Scenario Detection in Unlabeled Real Driving Data with a Rule-based State Machine supported by a Recurrent Neural Network

Montanari F, Ren H, Djanatliev A (2021)

Publication Language: English

Publication Type: Conference contribution, Original article

Publication year: 2021

DOI: 10.1109/vtc2021-spring51267.2021.9449032

Abstract

An arising idea in the automotive sector is to extract and collect scenarios from real driving data and use
them as test cases for the validation of automated driving functions. In this paper, we use a rule-based state machine to label the data for the training of a recurrent neural network (RNN) and combine both the state machine and the RNN for detecting driving scenarios. The state machine shows precise results and the idea of training the RNN on the resulted samples from the state machine shows promising results. A statistical comparison of the proposed methods shows that the state machine should be used if possible, however, if the signals needed for the state machine are not available the RNN can be used to support it.

Authors with CRIS profile

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

How to cite

APA:

Montanari, F., Ren, H., & Djanatliev, A. (2021). Scenario Detection in Unlabeled Real Driving Data with a Rule-based State Machine supported by a Recurrent Neural Network. In Proceedings of the 2021 IEEE 93rd Vehicular Technology Conference.

MLA:

Montanari, Francesco, Haoyu Ren, and Anatoli Djanatliev. "Scenario Detection in Unlabeled Real Driving Data with a Rule-based State Machine supported by a Recurrent Neural Network." Proceedings of the 2021 IEEE 93rd Vehicular Technology Conference 2021.

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