Dietmayer, Klaus

Deep Multi-Modal Object Detection and Semantic Segmentation for Autonomous Driving: Datasets, Methods, and Challenges

Feng, D., Haase-Schutz, C., Rosenbaum, L., Hertlein, H., Glaser, C., Timm, F., Wiesbeck, W., & Dietmayer, K. (2021) · IEEE Transactions on Intelligent Transportation Systems

Autonomous Vehicle Technology and SafetyAdvanced Neural Network Applications

Cited by 1,425

Health diagnosis and remaining useful life prognostics of lithium-ion batteries using data-driven methods

Nuhic, A., Terzimehic, T., Soczka-Guth, T., Buchholz, M., & Dietmayer, K. (2013) · Journal of Power Sources

Battery Modeling and ManagementElectric and Hybrid Vehicle Technologies

Cited by 664

Towards Safe Autonomous Driving: Capture Uncertainty in the Deep Neural Network For Lidar 3D Vehicle Detection

Feng, D., Rosenbaum, L., & Dietmayer, K. (2018) · arXiv

Autonomous Vehicle Technology and SafetyAdvanced Neural Network Applications

Cited by 260

On-board state-of-health monitoring of lithium-ion batteries using linear parameter-varying models

Remmlinger, J., Buchholz, M., Soczka-Guth, T., & Dietmayer, K. (2013) · Journal of Power Sources

Battery Modeling and ManagementElectric and Hybrid Vehicle Technologies

Cited by 105

Deep Active Learning for Efficient Training of a LiDAR 3D Object Detector

Feng, D., Wei, X., Rosenbaum, L., Maki, A., & Dietmayer, K. (2019) · 2019 IEEE Intelligent Vehicles Symposium (IV)

Machine Learning and AlgorithmsAdvanced Neural Network Applications

Cited by 85

Robust Semantic Segmentation in Adverse Weather Conditions by means of Sensor Data Fusion

Pfeuffer, A., & Dietmayer, K. (2019) · 2019 22th International Conference on Information Fusion (FUSION)

Autonomous Vehicle Technology and SafetyAdvanced Neural Network Applications

Cited by 51