TY - GEN
T1 - Spectranet
T2 - 12th IEEE Sensor Array and Multichannel Signal Processing Workshop, SAM 2022
AU - Zheng, Ruxin
AU - Sun, Shunqiao
AU - Scharff, David
AU - Wu, Teresa
N1 - Publisher Copyright:
© 2022 IEEE.
PY - 2022
Y1 - 2022
N2 - The potentials of automotive radar for autonomous driving have not been fully exploited due to the difficulty of extracting targets' information from the radar signals and the lack of radar datasets. In this paper, a novel signal processing pipeline is proposed to address the max ambiguous velocity reduction issue introduced by staggered time division multiplexing (TDM) scheme of high resolution imaging radar system with a large number of transmit antennas. A dataset of 1,410 synchronized frames (stereo cameras, LiDAR, radar) with three classes, i.e., bus, car, and people, is constructed from field experiments. Next, we implement a vanilla SpectraNet and show its promising performance on moving object detection and classification with a mean average precision (mAP) of 81.9% at an intersection over union (IoU) of 0.5.
AB - The potentials of automotive radar for autonomous driving have not been fully exploited due to the difficulty of extracting targets' information from the radar signals and the lack of radar datasets. In this paper, a novel signal processing pipeline is proposed to address the max ambiguous velocity reduction issue introduced by staggered time division multiplexing (TDM) scheme of high resolution imaging radar system with a large number of transmit antennas. A dataset of 1,410 synchronized frames (stereo cameras, LiDAR, radar) with three classes, i.e., bus, car, and people, is constructed from field experiments. Next, we implement a vanilla SpectraNet and show its promising performance on moving object detection and classification with a mean average precision (mAP) of 81.9% at an intersection over union (IoU) of 0.5.
KW - Automotive radar
KW - autonomous vehicles
KW - deep neural network
KW - machine learning
UR - http://www.scopus.com/inward/record.url?scp=85135380063&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85135380063&partnerID=8YFLogxK
U2 - 10.1109/SAM53842.2022.9827798
DO - 10.1109/SAM53842.2022.9827798
M3 - Conference contribution
AN - SCOPUS:85135380063
T3 - Proceedings of the IEEE Sensor Array and Multichannel Signal Processing Workshop
SP - 301
EP - 305
BT - 2022 IEEE 12th Sensor Array and Multichannel Signal Processing Workshop, SAM 2022
PB - IEEE Computer Society
Y2 - 20 June 2022 through 23 June 2022
ER -