Multi-agent Collaborative Perception for Autonomous Driving: Unsettled Aspects

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.Multi-agent Collaborative Perception for Autonomous Driving: Unsettled Aspects addresses three unsettled topics that demand immediate attention: (1) Establishing normative communication protocols to facilitate seamless information sharing among vehicles; (2) Defining collaboration strategies, including identifying specific collaboration projects, partners, and content, as well as establishing the integration mechanism; (3) Collecting sufficient data for MCP model training, including capturing diverse modal data and labeling various downstream tasks as accurately as possible.

  • Record URL:
  • Supplemental Notes:
    • Abstract reprinted with permission of SAE International.
  • Authors:
    • Chen, Guang
  • Publication Date: 2023-8-15


  • English

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Filing Info

  • Accession Number: 01890374
  • Record Type: Publication
  • Source Agency: SAE International
  • Report/Paper Numbers: EPR2023017
  • Files: TRIS, SAE
  • Created Date: Aug 22 2023 3:30PM