7 citations · 14 across the 5 of their papers we have counts for
6 papers · 1 filter
Vision-Based Mobile Robotics Obstacle Avoidance With Deep Reinforcement Learning
Patrick Wenzel, Torsten Schön, Laura Leal-Taixé +1
Obstacle avoidance is a fundamental and challenging problem for autonomous navigation of mobile robots. In this paper, we consider the problem of obstacle avoidance in simple 3D en…
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…
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…