1.4k citations · 1.5k across the 11 of their papers we have counts for
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Can We Trust You? On Calibration of a Probabilistic Object Detector for Autonomous Driving
Di Feng, Lars Rosenbaum, Claudius Glaeser +2
Reliable uncertainty estimation is crucial for perception systems in safe autonomous driving. Recently, many methods have been proposed to model uncertainties in deep learning base…
Learning Multimodal Fixed-Point Weights using Gradient Descent
Lukas Enderich, Fabian Timm, Lars Rosenbaum +1
Due to their high computational complexity, deep neural networks are still limited to powerful processing units. To promote a reduced model complexity by dint of low-bit fixed-poin…
Deep Multi-modal Object Detection and Semantic Segmentation for Autonomous Driving: Datasets, Methods, and Challenges
Di Feng, Christian Haase-Schütz, Lars Rosenbaum +5
Recent advancements in perception for autonomous driving are driven by deep learning. In order to achieve robust and accurate scene understanding, autonomous vehicles are usually e…
Deep Active Learning for Efficient Training of a LiDAR 3D Object Detector
Di Feng, Xiao Wei, Lars Rosenbaum +2
Training a deep object detector for autonomous driving requires a huge amount of labeled data. While recording data via on-board sensors such as camera or LiDAR is relatively easy,…