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Perception01:28

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Perception is a fundamental psychological process that enables individuals to organize, interpret, and consciously experience sensory information. This process is crucial for understanding and interacting with the world around us. It includes both bottom-up and top-down processing, each playing a distinct role in how we perceive our environment.
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The brain processes sensory information rapidly due to parallel processing, which involves sending data across multiple neural pathways at the same time. This method allows the brain to manage various sensory qualities, such as shapes, colors, movements, and locations, all concurrently. For instance, when observing a forest landscape, the brain simultaneously processes the movement of leaves, the shapes of trees, the depth between them, and the various shades of green. This enables a quick and...
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Related Experiment Video

Updated: May 14, 2025

Tactile Vibrating Toolkit and Driving Simulation Platform for Driving-Related Research
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Multi-Source Information Fusion for Environmental Perception of Intelligent Vehicles Using Sage-Husa Adaptive

Yibo Meng1, Huifang Kong1, Tiankuo Liu1

  • 1State Key Laboratory of High-Efficiency and High-Quality Conversion for Electric Power, Hefei University of Technology, Hefei 230002, China.

Sensors (Basel, Switzerland)
|April 12, 2025
PubMed
Summary

This study introduces an improved adaptive Kalman filtering algorithm for intelligent driving systems. The new method enhances multi-source information fusion accuracy by adapting to changing sensor performance, reducing positional errors.

Keywords:
Sage-Husa adaptive extended Kalman filteringenvironmental perceptionfading factorintelligent drivingmulti-source information fusion

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Area of Science:

  • Intelligent transportation systems
  • Robotics and autonomous systems
  • Sensor fusion and perception

Background:

  • Intelligent driving technology relies on multi-source information fusion for environmental perception.
  • Environmental variations can alter sensor performance, leading to fusion deviations.
  • Existing adaptive Kalman filtering methods may not fully address these dynamic changes.

Purpose of the Study:

  • To propose an improved multi-source information fusion algorithm for intelligent driving.
  • To enhance the adaptability and accuracy of sensor fusion under varying environmental conditions.
  • To mitigate fusion deviations caused by sensor performance fluctuations.

Main Methods:

  • Constructed a multi-source information fusion system using vehicle kinematic and sensor measurement models.
  • Developed the Sage-Husa adaptive fading extended Kalman filtering (SHAFEKF) algorithm.
  • Introduced a fading factor into the Sage-Husa adaptive extended Kalman filtering (SHAEKF) algorithm to prioritize recent data.

Main Results:

  • The proposed SHAFEKF algorithm achieved positional average errors of 0.137 and 0.071 in two scenarios.
  • Positional average errors were reduced by 2.8% and 13.4% compared to the SHAEKF algorithm.
  • Mean squared errors decreased by 64% and 72%, indicating improved accuracy and stability.

Conclusions:

  • The SHAFEKF algorithm demonstrates high accuracy and low fluctuation in multi-source information fusion.
  • The algorithm effectively enhances adaptability in intelligent driving perception systems.
  • The fading factor significantly improves the fusion performance under dynamic environmental conditions.