Details
| Originalsprache | Englisch |
|---|---|
| Titel des Sammelwerks | 2022 IEEE 25th International Conference on Intelligent Transportation Systems (ITSC) |
| Herausgeber (Verlag) | Institute of Electrical and Electronics Engineers Inc. |
| Seiten | 2321-2328 |
| Seitenumfang | 8 |
| ISBN (elektronisch) | 9781665468800 |
| ISBN (Print) | 978-1-6654-6881-7 |
| Publikationsstatus | Veröffentlicht - 2022 |
| Veranstaltung | 25th IEEE International Conference on Intelligent Transportation Systems, ITSC 2022 - Macau, China Dauer: 8 Okt. 2022 → 12 Okt. 2022 |
Publikationsreihe
| Name | IEEE Conference on Intelligent Transportation Systems, Proceedings, ITSC |
|---|---|
| Band | 2022-October |
Abstract
Intelligent vehicle technology is becoming more and more important to counteract high emission levels and climate change while at the same time maintaining a reliable and accessible transportation system. To this end, ego-vehicle speed prediction, i.e., the forecasting of the own vehicle speed in the near future, has emerged as an important research direction for enabling advanced driver assistance systems. In this paper, we propose the WHEELS (Walk-Ahead Ego Vehicle Speed) model for ego-vehicle speed prediction. Our WHEELS combines vehicle speed information retrieved with contextual information such as road network information, weather information, or the day of the week. At the core, WHEELS introduces the so-called walk-ahead that takes all possible further routes into account. We conducted an extensive evaluation on a large-scale real-world dataset collected from a ride-pooling service. Our experiments confirm that WHEELS reliably outperforms existing baselines and achieves an average performance gain of 19.4% compared to the best-performing baseline.
ASJC Scopus Sachgebiete
- Ingenieurwesen (insg.)
- Fahrzeugbau
- Ingenieurwesen (insg.)
- Maschinenbau
- Informatik (insg.)
- Angewandte Informatik
Ziele für nachhaltige Entwicklung
Zitieren
- Standard
- Harvard
- Apa
- Vancouver
- BibTex
- RIS
2022 IEEE 25th International Conference on Intelligent Transportation Systems (ITSC). Institute of Electrical and Electronics Engineers Inc., 2022. S. 2321-2328 (IEEE Conference on Intelligent Transportation Systems, Proceedings, ITSC; Band 2022-October).
Publikation: Beitrag in Buch/Bericht/Sammelwerk/Konferenzband › Aufsatz in Konferenzband › Forschung › Peer-Review
}
TY - GEN
T1 - Ego-Vehicle Speed Prediction with Walk-Ahead
AU - Matesanz, Philip
AU - Tempelmeier, Nicolas
AU - Nolting, Michael
AU - Funke, Thorben
N1 - Funding Information: This work was partially by the German Federal Ministry for Economic Affairs and Climate Action (BMWK) under the project “CampaNeo” (grant ID 01MD 19007A, 01MD19007B).
PY - 2022
Y1 - 2022
N2 - Intelligent vehicle technology is becoming more and more important to counteract high emission levels and climate change while at the same time maintaining a reliable and accessible transportation system. To this end, ego-vehicle speed prediction, i.e., the forecasting of the own vehicle speed in the near future, has emerged as an important research direction for enabling advanced driver assistance systems. In this paper, we propose the WHEELS (Walk-Ahead Ego Vehicle Speed) model for ego-vehicle speed prediction. Our WHEELS combines vehicle speed information retrieved with contextual information such as road network information, weather information, or the day of the week. At the core, WHEELS introduces the so-called walk-ahead that takes all possible further routes into account. We conducted an extensive evaluation on a large-scale real-world dataset collected from a ride-pooling service. Our experiments confirm that WHEELS reliably outperforms existing baselines and achieves an average performance gain of 19.4% compared to the best-performing baseline.
AB - Intelligent vehicle technology is becoming more and more important to counteract high emission levels and climate change while at the same time maintaining a reliable and accessible transportation system. To this end, ego-vehicle speed prediction, i.e., the forecasting of the own vehicle speed in the near future, has emerged as an important research direction for enabling advanced driver assistance systems. In this paper, we propose the WHEELS (Walk-Ahead Ego Vehicle Speed) model for ego-vehicle speed prediction. Our WHEELS combines vehicle speed information retrieved with contextual information such as road network information, weather information, or the day of the week. At the core, WHEELS introduces the so-called walk-ahead that takes all possible further routes into account. We conducted an extensive evaluation on a large-scale real-world dataset collected from a ride-pooling service. Our experiments confirm that WHEELS reliably outperforms existing baselines and achieves an average performance gain of 19.4% compared to the best-performing baseline.
UR - http://www.scopus.com/inward/record.url?scp=85141853696&partnerID=8YFLogxK
U2 - 10.1109/ITSC55140.2022.9922436
DO - 10.1109/ITSC55140.2022.9922436
M3 - Conference contribution
AN - SCOPUS:85141853696
SN - 978-1-6654-6881-7
T3 - IEEE Conference on Intelligent Transportation Systems, Proceedings, ITSC
SP - 2321
EP - 2328
BT - 2022 IEEE 25th International Conference on Intelligent Transportation Systems (ITSC)
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 25th IEEE International Conference on Intelligent Transportation Systems, ITSC 2022
Y2 - 8 October 2022 through 12 October 2022
ER -