Real-World Scenario Mining for the Assessment of Automated Vehicles
This work addresses the need for efficient scenario-based assessment methods in the automotive industry, though it appears incremental as it builds on existing techniques for real-world data capture.
The paper tackles the problem of capturing real-world scenarios for assessing automated vehicles by proposing a two-step method that automatically labels data with tags and mines scenarios based on these tags, enabling identification of shared characteristics across scenario types and broad applicability.
Scenario-based methods for the assessment of Automated Vehicles (AVs) are widely supported by many players in the automotive field. Scenarios captured from real-world data can be used to define the scenarios for the assessment and to estimate their relevance. Therefore, different techniques are proposed for capturing scenarios from real-world data. In this paper, we propose a new method to capture scenarios from real-world data using a two-step approach. The first step consists in automatically labeling the data with tags. Second, we mine the scenarios, represented by a combination of tags, based on the labeled tags. One of the benefits of our approach is that the tags can be used to identify characteristics of a scenario that are shared among different type of scenarios. In this way, these characteristics need to be identified only once. Furthermore, the method is not specific for one type of scenario and, therefore, it can be applied to a large variety of scenarios. We provide two examples to illustrate the method. This paper is concluded with some promising future possibilities for our approach, such as automatic generation of scenarios for the assessment of automated vehicles.