# Scenario-Independent Criticality Assessment and Prediction for Vulnerable Road Users in Autonomous Driving

arXiv: https://arxiv.org/abs/2609.11947 · HTML: https://mlsanity.com/paper/2609.11947/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 12 of its 31 references that are in the map.
- First posted on arXiv 24 Jul 2026.

## Builds on (most cited first, 12 of 12)

- [Optuna: A Next-generation Hyperparameter Optimization Framework](https://mlsanity.com/paper/1907.10902/) (2019)
- [On a Formal Model of Safe and Scalable Self-driving Cars](https://mlsanity.com/paper/1708.06374/) (2017)
- [DeepAccident: A Motion and Accident Prediction Benchmark for V2X Autonomous Driving](https://mlsanity.com/paper/2304.01168/) (2023)
- [Criticality Metrics for Automated Driving: A Review and Suitability Analysis of the State of the Art](https://mlsanity.com/paper/2108.02403/) (2021)
- [Pedestrian Behavior Prediction for Automated Driving: Requirements, Metrics, and Relevant Features](https://mlsanity.com/paper/2012.08418/) (2020)
- [Interaction-Dynamics-Aware Perception Zones for Obstacle Detection Safety Evaluation](https://mlsanity.com/paper/2206.12471/) (2022)
- [LSM: A Comprehensive Metric for Assessing the Safety of Lane Detection Systems in Autonomous Driving](https://mlsanity.com/paper/2407.07740/) (2024)
- [A Comprehensive Safety Metric to Evaluate Perception in Autonomous Systems](https://mlsanity.com/paper/2512.14367/) (2025)
- [SURE-Val: Safe Urban Relevance Extension and Validation](https://mlsanity.com/paper/2308.02266/) (2023)
- [Dynamic Risk Assessment Methodology with an LDM-based System for Parking Scenarios](https://mlsanity.com/paper/2404.04040/) (2024)
- [Criticality Metrics for Relevance Classification in Safety Evaluation of Object Detection in Automated Driving](https://mlsanity.com/paper/2512.15181/) (2025)
- [EPSM: A Novel Metric to Evaluate the Safety of Environmental Perception in Autonomous Driving](https://mlsanity.com/paper/2512.15195/) (2025)

## Abstract (arXiv)

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.