I am a PhD student in the research group of Prof. Dr.-Ing. Eckehard Steinbach at the Technical University of Munich (TUM). I studied Electrical and Computer Engineering at TUM for both Bachelor and Master. From 2016 to 2018, I was a software engineer at the Objective Software GmbH and Luxoft Inc and worked in cooperation with the BMW Group in the area of Automotive and Autonomous Driving. In January 2019, I joined the Chair of Media Technology at the Technical University of Munich as a research and teaching associate and PhD candidate.
My current research is focused on video processing, compression, and transmission of multi-camera systems for autonomous and teleoperated driving.
M.Sc. in Electrical and Computer Engineering, 2016
Technical University of Munich
B.Sc. in Electrical and Computer Engineering, 2014
Technical University of Munich
Selection of courses & visits
Teleoperated driving (TOD) is a possible solution to cope with failures of autonomous vehicles. In TOD, the human operator perceives the traffic situation via video streams of multiple cameras from a remote location. Adaptation mechanisms are needed in order to match the available transmission resources and provide the operator with the best possible situation awareness. This includes the adjustment of individual camera video streams according to the current traffic situation. The limited video encoding hardware in vehicles requires the combination of individual camera frames into a larger superframe video. While this enables the encoding of multiple camera views with a single encoder, it does not allow for rate/quality adaptation of the individual views. To this end, we propose a novel concept that uses preprocessing filters to enable individual rate/quality adaptations in the superframe video. The proposed preprocessing filters allow for the usage of existing multidimensional adaptation models in the same way as for individual video streams using multiple encoders. Our experiments confirm that the proposed concept is able to control the spatial, temporal and quality resolution of individual segments in the superframe video. Additionally, we demonstrate the usability of the proposed method by applying it in a multi-view teledriving scenario. We compare our approach to individually encoded video streams and a multiplexing solution without preprocessing. The results show that the proposed approach produces bitrates for the individual video streams which are comparable to the bitrates achieved with separate encoders. While achieving a similar bitrate for the most important views, our approach requires a total bitrate that is 40% smaller compared to the multiplexing approach without preprocessing.
Teledriving is a possible fallback mode to cope with failures of fully autonomous vehicles. One important requirement for teleoperated vehicles is a reliable low delay data transmission solution, which adapts to the current network conditions to provide the operator with the best possible situation awareness. Currently, there is no easily accessible solution for the evaluation of such systems and algorithms in a fully controllable environment available. To this end we propose an open source framework for teleoperated driving research using low-cost off-the-shelf components. The proposed system is an extension of the open source simulator CARLA, which is responsible for rendering the driving environment and providing reproducible scenario evaluation. As a proof of concept, we evaluated our teledriving solution against CARLA in remote and local driving scenarios. The proposed teledriving system leads to almost identical performance measurements for local and remote driving. In contrast, remote driving using CARLA’s client server communication results in drastically reduced operator performance. Further, the framework provides an interface for the adaptation of the temporal resolution and target bitrate of the compressed video streams. The proposed framework reduces the required setup effort for teleoperated driving research in academia and industry.
Posters & Publications