Critical Scenario creation can be optimized by using AI
Identify Safety Of The Intended Functionality (SOTIF) unknown-unsafe scenario creation from recorded and processed traffic data, using AI and simulation
With increasing automation levels, there is an exponential increase in the number and variety of scenarios that autonomous driving systems must be able to react to - all while maintaining a safe and comfortable environment. As a result, automated vehicle functionalities need to be tested and validated, driving millions of miles. AI-based optimization algorithms provide the solution to identifying critical scenarios from recorded / synthetic data and accelerates the development and testing of autonomous driving systems.
In this webinar, learn how to identify critical scenarios and mitigate unsafe situations by testing the AD function in critical scenarios, leveraging Siemens’ AI optimization-based methodology.
Learn how critical scenario creation equips development teams to:
- Collect data in an efficient and smart way
- Import collected data into simulation environment
- Extract the basic scenario elements (BSE) which are the most basic behaviors of the road users in lateral and longitudinal direction from collected/synthetic data
- Combine the basic scenario elements of all road users for massive scenario creation using a graph-based approach
- Identify SOTIF unknown-unsafe scenarios from massive scenarios using AI optimization algorithm