DOI: 10.21949/1403301
title: Finding Vulnerabilities of Autonomous Vehicle Stacks to Physical Adversaries [supporting dataset]

description: 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.

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description: 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.

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