Automated negotiation mechanisms for autonomous vehicles at intersections

Jianglin Qiao, Dongmo Zhang, Dave de Jonge, Simeon Simoff, Carles Sierra

Research output: Chapter in Book / Conference PaperChapterpeer-review

Abstract

The advent of autonomous vehicles heralds a new era in traffic management, presenting unprecedented opportunities and complex challenges. This paper aims to develop automated negotiation mechanisms for autonomous vehicles that navigate intersections without traditional traffic signals. We introduce a goal-oriented negotiation protocol grounded in the utilization of curvilinear coordinates. This approach is complemented by introducing a decision-making algorithm and a counteroffer algorithm for vehicles, both of which play pivotal roles in the negotiation protocol. Moreover, we provide evidence of the protocol's convergence and elucidate the time complexity of the underlying algorithm. We validate our algorithms with experiments using the AIM4 simulator, showcasing significant improvements in travel times compared to conventional traffic light systems and first-come-first-served methods. The results underscore our protocol's potential to reduce average travel time, enhancing overall traffic flow efficiency.
Original languageEnglish
Title of host publicationPRICAI 2024: Trends in Artificial Intelligence, 21st Pacific Rim International Conference on Artificial Intelligence, PRICAI 2024, Kyoto, Japan, November 18-24, 2024, Proceedings, Part IV
EditorsRafik Hadfi, Takayuki Ito, Patricia Anthony, Alok Sharma, Quan Bai
Place of PublicationSingapore
PublisherSpringer
Pages271-283
Number of pages13
ISBN (Electronic)9789819601257
ISBN (Print)9789819601240
DOIs
Publication statusPublished - 2025

Publication series

NameLecture Notes in Computer Science
Volume15284
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Keywords

  • Automated Negotiation
  • Autonomous Vehicles
  • Curvilinear Coordinate System
  • Multi-agent System

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