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Related Experiment Video

Updated: Oct 25, 2025

Evaluation of an Exclusive Spur Dike U-Turn Design with Radar-Collected Data and Simulation
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Cooperative Intersection with Misperception in Partially Connected and Automated Traffic.

Chenghao Li1, Zhiqun Hu1,2, Zhaoming Lu1

  • 1Beijing Laboratory of Advanced Information Networks, Beijing University of Posts and Telecommunications, Beijing 100876, China.

Sensors (Basel, Switzerland)
|August 10, 2021
PubMed
Summary
This summary is machine-generated.

Connected and automated vehicles (CAVs) can improve traffic safety and efficiency. A new data fusion model enhances perception, reducing accidents and congestion while improving energy use in mixed traffic environments.

Keywords:
connected and automated vehiclesperceptual errorsignalized intersection

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Area of Science:

  • Traffic Engineering
  • Intelligent Transportation Systems
  • Vehicle Dynamics

Background:

  • Connected and automated vehicles (CAVs) promise enhanced traffic efficiency and safety through intersection cooperation.
  • Sensor perceptual errors and the coexistence of CAVs with conventional vehicles present challenges to traffic flow.
  • Dynamic perceptual errors and time headway significantly impact traffic performance in cooperative intersection scenarios.

Purpose of the Study:

  • To develop a simulation model for analyzing the impact of perceptual errors and time headway on traffic performance at cooperative intersections.
  • To propose a data fusion scheme for improving perception accuracy and safety in mixed CAV and conventional vehicle traffic.
  • To evaluate the trade-offs between traffic efficiency, safety, energy consumption, and congestion under varying CAV penetration rates.

Main Methods:

  • Extended the Intelligent Driver Model (IDM) with the Ornstein-Uhlenbeck process to dynamically model perceptual errors.
  • Implemented a longitudinal control model for vehicle dynamics, platooning, and reduced deceleration.
  • Proposed a data fusion scheme using Differential Global Positioning System (DGPS) data and Kalman filtering to interpolate sensor data for accurate perception.

Main Results:

  • Perceptual error and time headway significantly affect crash rates, energy consumption, and congestion at cooperative intersections.
  • Larger vehicle intervals reduce accidents but excessive time headway can decrease traffic efficiency and energy conversion.
  • The proposed data fusion scheme outperforms independent on-board perception, improving traffic flow, reducing congestion, and enhancing energy efficiency across different CAV penetration rates.

Conclusions:

  • A dynamic simulation model effectively captures the influence of perceptual errors and time headway on cooperative intersection traffic.
  • Data fusion is crucial for mitigating perceptual errors, enhancing safety, and optimizing traffic flow in mixed CAV environments.
  • The study highlights a critical trade-off between safety and efficiency, with the proposed data fusion offering a balanced solution for future transportation systems.