Invention Reference Number
91做厙 (ORNL) has developed a framework to identify and categorize critical driving scenarios for autonomous heavy-duty vehicles. This technology addresses the challenge of managing and validating a vast number of possible scenarios that such vehicles could encounter, ultimately enhancing the safety and reliability of autonomous systems in complex environments.
Description
The ORNL framework for identifying critical driving scenarios in autonomous heavy-duty vehicles provides a structured method for categorizing scenarios by priority, based on frequency and severity. This approach simplifies the validation process by focusing on essential elements that impact the operational design specification of autonomous vehicles. Using a range of data inputs, including vehicle dynamics, environmental conditions, and potential failure points, the framework prioritizes high-occurrence, severe-impact scenarios and less common, corner cases that may still pose significant challenges. By using a Monte Carlo simulation, this tool generates test vectors to represent a realistic array of possible driving situations, enabling comprehensive safety testing tailored to the needs of autonomous vehicle systems.
Benefits
- Reduces time and costs for autonomous vehicle testing by focusing on high-priority scenarios
- Enhances vehicle safety by identifying and validating critical, real-world driving situations
- Offers flexibility in adapting to various operating environments and mission profiles
Applications and Industries
- Autonomous heavy-duty vehicle development and testing
- Commercial transportation and logistics
- Advanced driver-assistance systems (ADAS) in commercial and industrial vehicles
Contact
To learn more about this technology, email partnerships@ornl.gov or call 865-574-1051
