One core challenge in the development of automated vehicles is their capability to deal with a multitude of complex traffic scenarios with many, hard to predict traffic participants. As part of the iterative development process, it is necessary to detect critical scenarios and generate knowledge from them to improve the highly automated driving (HAD) function. In order to tackle this challenge, numerous datasets have been released in the past years, which act as the basis for the development and testing of such algorithms. Nevertheless, the remaining challenges are to find relevant scenes, such as safety-critical corner cases, in these datasets and to understand them completely.
Therefore, this paper presents a methodology to process and analyze naturalistic motion datasets in two ways: on the one hand, our approach maps scenes of the datasets to a generic semantic scene graph which allows for a high-level and objective analysis. Here, arbitrary criticality measures, e.g. TTC, RSS or SFF, can be set to automatically detect critical scenarios between traffic participants. On the other hand, the scenarios are recreated in a realistic virtual reality (VR) environment, which allows for a subjective close-up analysis from multiple, interactive perspectives.
Reliving the Dataset: Combining the Visualization of Road Users’ Interactions with Scenario Reconstruction in Virtual Reality
Lect. Notes Electrical Eng.
International Conference on Intelligent Transportation Engineering ; 2021 ; Beijing, China October 29, 2021 - October 31, 2021
2021 6th International Conference on Intelligent Transportation Engineering (ICITE 2021) ; Chapter : 39 ; 436-454
2022-06-01
19 pages
Article/Chapter (Book)
Electronic Resource
English
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