Studentische Arbeiten am Lehrstuhl für Medientechnik
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Offene Arbeiten
BAMAIDPFPIPSEMSHK
Titel
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How Well Perform Today's Autonomous Driving Models
How Well Perform Today's Autonomous Driving Models
Stichworte: Autonomous Driving
Beschreibung
This work can be done in German or English in a team of 2-4 members
At the current stage of autonomous driving, failures in complex situations are inevitable. A learning-based method to predict such failures could prevent dangerous situations or crashes. However, collecting real-life training data of crashes caused by autonomous vehicles is not feasible. A different solution is to use data from realistic simulations of a self-driving car, such as CARLA [1].
In this project, the objective is to setup available autonomous driving models such as [2-7] and use our existing data logging pipeline to evaluate these model's failure cases. The whole process should be further improved by extending our logging pipeline with an orchestration layer to manage all other services.
Tasks
Implementation and integration of orchestration layer into existing data logging pipeline
Setup of existing autonomous driving models
Collection of driving data with the implemented system
Evaluation of autonomous driving model failures and collection of failure data
References
[1] A. Dosovitskiy, „CARLA: An Open Urban Driving Simulator“, S. 16, 2017. [2] F. Codevilla, E. Santana, A. M. Lopez, und A. Gaidon, „Exploring the Limitations of Behavior Cloning for Autonomous Driving“, S. 10, 2019. [3] M. Toromanoff, E. Wirbel, und F. Moutarde, „End-to-End Model-Free Reinforcement Learning for Urban Driving using Implicit Affordances“, arXiv:1911.10868 [cs, stat], Nov. 2019. [4] NVIDIA Drive, https://www.nvidia.com/de-de/self-driving-cars/drive-platform/ [5] https://github.com/tech-rules/DAVE2-Keras [6] https://github.com/adityaguptai/Self-Driving-Car- [7] https://github.com/commaai/research
Kurzbeschreibung: Design the most intuitive interface for a general-purpose tele-assistant robot.
Beschreibung
By the advancement of robotics and communication networks such as 5G, telemedicine has become a key application for remote diagnosis and treatment. In this project, we want to perform a human study to investigate the human-robot interaction in order to design the most intuitive interface for a general-purpose tele-assistant robot.
Evaluation of Point Cloud Compression for Teleoperated Driving
Stichworte: Point Cloud Compression, Autonomous Driving
Beschreibung
This work can be done in German or English
LIDAR is one important sensor type for autonomous vehicles' perception. Human perception is mainly based on RGB data, in case of teleoperation captured by RGB cameras and transmitted to the remote operator through a communication network. In some situation a 3D representation of the scene might be helpful for the operator which could be achieved using LIDAR data. To avoid hight transmission rate LIDAR data need to be compressed. Existing methods for point cloud compression [1, 2, 3] don't have their focus on automotive LIDAR data.
The objective of this project is to setup existing point cloud compression implementations and compare them focusing on automotive point clouds.
Tasks
Setup available point cloud compression implementations * ROS PCL [1] * MPEG L-PCC [2] (available at MPEG Repo) * MPEG G-PCC [2] (available at MPEG Repo) * MPEG Anchor Implementation [3]
Evaluate implementations in terms of * Encoding time/complexity * Compression rate * Compression quality
A blind/referenceless subjective quality assessment for time-delayed teleoperation
Stichworte: quality metrics, teleoperation, time delay, haptics
Beschreibung
Using a teleoperation system with haptic feedback, the users can thus truly immerse themselves into a distant environment, i.e., modify it, and execute tasks without physically being present but with the feeling of being there. A typical teleoperation system with haptic feedback (referred to as a teleoperation system) comprises three main parts: the human operator OP)/master system, the teleoperator (TOP)/slave system, and the communication link/network in between [43]. During teleoperation, the slave and master devices exchange multimodal sensor information over the communication link. This work aims to develop a referenceless subjective quality index for time-delayed teleoperation system. This index is able to describe the subjective quality of experience based on a series of objective metrics.
Your work:
(1) build up a teleoperation system.
(2) design subjective test and collect enough subjective evaluation data as the ground truth.
(3) collect the objective metrics of teleoperation systems and design a training scheme based on the ground truth. Machine learning approaches may be needed.
(4) Evaluate the proposed quality index under different conditions.
Stichworte: Haptics, Vibrotactile, Signal Processing, Source Separation, Denoising
Beschreibung
Microscoping roughness sensations can be captured with the vibrations induced by sliding motion over a textured surface. However, the captured signals can be affected by various factors, such as other vibrations of external systems or sensor inaccuracies. An important goal is to be able to filter signals and remove noise components and imperfections. To do this, source separation is a promising approach. The idea is to separate different components in signals originating from different sources, respectively. After separation, it is possible to remove noise components and maintain only meaningful parts of the original signal. The student shall investigate different forms of distortions in vibrotactile signals and methods to separate and remove them.
Voraussetzungen
MATLAB, basics in signal processing, basics in audio processing
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