Generation of Risky Scenarios for Testing Automated Driving Visual Perception Based on Causal Analysis

Research output: Journal Publications and ReviewsRGC 21 - Publication in refereed journalpeer-review

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Author(s)

  • Yi Pan

Related Research Unit(s)

Detail(s)

Original languageEnglish
Pages (from-to)15991-16004
Journal / PublicationIEEE Transactions on Intelligent Transportation Systems
Volume25
Issue number11
Online published9 Jul 2024
Publication statusPublished - Nov 2024

Abstract

Automated driving systems (ADS) have made remarkable progress in recent years, yet their reliability and testability remain as significant challenges. The environmental conditions that ADS face are highly complex and may result in the disruption of autonomous vehicles. In this study, we propose an approach that leverages causal inference theory to analyze the impact of causal factors on automated driving visual modules. Our method uncovers the root key factors that affect visual perception performance. We further establish a Challenging Index to quantitatively characterize the causal effects of the key factors on perception failures. This quantitative index is subsequently utilized to generate risky scenarios. Through extensive experiments on various state-of-the-art automated driving visual algorithms, we demonstrate the effectiveness of the challenge index in evaluating the level of hazard in the deployment environment. Additionally, the proposed "challenge index guided search" method improves test efficiency by up to 8.95 times compared to the baselines while maintaining a balance between coverage diversity and the hazardous level of test scenarios. Our research offers a new perspective for analyzing and evaluating the impact of key factors on visual perception. This contributes to the reduction of test space and efficiency of the generation of high-value test scenarios, ultimately advancing the deployment of safer automated vehicles.

Research Area(s)

  • Automated driving system, visual perception, causal theory, safety test, evaluation

Citation Format(s)

Generation of Risky Scenarios for Testing Automated Driving Visual Perception Based on Causal Analysis. / Jiang, Zhengmin; Liu, Jia; Sun, Peng et al.
In: IEEE Transactions on Intelligent Transportation Systems, Vol. 25, No. 11, 11.2024, p. 15991-16004.

Research output: Journal Publications and ReviewsRGC 21 - Publication in refereed journalpeer-review