---
title: "TuSimple/tusimple-benchmark: papers citing it"
description: "9 papers in the tracked journals link to the GitHub repository TuSimple/tusimple-benchmark in their Crossref records, the first in 2021; most of them (3) in 2025."
url: https://scholar4ai.com/code/github-tusimple-tusimple-benchmark
markdown_url: https://scholar4ai.com/code/github-tusimple-tusimple-benchmark.md
license: CC BY-NC 4.0 (https://creativecommons.org/licenses/by-nc/4.0/)
share_text: "9 papers in the tracked journals link to the GitHub repository TuSimple/tusimple-benchmark in their Crossref records, the first in 2021; most of them (3) in 2025. (as of 2026-11-03)"
data_url: https://scholar4ai.com/data/rankings.json
updated: 2026-11-03T14:43:26+09:00
window: 2026-10-05 to 2026-11-03
source: OpenAlex (CC0)
---

# TuSimple/tusimple-benchmark: papers linking to it

Research papers in the 480 tracked journals that link to TuSimple/tusimple-benchmark (https://github.com/TuSimple/tusimple-benchmark), read from their Crossref records: by year, category and journal, the family tree of papers building on one another, and the linked repositories derived from it or that it derives from.

- GitHub
- 594 stars
- 186 forks
- Jupyter Notebook
- license Apache-2.0
- created 2017
- https://github.com/TuSimple/tusimple-benchmark

All repositories: https://scholar4ai.com/code.md

## Key findings

- 9 papers in the tracked journals link to the GitHub repository TuSimple/tusimple-benchmark in their Crossref records, the first in 2021; most of them (3) in 2025.
- By category: AI (9); the journals with the most are Pattern Recognition (3), Neural Computing and Applications (2) and Expert Systems with Applications (1).
- 2 of the papers build on one another: a later paper cites an earlier one that also links TuSimple/tusimple-benchmark.

## By year

*Papers linking to TuSimple/tusimple-benchmark by publication year*

| Year | Papers |
|---|---:|
| 2021 | 1 |
| 2022 | 1 |
| 2023 | 3 |
| 2025 | 3 |
| 2026 | 1 |

## Journals

*Journals whose papers link to TuSimple/tusimple-benchmark most*

| Journal | Papers |
|---|---:|
| [Pattern Recognition](https://scholar4ai.com/journals/pattern-recognition.md) | 3 |
| [Neural Computing and Applications](https://scholar4ai.com/journals/neural-computing-and-applications.md) | 2 |
| [Expert Systems with Applications](https://scholar4ai.com/journals/expert-systems-with-applications.md) | 1 |
| [Applied Intelligence](https://scholar4ai.com/journals/applied-intelligence.md) | 1 |
| [International Journal of Computer Vision](https://scholar4ai.com/journals/international-journal-of-computer-vision.md) | 1 |

## Family tree

Each paper sits under the earlier paper it builds on.

- [Real-Time Lane Detection by Using Biologically Inspired Attention Mechanism to Learn Contextual Information](https://doi.org/10.1007/s12559-021-09935-5) (Cognitive Computation, 2021)
  - [AR-NET: lane detection model with feature balance concerns for autonomous driving](https://doi.org/10.1007/s00521-023-09270-w) (Neural Computing and Applications, 2023) — cites the paper above

## Papers

*Papers linking to TuSimple/tusimple-benchmark, most cited first*

| Published | Title | Journal | Authors | Citations | Link |
|---|---|---|---|---:|---|
| 2022-09-21 | [Lane Detection with Versatile AtrousFormer and Local Semantic Guidance](https://doi.org/10.1016/j.patcog.2022.109053) | [Pattern Recognition](https://scholar4ai.com/journals/pattern-recognition.md) | Jiaxing Yang, Lihe Zhang, Huchuan Lu | 46 | cites it |
| 2023-07-08 | [LaneScanNET: A deep-learning approach for simultaneous detection of obstacle-lane states for autonomous driving systems](https://doi.org/10.1016/j.eswa.2023.120970) | [Expert Systems with Applications](https://scholar4ai.com/journals/expert-systems-with-applications.md) | P. Shunmuga Perumal, Yong Wang, M. Sujasree et al. | 31 | cites it |
| 2021-09-01 | [Real-Time Lane Detection by Using Biologically Inspired Attention Mechanism to Learn Contextual Information](https://doi.org/10.1007/s12559-021-09935-5) | [Cognitive Computation](https://scholar4ai.com/journals/cognitive-computation.md) | Lu Zhang, Fengling Jiang, Bin Kong et al. | 15 | cites it |
| 2025-03-24 | [LaneCorrect: Self-Supervised Lane Detection](https://doi.org/10.1007/s11263-025-02417-3) | [International Journal of Computer Vision](https://scholar4ai.com/journals/international-journal-of-computer-vision.md) | Ming Nie, Xinyue Cai, Hang Xu et al. | 7 | cites it |
| 2023-01-13 | [Non-residual unrestricted pruned ultra-faster line detection for edge devices](https://doi.org/10.1016/j.patcog.2023.109321) | [Pattern Recognition](https://scholar4ai.com/journals/pattern-recognition.md) | Pengpeng Chen, Dongjingdian Liu, Shouwan Gao | 5 | cites it |
| 2025-01-04 | [A comprehensive study of fisheye image compression and perception for autonomous driving](https://doi.org/10.1007/s00521-024-10831-w) | [Neural Computing and Applications](https://scholar4ai.com/journals/neural-computing-and-applications.md) | Basem Barakat, Ibrahim Sobh, Chup-Chung Wong et al. | 3 | cites it |
| 2023-12-08 | [AR-NET: lane detection model with feature balance concerns for autonomous driving](https://doi.org/10.1007/s00521-023-09270-w) | [Neural Computing and Applications](https://scholar4ai.com/journals/neural-computing-and-applications.md) | Guoxiang Tong, Chuanye Zu | 0 | cites it |
| 2025-12-27 | [Lane mark segmentation from sparse event image via lightweight large kernel network](https://doi.org/10.1007/s10489-025-07047-3) | [Applied Intelligence](https://scholar4ai.com/journals/applied-intelligence.md) | Mingyong Zhuang, Xinbo Chen, Yixiong Zhang et al. | 0 | cites it |
| 2026-03-08 | [A lane detection model with knowledge guided anchor feature enhancement mechanism](https://doi.org/10.1016/j.patcog.2026.113446) | [Pattern Recognition](https://scholar4ai.com/journals/pattern-recognition.md) | Zhixiong Nan, Wanying Xu, Zhe Yang et al. | 0 | cites it |

## How this is measured

Links are read from each paper's Crossref record as its publisher deposited it: the URLs and repository DOIs in its reference list (the software and data it cites) and its supplementary links (“is-supplemented-by”: its own code or data). A paper that gives its code only in the text, a footnote or a data availability statement is not counted, and some publishers deposit reference lists without their URLs, so every count here is a lower bound. Repository details come from the GitHub REST API (stars, forks, and the repository a fork was made from), the Hugging Face Hub API (likes, downloads, and the base model a model was derived from) and DataCite (Zenodo and other data DOIs, and the GitHub release a Zenodo record archives), read again every month.

## Cite this page

Scholar4AI. (2026). TuSimple/tusimple-benchmark: papers linking to it. Journal and Conference Research Rankings. Retrieved November 3, 2026, from https://scholar4ai.com/code/github-tusimple-tusimple-benchmark

```bibtex
@misc{scholar4ai-code-github-tusimple-tusimple-benchmark,
  author = {{Scholar4AI}},
  title = {TuSimple/tusimple-benchmark: papers linking to it},
  year = {2026},
  howpublished = {\url{https://scholar4ai.com/code/github-tusimple-tusimple-benchmark}},
  urldate = {2026-11-03},
  note = {Journal and Conference Research Rankings; data from OpenAlex (CC0); licensed CC BY-NC 4.0}
}
```

License: CC BY-NC 4.0 (https://creativecommons.org/licenses/by-nc/4.0/).