41 citations · 46 across the 5 of their papers we have counts for
6 papers
Cross-Dataset Experimental Study of Radar-Camera Fusion in Bird's-Eye View
Lukas Stäcker, Philipp Heidenreich, Jason Rambach +1
By exploiting complementary sensor information, radar and camera fusion systems have the potential to provide a highly robust and reliable perception system for advanced driver ass…
RC-BEVFusion: A Plug-In Module for Radar-Camera Bird's Eye View Feature Fusion
Lukas Stäcker, Shashank Mishra, Philipp Heidenreich +2
Radars and cameras belong to the most frequently used sensors for advanced driver assistance systems and automated driving research. However, there has been surprisingly little res…
Deployment of Deep Neural Networks for Object Detection on Edge AI Devices with Runtime Optimization
Lukas Stäcker, Juncong Fei, Philipp Heidenreich +4
Deep neural networks have proven increasingly important for automotive scene understanding with new algorithms offering constant improvements of the detection performance. However,…
MASS: Multi-Attentional Semantic Segmentation of LiDAR Data for Dense Top-View Understanding
Kunyu Peng, Juncong Fei, Kailun Yang +6
At the heart of all automated driving systems is the ability to sense the surroundings, e.g., through semantic segmentation of LiDAR sequences, which experienced a remarkable progr…
PillarSegNet: Pillar-based Semantic Grid Map Estimation using Sparse LiDAR Data
Juncong Fei, Kunyu Peng, Philipp Heidenreich +2
Semantic understanding of the surrounding environment is essential for automated vehicles. The recent publication of the SemanticKITTI dataset stimulates the research on semantic s…
SemanticVoxels: Sequential Fusion for 3D Pedestrian Detection using LiDAR Point Cloud and Semantic Segmentation
Juncong Fei, Wenbo Chen, Philipp Heidenreich +2
3D pedestrian detection is a challenging task in automated driving because pedestrians are relatively small, frequently occluded and easily confused with narrow vertical objects. L…