# LaneFormer: Real-Time Lane Execution and Detection via Transformer
## 4 Conclusions  
In this paper, we have proposed an end-to-end lane line detection network: a Lane-Former network based on an attention mechanism that can directly visualize lane lines. We use the modified ResNet32 network as a backbone to extract shallow-level features and adopt multiple down-sampling and up-sampling for fusion in stages to improve the ability to extract information on slender lane lines. When the extracted features are not significantly increased, the ability to use the feature correction module to visualize the extraction of features sends the features to the attention module. Use the attention module to enhance the contextual information of the extracted features, and then use the lane line model to fit the detected feature points. Our network fully captures the contextual information during the training process and fits the detected road lane points well. Our method achieves state-of-the-art detection performance in terms of parameter numbers and running time; besides, our model is more stable and reliable. In the next task, we will explore the encoding method of location information to find a better encoding matrix that can improve the models' accuracy or speed. At the same time, we will study the reasoning ability of the model so that it can be based on a small part of the curve in the urbanization road to infer the line of the lane that is occluded.  
Y.Y.Writing--original draft, Conceptualization, Methodology, Validation, Visualization; H.P.Writing--review & editing, Methodology; C.L.: Validation, Project administration;  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Least Squares Method** & **Acc** & **FP** & **FN** \\ \hline quadratic curve & 90.22 & 0.1259 & 0.0895 \\ \hline cubic curve & 93.22 & 0.0954 & 0.0715 \\ \hline quartic curve & 91.58 & 0.1061 & 0.0845 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Quantitative evaluation of different lane shape models on TuSimple validation set (%).  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Layer** & **1** & **2** & **3** & **4** & **5** \\ \hline
2 & 92.25 & 93.22 & - & - & - \\ \hline
4 & 92.05 & 93.11 & 94.52 & 95.15 & - \\ \hline
6 & 91.05 & 93.54 & 93.52 & 94.85 & 93.85 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Quantitative evaluation of different transformer decoder module number on TuSimple validation set (%). The horizontal coordinate is transformer decoder module number, the vertical coordinate is transformer encoder number, and the evaluation metric is mAP (mean Average Precision).  
W.Z.: Visualization, Resources; K.Y.: Data curation, Funding acquisition. All authors have read and agreed to the published version of the manuscript.
* This research was funded by the National Natural Science Foundation of China, grant number 51805312; Shanghai Sailing Program, grant number 18 YF1409400; Science and Technology Commission of Shanghai Municipality; Training and funding Program of Shanghai College young teachers, grant number ZZGCD15102.
* The data that support the findings of this study are openly available in [[[https://github.com/TuSimple/tusimple-benchmark](https://github.com/TuSimple/tusimple-benchmark)]([https://github.com/TuSimple/tusimple-benchmark](https://github.com/TuSimple/tusimple-benchmark)), accessed on 29 May 2022], reference number [37].  
The authors wish to express their appreciation to the reviewers for their helpful suggestions, which greatly improved the presentation of this paper.  
The authors declare that they have no conflict of interest to report regarding the present study.