Ego-Vehicle Speed Prediction with Walk-Ahead

authored by

Philip Matesanz, Nicolas Tempelmeier, Michael Nolting, Thorben Funke

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.

Organisation(s)

L3S Research Centre

External Organisation(s)

Volkswagen AG

Type

Conference contribution

Pages

2321-2328

No. of pages

8

Publication date

2022

Publication status

Published

Peer reviewed

Yes

ASJC Scopus subject areas

Automotive Engineering, Mechanical Engineering, Computer Science Applications

Sustainable Development Goals

SDG 9 - Industry, Innovation, and Infrastructure, SDG 13 - Climate Action

Electronic version(s)

https://doi.org/10.1109/ITSC55140.2022.9922436 (Access: Closed)