Foretify Evaluate
Data Curation
Unify, aggregate and cleanse data from both real-world and virtual drive logs to optimize the training and validation data set and identify gaps and redundancies
Search for similar or rare scenarios, to increase scenario diversity and relevance during AI model training
Prioritize high-value samples to improve AI model robustness and training effectiveness
Unified Coverage View
Visualize progress against a high-level coverage plan driven by aggregating physical and virtual logs
Single Run Debugger
Inspect key events and KPIs in the scenario context, focusing the debugging tools for improve efficiency
Formal Definition of Abstract Scenarios
Identify scenarios using a formal scenario language (OpenSCENARIO DSL) to ensure consistency and traceability across workflows
Sensor Data Curation
Identify scenarios based on visual characteristics using natural language, complementing formal scenario search techniques
An integrated solution leveraging NVIDIA Cosmos World Foundation Models
Apply a library of scenarios, KPIs and coverage definitions to automatically detect and classify the ODD coverage and performance from the drive logs
Apply automation to identify and analyze anomalies and critical issues. Compare different AV stack versions, to identify degradations.
Generate performance dashboards for transparent, data-driven views to all stakeholders
Explore Foretify Generate
Close the gaps and expose unknowns by truthfully replaying and automatically enriching real-world drive data with realistic variations and synthetic generation of edge cases at scale
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