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

Ahmad Khalil, Tobias Meuser, Yassin Alkhalili, Antonio Anta, Lukas Staecker, Ralf Steinmetz

2022

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.

Paper Citation

in Harvard Style

Khalil A., Meuser T., Alkhalili Y., Anta A., Staecker L. and Steinmetz R. (2022). Situational Collective Perception: Adaptive and Efficient Collective Perception in Future Vehicular Systems. In Proceedings of the 8th International Conference on Vehicle Technology and Intelligent Transport Systems - Volume 1: VEHITS, ISBN 978-989-758-573-9, pages 346-352. DOI: 10.5220/0011065000003191

in Bibtex Style

@conference{vehits22,
author={Ahmad Khalil and Tobias Meuser and Yassin Alkhalili and Antonio Anta and Lukas Staecker and Ralf Steinmetz},
title={Situational Collective Perception: Adaptive and Efficient Collective Perception in Future Vehicular Systems},
booktitle={Proceedings of the 8th International Conference on Vehicle Technology and Intelligent Transport Systems - Volume 1: VEHITS,},
year={2022},
pages={346-352},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0011065000003191},
isbn={978-989-758-573-9},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 8th International Conference on Vehicle Technology and Intelligent Transport Systems - Volume 1: VEHITS,
TI - Situational Collective Perception: Adaptive and Efficient Collective Perception in Future Vehicular Systems
SN - 978-989-758-573-9
AU - Khalil A.
AU - Meuser T.
AU - Alkhalili Y.
AU - Anta A.
AU - Staecker L.
AU - Steinmetz R.
PY - 2022
SP - 346
EP - 352
DO - 10.5220/0011065000003191