TL;DR: Applied Intuition provides AI-driven tools for developing, testing, and validating vehicle software, including advanced driver-assistance systems (ADAS) and autonomous driving (AD) systems.
Applied Intuition offers a unified platform for developing, testing, and validating vehicle software. It supports ADAS/AD development, off-road autonomy, and vehicle software across multiple domains. The platform provides simulation, data exploration, and validation tools, making it essential for automotive, trucking, defense, and other industries aiming to accelerate their development cycles and enhance system safety.
Who is Applied Intuition For
Applied Intuition is designed for a wide range of users in the automotive and related industries. Key users include:
- Automotive Manufacturers: Companies developing advanced driver-assistance systems and autonomous vehicles.
- Trucking Companies: Firms looking to integrate autonomous driving technology into their fleets.
- Defense Contractors: Organizations working on autonomous military vehicles.
- Construction and Mining Firms: Companies implementing autonomous solutions for heavy machinery.
- Agricultural Enterprises: Businesses utilizing autonomous technology in farming equipment.
- Vehicle Software Developers: Teams developing, testing, and deploying vehicle software.
Key Features of Applied Intuition
- ADAS and AD Development Platform: Develop, test, and validate advanced driver-assistance and autonomous driving systems.
- Vehicle Software Platform: Develop and update vehicle software across all domains.
- Off-Road Autonomy Stack: Integrate autonomous systems for diverse terrains.
- Simulation Tools: Comprehensive tools for prediction, planning, sensor, and log-based simulations.
- Data Tools: Explore log data, use synthetic datasets, and perform validation and verification.
- AI Integration: Utilize the latest AI technology for developing and training machine learning models.
Key Use Cases for Applied Intuition
- ADAS and AD Development: Safely develop and test advanced driver-assistance and autonomous driving systems.
- Vehicle Software Development: Build and update vehicle software across various domains.
- Off-Road Autonomy: Implement autonomous systems for off-road and diverse terrain applications.
- Simulation Testing: Use advanced simulation tools for predicting and planning vehicle behaviors.
- Data Exploration: Analyze log data and use synthetic datasets for machine learning.
- AI and ML Training: Train machine learning models on fleet data for improved performance.
