What is he best known for?
The fields of study he is best known for:
- Artificial intelligence
- Computer vision
- Machine learning
His main research concerns Artificial intelligence, Computer vision, Object detection, Video tracking and Sensor fusion.
In his research on the topic of Artificial intelligence, Probabilistic logic and Mixture model is strongly related with Machine learning.
His Computer vision research incorporates themes from Kalman filter and Laser scanning.
The Object detection study combines topics in areas such as Segmentation, Data mining, Feature, Lidar and Robustness.
Klaus Dietmayer has included themes like Covariance function and Vehicle tracking system in his Video tracking study.
His Sensor fusion research is multidisciplinary, incorporating elements of Signal and High-definition video.
His most cited work include:
- Three Decades of Driver Assistance Systems: Review and Future Perspectives (427 citations)
- The Labeled Multi-Bernoulli Filter (365 citations)
- Health diagnosis and remaining useful life prognostics of lithium-ion batteries using data-driven methods (252 citations)
What are the main themes of his work throughout his whole career to date?
Artificial intelligence, Computer vision, Object detection, Sensor fusion and Object are his primary areas of study.
His work carried out in the field of Artificial intelligence brings together such families of science as Radar and Pattern recognition.
The study incorporates disciplines such as Lidar, Advanced driver assistance systems, Radar engineering details and Laser scanning in addition to Computer vision.
In his work, Machine learning is strongly intertwined with Probabilistic logic, which is a subfield of Object detection.
Klaus Dietmayer focuses mostly in the field of Sensor fusion, narrowing it down to topics relating to Real-time computing and, in certain cases, Simulation.
As part of the same scientific family, Klaus Dietmayer usually focuses on Tracking, concentrating on Filter and intersecting with Algorithm.
He most often published in these fields:
- Artificial intelligence (58.18%)
- Computer vision (43.90%)
- Object detection (18.18%)
What were the highlights of his more recent work (between 2017-2021)?
- Artificial intelligence (58.18%)
- Computer vision (43.90%)
- Object detection (18.18%)
In recent papers he was focusing on the following fields of study:
Klaus Dietmayer mainly investigates Artificial intelligence, Computer vision, Object detection, Lidar and Object.
His Artificial intelligence course of study focuses on Machine learning and Kalman filter.
Advanced driver assistance systems is closely connected to Radar in his research, which is encompassed under the umbrella topic of Computer vision.
The various areas that Klaus Dietmayer examines in his Object detection study include Point cloud, Data mining, Probabilistic logic, Convolutional neural network and Sensor fusion.
Klaus Dietmayer combines subjects such as Artificial neural network, Odometry and RGB color model with his study of Lidar.
His studies deal with areas such as Occupancy grid mapping, Representation, Digital mapping and Task as well as Object.
Between 2017 and 2021, his most popular works were:
- Deep Multi-Modal Object Detection and Semantic Segmentation for Autonomous Driving: Datasets, Methods, and Challenges (148 citations)
- Towards Safe Autonomous Driving: Capture Uncertainty in the Deep Neural Network For Lidar 3D Vehicle Detection (108 citations)
- Dynamic Occupancy Grid Prediction for Urban Autonomous Driving: A Deep Learning Approach with Fully Automatic Labeling (71 citations)
In his most recent research, the most cited papers focused on:
- Artificial intelligence
- Computer vision
- Electrical engineering
The scientist’s investigation covers issues in Artificial intelligence, Object detection, Computer vision, Lidar and Sensor fusion.
As a part of the same scientific study, he usually deals with the Artificial intelligence, concentrating on Pattern recognition and frequently concerns with Recurrent neural network.
His Object detection study combines topics from a wide range of disciplines, such as Artificial neural network, Detector and Exploit.
In Computer vision, he works on issues like Radar, which are connected to Entropy.
His Lidar research integrates issues from Sampling, Data mining and Backscatter.
The concepts of his Occupancy grid mapping study are interwoven with issues in Video tracking, Representation, Convolutional neural network and Vehicle dynamics.
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