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Generating Edge Cases for Testing Autonomous Vehicles Using Real-World Data

Generating Edge Cases for Testing Autonomous Vehicles Using Real-World Data

Authors :
Dhanoop Karunakaran
Julie Stephany Berrio Perez
Stewart Worrall
Source :
Sensors, Vol 24, Iss 1, p 108 (2023)
Publication Year :
2023
Publisher :
MDPI AG, 2023.

Abstract

In the past decade, automotive companies have invested significantly in autonomous vehicles (AV), but achieving widespread deployment remains a challenge in part due to the complexities of safety evaluation. Traditional distance-based testing has been shown to be expensive and time-consuming. To address this, experts have proposed scenario-based testing (SBT), which simulates detailed real-world driving scenarios to assess vehicle responses efficiently. This paper introduces a method that builds a parametric representation of a driving scenario using collected driving data. By adopting a data-driven approach, we are then able to generate realistic, concrete scenarios that correspond to high-risk situations. A reinforcement learning technique is used to identify the combination of parameter values that result in the failure of a system under test (SUT). The proposed method generates novel, simulated high-risk scenarios, thereby offering a meaningful and focused assessment of AV systems.

Details

Language :
English
ISSN :
14248220
Volume :
24
Issue :
1
Database :
Directory of Open Access Journals
Journal :
Sensors
Publication Type :
Academic Journal
Accession number :
edsdoj.6f1f7b6a0d4b68b6868a3c9ff490aa
Document Type :
article
Full Text :
https://doi.org/10.3390/s24010108