Felix Rempe

Rudolf Diesel Industry Fellowship


BMW Group

Autonomous Driving

Prof. Klaus Bogenberger

Focus Group
Artificial Intelligence in Traffic Engineering and Control

Short CV

Felix Rempe studied Mechatronics at DHBW Stuttgart and Computational Science and Engineering at TU Munich, focusing on high-performance computing and machine learning. He was member of the Elitenetzwerk Bayern and graduated 2014 as M.Sc. with Honours. Right after, he started his PhD program with BMW Group and Bundeswehr University Munich. During his doctoral studies, he explored the usage of vehicle sensor data for accurate traffic state estimation and prediction. His PhD thesis in 2018 was awarded with “summa cum laude”. Afterwards, he worked on several research and development projects at BMW Group involving data-driven development and autonomous driving: the design of future mobility systems with autonomous taxis, the utilization of vehicle sensor data for the enrichment of high-definition digital maps as well as data-based design & virtual validation of highly automated driving systems. During this time, he published several papers on machine learning in traffic engineering applications. He is member of „AA 3.10 Forschungsgesellschaft für Straßen- und Verkehrswesen (FGSV)“ for traffic flow theory.

Selected Awards

  • 2018: PhD thesis awarded with summa cum laude
  • 2014: M.Sc. with Honours award for completing the Honour’s track at TUM
  • 2012: BayLat, Grant for study exchange program Latin America

Research Interest

Mobility Systems, Machine Learning, Data Exploration and Analyses Methods, Traffic Flow Theory

Selected Publications

  • Rempe, Felix; Franeck, Philipp; Bogenberger, Klaus; On the estimation of traffic speeds with Deep Convolutional Neural Networks given probe data, Transportation research part C: emerging technologies,134, 103448, 2022, Pergamon
  • Samara, Adam; Rempe, Felix; Göttlich, Simone; A novel approach for vehicle travel time distribution: copula-based dependent discrete convolution, Transportation Letters, 14, 7, 740-751, 2022, Taylor & Francis
  • Zhang, Yunfei; Loder, Allister; Rempe, Felix; Bogenberger, Klaus; Temporal Aggregated Analysis of GPS Trajectory Data Using Two-Fluid Model, Transportation Research Record,2022, SAGE Publications Sage CA: Los Angeles, CA
  • Rempe, Felix; Loder, Allister; Bogenberger, Klaus; Estimating motorway traffic states with data fusion and physics-informed deep learning, 2021 IEEE International Intelligent Transportation Systems Conference (ITSC), 2208-2214, 2021
  • Samara, Adam; Rempe, Felix; Göttlich, Simone; Vehicle Routing Optimized for Autonomous Driving,2021 IEEE Intelligent Vehicles Symposium (IV), 1162-1167, 2021
  • Kessler, Lisa; Rempe, Felix; Bogenberger, Klaus; Multi-Sensor Data Fusion for Accurate Traffic Speed and Travel Time Reconstruction, Frontiers in Future Transportation, 2, 27, 2021
  • Samara, Adam; Rempe, Felix; Göttlich, Simone; Modelling arterial travel time distribution using copulas, 2020 IEEE 23rd International Conference on Intelligent Transportation Systems (ITSC), 1-6, 2020
  • Rempe, Felix; Franeck, Philipp; Fastenrath, Ulrich; Bogenberger, Klaus; A phase-based smoothing method for accurate traffic speed estimation with floating car data, Transportation Research Part C: Emerging Technologies, 85, 644-663, 2017
  • Rempe, Felix; Kessler, Lisa; Bogenberger, Klaus; Fusing probe speed and flow data for robust short-term congestion front forecasts, 2017 5th IEEE International Conference on Models and Technologies for Intelligent Transportation Systems (MT-ITS), 31-36, 2017
  • Rempe, Felix; Huber, Gerhard; Bogenberger, Klaus; Spatio-temporal congestion patterns in urban traffic networks, Transportation Research Procedia, 15, 513-524, 2016
  • Rempe, Felix; Huber, Gerhard; Bogenberger, Klaus; Travel time prediction in partitioned road networks based on floating car data, 2016 IEEE 19th International Conference on Intelligent Transportation Systems (ITSC), 1982-1987, 2016