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A word about Human Danger Recognition
Human danger recognition — the innate ability to sense and respond to risk before an accident occurs — is universal across regions and cultures. It defines how people intuitively avoid accidents even in unpredictable conditions.
In this database example the driver detected that the mother was walking behind the stopped vehicles—just before she became occluded—and instinctively slowed down and steered slightly away from the center lane, avoiding an accident.
Resembler provides scenarios as these as A-B test case to verify whether an automated system would have exhibited the same risk-anticipating behavior.
Built on one of the largest verified accident datasets in the world, Resembler uses Singapore as its living testbed — a uniquely dense, multi-modal, and highly regulated city where urban, industrial, highway, port, and airport traffic coexist under near-perfect control. This diversity makes Singapore the ideal environment to observe, model, and verify human-like safety behavior before deploying in any city worldwide.
Solutions
Global Coverage and Behavioral Normalization
Our database provides over 500 million kilometers of real-world accident and incident coverage, with an additional 10% of comparative data from Europe, the US, and Australia.
Research confirms that human avoidance behavior—how people interpret complex traffic situations and act to prevent accidents—is consistent worldwide, enabling accurate cross-regional modeling.
Using more than 9 Billion kilometers of accident statistics from Singapore, we support normalizing data to regional conditions, adapting exposure and event likelihood across different regions, city zones, countries, and environments to ensure realistic and comparable safety analysis.
ODD Comparison and Regional Adaptation
Resembler.ai offers an ODD Comparison Tool to verify and compare Operational Design Domains (ODDs) and deployment areas across regions.
Based on real accidents and incidents, the tool integrates human accident avoidance behavior into the selected infrastructure and environment.
The tool highlights differences in road design, traffic dynamics, and environmental factors that influence autonomous driving performance — enabling city planners and developers to make data-driven improvements and reduce deployment risk for automated systems.
Visual Search and Semantic Querying
The Resembler Icon Query Language (RIQL) makes complex data exploration simple and visual.
Using hundreds of proprietary semantic elements, RIQL allows users to search, filter, and compare accidents and incidents across our 500M+ km AV dataset with intuitive icon-based logic — taking the complexity out of deep analysis.
Built on insights from the AV Human Benchmark Dataset, RIQL enables rapid understanding of how autonomous systems perform in real-world conditions, supporting faster validation, safer algorithms, and more transparent performance assessment.
Annotated Risk Frames
Resembler.ai’s dataset, enriched with expert-led annotations, includes bounding boxes, risk progression data, and contextual tags derived from real-world accidents and incidents.
The database highlights frames where risk emerges before an incident occurs, carefully selected from the best-of-best and worst-of-worst examples of defensive driving.
These annotations capture how humans perceive emerging risk and often intelligently circumvent situations where others fail to follow traffic rules.
The data can be fed directly into ADAS and AD vision systems, or — through OpenDRIVE and CARLA — into the planning and intention-prediction stack, helping developers verify, benchmark, and enhance safety performance across diverse environments.
Flexible Pipeline
Resembler.ai supports a flexible pipeline compatible with multiple data formats, including OSM, OpenDRIVE, OpenSCENARIO, LTA Datamall, and CARLA — enabling seamless integration into existing workflows and simulation environments.
Whether you’re an insurance provider refining risk models or a developer improving autonomous systems, Resembler.ai delivers the tools and insights needed for accurate validation and continuous improvement.
The web-based AI platform includes advanced capabilities such as risk assessment, accident link-back, digital-twin generation, and risk mitigation analytics, helping organizations predict, verify, and prevent accidents — ultimately making transportation safer and more reliable worldwide.
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