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Improved MobileNet V3-Based Identification Method for Road Adhesion Coefficient.

Binglin Li¹, Jianqiang Xu¹, Yufeng Lian¹

¹School of Electrical and Electronic Engineering, Changchun University of Technology, Changchun 130012, China.

Sensors (Basel, Switzerland)

|September 14, 2024

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This study presents an improved MobileNet V3 model for predicting road surface conditions and recognizing road adhesion coefficients, enhancing vehicle safety systems. The new method achieved 95.53% classification precision, outperforming existing techniques.

Area of Science:

Automotive Engineering
Computer Vision
Machine Learning

Background:

Automobile active safety systems require timely control strategy adjustments for complex conditions.
Accurate road surface state and adhesion coefficient recognition are crucial for improving driving safety.

Purpose of the Study:

To develop an enhanced MobileNet V3 model for predicting road surface information and recognizing road adhesion coefficients.
To improve the adaptability and safety of automobile active safety systems.

Main Methods:

The study enhanced the MobileNet V3 model by replacing the Squeeze-and-Excitation (SE) module with the Convolutional Block Attention Module (CBAM) for improved feature extraction.
The cross-entropy loss function was replaced with the Bias Loss function to mitigate random prediction issues and enhance identification accuracy.

Keywords:

Bias Loss function Convolutional Block Attention Module MobileNet V3 ROS robot platform road adhesion coefficient identification

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The proposed method was validated using a four-wheel-drive ROS robot platform.

Main Results:

The enhanced MobileNet V3 model achieved a classification precision of 95.53% in identifying road adhesion coefficients.
The CBAM module effectively improved feature extraction by considering both spatial and channel dimensions.
The Bias Loss function reduced prediction errors, leading to higher identification accuracy.

Conclusions:

The proposed method significantly improves the accuracy of road adhesion coefficient identification compared to existing approaches.
The enhanced model offers a promising solution for real-time road surface state prediction in active safety systems.
This research contributes to the advancement of intelligent vehicle safety technologies.