US 12,403,936 B2
Method for controlling a driver assistance system during operation of a vehicle
Nikhil Kapoor, Wolfsburg (DE); Jan David Schneider, Wolfsburg (DE); and Serin Varghese, Braunschweig (DE)
Assigned to VOLKSWAGEN AKTIENGESELLSCHAFT, Wolfsburg (DE)
Appl. No. 18/043,449
Filed by Volkswagen Aktiengesellschaft, Wolfsburg (DE)
PCT Filed Jul. 6, 2021, PCT No. PCT/EP2021/068721
§ 371(c)(1), (2) Date Feb. 28, 2023,
PCT Pub. No. WO2022/048812, PCT Pub. Date Mar. 10, 2022.
Claims priority of application No. 10 2020 211 097.2 (DE), filed on Sep. 2, 2020.
Prior Publication US 2023/0331254 A1, Oct. 19, 2023
Int. Cl. B60W 60/00 (2020.01); B60W 50/00 (2006.01); B60W 50/14 (2020.01); G06F 18/2131 (2023.01); G06F 21/56 (2013.01); G06N 3/08 (2023.01)
CPC B60W 60/0015 (2020.02) [B60W 50/0098 (2013.01); B60W 50/14 (2013.01); B60W 60/0053 (2020.02); G06F 18/2131 (2023.01); G06F 21/56 (2013.01); B60W 2420/403 (2013.01); B60W 2420/408 (2024.01); G06N 3/08 (2013.01)] 17 Claims
OG exemplary drawing
 
1. A method for controlling a driver assistance system during operation of a vehicle, wherein the driver assistance system comprises at least one sensor for observing an environment of the vehicle and an electronic control unit using a neural network for analyzing sensor data of the at least one sensor and providing perception tasks for the vehicle based on the analyzed sensor data, wherein the sensor data comprises one or more of optical, acoustical, and electromagnetic data, the method comprising:
providing a data set of the sensor data by the at least one sensor in a spatial domain;
transforming the data set of the sensor data by the electronic control unit using frequency analysis into a frequency spectrum in a frequency domain; and
analyzing the frequency spectrum of the data set in order to detect an adversarially attacked data set; wherein
wherein in response to an adversarially attacked data set being detected from analyzing the frequency spectrum, at least the following is conducted: selectively activating a security mechanism against an adversarial attack;
wherein the security mechanism comprises one or more of:
activating a pre-processing technique to remove adversarial attack from the data set;
using a denoising filter for cleaning the adversarially attacked data set;
using a hardened neural network for analyzing the adversarially attacked data set;
using multiple other neural networks with different architectures in an ensemble and performing a consolidation of their output for analyzing the adversarially attacked data set;
applying rule-based technique for analyzing the adversarially attacked data set, especially using external, human-readable devices; and
requesting a user of the vehicle for analyzing the adversarially attacked data set; and
wherein the neural network or a special detecting neural network used for detecting the adversarially attacked data set are trained in the frequency domain on frequency spectrums of clean data sets and on frequency spectrums of prepared adversarially attacked data sets in order to learn differences between the frequency spectrums of the clean data sets and the frequency spectrums of the adversarially attacked data sets in the frequency domain.