Concretize: A Model-Driven Tool for Scenario-Based Autonomous Vehicle Testing
To achieve rigorous certification of autonomous vehicles (AVs), testing approaches must handle all possible, practically relevant traffic scenarios. This is achievable through the handling of relevant abstractions within the scenario specification language and throughout the scenario generation process. While many scenario generation approaches exist, they are often limited to generating instances of a fixed (pre-defined) scenario and lack tool support. In this paper, we introduce Concretize, a model-driven AV testing framework. It (1) allows users to define scenario specifications using an abstract domain-specific language, and (2) generates conforming concrete (exact) scenarios, which are (3) visualized via a user-friendly web interface. Scenarios are also (4) executed in simulation, in which case Concretize (5) auto-generates figures depicting the monitored safety behavior of the AV-under-test wrt. scenario components at various abstraction levels. Video demonstration: https://youtu.be/inaD8jd7YxI.
WOS:001351589800014
McGill University
École Polytechnique Fédérale de Lausanne
McGill University
2024-01-01
NEW YORK
979-8-4007-0622-6
66
70
REVIEWED
EPFL
| Event name | Event acronym | Event place | Event date |
Linz, AUSTRIA | 2024-09-22 - 2024-09-27 | ||