Training and Validation
of AI-Powered Autonomy with
Data Automation
Foretellix’s development toolchain optimizes data-driven training and validation of the AI-powered AV stacks.
By curating and evaluating traffic scenarios from real-world drives and augmenting them with synthetically generated scenarios, Foretify generates training and validation data to improve the performance and safety of the AV stack, throughout the development processes, creating a rapid feedback loop.
Difficult to locate and prioritize the valuable training data within the petabytes of data being collected
Linear scaling of models and data increases costs with limited payoff
Improving model generalization to handle edge cases (long-tail) by solely relying on real-world data collection has diminishing returns
Inefficient and ineffective for curation, triage and edge case training
Difficult to capture, measure and evaluate the Operational Design Domain (ODD) coverage
A manual and inefficient process for triaging and tracing errors back to specific root causes
Difficult to develop realistic test scenarios at scale to find edge-cases and unknowns
There is a need for correlation and AV performance predictability between synthetic driving scenarios and real-world driving
Data Automation for Effective AI Training & Validation
Automate driving data evaluation by applying advanced scenario metrics for triaging, accelerating the data flywheel from issue detection to validated improvement
Evaluate KPIs and ODD coverage to quickly identify and prioritize scenarios for accelerated AI model training
Truthfully replay real-world drives, inserting variations of the actors’ behavior to train and validate changes in the AV’s behavior
Generate hyper-realistic behavior and environmental variations for end-to-end simulation, grounded in physics with NVIDIA Omniverse and Cosmos
Automatically identify gaps in the ODD coverage and generate the relevant training and validation data required to scale
Intelligent scenario generation engine ensures that only useful and realistic scenarios are created enabling efficient AV development at scale
Deep visibility into scenario execution, model behavior and coverage metrics
Custom tailored views and KPIs according to specific validation workflows
Why Foretellix’s Data Automation Toolchain is Essential for AI Training & Validation
Maximize the value of your massive amounts of existing driving data with automated unification, curation, and prioritization of your real-world and simulated drive logs
Streamline large-scale training workflows by generalizing and abstracting behavior and environment events
Streamline large-scale training workflows by generalizing and abstracting both behavior and environment events
Efficiently generate targeted synthetic datasets to reduce reliance on costly real-world data collection, optimizing compute resources without compromising learning effectiveness
Automate Your AI Training and Validation Data Pipeline