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Communication Delay Outlier Detection and Compensation for Teleoperation Using Stochastic State Estimation.

Eugene Kim¹, Myeonghwan Hwang¹, Taeyoon Lim¹

¹Korea Institute of Industrial Technology, Gwangju 61012, Republic of Korea.

Sensors (Basel, Switzerland)

|February 24, 2024

Summary

This summary is machine-generated.

This study addresses safety challenges in autonomous driving, particularly concerning teleoperation. A new stochastic method effectively detects and classifies communication delay outliers, enhancing vehicle control system resilience.

Keywords:

autonomous vehicle communication delay outlier detection teleoperation

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Area of Science:

Robotics
Computer Science
Electrical Engineering

Background:

Autonomous driving systems face safety challenges in complex urban and dynamic environments.
Teleoperation is a proposed solution for autonomous vehicle disengagements but introduces risks due to wireless communication instability.
Communication delay outliers can impair teleoperator situational awareness and corrupt control signals.

Purpose of the Study:

To develop a method for detecting and classifying communication delay outliers in teleoperated autonomous vehicles.
To enhance the safety and reliability of autonomous driving systems during teleoperation.

Main Methods:

A stochastic approach was employed to estimate passive communication delays and identify outliers.
The framework was designed to detect outliers independently of real-time wireless communication quality.

Main Results:

Communication delay outliers were quantified, representing 1.33% of observed delays.
The proposed framework successfully detected and classified these outliers automatically.
The system demonstrated resilience against communication delay outliers, mitigating performance degradation.

Conclusions:

The developed method provides a robust solution for managing communication delay outliers in teleoperation.
This contributes to improving the safety and reliability of autonomous driving technologies in real-world scenarios.