Generating Edge Cases for Testing Autonomous Vehicles Using Real-World Data
Author:
Karunakaran Dhanoop1, Berrio Perez Julie Stephany2ORCID, Worrall Stewart2
Affiliation:
1. Emerging Technologies, IAG, Sydney, NSW 2000, Australia 2. Australian Centre for Robotics, University of Sydney, Sydney, NSW 2008, Australia
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.
Funder
Australian Centre for Robotics University of Sydney Insurance Australia Group Cooperative Research Centres program
Subject
Electrical and Electronic Engineering,Biochemistry,Instrumentation,Atomic and Molecular Physics, and Optics,Analytical Chemistry
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