Autonomous vehicles (AVs) have significant potentials to improve road safety, travel experience and transportation footprint, as well as the mobility and accessibility for all. However, the safety and security of AVs themselves have raised many concerns in recent years. As AVs use a plethora of sensors, including cameras and radars to detect, classify and track objects and obstacles on the road, their reliability and resilience have significant impacts on AV safety. Although risks from malicious attacks targeted at cameras and other sensors in AV systems have been studied, there is a lack of in-depth understanding of the vulnerability of millimeter-wave radar sensors in AVs. This EArly-concept Grant for Exploratory Research (EAGER) project seeks to explore insights to improve the security and resilience of AVs by investigating advanced attack and defense methods for millimeter-wave radar sensor-based systems. Since these radars are used exclusively today for adaptive cruise control, blind-spot detection, and collision avoidance, this project will benefit many safety-critical applications. The PIs plan to expand research opportunities for K-12 and underrepresented students, and integrate diversity in broadening participation in engineering through the educational programs of National Summer Transportation Institute (NSTI), Institute for Sustainable Transportation and Logistics (ISTL) and Louis Stokes Alliance for Minority Participation (LSAMP) at the University at Buffalo. The PIs will disseminate the results of the project through publications, talks, and demos, and integrate research materials into specific courses and education curricula. All newly developed research and teaching materials will be publicly accessible on the project website.
This project will first demonstrate that the millimeter-wave radar sensors can be spoofed and jammed while AVs are on the road through a non-cooperative over-the-air synchronization method. It can accurately identify the frequency band, modulation scheme and waveform patterns of victim radars to launch stealthy attacks. The team will conduct a proof-of-concept demo to attack real-world AV radars with fast-chirp signals. Second, the project will explore both hardware and software/algorithm-based defense mechanisms for avoiding such attacks, including beam feature based physical layer capacity estimation, machine learning physical identification, non-cooperative passive front-end architectures and band-limited coherent noise radars. The innovation of the project comes from not only the use of adaptive finite state machine based approaches that combine inter- and intra-chirp-sequence synchronization, but also data modeling techniques to efficiently adjust the attacker?s waveform parameters. Moreover, the project is among the first to explore the use of unique radiometrics, noise radars and passive radars as effective defense mechanisms against the attacks to AVs. Research thrusts in this project will significantly advance the state-of-the-art knowledge of the security of millimeter-wave sensors, and provide insights on developing more undeceivable, disclosure-resistant and robust AV radar solutions.
This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.
