Dipl.-Inform. Jens Doll
Wissenschaftlicher Mitarbeiter
Werdegang
Jens Doll studierte Informatik am Karlsruher Institut für Technologie (KIT). Seine Studienschwerpunkte lagen dabei in Systemarchitektur, Compilerbau und Algorithmik.
Seine Diplomarbeit mit dem Titel "Adaptive Pfadplanung basierend auf räumlicher Partitionierung" verfasste er in der Abteilung Technisch-Kognitive Systeme am Forschungszentrum Informatik (FZI).
Seit Mai 2016 arbeitet er als wissenschaftlicher Mitarbeiter in der Abteilung Technisch-Kognitive Systeme (TKS) des FZI.
Publikationen
Konferenzbeitrag (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.
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Kontakt
Telefon: +49 721 9654-353
E-Mail: doll@fzi.de- Integrating End-to-End Learned Steering into Probabilistic Autonomous DrivingInfoDetails