Data annotation

Safety-Critical Scenario Annotation: Labeling Dangerous & Edge Case Situations

In modern AI and autonomous systems, such as autonomous vehicles, medical robotics, and industrial automation, safety is a critical consideration. One key approach to safety is safety-critical scenario Annotation, the process of labeling events and conditions that may pose a hazard or represent an extreme, atypical case (edge case).

Labeling hazardous and edge cases involves identifying potential risks, classifying events by severity, and documenting the context in which they occur.

Key Takeaways

Rare events in driving logs require targeted curation to boost test coverage and model robustness.
Embedding-based selection replaces hand-tuned scores, leading to more realistic adversary selection.
Labeling vehicle roles and maneuvers enables precise analysis and behavior-driven testing.
Integration of annotated scenarios into training improves downstream performance and worst-case metrics.

Objective measures of risk: TTC, PET, jerk, and RSS-inspired constraints

Metric	Full Name	Description	Interpretation / Use
TTC	Time-to-Collision	The time remaining until a potential collision between objects at their current speed and trajectory	Lower TTC indicates higher risk; used for active braking or driver warning systems
PET	Post-Encroachment Time	The time between one object entering another’s path and the moment of potential contact	Lower PET indicates higher risk of conflict; evaluates near-miss situations, e.g., at intersections
Jerk	Rate of Change of Acceleration	Measures sudden changes in motion that can affect passenger safety	High jerk indicates potential danger, discomfort, or vehicle instability; important for planning smooth maneuvers
RSS-inspired constraints	Responsibility-Sensitive Safety Constraints	A set of rules ensuring safe distance and speed depending on conditions	Ensures that even in edge cases, autonomous systems maintain safe behavior; integrated into motion planning

From specifications to simulations: an end-to-end critical scenario identification approach

Formalize functional and safety specifications that define critical conditions and limit scenarios for the system.
Define risk criteria and safety metrics such as TTC, PET, jerk, and constraints inspired by the responsibility-sensitive safety approach.
Generate specific scenarios based on the specifications, including extreme cases and rare but dangerous events.
Conduct simulations and verification to evaluate the system's response in each scenario without risking real participants.
Analyze and identify critical scenarios to improve the safety of autonomous systems and reduce the probability of accidents in the real world.

Closed-loop and adversarial scenario generation trends

Closed-loop and adversarial scenario-generation methods are used to test autonomous systems under complex, dangerous conditions. The closed-loop approach enables the simulation system to interact with the autonomous agent in real time, creating dynamic safety scenarios that replicate real-world road conditions. Adversarial scenario generation aims to create deliberately designed dangerous situations and extreme cases that increase the autonomous agent's collision risk.

Both approaches can be used sequentially. First, the system is tested in a closed-loop environment to evaluate the basic behavior, and then adversarial scenario generation is used to analyze the most critical scenarios.

Crash-grounded representation learning: embedding realistic unsafe behaviors

Crash-grounded representation learning is used to train models based on data that reflect real-world dangerous behaviors. The goal is to build representations that enable the system to recognize and analyze dangerous situations and critical events typical of the real road environment.

This approach involves integrating information on historical accident scenarios and incidents to build models that assess collision risks across different safety scenarios. As a result, the system can account for rare and complex situations during planning and decision-making.

Crash-grounded representation learning enables the combination of simulation and real-world data, thereby increasing the relevance of training and providing a more accurate assessment of potentially dangerous situations. The use of such models enables systematic analysis of critical events and enhances autonomous systems' ability to respond to dangerous situations.

Turning video into trajectories: processing accident datasets for annotation

Video footage of accidents is converted into digital trajectories of objects, enabling the systematic detection of dangerous situations and the assessment of collision risks. The process includes object detection, tracking of their movement, and construction of coordinate sequences that reflect the behavior of vehicles and road users over time. The resulting trajectories are used to annotate high-risk scenarios and form training and test sets for autonomous systems.

This approach allows for the standardization of data from different sources, increases annotation accuracy, and ensures the reproducibility of experiments. It enables large-scale analysis of critical events and supports the assessment of potential collision risks across different safety scenarios.

Human-in-the-loop and expert-in-the-loop: resolving ambiguity at scale

Approach	Description	Role in Data Processing	Use for safety scenarios
Human-in-the-loop (HITL)	Involves non-expert or trained users in data annotation and verification of algorithm outputs	Helps refine annotations and reduce uncertainty in data labeling	Used for identifying dangerous situations and clarifying critical events in large datasets
Expert-in-the-loop (EITL)	Involves experts (e.g., safety engineers or traffic accident analysts) for quality control and complex annotations	Ensures high accuracy and consistency of annotations in ambiguous or complex cases	Used to validate scenarios with high collision risks and confirm complex safety scenarios

Quality assurance for the rare: edge-case-first QA and audit mechanisms

An edge-case-first approach to quality assurance prioritizes testing and analysis of scenarios that occur rarely but have a high potential to cause critical events or dangerous situations. Instead of focusing on typical scenarios, this method enables systematic detection of problems in safety scenarios where increased collision risk is possible.

Edge-case-first QA uses various methods: analysis of historical accident data, simulations of rare situations, and adversarial scenario generation. These approaches allow for testing the response of an autonomous system to extreme or atypical events that are not usually included in standard test suites.

Audit mechanisms provide a systematic check of the quality of annotations, correctness of trajectory formation, and accuracy of simulations. Special attention is paid to critical events, where an incorrect annotation or omitted detail can lead to incorrect conclusions about safety. The audit includes data verification by various process participants, including human-in-the-loop and expert-in-the-loop methods, which provide greater reliability in assessing dangerous situations.

The combination of edge-case-first QA and audits enables building a structured quality control system that allocates resources to the riskiest scenarios, increases the reliability of collision risk assessments, and ensures reliable verification of autonomous systems in complex safety scenarios.

KPIs that matter: measuring dataset value and model robustness under stress

Define key performance indicators (KPIs) to assess the quality of datasets and model behavior in complex safety scenarios.
Assess the value of a dataset by its ability to capture a variety of critical events and dangerous situations, including rare or extreme cases.
Measure the robustness of models under stress, such as when the system is exposed to high collision risk or atypical scenarios.
Use KPIs to compare different datasets and annotation strategies to determine which ones provide the most complete representation of safety scenarios.
Analyze KPI results to identify model and data weaknesses and improve the reliability of autonomous systems in real-world environments.

Summary

Identification and analysis of critical scenarios in autonomous systems is based on systematic work with data, annotations, and models. Methods for converting video data into trajectories for accurate object motion reproduction and safety scenario generation are included, as well as approaches to representing rare and extremely dangerous situations. Human-in-the-loop and expert-in-the-loop methods improve annotation accuracy, while edge-case-first approaches and audit mechanisms provide quality control in scenarios with high potential for collisions.

Models are trained on both simulated and real data, enabling them to evaluate their behavior under various critical events. KPIs are used to measure the value of datasets and the robustness of models under stress, which provides systematic verification and analysis. A comprehensive approach to scenario identification, annotation, and testing enhances autonomous systems' ability to respond to complex conditions and supports overall safety.

FAQ

What are safety scenarios in autonomous systems?

Safety scenarios are defined situations that an autonomous system may encounter, including both typical and unusual conditions. They help evaluate how the system responds to potential critical events and dangerous situations.

Why is identifying critical events important?

Identifying critical events enables targeted testing in high-risk situations where collision risk is elevated. This helps improve the reliability of autonomous systems in real-world operation.

What is the role of edge-case scenarios?

Edge-case scenarios represent rare or extreme situations that are not common in regular operation. They are crucial for detecting dangerous situations that standard tests might miss.

How do closed-loop simulations help?

Closed-loop simulations allow the system to interact dynamically with its environment. They enable the evaluation of system behavior in diverse safety scenarios and under varying collision risks.

What is adversarial scenario generation?

Adversarial scenario generation involves creating situations specifically designed to challenge the system. It helps uncover vulnerabilities to critical events and dangerous situations that may not occur naturally.

How does human-in-the-loop (HITL) support data annotation?

HITL involves humans in the annotation and verification process to improve accuracy. This approach ensures that safety scenarios and critical events are correctly labeled.

Why use expert-in-the-loop (EITL)?

EITL engages domain experts to validate complex or ambiguous cases. It is particularly useful for confirming annotations related to collision risks and rare, dangerous situations.

What is crash-grounded representation learning?

Crash-grounded representation learning uses data from real incidents to train models. It allows autonomous systems to recognize patterns in critical events and anticipate dangerous situations.

How are KPIs applied in autonomous system testing?

KPIs measure dataset coverage and model performance under stress. They help assess how well safety scenarios are represented and how effectively systems respond to collision risks.

What is the purpose of edge-case-first QA and audit mechanisms?

Edge-case-first QA prioritizes testing rare but high-risk scenarios. Audits ensure accurate annotations and reliable evaluation of critical events and dangerous situations, improving overall system robustness.