SCTrans: Scenario Dataset for Simulation Testing of Autonomous Driving Systems
Authors/Creators
Description
In this work, we introduce a transformation-based approach, namely SCTrans, which utilizes existing real-world traffic scenario datasets to create simulation scenario files. For more approach details, please see our ICSE-24 paper.
To curate an initial version of our scenario dataset, we randomly selected 1,994 valid traffic scenario recording files as data sources, from representative traffic scenario datasets, including CommonRoad[1], inD[2], and highD[3]. Finally, we got 1,994 simulation scenario files and 426 map description files, which are compatible with advanced ADS testing platforms (e.g., LGSVL[4]+Apollo[5] and Carla[6]+Autoware[7]. For ease of using our scenarios, we provide scenario-runner scripts for LGSVL and Carla, available at our Github Repo[8]. Besides, for ease of scenario selection, we manually label all our simulation scenarios according to ISO 21448 (i.e., guiding principles for ADS SOTIF testing). The labeling results are listed in scenario-tag.xlsx.
To obtain access to this dataset, please read our open-source policy (i.e., conditions of restricted access to this repo).
======== How to Use ========
Please refer to our Github Website. We provide the refined versions of the LGSVL & Carla scenario runners. The scenario-runner directory contains the complete, modified source code along with comprehensive usage instructions.
Files
Restricted
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Request access
If you would like to request access to these files, please fill out the form below.
You need to satisfy these conditions in order for this request to be accepted:
If you are interested in SCTrans and want to obtain access to our source code and dataset, please read the following instructions and send your request email to Jiarun Dai (jrdai14@fudan.edu.cn) and Bufan Gao (bfgao22@m.fudan.edu.cn).
======= Instructions about Request Email =======
For Academic Researchers:
If you are a student(or postdoc), please have your advisor (or host) send us an access email. If you are a faculty member, please send us an email from your university email account.
In your email, please include your name, affiliation, and home page (if we do not recognize each other). This information is required only for verification purposes. Please note that if we are unable to determine your identity or affiliation, your request may be ignored.
If your papers or articles use our dataset or our tool, please cite our ICSE 2024 paper.
For Industry Researchers:
If you are currently in the industry (including research labs), please send us an email from your company’s email account.
In the email, please briefly introduce yourself (e.g., name and title - in case we don’t know each other) and your company.
Mail Content:
In your email, please kindly attach a justification letter (in PDF format) printed on official letterhead. This letter should recognize the “SCTrans” project from Fudan University and explicitly outline the purpose behind your dataset or source code request. Moreover, confirm your commitment to utilizing the dataset or source code solely for non-commercial purposes and assure that it will not be disseminated to others without our prior consent.
It is imperative to stress that we will disregard emails that do not adhere to the instructions provided above. Furthermore, please be aware that we reserve the right to publicly display on the SCTrans homepage the names of universities, research laboratories, and companies that have submitted dataset access requests. By sending an email to us requesting access to our source code or dataset, you acknowledge and agree to abide by the aforementioned policy.
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