Limited.

This article is published under the Creative Commons Attribution (CC BY 4.0) licence. Anyone may reproduce, distribute, translate and create derivative works of this article (for both commercial and noncommercial purposes), subject to full attribution to the original publication and authors. The full terms of this licence may be seen at http://creativecommons.org/licences/by/4.0/legalcode
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