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CBAM meets DropBlock: enhancing robot steering-angle prediction with hybrid attention and structured dropout.

Jing Niu¹, Guanghao Gao², Chuanyan Shen²

¹School of Mechatronics and Automotive Engineering, Tianshui Normal University, Tianshui, 741001, China. sensily@163.com.

Scientific Reports

|June 2, 2026

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This study introduces a new lightweight network for robot steering angle prediction in complex environments. The method improves accuracy by focusing on key features and reducing interference from irrelevant scene details.

Area of Science:

Robotics
Computer Vision
Artificial Intelligence

Background:

Accurate steering angle prediction is vital for intelligent auto-navigation in complex scenarios.
Traditional end-to-end systems struggle with prediction accuracy and feature interference.

Purpose of the Study:

To propose a lightweight steering angle prediction network.
To enhance prediction accuracy by addressing interference from complex scene features.

Main Methods:

A three-stage progressive framework with multi-scale feature pyramid, channel/spatial attention decoupling, and dynamic region feature dropout.
Bi-domain attention fused region mask and de-fuzzy network for cooperative optimization.
Dual-channel feature compression, multilayer perceptron for channel weight generation, and gated convolutional kernel for spatial attention.

Keywords:

CBAM Deblurring network DropBlock Model lightweighting Robot steering-angle prediction

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Main Results:

The proposed method effectively strengthens key semantic features like lane lines and obstacles.
Interfering information from non-critical regions (road texture, sky) is suppressed.
Experimental results demonstrate strong performance in steering angle prediction.

Conclusions:

The developed lightweight network shows significant improvements in steering angle prediction accuracy.
The cooperative optimization strategy effectively handles complex scenarios.
The method holds promising applications for intelligent auto-navigation systems.