2019 - IEEE Fellow For contributions to machine vision for automated driving
Christoph Stiller focuses on Artificial intelligence, Computer vision, Trajectory, Object detection and Advanced driver assistance systems. His Artificial intelligence research includes themes of Machine learning and Global Positioning System. His study in the fields of Stereopsis, Stereo cameras and Image segmentation under the domain of Computer vision overlaps with other disciplines such as Reliability.
His Trajectory research is multidisciplinary, relying on both Partially observable Markov decision process, Probabilistic logic, Solver and Motion planning. His Object detection research incorporates themes from Optical flow, Robotics, Inertial navigation system and Inertial measurement unit. He interconnects Bayesian network, Inference, Vehicle dynamics and Vehicle safety in the investigation of issues within Advanced driver assistance systems.
Christoph Stiller spends much of his time researching Artificial intelligence, Computer vision, Trajectory, Motion planning and Object detection. His Artificial intelligence study combines topics in areas such as Lidar, Machine learning and Pattern recognition. The Computer vision study combines topics in areas such as Kalman filter and Robustness.
His Trajectory research includes elements of Mathematical optimization and Vehicle dynamics. His work in Motion planning covers topics such as Real-time computing which are related to areas like Autonomous system. His study in Object detection is interdisciplinary in nature, drawing from both Grid, Detector and Benchmark.
His primary areas of study are Artificial intelligence, Computer vision, Trajectory, Grid and Object detection. The study incorporates disciplines such as Task and Pattern recognition in addition to Artificial intelligence. His research integrates issues of Lidar and Grid reference in his study of Computer vision.
The various areas that Christoph Stiller examines in his Trajectory study include Matching, Overtaking and Vehicle dynamics. His research investigates the connection with Object detection and areas like Detector which intersect with concerns in Sensor fusion, Pyramid, Robustness and Feature. Christoph Stiller combines subjects such as Collision, Obstacle, Collision avoidance and Motion planning with his study of Control theory.
His scientific interests lie mostly in Artificial intelligence, Computer vision, Motion planning, Object detection and Grid. He has researched Artificial intelligence in several fields, including Collision, Machine learning and GNSS applications. He has included themes like Salient, Convolutional neural network and Odometry in his Computer vision study.
His Motion planning research integrates issues from Automation, Systems engineering, Real-time computing, Intelligent driver model and Upstream. In his research, Data set is intimately related to Sensor fusion, which falls under the overarching field of Object detection. His biological study spans a wide range of topics, including Transformation, Segmentation, Image segmentation and Grid reference.
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Vision meets robotics: The KITTI dataset
A Geiger;P Lenz;C Stiller;R Urtasun.
The International Journal of Robotics Research (2013)
StereoScan: Dense 3d reconstruction in real-time
Andreas Geiger;Julius Ziegler;Christoph Stiller.
ieee intelligent vehicles symposium (2011)
Three Decades of Driver Assistance Systems: Review and Future Perspectives
Klaus Bengler;Klaus Dietmayer;Berthold Farber;Markus Maurer.
ieee intelligent transportation systems (2014)
Making Bertha Drive?An Autonomous Journey on a Historic Route
Julius Ziegler;Philipp Bender;Markus Schreiber;Henning Lategahn.
IEEE Intelligent Transportation Systems Magazine (2014)
Estimating motion in image sequences
C. Stiller;J. Konrad.
IEEE Signal Processing Magazine (1999)
3D Traffic Scene Understanding From Movable Platforms
Andreas Geiger;Martin Lauer;Christian Wojek;Christoph Stiller.
IEEE Transactions on Pattern Analysis and Machine Intelligence (2014)
Trajectory planning for Bertha — A local, continuous method
Julius Ziegler;Philipp Bender;Thao Dang;Christoph Stiller.
intelligent vehicles symposium (2014)
Segmentation of 3D lidar data in non-flat urban environments using a local convexity criterion
Frank Moosmann;Oliver Pink;Christoph Stiller.
ieee intelligent vehicles symposium (2009)
Team AnnieWAY's autonomous system for the 2007 DARPA Urban Challenge
Sören Kammel;Julius Ziegler;Benjamin Pitzer;Moritz Werling.
Journal of Field Robotics (2008)
Frank Moosmann;Christoph Stiller.
ieee intelligent vehicles symposium (2011)
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