7 citations · 14 across the 5 of their papers we have counts for
8 papers
Lateral Ego-Vehicle Control without Supervision using Point Clouds
Florian Müller, Qadeer Khan, Daniel Cremers
Existing vision based supervised approaches to lateral vehicle control are capable of directly mapping RGB images to the appropriate steering commands. However, they are prone to s…
Self-Supervised Steering Angle Prediction for Vehicle Control Using Visual Odometry
Qadeer Khan, Patrick Wenzel, Daniel Cremers
Vision-based learning methods for self-driving cars have primarily used supervised approaches that require a large number of labels for training. However, those labels are usually…
Towards Generalizing Sensorimotor Control Across Weather Conditions
Qadeer Khan, Patrick Wenzel, Daniel Cremers +1
The ability of deep learning models to generalize well across different scenarios depends primarily on the quality and quantity of annotated data. Labeling large amounts of data fo…
GN-Net: The Gauss-Newton Loss for Multi-Weather Relocalization
Lukas von Stumberg, Patrick Wenzel, Qadeer Khan +1
Direct SLAM methods have shown exceptional performance on odometry tasks. However, they are susceptible to dynamic lighting and weather changes while also suffering from a bad init…
Towards Self-Supervised High Level Sensor Fusion
Qadeer Khan, Torsten Schön, Patrick Wenzel
In this paper, we present a framework to control a self-driving car by fusing raw information from RGB images and depth maps. A deep neural network architecture is used for mapping…
Semantic Label Reduction Techniques for Autonomous Driving
Qadeer Khan, Torsten Schön, Patrick Wenzel
Semantic segmentation maps can be used as input to models for maneuvering the controls of a car. However, not all labels may be necessary for making the control decision. One would…