---
title: "torcs: papers citing this SourceForge repository"
description: "6 papers in the tracked journals link to the SourceForge repository torcs in their Crossref records, the first in 2019; most of them (3) in 2020."
url: https://scholar4ai.com/code/sourceforge-torcs
markdown_url: https://scholar4ai.com/code/sourceforge-torcs.md
license: CC BY-NC 4.0 (https://creativecommons.org/licenses/by-nc/4.0/)
share_text: "6 papers in the tracked journals link to the SourceForge repository torcs in their Crossref records, the first in 2019; most of them (3) in 2020. (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)
---

# torcs: papers linking to it

Research papers in the 480 tracked journals that link to torcs (https://sourceforge.net/projects/torcs/), 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.

- SourceForge
- https://sourceforge.net/projects/torcs/

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

## Key findings

- 6 papers in the tracked journals link to the SourceForge repository torcs in their Crossref records, the first in 2019; most of them (3) in 2020.
- By category: AI (4) and Operations Research (2); the journals with the most are Transportation Research Part C Emerging Technologies (2), Expert Systems with Applications (1) and Applied Intelligence (1).
- 2 of the papers build on one another: a later paper cites an earlier one that also links torcs.

## By year

*Papers linking to torcs by publication year*

| Year | Papers |
|---|---:|
| 2019 | 1 |
| 2020 | 3 |
| 2021 | 1 |
| 2025 | 1 |

## Journals

*Journals whose papers link to torcs most*

| Journal | Papers |
|---|---:|
| [Transportation Research Part C Emerging Technologies](https://scholar4ai.com/journals/transportation-research-part-c-emerging-technologies.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 |
| [Neural Computing and Applications](https://scholar4ai.com/journals/neural-computing-and-applications.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.

- [Automated vehicle’s behavior decision making using deep reinforcement learning and high-fidelity simulation environment](https://doi.org/10.1016/j.trc.2019.08.011) (Transportation Research Part C Emerging Technologies, 2019)
  - [A survey on autonomous vehicle control in the era of mixed-autonomy: From physics-based to AI-guided driving policy learning](https://doi.org/10.1016/j.trc.2021.103008) (Transportation Research Part C Emerging Technologies, 2021) — cites the paper above

## Papers

*Papers linking to torcs, most cited first*

| Published | Title | Journal | Authors | Citations | Link |
|---|---|---|---|---:|---|
| 2021-03-12 | [A survey on autonomous vehicle control in the era of mixed-autonomy: From physics-based to AI-guided driving policy learning](https://doi.org/10.1016/j.trc.2021.103008) | [Transportation Research Part C Emerging Technologies](https://scholar4ai.com/journals/transportation-research-part-c-emerging-technologies.md) | Xuan Sharon Di, Rongye Shi | 235 | cites it |
| 2019-08-20 | [Automated vehicle’s behavior decision making using deep reinforcement learning and high-fidelity simulation environment](https://doi.org/10.1016/j.trc.2019.08.011) | [Transportation Research Part C Emerging Technologies](https://scholar4ai.com/journals/transportation-research-part-c-emerging-technologies.md) | Yingjun Ye, Xiaohui Zhang, Jian Sun | 180 | cites it |
| 2020-08-08 | [Deep learning and control algorithms of direct perception for autonomous driving](https://doi.org/10.1007/s10489-020-01827-9) | [Applied Intelligence](https://scholar4ai.com/journals/applied-intelligence.md) | Der‐Hau Lee, Kuan-Lin Chen, Kuan-Han Liou et al. | 68 | cites it |
| 2025-11-08 | [A survey of transformer architectures for autonomous driving](https://doi.org/10.1016/j.eswa.2025.130338) | [Expert Systems with Applications](https://scholar4ai.com/journals/expert-systems-with-applications.md) | Fulin Chu, Haoyu Li, Lili Xie et al. | 18 | cites it |
| 2020-03-14 | [Coping with opponents: multi-objective evolutionary neural networks for fighting games](https://doi.org/10.1007/s00521-020-04794-x) | [Neural Computing and Applications](https://scholar4ai.com/journals/neural-computing-and-applications.md) | Steven Künzel, Silja Meyer-Nieberg | 7 | cites it |
| 2020-08-19 | [GADE: A Generative Adversarial Approach to Density Estimation and its Applications](https://doi.org/10.1007/s11263-020-01360-9) | [International Journal of Computer Vision](https://scholar4ai.com/journals/international-journal-of-computer-vision.md) | M. Ehsan Abbasnejad, Qinfeng Shi, Anton van den Hengel et al. | 6 | 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). torcs: papers linking to it. Journal and Conference Research Rankings. Retrieved November 3, 2026, from https://scholar4ai.com/code/sourceforge-torcs

```bibtex
@misc{scholar4ai-code-sourceforge-torcs,
  author = {{Scholar4AI}},
  title = {torcs: papers linking to it},
  year = {2026},
  howpublished = {\url{https://scholar4ai.com/code/sourceforge-torcs}},
  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/).