Model-based selective image downsampling in remote driving applications
Paper in proceeding, 2022

The large-scale deployment of remote driving technologies can be limited by the large (upstream) link capacity necessary to stream the video from the onboard cameras to the remote driver. This paper proposes an algorithm that reduces the size of the upstream video, by selectively downsampling portions of the frame recorded by the onboard cameras. Portions of the frame to downsample are selected, which surround those road users (RUs) that are not likely to collide with the remotely driven vehicle. In this paper, we propose to select such RUs by using reachability analysis techniques. In order to limit the computational overhead introduced by the proposed algorithms, we resort to reachability analysis based on ellipsoidal sets. The algorithm is demonstrated in a simulation environment in a T-junction by showing a significative reduction of the frames size.

Author

Maziar Ebrahimi Dehshalie

University of Modena and Reggio Emilia

Francesco Prignoli

University of Modena and Reggio Emilia

Paolo Falcone

University of Modena and Reggio Emilia

Chalmers, Electrical Engineering, Systems and control

Marko Bertogna

University of Modena and Reggio Emilia

IEEE Conference on Intelligent Transportation Systems, Proceedings, ITSC

Vol. 2022-October 3225-3230
9781665468800 (ISBN)

25th IEEE International Conference on Intelligent Transportation Systems, ITSC 2022
Macau, China,

Subject Categories

Telecommunications

Media Engineering

Computer Science

DOI

10.1109/ITSC55140.2022.9922590

More information

Latest update

10/25/2023