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Evaluation of Camera Recognition Performance under Blockage Using Virtual Test Drive Toolchain.

Sungho Son^1,2, Woongsu Lee¹, Hyungi Jung¹

¹Department of Future Vehicle Research, Korea Automobile Testing and Research Institute, Hwaseong 18247, Republic of Korea.

Sensors (Basel, Switzerland)

|October 14, 2023

Summary

This summary is machine-generated.

This study introduces a new method to test camera recognition for autonomous vehicles. Blockage concentration significantly impacts object detection, guiding improved camera safety and lens cleaning schedules.

Keywords:

autonomous vehicles blockage color camera sensor object color object recognition

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Area of Science:

Computer Vision
Autonomous Systems
Sensor Technology

Background:

Camera sensors are crucial for autonomous vehicles due to their cost-effectiveness.
Evaluating camera performance under real-world conditions, like obstructions, is vital for safety.

Purpose of the Study:

To develop and validate a technology for assessing camera sensor object recognition performance.
To analyze the impact of various blockage factors on camera recognition algorithms.

Main Methods:

Utilized a virtual test drive toolkit and camera simulator.
Systematically varied blockage concentration, blockage color, object type, and object color.
Analyzed the effects of these factors on object recognition performance.

Main Results:

Blockage concentration was the most significant factor affecting recognition, followed by object type, blockage color, and object color.
Black blockages performed better than gray or yellow.
Blockage color influenced the recognition of specific object types.

Conclusions:

Proposed a simulation-based evaluation method for camera recognition performance under blockage conditions.
Established an algorithm evaluation environment for manufacturers.
Provided insights for optimizing camera lens cleaning timing and enhancing vehicle safety.