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Data Filtering Method for Intelligent Vehicle Shared Autonomy Based on a Dynamic Time Warping Algorithm.

Zhenhai Gao1,2, Tong Yu1,2, Tianjun Sun1,2

  • 1State Key Laboratory of Automotive Simulation and Control, Jilin University, Changchun 130022, China.

Sensors (Basel, Switzerland)
|December 11, 2022
PubMed
Summary
This summary is machine-generated.

This study introduces an automatic model to efficiently record human-vehicle difference data for autonomous driving systems. The new method significantly reduces invalid data and storage needs, speeding up algorithm development.

Keywords:
autonomous vehicledata miningdiscrepancy trigger controlintelligent vehicle shared autonomy

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Area of Science:

  • Intelligent Transportation Systems
  • Autonomous Driving Technology
  • Big Data Analytics

Background:

  • Autonomous driving systems generate vast amounts of data, complicating testing and verification.
  • Traditional data acquisition methods face challenges with efficiency, quality, and time limitations.
  • Effective data extraction is crucial for reducing the autonomous driving algorithm development cycle.

Purpose of the Study:

  • To develop an automatic trigger recording control model for human-vehicle difference feature data.
  • To address the challenges of low efficiency, poor quality, and long-time limits in traditional data acquisition.
  • To minimize data dimensions and improve data mining efficiency in autonomous driving.

Main Methods:

  • Utilized dynamic time warping algorithm for data filtering.
  • Implemented a data filtering approach under a dynamic time window.
  • Focused on driver-dominated vehicle movement and virtual decision-making of autonomous systems as a reference.

Main Results:

  • The suggested model decreased recorded invalid data by an average of 75.35%.
  • Achieved data storage savings of approximately 2.65 TB per hour.
  • Outperformed industrial-grade methods, saving an average of 307 GB of storage space per hour.

Conclusions:

  • The proposed model offers an effective solution for intelligent data recording in autonomous driving.
  • Significant reductions in invalid data and storage requirements accelerate algorithm development.
  • This approach enhances the efficiency and quality of data acquisition for autonomous vehicle testing.