Abstract
In this paper, we present a physical-layer attack and interference resilient automotive radar system, and derive analytical upper bounds for the probability of not detecting an attack, and the probability of false attack alarm. We consider a quite general attack model and prove that if the attack signal level is above a defined relative threshold, both the probability of false attack alarm and the probability of not detecting an attack converge to zero exponentially with the number of samples acquired during a single chirp, and the number of chirps used in a frame. We also derive an analytical formula for this relative threshold, and prove that by selecting shorter frame durations, and using lower noise RF equipment, the threshold can be made as small as possible. Basically, by proper selection of radar parameters arbitrarily small attack signals can be detected almost always with almost no false alarms. We also present a numerical example using real measured data obtained from two 77 GHz automotive radars operated at the same time. Also using real data, we show that the proposed system reduces the negative effects of undetected weak attacks which are below the above mentioned threshold.
Highlights
A UTONOMOUS vehicles (AV), and advanced driver assistance systems (ADAS) rely on highly advanced sensors including millimeter wave radars, lidars, and multiple camera based vision systems
A standard frequency modulated continuous wave (FMCW) AV radar offers no protection against physical-layer attacks, but using advanced signal processing techniques, a high degree of attack and interference resilience can be achieved
We present a new attack resilient automotive radar system, derive analytical bounds on its attack detection performance, prove that the total risk can be made as small as possible, and demonstrate
Summary
A UTONOMOUS vehicles (AV), and advanced driver assistance systems (ADAS) rely on highly advanced sensors including millimeter wave radars, lidars, and multiple camera based vision systems. These attack detectors have an estimator which can be an analytical expression derived from a probabilistic model, or can be completely artifical intelligence (AI) based For all such designs false attack alarms are always possible because observed differences may be due to natural measurement noise and/or estimation error. Thresholds should be selected to minimize false alarms without significantly degrading the attack detection capability, see [15] for a related edge case where gradually increasing attacks are used to bypass detection and to destabilize the overall system These estimator based techniques are known to be quite useful for detecting attacks at the communication layer, but the accuracy of the estimation model is of critical importance for reliable operation. We have considered only the forward looking direction, but we would like to reiterate that experimental data is used only as a secondary supporting evidence
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