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Related Experiment Video

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Evaluating the Effect of Roadside Parking on a Dual-Direction Urban Street
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Networked Roadside Perception Units for Autonomous Driving.

Manabu Tsukada1, Takaharu Oi1, Masahiro Kitazawa1

  • 1Graduate School of Information Science and Technology, The University of Tokyo, 7-3-1 Hongo, Bunkyo-ku, Tokyo 113-8656, Japan.

Sensors (Basel, Switzerland)
|September 22, 2020
PubMed
Summary

This study introduces a roadside perception unit (RSPU) and AutoC2X software for cooperative perception in autonomous driving. Networked RSPUs enhance safety and efficiency by enabling broader sensor data sharing for vulnerable road users.

Keywords:
V2Xautonomous vehiclecooperative ITScooperative automated vehicles (CAV)cooperative perceptionopen-source software

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Area of Science:

  • Intelligent Transportation Systems (ITS)
  • Autonomous Driving Technologies
  • Wireless Communication

Background:

  • Vehicle-to-Everything (V2X) communication is vital for enhancing autonomous driving safety and efficiency.
  • Cooperative perception, through sensor data sharing, is crucial for integrating vulnerable road users into intelligent transport systems.

Purpose of the Study:

  • To introduce a roadside perception unit (RSPU) integrating sensors and roadside units (RSUs) for infrastructure-based cooperative perception.
  • To propose AutoC2X software for enabling cooperative perception between RSPUs and vehicles.
  • To present the concept of networked RSPUs for expanded cooperative perception capabilities.

Main Methods:

  • Development and evaluation of an integrated RSPU system.
  • Design and implementation of AutoC2X software for cooperative perception.
  • Field testing, numerical analysis, and simulation experiments to assess system performance.
  • Proposal and analysis of a priority algorithm for message delivery.

Main Results:

  • The RSPU system demonstrated message delivery to autonomous vehicles within 100 ms, even in worst-case scenarios.
  • Networked RSPUs were shown to facilitate broader cooperative perception.
  • Simulation results indicated the priority algorithm achieves a wide perception range with high delivery ratios and low latency, particularly in heavy traffic.

Conclusions:

  • The developed RSPU system and AutoC2X software effectively enable infrastructure-based cooperative perception.
  • Networked RSPUs significantly enhance the scope and reliability of cooperative perception systems.
  • The proposed priority algorithm optimizes performance under demanding traffic conditions, improving autonomous driving safety.