AICP: Augmented Informative Cooperative Perception
Pengyuan Zhou, Pranvera Kortoci, Yui-Pan Yau, Tristan Braud, Xiujun, Wang, Benjamin Finley, Lik-Hang Lee, Sasu Tarkoma, Jussi Kangasharju, Pan Hui

TL;DR
AICP is a fast-filtering cooperative perception system for connected vehicles that optimizes shared data relevance, reducing network load and enhancing driver situational awareness through efficient data selection and presentation.
Contribution
It introduces a novel fast-filtering mechanism with a custom data structure and sorting algorithm to select the most informative data for vehicle perception systems.
Findings
Adds only 12.6 ms latency in prototype implementation
Effectively filters less relevant data packets
Reduces channel busy time in network performance tests
Abstract
Connected vehicles, whether equipped with advanced driver-assistance systems or fully autonomous, require human driver supervision and are currently constrained to visual information in their line-of-sight. A cooperative perception system among vehicles increases their situational awareness by extending their perception range. Existing solutions focus on improving perspective transformation and fast information collection. However, such solutions fail to filter out large amounts of less relevant data and thus impose significant network and computation load. Moreover, presenting all this less relevant data can overwhelm the driver and thus actually hinder them. To address such issues, we present Augmented Informative Cooperative Perception (AICP), the first fast-filtering system which optimizes the informativeness of shared data at vehicles to improve the fused presentation. To this…
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Taxonomy
TopicsIoT and Edge/Fog Computing · Vehicular Ad Hoc Networks (VANETs) · Advanced Wireless Communication Technologies
