7 citations · 27 across the 15 of their papers we have counts for
18 papers
Generation of Musical Timbres using a Text-Guided Diffusion Model
Weixuan Yuan, Qadeer Khan, Vladimir Golkov
In recent years, text-to-audio systems have achieved remarkable success, enabling the generation of complete audio segments directly from text descriptions. While these systems als…
MA-DV2F: A Multi-Agent Navigation Framework using Dynamic Velocity Vector Field
Yining Ma, Qadeer Khan, Daniel Cremers
In this paper we propose MA-DV2F: Multi-Agent Dynamic Velocity Vector Field. It is a framework for simultaneously controlling a group of vehicles in challenging environments. DV2F…
Enhancing the Performance of Multi-Vehicle Navigation in Unstructured Environments using Hard Sample Mining
Yining Ma, Ang Li, Qadeer Khan +1
Contemporary research in autonomous driving has demonstrated tremendous potential in emulating the traits of human driving. However, they primarily cater to areas with well built r…
Enhancing Multimodal Compositional Reasoning of Visual Language Models with Generative Negative Mining
Ugur Sahin, Hang Li, Qadeer Khan +2
Contemporary large-scale visual language models (VLMs) exhibit strong representation capacities, making them ubiquitous for enhancing image and text understanding tasks. They are o…
Robust Autonomous Vehicle Pursuit without Expert Steering Labels
Jiaxin Pan, Changyao Zhou, Mariia Gladkova +2
In this work, we present a learning method for lateral and longitudinal motion control of an ego-vehicle for vehicle pursuit. The car being controlled does not have a pre-defined r…
Multi Agent Navigation in Unconstrained Environments using a Centralized Attention based Graphical Neural Network Controller
Yining Ma, Qadeer Khan, Daniel Cremers
In this work, we propose a learning based neural model that provides both the longitudinal and lateral control commands to simultaneously navigate multiple vehicles. The goal is to…
LiDAR View Synthesis for Robust Vehicle Navigation Without Expert Labels
Jonathan Schmidt, Qadeer Khan, Daniel Cremers
Deep learning models for self-driving cars require a diverse training dataset to manage critical driving scenarios on public roads safely. This includes having data from divergent…
Multi-Vehicle Trajectory Prediction at Intersections using State and Intention Information
Dekai Zhu, Qadeer Khan, Daniel Cremers
Traditional approaches to prediction of future trajectory of road agents rely on knowing information about their past trajectory. This work rather relies only on having knowledge o…
Biologically Inspired Neural Path Finding
Hang Li, Qadeer Khan, Volker Tresp +1
The human brain can be considered to be a graphical structure comprising of tens of billions of biological neurons connected by synapses. It has the remarkable ability to automatic…
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…
4Seasons: A Cross-Season Dataset for Multi-Weather SLAM in Autonomous Driving
Patrick Wenzel, Rui Wang, Nan Yang +5
We present a novel dataset covering seasonal and challenging perceptual conditions for autonomous driving. Among others, it enables research on visual odometry, global place recogn…
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…
Latent Space Reinforcement Learning for Steering Angle Prediction
Qadeer Khan, Torsten Schön, Patrick Wenzel
Model-free reinforcement learning has recently been shown to successfully learn navigation policies from raw sensor data. In this work, we address the problem of learning driving p…
Modular Vehicle Control for Transferring Semantic Information Between Weather Conditions Using GANs
Patrick Wenzel, Qadeer Khan, Daniel Cremers +1
Even though end-to-end supervised learning has shown promising results for sensorimotor control of self-driving cars, its performance is greatly affected by the weather conditions…