# Criticality Metrics for Automated Driving: A Review and Suitability Analysis of the State of the Art

arXiv: https://arxiv.org/abs/2108.02403 · HTML: https://mlsanity.com/paper/2108.02403/

## Key facts

- Cited by 31 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 5 Aug 2021.

## Built on by (most cited first, 30 of 31)

- [Using Ontologies for the Formalization and Recognition of Criticality for Automated Driving](https://mlsanity.com/paper/2205.01532/) (2022)
- [From Model-Based to Data-Driven Simulation: Challenges and Trends in Autonomous Driving](https://mlsanity.com/paper/2305.13960/) (2023)
- [LSM: A Comprehensive Metric for Assessing the Safety of Lane Detection Systems in Autonomous Driving](https://mlsanity.com/paper/2407.07740/) (2024)
- [CLICv2: Image Complexity Representation via Content Invariance Contrastive Learning](https://mlsanity.com/paper/2503.06641/) (2025)
- [A Counterfactual Safety Margin Perspective on the Scoring of Autonomous Vehicles' Riskiness](https://mlsanity.com/paper/2308.01050/) (2023)
- [Bridging Data-Driven and Knowledge-Driven Approaches for Safety-Critical Scenario Generation in Automated Vehicle Validation](https://mlsanity.com/paper/2311.10937/) (2023)
- [Investigating Driving Interactions: A Robust Multi-Agent Simulation Framework for Autonomous Vehicles](https://mlsanity.com/paper/2402.04720/) (2024)
- [Accurately Predicting Probabilities of Safety-Critical Rare Events for Intelligent Systems](https://mlsanity.com/paper/2403.13869/) (2024)
- [An Approach to Systematic Data Acquisition and Data-Driven Simulation for the Safety Testing of Automated Driving Functions](https://mlsanity.com/paper/2405.01776/) (2024)
- [A Unified Probabilistic Approach to Traffic Conflict Detection](https://mlsanity.com/paper/2407.10959/) (2024)
- [Learning collision risk proactively from naturalistic driving data at scale](https://mlsanity.com/paper/2505.13556/) (2025)
- [Grasping Causality for the Explanation of Criticality for Automated Driving](https://mlsanity.com/paper/2210.15375/) (2022)
- [Efficient Data Representation for Motion Forecasting: A Scene-Specific Trajectory Set Approach](https://mlsanity.com/paper/2407.20732/) (2024)
- [Edge Case Detection in Automated Driving: Methods, Challenges, and Future Directions](https://mlsanity.com/paper/2410.08491/) (2024)
- [Traffic and Safety Rule Compliance of Humans in Diverse Driving Situations](https://mlsanity.com/paper/2411.01909/) (2024)
- [CRASH: Challenging Reinforcement-Learning Based Adversarial Scenarios For Safety Hardening](https://mlsanity.com/paper/2411.16996/) (2024)
- [Reinforcement Learning for Testing Interdependent Requirements in Autonomous Vehicles: An Empirical Study](https://mlsanity.com/paper/2502.15792/) (2025)
- [Criticality Metrics for Relevance Classification in Safety Evaluation of Object Detection in Automated Driving](https://mlsanity.com/paper/2512.15181/) (2025)
- [Can Attribution Predict Risk? From Multi-View Attribution to Planning Risk Signals in End-to-End Autonomous Driving](https://mlsanity.com/paper/2605.06264/) (2026)
- [Navigating the Edge with the State-of-the-Art Insights into Corner Case Identification and Generation for Enhanced Autonomous Vehicle Safety](https://mlsanity.com/paper/2503.00077/) (2025)
- [Advancing Autonomous Driving System Testing: Demands, Challenges, and Future Directions](https://mlsanity.com/paper/2512.11887/) (2025)
- [Risk-Controllable Multi-View Diffusion for Driving Scenario Generation](https://mlsanity.com/paper/2603.11534/) (2026)
- [Conservative Estimation of Perception Relevance of Dynamic Objects for Safe Trajectories in Automotive Scenarios](https://mlsanity.com/paper/2307.10873/) (2023)
- [Improving AEBS Validation Through Objective Intervention Classification Leveraging the Prediction Divergence Principle](https://mlsanity.com/paper/2507.07872/) (2025)
- [Bayesian Optimization applied for accelerated Virtual Validation of the Autonomous Driving Function](https://mlsanity.com/paper/2507.22769/) (2025)
- [From Real-World Traffic Data to Relevant Critical Scenarios](https://mlsanity.com/paper/2512.07482/) (2025)
- [Effort-Based Criticality Metrics for Evaluating 3D Perception Errors in Autonomous Driving](https://mlsanity.com/paper/2603.28029/) (2026)
- [CooperDrive: Enhancing Driving Decisions Through Cooperative Perception](https://mlsanity.com/paper/2604.14454/) (2026)
- [Do Not Forget the Obvious - RISC: A Risk-Informed Slice-Coverage Protocol for Safe Autonomous Driving](https://mlsanity.com/paper/2608.12051/) (2026)
- [Scenario-Independent Criticality Assessment and Prediction for Vulnerable Road Users in Autonomous Driving](https://mlsanity.com/paper/2609.11947/) (2026)

## Abstract (arXiv)

The large-scale deployment of automated vehicles on public roads has the potential to vastly change the transportation modalities of today's society. Although this pursuit has been initiated decades ago, there still exist open challenges in reliably ensuring that such vehicles operate safely in open contexts. While functional safety is a well-established concept, the question of measuring the behavioral safety of a vehicle remains subject to research. One way to both objectively and computationally analyze traffic conflicts is the development and utilization of so-called criticality metrics. Contemporary approaches have leveraged the potential of criticality metrics in various applications related to automated driving, e.g. for computationally assessing the dynamic risk or filtering large data sets to build scenario catalogs. As a prerequisite to systematically choose adequate criticality metrics for such applications, we extensively review the state of the art of criticality metrics, their properties, and their applications in the context of automated driving. Based on this review, we propose a suitability analysis as a methodical tool to be used by practitioners. Both the proposed method and the state of the art review can then be harnessed to select well-suited measurement tools that cover an application's requirements, as demonstrated by an exemplary execution of the analysis. Ultimately, efficient, valid, and reliable measurements of an automated vehicle's safety performance are a key requirement for demonstrating its trustworthiness.