ビークル インテリジェンス ツール

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.

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.

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.

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.

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.

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.

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.

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.

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.