A Recursive Gaussian Process based Online Driving Style Analysis

authored by

Daniel Fink, Tobias Dues, Karl-Philipp Kortmann, Pascal Blum, Christoph Schweers, Ahmed Trabelsi

Abstract

Advanced driver assistance systems improve the driving comfort and contribute to enhance safety and energy efficiency in automotive traffic. However, whether these systems are actually used, depends on the driver's satisfaction with the system's way of driving. A promising approach to met the driver's individual preferences, is to personalize the assistance system. This paper presents a recursive Gaussian Process based analysis to determine the driver's preferences, during manual vehicle guidance, separately for various driving maneuvers. The recursive process enables an online capable analysis where no maneuver data has to be stored. In addition, an event detection approach to identify relevant driving situations is proposed. The gained information about the driver's preferences can be accessed by modern assistance systems to individually parameterize the driving behavior for example in curves or for general velocity adjustments at speed limit changes.

Organisation(s)

Institute of Mechatronic Systems
Identification & Control

External Organisation(s)

IAV GmbH

Type

Conference contribution

Pages

3187-3192

No. of pages

6

Publication date

2023

Publication status

Published

Peer reviewed

Yes

ASJC Scopus subject areas

Electrical and Electronic Engineering

Sustainable Development Goals

SDG 7 - Affordable and Clean Energy

Electronic version(s)

https://doi.org/10.23919/acc55779.2023.10156499 (Access: Closed)