Yuzhe Ma, Jon Sharp, Ruizhe Wang, Earlence Fernandes, and Jerry Zhu (University of Wisconsin–Madison)

Kalman Filter (KF) is widely used in various domains to perform sequential learning or variable estimation. In the context of autonomous vehicles, KF constitutes the core component of many Advanced Driver Assistance Systems (ADAS), such as Forward Collision Warning (FCW). It tracks the states (distance, velocity etc.) of relevant traffic objects based on sensor measurements. The tracking output of KF is often fed into downstream logic to produce alerts, which will then be used by human drivers to make driving decisions in near-collision scenarios. In this work, we demonstrate planning-based attacks on Forward Collision Warning — a machine-human hybrid system that uses KF. Based on our work published at the AAAI2021 conference, we use an MPC-based algorithm and show how an attacker can sequentially perturb vision measurements to change the FCW alert signals at desired points in time. We simulate our attack on CARLA using standard test protocols from the National Highway Traffic Safety Administration.

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DRIVETRUTH: Automated Autonomous Driving Dataset Generation for Security Applications

Raymond Muller (Purdue University), Yanmao Man (University of Arizona), Z. Berkay Celik (Purdue University), Ming Li (University of Arizona) and Ryan Gerdes (Virginia Tech)

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(Short) WIP: End-to-End Analysis of Adversarial Attacks to Automated...

Hengyi Liang, Ruochen Jiao (Northwestern University), Takami Sato, Junjie Shen, Qi Alfred Chen (UC Irvine), and Qi Zhu (Northwestern University) Best Short Paper Award Winner!

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My Past Dictates my Present: Relevance, Exposure, and Influence...

Shujaat Mirza, Christina Pöpper (New York University)

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Bringing Balance to the Force: Dynamic Analysis of the...

Abdallah Dawoud (CISPA Helmholtz Center for Information Security), Sven Bugiel (CISPA Helmholtz Center for Information Security)

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