[Submitted on 24 Jul 2026]
Title:Scenario-Independent Criticality Assessment and Prediction for Vulnerable Road Users in Autonomous Driving
View a PDF of the paper titled Scenario-Independent Criticality Assessment and Prediction for Vulnerable Road Users in Autonomous Driving, by J\"org Gamerdinger and 4 other authors
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Abstract:Increasing safety is the primary objective of automated vehicles. Achieving this goal requires reliable safety metrics that incorporate safety-relevant factors such as object type, velocity, and criticality. A key capability of such metrics is the distinction between critical and non-critical objects, which is addressed through criticality or relevance estimation. Existing criticality metrics are typically designed for specific scenarios and primarily focus on vehicle-to-vehicle interactions. In this paper, we therefore propose a novel criticality metric tailored to vulnerable road users (VRUs), which require special consideration due to their less predictable motion behavior. Furthermore, to avoid the complexity introduced by scenario-specific metrics, we introduce a scenario-independent criticality prediction framework applicable to all traffic participant classes. The effectiveness of both the proposed VRU-centric criticality metric and the criticality prediction framework is evaluated using the DeepAccident dataset, which contains a diverse set of safety-critical traffic scenarios. The proposed VRU-centric criticality metric improves pedestrian criticality classification performance by up to 50 %. In addition, the proposed criticality prediction framework outperforms state-of-the-art metrics by 275 %, achieving an F1-score of 0.96 and enabling scenario-independent criticality assessment across all object classes. These results demonstrate the strong potential of the proposed approaches to enhance criticality assessment for safety evaluation in automated driving systems.
Comments: Accepted at IEEE VTC Fall 2026
Subjects:
Robotics (cs.RO); Computer Vision and Pattern Recognition (cs.CV); Computers and Society (cs.CY); Machine Learning (cs.LG)
Cite as: arXiv:2609.11947 [cs.RO]
(or arXiv:2609.11947v1 [cs.RO] for this version)
https://doi.org/10.48550/arXiv.2609.11947
arXiv-issued DOI via DataCite
Submission history
From: Jörg Gamerdinger [view email] [v1] Fri, 24 Jul 2026 13:44:05 UTC (666 KB)
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