257 citations
Counting papers with at most 25 authors, so collaboration author lists do not stand in for the people here.
- Denso (United States)US3 papers
- Technical University of MunichDE3 papers
- Chalmers University of TechnologySE2 papers
- AIT Austrian Institute of Technology GmbHAT1 paper
- Fraunhofer Institute for Cognitive SystemsDE1 paper
- Humboldt-Universität zu BerlinDE1 paper
- IMT School for Advanced Studies LuccaIT1 paper
- LMU KlinikumDE1 paper
- Ludwig-Maximilians-Universität MünchenDE1 paper
- Universität UlmDE1 paper
- University of GothenburgSE1 paper
10 papers
Predictive Energy Management for Battery Electric Vehicles with Hybrid Models
Yu-Wen Huang, Christian Prehofer, William Lindskog +3
This paper addresses the problem of predicting the energy consumption for the drivers of Battery electric vehicles (BEVs). Several external factors (e.g., weather) are shown to hav…
A Federated Learning Benchmark on Tabular Data: Comparing Tree-Based Models and Neural Networks
William Lindskog, Christian Prehofer
Federated Learning (FL) has lately gained traction as it addresses how machine learning models train on distributed datasets. FL was designed for parametric models, namely Deep Neu…
Histogram-Based Federated XGBoost using Minimal Variance Sampling for Federated Tabular Data
William Lindskog, Christian Prehofer, Sarandeep Singh
Federated Learning (FL) has gained considerable traction, yet, for tabular data, FL has received less attention. Most FL research has focused on Neural Networks while Tree-Based Mo…
Federated Learning for Tabular Data using TabNet: A Vehicular Use-Case
William Lindskog, Christian Prehofer
In this paper, we show how Federated Learning (FL) can be applied to vehicular use-cases in which we seek to classify obstacles, irregularities and pavement types on roads. Our pro…
EC-IoU: Orienting Safety for Object Detectors via Ego-Centric Intersection-over-Union
Brian Hsuan-Cheng Liao, Chih-Hong Cheng, Hasan Esen +1
This paper presents Ego-Centric Intersection-over-Union (EC-IoU), addressing the limitation of the standard IoU measure in characterizing safety-related performance for object dete…
Specification-Guided Critical Scenario Identification for Automated Driving
Adam Molin, Edgar A. Aguilar, Dejan Ničković +3
To test automated driving systems, we present a case study for finding critical scenarios in driving environments guided by formal specifications. To that aim, we devise a framewor…
USC: Uncompromising Spatial Constraints for Safety-Oriented 3D Object Detectors in Autonomous Driving
Brian Hsuan-Cheng Liao, Chih-Hong Cheng, Hasan Esen +1
In this work, we consider the safety-oriented performance of 3D object detectors in autonomous driving contexts. Specifically, despite impressive results shown by the mass literatu…
Are Transformers More Robust? Towards Exact Robustness Verification for Transformers
Brian Hsuan-Cheng Liao, Chih-Hong Cheng, Hasan Esen +1
As an emerging type of Neural Networks (NNs), Transformers are used in many domains ranging from Natural Language Processing to Autonomous Driving. In this paper, we study the robu…
Safety-Aware Hardening of 3D Object Detection Neural Network Systems
Chih-Hong Cheng
We study how state-of-the-art neural networks for 3D object detection using a single-stage pipeline can be made safety aware. We start with the safety specification (reflecting the…
Survey on Misbehavior Detection in Cooperative Intelligent Transportation Systems
Rens W. van der Heijden, Stefan Dietzel, Tim Leinmüller +1
Cooperative Intelligent Transportation Systems (cITS) are a promising technology to enhance driving safety and efficiency. Vehicles communicate wirelessly with other vehicles and i…