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Toward Unsupervised Test Scenario Extraction for Automated Driving Systems from Urban Naturalistic Road Traffic Data
ISSN: 2574-0741, e-ISSN: 2574-075X
Published February 02, 2023 by SAE International in United States
- Level 3 (Conditional driving automation),
- Level 4 (High driving automation),
- Level 5 (Full driving automation),
- Automated driving systems,
- Machine learning,
- Big data,
- Artificial intelligence (AI),
- Test procedures,
- Vehicle to vehicle (V2V),
- Driver behavior,
- Safety testing and procedures,
- Neural networks
Citation: Weber, N., Thiem, C., and Konigorski, U., "Toward Unsupervised Test Scenario Extraction for Automated Driving Systems from Urban Naturalistic Road Traffic Data," SAE Intl. J CAV 6(3):263-281, 2023, https://doi.org/10.4271/12-06-03-0017.
Scenario-based testing is a promising approach to solving the challenge of proving the safe behavior of vehicles equipped with automated driving systems (ADS). Since an infinite number of concrete scenarios can theoretically occur in real-world road traffic, the extraction of scenarios relevant in terms of the safety-related behavior of these systems is a key aspect for their successful verification and validation. Therefore, a method for extracting multimodal urban traffic scenarios from naturalistic road traffic data in an unsupervised manner, minimizing the amount of (potentially biased) prior expert knowledge, is proposed. Rather than an (elaborate) rule-based assignment by extracting concrete scenarios into predefined functional scenarios, the presented method deploys an unsupervised machine learning pipeline. The approach allows for exploring the unknown nature of the data and their interpretation as test scenarios that experts could not have anticipated. The method is evaluated for naturalistic road traffic data at urban intersections from the inD and the Silicon Valley Intersections datasets. For this purpose, it is analyzed with which clustering approach (K-means, hierarchical clustering, and DBSCAN) the scenario extraction method performs best (referring to an elaborate rule-based implementation). Subsequently, using hierarchical clustering the results show both a jump in the overall accuracy of around 20% when moving from 4 to 5 clusters and a saturation effect starting at 41 clusters with an overall accuracy of 84%. These observations can be a valuable contribution in the context of the trade-off between the number of functional scenarios (i.e., clustering accuracy) and testing effort. The possible reasons for the observed accuracy variations of different clusters, each with a fixed total number of given clusters, are discussed. The findings encourage the use of this type of data and unsupervised machine learning approaches as valuable pillars for the systematic construction of a relevant scenario database with sufficient coverage for testing ADS.