AttBi-ResU: A Smart 3D Vision-Based Lane Detection System for Autonomous Vehicles

Engineering notes

Key topics: autonomous driving, autonomous vehicle, lane detection. See the paper for implementation details and experimental results.

Chinese explanation / 中文解读

中文解读待补充：本站会优先为端到端自动驾驶、BEV感知、3D目标检测、轨迹预测、路径规划、LiDAR感知等高价值论文补充中文说明。

Original abstract

To enhance driving safety and minimize the risk of collisions, autonomous vehicles rely on lane-detection systems capable of issuing timely warnings during unexpected lane departures. Current methods often suffer from complex scenes, fluctuating illumination, high false-positive rates, and imprecise localization. To overcome these constraints, we present a novel three-stage vision-based pipeline. In the first stage, raw RGB images sourced from public repositories are converted into grayscale. This process leverages a bilateral entropy-based adaptive histogram equalization module, designed to enhance image contrast while effectively mitigating noise. An attention-augmented Bidirectional Long Short-Term Memory (Bi-LSTM) feature extractor feeds a residual-dilation U-Net, enabling precise spatiotemporal segmentation of lane regions. Finally, to minimize training loss, the model’s hyperparameters are adjusted using an enhanced Krill Herd Optimization (KHO) process.

Links and sources

• Official / arXiv page

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