Details
| Translated title of the contribution | Online-Fahrstilanalyse mittels rekursiver Gauß-Prozess-Regerssion |
|---|---|
| Original language | English |
| Title of host publication | 2023 American Control Conference (ACC) |
| Place of Publication | San Diego, USA |
| Pages | 3187-3192 |
| Number of pages | 6 |
| ISBN (electronic) | 979-8-3503-2806-6 |
| Publication status | Published - 2023 |
| Event | 2023 American Control Conference, ACC 2023 - San Diego, United States Duration: 31 May 2023 → 2 Jun 2023 https://acc2023.a2c2.org/ |
Publication series
| Name | Proceedings of the ... American Control Conference |
|---|---|
| ISSN (Print) | 0743-1619 |
| ISSN (electronic) | 2378-5861 |
Abstract
ASJC Scopus subject areas
- Engineering(all)
- Electrical and Electronic Engineering
Sustainable Development Goals
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2023 American Control Conference (ACC). San Diego, USA, 2023. p. 3187-3192 (Proceedings of the ... American Control Conference).
Research output: Chapter in book/report/conference proceeding › Conference contribution › Research › peer review
}
TY - GEN
T1 - A Recursive Gaussian Process based Online Driving Style Analysis
AU - Fink, Daniel
AU - Schweers, Christoph
AU - Trabelsi, Ahmed
A2 - Dues, Tobias
A2 - Kortmann, Karl-Philipp
A2 - Blum, Pascal
PY - 2023
Y1 - 2023
N2 - 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.
AB - 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.
UR - http://www.scopus.com/inward/record.url?scp=85167810233&partnerID=8YFLogxK
U2 - 10.23919/acc55779.2023.10156499
DO - 10.23919/acc55779.2023.10156499
M3 - Conference contribution
SN - 978-1-6654-6952-4
T3 - Proceedings of the ... American Control Conference
SP - 3187
EP - 3192
BT - 2023 American Control Conference (ACC)
CY - San Diego, USA
T2 - 2023 American Control Conference, ACC 2023
Y2 - 31 May 2023 through 2 June 2023
ER -