Generation of Risky Scenarios for Testing Automated Driving Visual Perception Based on Causal Analysis
Research output: Journal Publications and Reviews › RGC 21 - Publication in refereed journal › peer-review
Author(s)
Related Research Unit(s)
Detail(s)
Original language | English |
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Pages (from-to) | 15991-16004 |
Journal / Publication | IEEE Transactions on Intelligent Transportation Systems |
Volume | 25 |
Issue number | 11 |
Online published | 9 Jul 2024 |
Publication status | Published - Nov 2024 |
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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.
In: IEEE Transactions on Intelligent Transportation Systems, Vol. 25, No. 11, 11.2024, p. 15991-16004.
Research output: Journal Publications and Reviews › RGC 21 - Publication in refereed journal › peer-review