학술논문

Scene-Extrapolation: Generating Interactive Traffic Scenarios
Document Type
Conference
Source
2024 IEEE Intelligent Vehicles Symposium (IV) Intelligent Vehicles Symposium (IV), 2024 IEEE. :2483-2490 Jun, 2024
Subject
Communication, Networking and Broadcast Technologies
Power, Energy and Industry Applications
Robotics and Control Systems
Signal Processing and Analysis
Transportation
Measurement
Machine learning
Autonomous vehicles
Testing
Language
ISSN
2642-7214
Abstract
Verifying highly automated driving functions can be challenging, requiring identifying relevant test scenarios. Scenario-based testing will likely play a significant role in verifying these systems, predominantly occurring within simulation. In our approach, we use traffic scenes as a starting point (seedscene) to address the individuality of various highly automated driving functions and to avoid the problems associated with a predefined test traffic scenario. Different highly autonomous driving functions, or their distinct iterations, may display different behaviors under the same operating conditions. To make a generalizable statement about a seed-scene, we simulate possible outcomes based on various behavior profiles. We utilize our lightweight simulation environment and populate it with rule-based and machine learning behavior models for individual actors in the scenario. We analyze resulting scenarios using a variety of criticality metrics. The density distributions of the resulting criticality values enable us to make a profound statement about the significance of a particular scene, considering various eventualities.