What is he best known for?
The fields of study he is best known for:
- Artificial intelligence
- Computer vision
- Algorithm
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.
His most cited work include:
- Vision meets robotics: The KITTI dataset (3500 citations)
- StereoScan: Dense 3d reconstruction in real-time (766 citations)
- Making Bertha Drive?An Autonomous Journey on a Historic Route (488 citations)
What are the main themes of his work throughout his whole career to date?
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.
He most often published in these fields:
- Artificial intelligence (53.14%)
- Computer vision (40.96%)
- Trajectory (11.44%)
What were the highlights of his more recent work (between 2018-2021)?
- Artificial intelligence (53.14%)
- Computer vision (40.96%)
- Trajectory (11.44%)
In recent papers he was focusing on the following fields of study:
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.
Between 2018 and 2021, his most popular works were:
- INTERACTION Dataset: An INTERnational, Adversarial and Cooperative moTION Dataset in Interactive Driving Scenarios with Semantic Maps. (66 citations)
- Realtime 3D Object Detection for Automated Driving Using Stereo Vision and Semantic Information (21 citations)
- Accurate and Efficient Self-Localization on Roads using Basic Geometric Primitives (19 citations)
In his most recent research, the most cited papers focused on:
- Artificial intelligence
- Computer vision
- Algorithm
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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