Scene-Extrapolation: Generating Interactive Traffic Scenarios
<|reference_start|>Scene-Extrapolation: Generating Interactive Traffic Scenarios: 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 (seed-scene) 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.<|reference_end|>
@article{zipfl2024scene-extrapolation:,
    title={Scene-Extrapolation: Generating Interactive Traffic Scenarios},
    author={Maximilian Zipfl, Barbara Sch"utt, J. Marius Z"ollner},
    journal={arXiv preprint arXiv:2404.17224},
    year={2024},
    archivePrefix={arXiv},
    eprint={2404.17224},
    primaryClass={cs.RO}
}