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Authorized Traffic Controller Hand Gesture Recognition for Situation-Aware Autonomous Driving.

Ashutosh Mishra¹, Jinhyuk Kim¹, Jaekwang Cha¹

¹Yonsei Institute of Convergence Technology, Yonsei University, Incheon 21983, Korea.

Sensors (Basel, Switzerland)

|December 10, 2021

Summary

This summary is machine-generated.

This study introduces a situation-aware traffic control hand-gesture recognition system for autonomous vehicles (AVs). The system enhances AVs

Keywords:

3D hand-pose modeling authorized traffic controller autonomous vehicle irregular situation situation-aware traffic control hand gesture recognition

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Area of Science:

Computer Vision
Robotics
Artificial Intelligence

Background:

Autonomous vehicles (AVs) require human-level understanding of traffic control gestures for safe operation.
Existing vision-based systems struggle with recognizing authorized traffic controllers (ATCs) and hand gestures, especially when gloves are worn.
Situational awareness is crucial for AVs to interpret and respond to irregular traffic control scenarios.

Purpose of the Study:

To develop a situation-aware traffic control hand-gesture recognition system for autonomous driving.
To improve the ability of AVs to recognize and interpret gestures from authorized traffic controllers (ATCs).
To address limitations in current vision-based systems, such as recognizing ATCs with gloves.

Main Methods:

Proposed a system integrating ATC detection and gesture recognition.
Utilized a 3D hand model for gesture recognition to overcome challenges posed by gloves.
Developed a database with approximately 60 minutes of training and testing videos (35,291 frames) captured at 24 frames per second.

Main Results:

The proposed system demonstrated correct recognition of traffic control hand gestures.
The 3D hand model approach effectively mitigated issues related to recognizing gestures on gloved hands.
The system successfully recognized various authorized traffic controllers and their gestures.

Conclusions:

The developed situation-aware system significantly enhances AVs' capability to understand traffic control gestures.
This research extends the operational domain of autonomous vehicles by improving their interaction with human traffic controllers.
The system provides a foundation for more robust and reliable autonomous driving in complex traffic environments.