A Recursive Gaussian Process based Online Driving Style Analysis

verfasst von

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.

Organisationseinheit(en)

Institut für Mechatronische Systeme
Identifikation & Regelung

Externe Organisation(en)

IAV GmbH

Typ

Aufsatz in Konferenzband

Seiten

3187-3192

Anzahl der Seiten

6

Publikationsdatum

2023

Publikationsstatus

Veröffentlicht

Peer-reviewed

Ja

ASJC Scopus Sachgebiete

Elektrotechnik und Elektronik

Ziele für nachhaltige Entwicklung

SDG 7 – Erschwingliche und saubere Energie

Elektronische Version(en)

https://doi.org/10.23919/acc55779.2023.10156499 (Zugang: Geschlossen)