Real-World Scenario Mining for the Assessment of Automated Vehicles

TL;DR

This paper introduces a two-step method for mining real-world scenarios for automated vehicle assessment, involving automatic data labeling and scenario extraction based on shared tags, applicable across various scenario types.

Contribution

The paper presents a novel, flexible approach for scenario mining from real-world data using automatic tagging and scenario characterization, enhancing scalability and reusability.

Findings

Method successfully labels data automatically.

Scenarios are characterized by shared tags.

Applicable to diverse scenario types.

Abstract

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.