Easily integrate complex software systems as the industry shifts from hardware-defined vehicles to software-defined vehicles (SDVs). Develop, test, and validate systems continuously to meet production timelines using the latest generative AI techniques.
Ensure safety across all dimensions including ODD, requirements, geographic, and scenario metrics. Validate ADAS and AD systems against complex and dangerous situations that could arise in the real world.
Use a modular platform that supports a variety of formats and environments. Establish flexible processes and meet unique software development needs. Facilitate global collaboration across teams and time zones.
“Shift left” with virtual testing such as SIL and HIL to reduce costly on-road tests. Use simulation efficiently to manage data and compute costs with fine-tuned technologies.
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.
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.
Simulation tools play a crucial role in ADAS and AD 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.
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.