activity
20182023
most citedDeep Multi-modal Object Detection and Semantic Segmentation for Autonomous Driving: Datasets, Methods, and Challenges

1.4k citations · 1.8k across the 15 of their papers we have counts for

collaborators
Showing 2018Show all

2 papers · 1 filter

cs.RO2018

Leveraging Heteroscedastic Aleatoric Uncertainties for Robust Real-Time LiDAR 3D Object Detection

Di Feng, Lars Rosenbaum, Fabian Timm +1

We present a robust real-time LiDAR 3D object detector that leverages heteroscedastic aleatoric uncertainties to significantly improve its detection performance. A multi-loss funct…

cs.RO2018★ 24 cited

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

Di Feng, Lars Rosenbaum, Klaus Dietmayer

To assure that an autonomous car is driving safely on public roads, its object detection module should not only work correctly, but show its prediction confidence as well. Previous…