Search Results for author:
Found 7 papers, 1 papers with code
Simultaneous Clutter Detection and Semantic Segmentation of Moving Objects for Automotive Radar Data
The unique properties of radar sensors, such as their robustness to adverse weather conditions, make them an important part of the environment perception system of autonomous vehicles.
LS-VOS: Identifying Outliers in 3D Object Detections Using Latent Space Virtual Outlier Synthesis
LiDAR-based 3D object detectors have achieved unprecedented speed and accuracy in autonomous driving applications.
Towards Robust 3D Object Detection In Rainy Conditions
In this way, the detected objects are less affected by the adverse weather in the scene, resulting in a more accurate perception of the environment.
Energy-based Detection of Adverse Weather Effects in LiDAR Data
Autonomous vehicles rely on LiDAR sensors to perceive the environment.
Detection of Condensed Vehicle Gas Exhaust in LiDAR Point Clouds
We address this problem by presenting a two-step approach for the detection of condensed vehicle gas exhaust.
Robust 3D Object Detection in Cold Weather Conditions
Second, we introduce a point cloud augmentation process that can be used to add gas exhaust to datasets recorded in good weather conditions.
R4Dyn: Exploring Radar for Self-Supervised Monocular Depth Estimation of Dynamic Scenes
While self-supervised monocular depth estimation in driving scenarios has achieved comparable performance to supervised approaches, violations of the static world assumption can still lead to erroneous depth predictions of traffic participants, posing a potential safety issue.
