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<p>Location-based services are widely used by both humans and autonomous vehicles in their daily operations. Envisioning an ecosystem where humans and autonomous vehicles collaborate, we extend <i>Near</i>-a social platform that enables user interaction while preserving privacy-to support physical interactions between human users and autonomous vehicles without disclosing precise locations. To achieve this, <i>Near for All</i> introduces new services and employs a clustering-based approach to recommend suitable meeting locations. Furthermore, we conduct a rigorous experimental evaluation that highlights the privacy and performance properties of our framework and demonstrates that the proposed clustering methodology effectively supports this functionality.</p>

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Near for All: Clustering-Based Privacy-Preserving Meetings for Human–Autonomous Vehicle Ecosystems

  • 期刊论文

  • 发表日期：

    2026年5月21日

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  • Panteleimon Stanimeros,
  • Alexandros Karakasidis

摘要

Location-based services are widely used by both humans and autonomous vehicles in their daily operations. Envisioning an ecosystem where humans and autonomous vehicles collaborate, we extend Near-a social platform that enables user interaction while preserving privacy-to support physical interactions between human users and autonomous vehicles without disclosing precise locations. To achieve this, Near for All introduces new services and employs a clustering-based approach to recommend suitable meeting locations. Furthermore, we conduct a rigorous experimental evaluation that highlights the privacy and performance properties of our framework and demonstrates that the proposed clustering methodology effectively supports this functionality.