How is SDS for Automotive different?
Applied Intuition’s offerings
Autonomy stack components
An E2E stack for urban autonomy and a 2-stage E2E stack for broader deployment, each with modular ML components that deliver precise, reliable performance in complex driving scenarios, including challenging weather and road conditions.
Data engine
Applied Intuition’s data engine powers E2E autonomy through an integrated data flywheel, where model performance drives intelligent curation of diverse, high-quality datasets — enabling rapid, efficient autonomy stack iteration
The data engine provides robust pipelines to collect, ingest, process, label, curate, and mine petabytes of multi-modal vehicle and sensor data, built on scalable infrastructure that manages compute, tracks experiments, and accelerates model iteration and deployment
The data engine provides robust pipelines to collect, ingest, process, label, curate, and mine petabytes of multi-modal vehicle and sensor data, built on scalable infrastructure that manages compute, tracks experiments, and accelerates model iteration and deployment
Reference hardware architecture
Reference hardware architecture contains compute, sensors, and sensor placements to help OEMs get started faster and balance compute- and cost-efficiency—without experimentation—to accelerate deployment
Tooling for system integration and validation
Proven simulation, verification, and data tools from successful automotive engagements, ensuring robust, efficient, and reliable integration with vehicle software and hardware systems
Scenario-based testing frameworks covering millions of situations, with metric computation and baseline comparison to prevent regressions and measure improvements
Neural simulation and reactive agent capabilities for end-to-end model development, supporting closed-loop training and validation
Scenario-based testing frameworks covering millions of situations, with metric computation and baseline comparison to prevent regressions and measure improvements
Neural simulation and reactive agent capabilities for end-to-end model development, supporting closed-loop training and validation
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