Micro AI-For-Mobility – Research Platform for AI-Based Control Methods

The AI for Micro Mobility (μAFM) is the new research platform for investigation of novel active and passive electronic bicycle assistance control methods within the Department of Vehicle System Dynamics at the German Aerospace Center (DLR). Autonomous driving and artificial intelligence offer completely new possibilities for control systems. However, no studies have yet investigated the holistic approach, from the perception sensors to the control algorithms, with the aim of protecting vulnerable road users, particularly in the context of bicycles. The objective of our research is to contribute to this field of study. To this end, we equipped a production electric serial hybrid power train bicycle with a set of state-of-the-art sensors and computational hardware. The hardware and software integration of the various sensors as well as the network architecture is presented. Paving the path for further research in this field. Concluding, two real-word experiments are conducted to validate and investigate the performance of the setup for further control algorithms studies.

Authors

Institutions

Publication Details

Published
2024-10-07
DOI
https://doi.org/10.1109/vtc2024-fall63153.2024.10757935
Primary Topic
Transportation and Mobility Innovations
Type
article
Field-Weighted Citation Impact
0.00
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Micro AI-For-Mobility – Research Platform for AI-Based Control Methods

Jonathan Brembeck, Daniel Baumgartner, Daniel Pölzleitner, Daniel Diegel
Transportation and Mobility Innovations
article

Micro AI-For-Mobility – Research Platform for AI-Based Control Methods

Jonathan Brembeck, Daniel Baumgartner, Daniel Pölzleitner, Daniel Diegel
article en

Abstract

The AI for Micro Mobility (μAFM) is the new research platform for investigation of novel active and passive electronic bicycle assistance control methods within the Department of Vehicle System Dynamics at the German Aerospace Center (DLR). Autonomous driving and artificial intelligence offer completely new possibilities for control systems. However, no studies have yet investigated the holistic approach, from the perception sensors to the control algorithms, with the aim of protecting vulnerable road users, particularly in the context of bicycles. The objective of our research is to contribute to this field of study. To this end, we equipped a production electric serial hybrid power train bicycle with a set of state-of-the-art sensors and computational hardware. The hardware and software integration of the various sensors as well as the network architecture is presented. Paving the path for further research in this field. Concluding, two real-word experiments are conducted to validate and investigate the performance of the setup for further control algorithms studies.

Deutsches Zentrum für Luft- und Raumfahrt e. V. (DLR) (DE)
Openalex Percentile: Top 72%
Transportation and Mobility Innovations
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.

Micro AI-For-Mobility – Research Platform for AI-Based Control Methods — Jonathan Brembeck, Daniel Baumgartner, et al. · (2024) | TGRS Research Map | TGRS