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Delay-Compensated Lane-Coordinate Vehicle State Estimation Using Low-Cost Sensors.

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  • 1School of Mechanical Engineering, Pusan National University, Busan 46241, Republic of Korea.

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Summary
This summary is machine-generated.

This study introduces a new method for accurate vehicle state estimation using low-cost sensors, crucial for advanced driver assistance systems (ADAS) and autonomous driving. The approach effectively compensates for signal delays, improving safety and reliability.

Keywords:
delay compensatorextended kalman filterlane coordinatesensor fusion

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Area of Science:

  • Robotics and Control Systems
  • Computer Vision for Autonomous Systems
  • Sensor Fusion for Vehicle Dynamics

Background:

  • Robust vehicle state estimation in a lane coordinate system is vital for Advanced Driver Assistance Systems (ADAS) and autonomous driving.
  • Low-cost sensors like cameras, IMUs, and steering angle sensors present challenges due to complex vehicle dynamics and vision system signal delays.
  • Accurate real-time estimation of lateral position, velocity, and heading angle is difficult to achieve with existing methods.

Purpose of the Study:

  • To develop a lane-coordinate-based vehicle state estimator that overcomes the limitations of low-cost sensors and signal delays.
  • To enable reliable and continuous estimation of critical vehicle states for ADAS and autonomous driving applications.
  • To improve the accuracy and reduce the phase lag in vehicle state estimation compared to conventional methods.

Main Methods:

  • Integration of a vehicle dynamics-based bicycle model with an Extended Kalman Filter (EKF).
  • Implementation of a signal delay compensation algorithm to predict vehicle state evolution during camera processing.
  • Development of an efficient camera processing pipeline with pre-trained networks for lane segmentation and state extraction.

Main Results:

  • The proposed estimator provides continuous, high-accuracy, and delay-compensated lane-coordinate-based vehicle states.
  • Significant reduction in estimation errors and phase lag compared to camera-only methods and estimators without delay compensation.
  • Successful validation using real vehicle driving data on both straight and curved roads.

Conclusions:

  • The developed estimator effectively addresses challenges in lane-based vehicle state estimation using low-cost sensors.
  • The method enables reliable, real-time acquisition of critical vehicle state information for ADAS and autonomous driving.
  • The approach offers a practical solution for enhancing the safety and performance of automated driving systems.