TY - GEN
T1 - Toward Neuromorphic Perception
T2 - 26th IEEE International Conference on Intelligent Transportation Systems, ITSC 2023
AU - Zhuang, Genghang
AU - Bing, Zhenshan
AU - Huang, Kai
AU - Knoll, Alois
N1 - Publisher Copyright:
© 2023 IEEE.
PY - 2023
Y1 - 2023
N2 - As a prerequisite of high vehicle autonomy, lane segmentation is a significant perception task for advanced autonomous driving. In recent years, spiking neural networks (SNNs) have garnered the attention of researchers due to their appealing power efficiency, which provides the potential to improve energy consumption for the perception system on power-constrained autonomous vehicles. In this paper, we propose a spiking neural network targeted for LiDAR sensors to solve the lane segmentation problem. By encoding the LiDAR point cloud into spikes, the proposed SNN constructed in an end-to-end fully convolutional network structure is capable of processing the LiDAR input through the network to segment the lane area effectively. Experiments conducted on the KITTI dataset for urban scenes and the power consumption evaluation demonstrate the high performance and energy efficiency of the proposed SNN for LiDAR-based lane segmentation.
AB - As a prerequisite of high vehicle autonomy, lane segmentation is a significant perception task for advanced autonomous driving. In recent years, spiking neural networks (SNNs) have garnered the attention of researchers due to their appealing power efficiency, which provides the potential to improve energy consumption for the perception system on power-constrained autonomous vehicles. In this paper, we propose a spiking neural network targeted for LiDAR sensors to solve the lane segmentation problem. By encoding the LiDAR point cloud into spikes, the proposed SNN constructed in an end-to-end fully convolutional network structure is capable of processing the LiDAR input through the network to segment the lane area effectively. Experiments conducted on the KITTI dataset for urban scenes and the power consumption evaluation demonstrate the high performance and energy efficiency of the proposed SNN for LiDAR-based lane segmentation.
UR - http://www.scopus.com/inward/record.url?scp=85186502712&partnerID=8YFLogxK
U2 - 10.1109/ITSC57777.2023.10422133
DO - 10.1109/ITSC57777.2023.10422133
M3 - Conference contribution
AN - SCOPUS:85186502712
T3 - IEEE Conference on Intelligent Transportation Systems, Proceedings, ITSC
SP - 2448
EP - 2453
BT - 2023 IEEE 26th International Conference on Intelligent Transportation Systems, ITSC 2023
PB - Institute of Electrical and Electronics Engineers Inc.
Y2 - 24 September 2023 through 28 September 2023
ER -
