Finding Vulnerabilities of Autonomous Vehicle Stacks to Physical Adversaries [supporting dataset]

Details

• Creators:
  Celik, Z Berkay ; Cardenas, Alvaro ; Fremont, Daniel J ; Ukkusuri, Satish V.
• Corporate Creators:
  National Center for Transportation Cybersecurity and Resiliency (TraCR) National University Transportation Center (UTC) ; Clemson University ; Purdue University ; University of California, Santa Cruz
• Corporate Contributors:
  United States. Department of Transportation. University Transportation Centers (UTC) Program ; United States. Department of Transportation. Office of the Assistant Secretary for Research and Technology
• Subject/TRT Terms:
  Autonomous Vehicles Computer Security Crimes Driving Behavior Physical Phenomena Risk Assessment Safety
• Publication/ Report Number:
  5
• DOI:
  https://doi.org/10.21949/1403301
• Resource Type:
  Dataset
• Right Statement:
  Open Access; https://creativecommons.org/licenses/by/4.0/
• Geographical Coverage:
  United States
• Corporate Publisher:
  National Center for Transportation Cybersecurity and Resiliency (TraCR) National University Transportation Center (UTC)
• Abstract:
  Autonomous Driving (AD) vehicles must interact and respond in real-time to multiple sensor signals indicating the behavior of other agents in the environment, such as other vehicles, and pedestrians near the ego vehicle (i.e., the vehicle itself). While autonomous vehicle (AV) developers tend to generate numerous test cases in simulations to detect safety and security problems, to the best of our knowledge, they are not testing for malicious physical interactions from attackers, such as by placing emergency cones in the hood of an AV or driving maneuvers that nearby human drivers or other AVs might perform. The main goal of our project is to develop automatic testing tools to evaluate the safety and security of autonomous vehicle stacks against unanticipated critical physical conditions created by attackers. Specifically, we aim to demonstrate adversarial driving maneuvers in different real-world scenarios, highlighting the potential consequences for AV safety and security, build an attack framework in a simulation environment to study the optimal discovery of adversarial driving maneuvers, and contribute to the development of a skilled AV security workforce. In this way, this effort aims to enable the deployment of increasingly trustworthy transportation systems.
  
  The total size of the ZIP file is 4 MB. The .txt file type is a common text file, which can be opened with a basic text editor. The most common software used to open .txt files are Microsoft Windows Notepad, Sublime Text, Atom, and TextEdit (for more information on .txt files and software, please visit https://www.file-extensions.org/txt-file-extension). The .gif file extension is standard bit-mapped graphics file format very widespread on Internet, which was originally developed by CompuServe in the late 1980s. Files with the GIF extension can be viewed through the local photo viewing software or in the web browser. File extension .json is associated to JavaScript Object Notation file format, a lightweight, text-based, language-independent data interchange format. JSON defines a small set of formatting rules for the portable representation of structured data. It is used by various applications as alternative option to XML file format. The data in a json file are stored in simple text file format and the content is viewable in any simple text editor (for more information on .json files and software, please visit https://www.file-extensions.org/json-file-extension). The .py file extension is commonly used for files containing source code written in Python programming language. Python is a dynamic object-oriented programming language that can be used for many kinds of software development (for more information on .py files and software, please visit https://www.file-extensions.org/py-file-extension). The .ipynb file type is a document file created in Jupyter Notebook. These files store browser-based notebooks with support for code, rich text, mathematical expressions, inline plots and other rich media. These files can be opened using the open-source application Jupyter Notebook (for more information on .ipynb files and software, please visit https://www.file-extensions.org/ipynb-file-extension).
  
  
• Content Notes:
  National Transportation Library (NTL) Curation Note: This dataset has been curated to CoreTrustSeal's curation level "A. Active Preservation". To find out more information on CoreTrustSeal's curation levels, please consult their "Curation & Preservation Levels" CoreTrustSeal Discussion Paper" (https://doi.org/10.5281/zenodo.11476980). NTL staff last accessed this dataset at its repository URL on 2025-08-14. If, in the future, you have trouble accessing this dataset, please email NTLDataCurator@dot.gov describing your problem. NTL staff will do its best to assist you at that time.
  
  Public Access Note: This item is made available under the terms of the Creative Commons Attribution 4.0 International (CC BY 4.0) license https://creativecommons.org/licenses/by/4.0/. Use the following citation:
  
  
• Format:
  ZIP
• Funding:
  69A3552344812, 69A3552348317
• Collection(s):
  University Transportation Centers Autonomous Vehicles
• Main Document Checksum:
  urn:sha-512:b01351b6b6fcb6c099510b7770d63e63f69dba19650ec33169c138bc17a0975b34955669474b129c121c30331e012a78960fbd9a10f4227c813273ffe18469ad
• Download URL:
  https://rosap.ntl.bts.gov/view/dot/85971/dot_85971_DS1.zip
• File Type:
  [ZIP - 5.54 MB ]