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Context-Aware Collaborative Perception: Estimating Relevance through Knowledge Representation

Authors:
Romain Tessier
Oyunchimeg Shagdar
Bruno Monsuez

Keywords: Collective Perception; V2X; Ontology; Context-aware; Semantic-Communication.

Abstract:
Automated driving systems have made significant strides in real-time perception and response to complex driving scenarios. However, these systems struggle when road users are beyond sensor range or obstructed by obstacles, limiting their ability to make informed decisions. Cooperative Intelligent Transport Systems (C-ITS) offer a promising solution by enabling vehicles to share real-time data with nearby vehicles and infrastructure. While this enhances collaborative perception, a major challenge is managing the high volume of sensor data exchanged, which are not always useful for the receiver. This can lead to data congestion, latency, and misinterpretation. Our solution addresses these issues by using an ontology to represent a vehicle’s observable scene and assess information relevance. Additionally, the ontology serves as a knowledge base, facilitating semantic communication that allows more effective interpretation of received messages. This approach aims to improve both the safety and efficiency of cooperative systems in automated driving environments.

Pages: 1 to 5

Copyright: Copyright (c) IARIA, 2025

Publication date: March 9, 2025

Published in: conference

ISSN: 2327-2058

ISBN: 978-1-68558-233-3

Location: Lisbon, Portugal

Dates: from March 9, 2025 to March 13, 2025