The documents distributed by this server have been provided by the contributing authors as a means to ensure timely dissemination of scholarly and technical work on a non-commercial basis. Copyright and all rights therein are maintained by the authors or by other copyright holders, not withstanding that they have offered their works here electronically. It is understood that all persons copying this information will adhere to the terms and constraints invoked by each author's copyright. These works may not be reposted without the explicit permission of the copyright holder.

Situational Collective Perception: Adaptive and Efficient Collective Perception in Future Vehicular Systems

Key:KMA+22
Author:Ahmad Khalil, Tobias Meuser, Yassin Alkhalili, Antonio Fernandez Anta, Lukas Staecker, Ralf Steinmetz
Date:June 2022
Kind:In proceedings - use for conference & workshop papers
Publisher:SCITEPRESS – Science and Technology Publications
Address:10.5220/0000159900003191
Editor:Jeroen Ploeg, Markus Helfert, Karsten Berns and Oleg Gusikhin
Pages:7
ISBN:978-989-758-573-9
Language:English
Keywords:Collective Perception, Vehicular Networks, Intelligent Transportation Systems, V2X, Federated Learning
Abstract:With the emerge of Vehicle-to-everything (V2X) communication, vehicles and other road users can perform Collective Perception (CP), whereby they exchange their individually detected environment to increase the collective awareness of the surrounding environment. To detect and classify the surrounding environmental objects, preprocessed sensor data (e.g., point-cloud data generated by a Lidar) in each vehicle is fed and classified by onboard Deep Neural Networks (DNNs). The main weakness of these DNNs is that they are commonly statically trained with context-agnostic data sets, limiting their adaptability to specific environments. This may eventually prevent the detection of objects, causing safety disasters. Inspired by the Federated Learning (FL) approach, in this work we tailor a collective perception architecture, introducing Situational Collective Perception (SCP) based on dynamically trained and situational DNNs, and enabling adaptive and efficient collective perception in future vehicular networks.
Full paper (pdf)

[Export this entry to BibTeX]

[back]