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A Unified Multiple-Target Positioning Framework for Intelligent Connected Vehicles.

Zhongyang Xiao1, Diange Yang2, Fuxi Wen3,4

  • 1State Key Laboratory of Automotive Safety and Energy, School of Vehicle and Mobility, Tsinghua University, Beijing 100084, China. xiaozy15@mails.tsinghua.edu.cn.

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Accurate positioning for intelligent transport systems is improved by fusing on-board sensors and vehicle-to-everything (V2X) communication data. This unified framework enhances target sensing integrity and precision for automated vehicles in diverse traffic scenarios.

Keywords:
high-definition mapintelligent and connected vehiclesintelligent transport systemtarget positioningvehicle-to-everythingvehicular localization

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Area of Science:

  • Intelligent Transportation Systems
  • Robotics and Autonomous Systems
  • Sensor Fusion and Data Integration

Background:

  • Individual vehicle self-positioning and on-board perception systems have limitations in precision and integrity.
  • Emerging vehicle-to-everything (V2X) communication offers new data sources like dynamic high-definition (HD) maps.
  • Accurate multi-target positioning is critical for the advancement of future intelligent transport systems.

Purpose of the Study:

  • To propose a unified theoretical framework for multi-target positioning.
  • To fuse heterogeneous information from on-board sensors and V2X technology.
  • To enhance the precision and integrity of positioning for vehicles and other road elements.

Main Methods:

  • Development of a unified theoretical framework for data fusion.
  • Integration of data from low-cost global navigation satellite systems (GNSS) with inertial navigation systems (INS).
  • Utilization of on-board sensors and dynamically updated high-definition (HD) maps via V2X communication.

Main Results:

  • The proposed framework achieves multi-target positioning precision suitable for high-level automated vehicles.
  • Significant improvements in target sensing integrity through sensor information sharing and map data exploitation.
  • Enhanced adaptability to various traffic scenarios compared to existing techniques.

Conclusions:

  • The fusion of multi-source heterogeneous information provides a robust solution for multi-target positioning.
  • The framework meets the stringent requirements of advanced automated driving systems.
  • This approach significantly advances the capabilities of intelligent transport systems.