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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Demo #14: In-Vehicle Communication Using Named Data Networking

Zachariah Threet (Tennessee Tech), Christos Papadopoulos (University of Memphis), Proyash Poddar (Florida International University), Alex Afanasyev (Florida International University), William Lambert (Tennessee Tech), Haley Burnell (Tennessee Tech), Sheikh Ghafoor (Tennessee Tech) and Susmit Shannigrahi (Tennessee Tech)

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Model-Agnostic Defense for Lane Detection against Adversarial Attack

Henry Xu, An Ju, and David Wagner (UC Berkeley) Baidu Security Auto-Driving Security Award Winner ($1000 cash prize)!

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Google/Apple Exposure Notification Due Diligence

Douglas Leith and Stephen Farrell (Trinity College Dublin)

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