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Research on Lane a Compensation Method Based on Multi-Sensor Fusion.

Yushan Li1, Wenbo Zhang2, Xuewu Ji3

  • 1College of Transportation, Shandong University of Science and Technology, Qingdao 266590, China. skd992034@sdust.edu.cn.

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Summary
This summary is machine-generated.

This study introduces a novel lane compensation method for autonomous driving, fusing GPS, IMU, and vision sensors. The system accurately predicts lane curvature during vision sensor failures, ensuring robust control.

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Area of Science:

  • Robotics
  • Computer Vision
  • Sensor Fusion

Background:

  • Vision sensors in autonomous driving are susceptible to errors from shadows, lighting changes, and line breaks.
  • These vision sensor failures critically impact unmanned driving control systems, necessitating real-time lane prediction and compensation.

Purpose of the Study:

  • To develop and validate a robust lane compensation method for autonomous driving systems.
  • To address the challenges posed by vision sensor unreliability through multi-sensor fusion.

Main Methods:

  • Implemented a multi-sensor fusion approach combining Global Positioning System (GPS), Inertial Measurement Unit (IMU), and vision sensors.
  • Utilized a Kalman filter for estimating vehicle velocity and yaw angle from GPS and IMU data.
  • Developed a vehicle kinematics model and employed a cubic polynomial function to represent and predict lane curvature.

Main Results:

  • The proposed method effectively compensates for vision sensor failures by predicting real-time lane information.
  • Simulation and vehicle tests confirmed the system's real-time performance, robustness, and accuracy.

Conclusions:

  • Multi-sensor fusion of GPS, IMU, and vision data provides a reliable solution for lane compensation in autonomous driving.
  • The developed prediction method enhances the stability and safety of unmanned driving systems under challenging environmental conditions.