Dr-Ing. Florian Kuhnt

Senior Expert

Werdegang

Dr.-Ing. Florian Kuhnt ist Senior Expert für Automatisiertes Fahren und Situationsverstehen. 2020 promovierte er am Karlsruher Institut für Technologie (KIT) zum Thema „Holistic Temporal Situation Interpretation for Traffic Participant Prediction“. Er arbeitet als Projektleiter und wissenschaftlicher Koordinator in zahlreichen deutschen und internationalen Forschungsprojekten sowie Industriekooperationen, unter anderem:

• EVA Shuttle Busse
• RobustSENSE
• tech center a-drive
• Audi Autonomous Driving Cup

 

Publications

Conference Proceedings (10)

1. Integrating End-to-End Learned Steering into Probabilistic Autonomous DrivingInfoDetails

   Christian Hubschneider and André Bauer and Jens Doll and Michael Weber and Sebastian Klemm and Florian Kuhnt and J. Marius Zöllner, 2017

   In this paper, we propose an integrated approach of combining end-to-end learned trajectory proposals with a probabilistic sampling based planning algorithm for autonomous driving. A convolutional neural network is trained based on monocular image data to predict prospective steering angles. By using a local history of image data, we achieve an implicit spatial representation of parking cars or other obstacles commonly found in urban and residential areas. Through this local history, calculated using the vehicle's velocity data, the trajectory proposals are not only capable of lane following, but also comfortably circumnavigate obstacles. Training data is collected by recording video data and the vehicles CAN bus during human driving, thus imitating human behavior. The integration of end-to-end learning into a modularized architecture allows for additional safety constraints and complementary sensor information to be combined with intuitive steering. Our first results take a promising step towards general architectures for autonomous vehicles that combine deep learning with factorized probabilistic modeling.

2. Automated Vehicle System Architecture with Performance AssessmentDetails

   Tas, \"Omer Sahin and Hörmann, Stefan and Schäufele, Bernd and Kuhnt, Florian, 2017

3. Integrating End-to-End Learned Steering into Probabilistic Autonomous DrivingInfoDetails

   Christian Hubschneider and André Bauer and Jens Doll and Michael Weber and Sebastian Klemm and Florian Kuhnt and J. Marius Zöllner, 2017

   In this paper, we propose an integrated approach of combining end-to-end learned trajectory proposals with a probabilistic sampling based planning algorithm for autonomous driving. A convolutional neural network is trained based on monocular image data to predict prospective steering angles. By using a local history of image data, we achieve an implicit spatial representation of parking cars or other obstacles commonly found in urban and residential areas. Through this local history, calculated using the vehicle's velocity data, the trajectory proposals are not only capable of lane following, but also comfortably circumnavigate obstacles. Training data is collected by recording video data and the vehicles CAN bus during human driving, thus imitating human behavior. The integration of end-to-end learning into a modularized architecture allows for additional safety constraints and complementary sensor information to be combined with intuitive steering. Our first results take a promising step towards general architectures for autonomous vehicles that combine deep learning with factorized probabilistic modeling.

4. Understanding Interactions between Traffic Participants based on Learned BehaviorsDetails

   Kuhnt, Florian and Schulz, Jens and Schamm, Thomas and Zöllner, J Marius, 2016

5. Functional System Architectures towards Fully Automated DrivingDetails

   Tas, \"Omer Sahin and Kuhnt, Florian and Zöllner, J. Marius and Stiller, Christoph, 2016

6. Testing and Validating High Level Components for Automated Driving : Simulation Framework for Traffic ScenariosDetails

   Zofka, Marc Ren\'e and Klemm, Sebastian and Kuhnt, Florian and Schamm, Thomas and Zöllner, J. Marius, 2016

7. DSRC and Radar Object Matching for Cooperative Driver Assistance SystemsDetails

   Chen, Qi and Yuan, Ting and Gern, Axel and Roth, Tobias and Kuhnt, Florian and Breu, Jakob and Bogdanovic, Miro and Weiss, Christian, 2015

8. Towards a Unified Traffic Situation Estimation Model – Street-dependent Behaviour and Motion Models –Details

   Kuhnt, Florian and Kohlhaas, Ralf and Schamm, Thomas and Zöllner, J. Marius, 2015

9. Data-Driven Simulation and Parametrization of Traffic Scenarios for the Development of Advanced Driver Assistance SystemsDetails

   Zofka, Marc Ren\'e and Kuhnt, Florian and Kohlhaas, Ralf and Rist, Christoph and Schamm, Thomas and Zöllner, J. Marius, 2015

10. Particle filter map matching and trajectory prediction using a spline based intersection modelDetails

    Kuhnt, Florian and Kohlhaas, Ralf and Jordan, Rüdiger and Gu\ssner, Thomas and Gumpp, Thomas and Schamm, Thomas and Zollner, J. Marius, 2014

Theses (1)

1. Probabilistische Kollisionsprädiktion für Segway-TransporterDetails

   Kuhnt, Florian, 2011

Advised Thesis (4)

1. Development of Data Matching Algorithms for Vehicle Tracking by Car2Car Communication and Radar SystemsDetails

   Roth, Tobias, 2015

2. Erkennung von Interaktionen zwischen Verkehrsteilnehmern zur VerhaltensprädiktionDetails

   Schulz, Jens, 2015

3. Fusion von Laserscanner- und Tiefenbildkameradaten zur Umweltmodellierung für den Versuchsträger CoCarDetails

   Benno Evers, 2014

4. Situationsabhängige Prädiktion von Fahrzeugbewegungen in urbaner Umgebung für FahrerassistenzsystemeDetails

   Patrick Bartler, 2014

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Kontakt

Telefon: +49 721 9654-364
E-Mail: kuhnt@dont-want-spam.fzi.de

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