Pattern Recognition for Driving Scenario Detection in Real Driving Data

Montanari F, German R, Djanatliev A (2020)

Publication Language: English

Publication Type: Conference contribution, Original article

Publication year: 2020

Pages Range: 590 - 597

ISBN: 978-1-7281-6673-5

URI: https://ieeexplore.ieee.org/abstract/document/9304560

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.

Authors with CRIS profile

Francesco Montanari Computer Science 7 (Computer Networks and Communication Systems) Reinhard German Computer Science 7 (Computer Networks and Communication Systems) Anatoli Djanatliev Computer Science 7 (Computer Networks and Communication Systems)

How to cite

APA:

Montanari, F., German, R., & Djanatliev, A. (2020). Pattern Recognition for Driving Scenario Detection in Real Driving Data. In Proceedings of the 2020 IEEE Intelligent Vehicles Symposium (IV) (pp. 590 - 597).

MLA:

Montanari, Francesco, Reinhard German, and Anatoli Djanatliev. "Pattern Recognition for Driving Scenario Detection in Real Driving Data." Proceedings of the 2020 IEEE Intelligent Vehicles Symposium (IV) 2020. 590 - 597.

BibTeX: Download