Forschung

• Vehicle Prediction
• Trajectory prediction and Mapping

Veröffentlichungen

Marvin Klemp, Kevin Rösch, Royden Wagner, Jannik Quehl, Martin Lauer. LDFA: Latent Diffusion Face Anonymization for Self-driving Applications. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, Seiten 3198--3204, 2023. [ DOI ]

Florian Wirth, Jannik Quehl, Jeffrey Ota, Christoph Stiller. PointAtMe: Efficient 3D Point Cloud Labeling in Virtual Reality. In Proc. IEEE Intelligent Vehicles Symposium (IV), Paris, France, June 2019. [ DOI | .pdf ]

Jannik Quehl, Shengchao Yan, Sascha Wirges, Jan-Hendrik Pauls, Martin Lauer. Estimating Object Shape and Movement Using Local Occupancy Grid Maps. IFAC-PapersOnLine, 52(8):87 -- 92, 2019. 10th IFAC Symposium on Intelligent Autonomous Vehicles IAV 2019. [ DOI | http ]

Jannik Quehl, Haohao Hu, Sascha Wirges, Martin Lauer. An Approach to Vehicle Trajectory Prediction Using Automatically Generated Traffic Maps. In Proc. IEEE Int. Conf. Intelligent Vehicles, June 2018. [ http ]

Steffen Busch, Jannik Quehl, Claus Brenner. High Definition Mapping Using LiDAR Traced Trajectories. In Publikationen der Deutschen Gesellschaft für Photogrammetrie, Fernerkundung und Geoinformation e.V., Seiten 86--102, 2018. [ .pdf ]

Jannik Quehl, Haohao Hu, Ömer Sahin Tas, Eike Rehder, Martin Lauer. How Good is My Prediction? Finding a Similarity Measure for Trajectory Prediction Evaluation. In Proc. IEEE Intell. Trans. Syst. Conf., Yokohama, Japan, October 2017. [ http ]

Eike Rehder, Jannik Quehl, Christoph Stiller. Driving Like a Human: Imitation Learning for Path Planning using Convolutional Neural Networks. In International Conference on Robotics and Automation Workshops, 2017.