Abstract
<div class="section abstract"><div class="htmlview paragraph">This report delves into the field of multi-agent collaborative perception (MCP)
for autonomous driving: an area that remains unresolved. Current single-agent
perception systems suffer from limitations, such as occlusion and sparse sensor
observation at a far distance.</div><div class="htmlview paragraph"><b>Multi-agent Collaborative Perception for Autonomous Driving: Unsettled
Aspects</b> addresses three unsettled topics that demand immediate
attention: <ul class="list disc"><li class="list-item"><div class="htmlview paragraph">Establishing normative
communication protocols to facilitate seamless information sharing
among vehicles</div></li><li class="list-item"><div class="htmlview paragraph">Definiting collaboration
strategies, including identifying specific collaboration projects,
partners, and content, as well as establishing the integration
mechanism</div></li><li class="list-item"><div class="htmlview paragraph">Collecting sufficient data
for MCP model training, including capturing diverse modal data and
labeling various downstream tasks as accurately as
possible</div></li></ul></div><div class="htmlview paragraph"><a href="https://www.sae.org/publications/edge-research-reports" target="_blank">Click
here to access the full SAE EDGE</a><sup>TM</sup><a href="https://www.sae.org/publications/edge-research-reports" target="_blank">
Research Report portfolio.</a></div></div>
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