# Tools for Vehicle Intelligence

## The end-to-end platform for building and validating intelligent systems.

## **Build and Scale Physical AI Systems**

### Train models, process data, and orchestrate autonomy workflows across any environment.

Applied Intuition’s Tools for Vehicle Intelligence is built for petabyte-scale ingestion, curation, and processing across fleets and long-running programs. Reliable orchestration, cost-aware execution, and reproducible lineage keep workflows stable as data volume and model complexity grow.

A powerful SDK lets teams construct and manage complex workflows across cloud, on-prem, and air-gapped environments. Modular primitives allow teams to bring their own models, simulators, and metrics while integrating existing infrastructure—preserving interoperability and lineage end to end.

## **A Data Flywheel That Compounds Performance**

Turn real-world sensor data into training, simulation, and evaluation at scale.

The platform converts real-world sensor data into curated, labeled segments that power downstream training, simulation, and evaluation. Each stage of the pipeline generates metrics and insights that feed directly into the next iteration.

Over time, this closed loop continuously improves autonomy performance. As more data flows through the system, models become more robust, edge cases are identified earlier, and safety progress compounds across the entire autonomy stack.

## **Agent-Driven Workflows for Physical AI Development**

Transform advanced AI research into reliable autonomy workflows.

The platform is built on state-of-the-art AI, including vision-language models for data understanding and curation, World Foundation Models, and neural reconstruction and rendering for high-fidelity simulation. ML behavior agents generate realistic outcomes and controllable scenario variation for training and validation workflows.

A dedicated research-to-production pipeline continuously turns cutting-edge methods into reliable, scalable product capabilities. The system is designed to be orchestrated by agents, with tokenized UI designs and MCP-ready interfaces. These agents understand the context, data types, and workflows required for physical AI development and can drive complex tasks end-to-end.

## FAQ

### What is ADAS?

Advanced driver-assistance systems are technologies used in vehicles to enhance safety and driving. They rely on inputs from multiple data sources, including automotive imaging, lidar, radar, computer vision, and sensors to assist drivers with tasks like parking, lane keeping, and collision avoidance.

### What is AD?

Automated driving is a technology involved in the development and deployment of systems that enable vehicles to drive themselves without human intervention. This encompasses the use of various technologies including sensors, software algorithms, and machine learning to perceive the environment, make decisions, and control the vehicle effectively and safely.

### What are the 6 levels of vehicle autonomy?

The Society of Automotive Engineers (SAE) defines 6 levels of driving automation, ranging from 0 (fully manual) to 5 (fully autonomous). Each level describes the degree of autonomy in the vehicle, indicating how much control the system has versus the human driver. These levels have been adopted by the U.S. Department of Transportation.

- **Level 0:** No automation. Human performs all driving tasks.
- **Level 1:** Driver assistance. A single vehicle function is automated, such as cruise control.
- **Level 2:** Partial automation. Vehicle handles steering and acceleration but human can take control at any time.
- **Level 3:** Conditional automation. Environmental detection enables the vehicle to handle certain tasks, but human oversight is required.
- **Level 4:** High automation. Vehicle performs all tasks under specific circumstances, such as dedicated lanes. Human can take control at any time.
- **Level 5:** Full automation. Vehicle performs all driving functions in all conditions. No human intervention is required.

### What are the key components of an ADAS and AD development platform?

A comprehensive ADAS and AD development platform typically includes simulation, validation, and data management tooling. These components work together to enable rapid development, testing, and deployment of ADAS and AD software.

### What types of testing are essential for validating ADAS and AD systems?

Essential testing types for ADAS and AD systems include real-world testing, test track testing, and virtual testing. These tests assess the reliability and effectiveness of the systems under various scenarios. Functional safety tests ensure that the systems react correctly to inputs and failures to validate their performance across different environments.

### How do simulation tools contribute to the development of vehicle software?

Simulation tools play a crucial role in vehicle software development by providing a safe, scalable, and cost-effective environment for testing and refining algorithms. They help teams model and simulate complex driving scenarios and sensor interactions that are impractical or risky to test in real-world conditions.

### What role does artificial intelligence (AI) play in the development of vehicle software?

AI enables vehicles to make decisions in real time, learn from diverse environmental conditions, and improve through machine learning models, thereby enhancing the ability to handle complex and dynamic road situations.

### What is the V-model in vehicle software development?

The V-model is a structured approach commonly used in the automotive industry, particularly for safety-critical systems. It starts with product and system requirements, designing functionality, and results in the implementation of the software. Then, the software is tested with software-in-the-loop, hardware-in-the-loop, deployed, and validated for production.

### What are the challenges associated with the traditional development V-model?

With the traditional V-model for development, software integration is happening infrequently, and there are too many dependencies on hardware. Additionally, there are local, siloed tools and slow cycles leading to bugs and production delays. Applied Intuition's tooling, by contrast, enables more consistent updating and parallel communication across all development steps.