TY - GEN
T1 - Radar Fault Detection via Camera-Radar Branches Learning Network
AU - Ning, Dian
AU - Han, Dong Seog
N1 - Publisher Copyright:
© 2023 IEEE.
PY - 2023
Y1 - 2023
N2 - Radars are widely used in autonomous driving technology. Self-driving usually relies on radar signals to recognize pedestrians and vehicles, identify the surrounding environments reliably, avoid car crashes and navigation, and provide a reliable route to avoid collisions. The radar plays an important role in vehicle systems, and maintaining its proper functioning is necessary for the safety of self-driving systems to be considered. However, sensor faults are unavoidable. When the radar sensor is faulty, the radar signal will not receive the correct feedback information. Currently, it is hard to detect fault errors in radars, and the algorithm is complicated to work with. To analyze the radar cross section (RCS) signal and distance relationship, we used the RCS signal feature and combined the real-time features of the vehicle camera with the convolutional neural network (CNN) model to identify the fault information as expected. The paper uses a new data generator feature and deep learning model, recognizes the input signal as normal and abnormal, and the accuracy improves to 95.54%.
AB - Radars are widely used in autonomous driving technology. Self-driving usually relies on radar signals to recognize pedestrians and vehicles, identify the surrounding environments reliably, avoid car crashes and navigation, and provide a reliable route to avoid collisions. The radar plays an important role in vehicle systems, and maintaining its proper functioning is necessary for the safety of self-driving systems to be considered. However, sensor faults are unavoidable. When the radar sensor is faulty, the radar signal will not receive the correct feedback information. Currently, it is hard to detect fault errors in radars, and the algorithm is complicated to work with. To analyze the radar cross section (RCS) signal and distance relationship, we used the RCS signal feature and combined the real-time features of the vehicle camera with the convolutional neural network (CNN) model to identify the fault information as expected. The paper uses a new data generator feature and deep learning model, recognizes the input signal as normal and abnormal, and the accuracy improves to 95.54%.
KW - Anomaly Detection
KW - Convolutional Neural Network(CNN)
KW - Radar Cross Section(RCS)
UR - http://www.scopus.com/inward/record.url?scp=85151949448&partnerID=8YFLogxK
U2 - 10.1109/ICAIIC57133.2023.10067071
DO - 10.1109/ICAIIC57133.2023.10067071
M3 - Conference contribution
AN - SCOPUS:85151949448
T3 - 5th International Conference on Artificial Intelligence in Information and Communication, ICAIIC 2023
SP - 463
EP - 467
BT - 5th International Conference on Artificial Intelligence in Information and Communication, ICAIIC 2023
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 5th International Conference on Artificial Intelligence in Information and Communication, ICAIIC 2023
Y2 - 20 February 2023 through 23 February 2023
ER -