# Optimizing Lane Detection in Autonomous Vehicles Using Cascading Attention Mechanisms in DeepLabv3+
> Dewiani

URL kanonis: https://discover.unhas.ac.id/publications/pub_scopus_105037821620
Jurnal / Konferensi: Engineering Technology and Applied Science Research
Tahun terbit: 2026
DOI: https://doi.org/10.48084/etasr.15315
ISSN: 22414487
Kuartil SJR: Q2
Citations: 0

## Authors
- Dewiani

## Abstract
The high rate of traffic accidents caused by human error highlights the urgent need for reliable lane detection in autonomous vehicles. Traditional segmentation models, such as U-Net, struggle to capture multi-scale contextual features, leading to inaccurate recognition of narrow or visually ambiguous lane markings. This study introduces an enhanced DeepLabv3+ architecture, augmented with cascading attention mechanisms, Convolutional Block Attention Module (CBAM), Efficient Channel Attention (ECA), and Squeeze-and-Excitation (SE), to improve feature extraction and boundary precision. The proposed method addresses limitations in existing models by leveraging these attention modules to better capture contextual information at different scales. A dataset consisting of 374 annotated road images from Makassar, Indonesia, was used for training and evaluation. The model achieved a mean Intersection over Union (IoU) of 97.34% and a Root Mean Square Error (RMSE) of 0.0377 in tire-to-lane distance estimation, outperforming traditional architectures. These results demonstrate that the proposed framework provides robust, real-time lane detection, making it highly suitable for autonomous vehicle navigation in dynamic and complex urban environments.

## Keywords
- Intersection (aeronautics)
- Computer science
- Block (permutation group theory)
- Artificial intelligence
- Feature (linguistics)
- Segmentation
- Feature extraction
- Channel (broadcasting)
- Word error rate
- Scheme (mathematics)
- Mean squared error
- Computer vision
- Boundary (topology)
- Advanced driver assistance systems
- Intelligent transportation system
- Real-time computing
- Pattern recognition (psychology)
- Error detection and correction
- Root (linguistics)
- Baseline (sea)
- Machine learning
- Human error
- Object detection
- Image (mathematics)

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Sumber: Discover Unhas — RIMS Universitas Hasanuddin.
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