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Pattern Recognition for Driving Scenario Detection in Real Driving Data

Montanari F, German R, Djanatliev A (2020)

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Publication Type: Conference contribution

Publication year: 2020

Publisher: Institute of Electrical and Electronics Engineers Inc.

Pages Range: 590-597

Conference Proceedings Title: IEEE Intelligent Vehicles Symposium, Proceedings

Event location: Virtual, Las Vegas, NV

DOI: 10.1109/IV47402.2020.9304560

Abstract

For the scenario-based development and testing of automated and connected driving an unknown huge number of different driving scenarios is needed. In this paper we propose an approach that extracts driving scenarios from real driving data without any requirement of predefinitions or rules. Instead of searching for specific scenarios in the data, we cluster recurring patterns and interpret the resulting clusters as potential scenario groups. The method shows promising results. In the exemplary clustering we are able to detect four main scenario groups and corner cases within the clusters. With an huge amount of data this method could be used in the future to set up a scenario database in an automatic manner.

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How to cite

APA:

Montanari, F., German, R., & Djanatliev, A. (2020). Pattern Recognition for Driving Scenario Detection in Real Driving Data. In IEEE Intelligent Vehicles Symposium, Proceedings (pp. 590-597). Virtual, Las Vegas, NV, US: Institute of Electrical and Electronics Engineers Inc..

MLA:

Montanari, Francesco, Reinhard German, and Anatoli Djanatliev. "Pattern Recognition for Driving Scenario Detection in Real Driving Data." Proceedings of the 31st IEEE Intelligent Vehicles Symposium, IV 2020, Virtual, Las Vegas, NV Institute of Electrical and Electronics Engineers Inc., 2020. 590-597.

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