TU Darmstadt / ULB / TUbiblio

Rule-Based Driving Style Classification Using Acceleration Data Profiles

Jardin, Philippe and Moisidis, Ioannis and Zetina, Siegfried Saenger and Rinderknecht, Stephan (2020):
Rule-Based Driving Style Classification Using Acceleration Data Profiles.
Rhodes, Greece, 2020 IEEE 23rd International Conference on Intelligent Transportation Systems (ITSC), 20-23 Sept. 2020, ISBN 978-1-7281-4149-7,
DOI: 10.1109/ITSC45102.2020.9294611,
[Conference or Workshop Item]

Abstract

Driving style classification can be useful for various intelligent vehicle applications and can improve driving comfort in new driveability functions. Within this contribution, we present a rule-based classification algorithm, which uses aggregated speed-depended acceleration data driving profiles from recorded experiments. We observe, that high values in longitudinal and lateral acceleration occur less frequently the greater they are. If a dynamic driving style is present, high values also occur more frequently compared to the average. From that observation we use the occurrence probability of an acceleration data pair as an indicator for driving style. The proposed approach compares the expected average acceleration according to the driving profile with the actual time series data. Based on a sample time series from recorded experiment data, the driving style is then classified into three different classes: calm, moderate and dynamic. Because the approach relies on a limited amount of parameters with low sensitivity, the classification offers high robustness and is not prone to over-fitting. It reaches overall 68.49% accuracy on recorded real life driving data with various driving context.

Item Type: Conference or Workshop Item
Erschienen: 2020
Creators: Jardin, Philippe and Moisidis, Ioannis and Zetina, Siegfried Saenger and Rinderknecht, Stephan
Title: Rule-Based Driving Style Classification Using Acceleration Data Profiles
Language: English
Abstract:

Driving style classification can be useful for various intelligent vehicle applications and can improve driving comfort in new driveability functions. Within this contribution, we present a rule-based classification algorithm, which uses aggregated speed-depended acceleration data driving profiles from recorded experiments. We observe, that high values in longitudinal and lateral acceleration occur less frequently the greater they are. If a dynamic driving style is present, high values also occur more frequently compared to the average. From that observation we use the occurrence probability of an acceleration data pair as an indicator for driving style. The proposed approach compares the expected average acceleration according to the driving profile with the actual time series data. Based on a sample time series from recorded experiment data, the driving style is then classified into three different classes: calm, moderate and dynamic. Because the approach relies on a limited amount of parameters with low sensitivity, the classification offers high robustness and is not prone to over-fitting. It reaches overall 68.49% accuracy on recorded real life driving data with various driving context.

Place of Publication: Rhodes, Greece
ISBN: 978-1-7281-4149-7
Divisions: 16 Department of Mechanical Engineering
16 Department of Mechanical Engineering > Institute for Mechatronic Systems in Mechanical Engineering (IMS)
Event Title: 2020 IEEE 23rd International Conference on Intelligent Transportation Systems (ITSC)
Event Dates: 20-23 Sept. 2020
Date Deposited: 05 Jan 2021 06:30
DOI: 10.1109/ITSC45102.2020.9294611
Export:
Suche nach Titel in: TUfind oder in Google
Send an inquiry Send an inquiry

Options (only for editors)
Show editorial Details Show editorial Details