Towards an Ontology for Scenario Definition for the Assessment of Automated Vehicles: An Object-Oriented Framework
arXiv:2001.11507 · doi:10.1109/TIV.2022.3144803
Abstract
The development of new assessment methods for the performance of automated vehicles is essential to enable the deployment of automated driving technologies, due to the complex operational domain of automated vehicles. One contributing method is scenario-based assessment in which test cases are derived from real-world road traffic scenarios obtained from driving data. Given the complexity of the reality that is being modeled in these scenarios, it is a challenge to define a structure for capturing these scenarios. An intensional definition that provides a set of characteristics that are deemed to be both necessary and sufficient to qualify as a scenario assures that the scenarios constructed are both complete and intercomparable. In this article, we develop a comprehensive and operable definition of the notion of scenario while considering existing definitions in the literature. This is achieved by proposing an object-oriented framework in which scenarios and their building blocks are defined as classes of objects having attributes, methods, and relationships with other objects. The object-oriented approach promotes clarity, modularity, reusability, and encapsulation of the objects. We provide definitions and justifications of each of the terms. Furthermore, the framework is used to translate the terms in a coding language that is publicly available.
16 pages, 7 figures, 1 table
References in corpus (2)
Cited by in corpus (5)
- Scenario Parameter Generation Method and Scenario Representativeness Metric for Scenario-Based Assessment of Automated Vehicles
- One Ontology to Rule Them All: Corner Case Scenarios for Autonomous Driving
- A Quantitative Method to Determine What Collisions Are Reasonably Foreseeable and Preventable
- Coverage Metrics for a Scenario Database for the Scenario-Based Assessment of Automated Driving Systems
- Scenario Extraction from a Large Real-World Dataset for the Assessment of Automated Vehicles