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A Methodology for Capturing Joint Visual Attention Using Mobile Eye-Trackers

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Lane Mark Detection with Pre-Aligned Spatial-Temporal Attention.

Yiman Chen¹, Zhiyu Xiang²

¹College of Information Science & Electronic Engineering, Zhejiang University, Hangzhou 310027, China.

Sensors (Basel, Switzerland)

|February 15, 2022

Summary

This summary is machine-generated.

This study introduces a new lane detection network using multiple successive images to improve accuracy in challenging driving conditions like occlusions and shadows. The method enhances stability and achieves state-of-the-art performance.

Keywords:

Spatial-Temporal Attention lane mark detection pre-aligned multiple frames

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Area of Science:

Computer Vision
Artificial Intelligence
Robotics

Background:

Lane mark detection is crucial for autonomous driving systems.
Current deep learning methods often process single images, limiting performance in complex scenarios like occlusions and shadows.
Utilizing successive image frames can enhance lane detection stability and robustness.

Purpose of the Study:

To develop a novel lane detection network that leverages information from multiple successive frames.
To improve the stability and accuracy of lane mark detection, especially in challenging environmental conditions.
To introduce and evaluate a Spatial-Temporal Attention Module (STAM) for feature aggregation.

Main Methods:

A novel deep learning network architecture is proposed, accepting pre-aligned multiple successive frames as input.
A Spatial-Temporal Attention Module (STAM) is designed to adaptively integrate feature information from historical frames into the current frame.
The network architecture and STAM variations were optimized and evaluated.

Main Results:

The proposed method demonstrates improved lane mark detection performance compared to single-frame approaches.
Experiments on the Tusimple and ApolloScape datasets show state-of-the-art results.
The STAM effectively aggregates temporal information, leading to more stable predictions.

Conclusions:

Leveraging successive frames with a spatial-temporal attention mechanism significantly enhances lane detection robustness.
The proposed network offers a more reliable solution for autonomous driving in diverse and challenging scenarios.
The method achieves state-of-the-art performance, paving the way for safer autonomous navigation.