2020 25th International Conference on Pattern Recognition (ICPR)
2021
TLDR
This is the first attempt to use SNN to perform time and energy efficient object recognition directly on LiDAR temporal pulses in the setting of autonomous driving.
Abstract
Accurate real-time object recognition from sensory data has long been a crucial and challenging task for autonomous driving. Even though deep neural networks (DNNs) have been widely applied in this area, their considerable processing latency, power consumption, as well as computational complexity have been challenging issues for real-time autonomous driving applications. In this paper, we propose an approach to address the real-time object recognition problem utilizing spiking neural networks (SNNs). The proposed SNN model works directly with raw LiDAR temporal pulses without the pulse-to-point cloud preprocessing procedure, which can significantly reduce delay and power consumption. Being evaluated on various datasets derived from LiDAR and dynamic vision sensor (DVS), including Sim LiDAR, KITTI, and DVS-barrel, our proposed model has shown remarkable time and power efficiency, while achieving comparable recognition performance as the state-of-the-art methods. This paper highlights the SNN's great potentials in autonomous driving and related applications. To the best of our knowledge, this is the first attempt to use SNN to perform time and energy efficient object recognition directly on LiDAR temporal pulses in the setting of autonomous driving”
Hi McHale
Easy to understand why you can see the benefit of unconnected ADAS. LOL
You will remember Shibo (Bob) Zhou and his links to Brainchip as will many others I feel sure so the above paper is for newer shareholders really as it reinforces the dots that link Valeos’ EAP, Brainchip and Lidar for ADAS.
Happy New Year.
My opinion only DYOR FF
AKIDA BALLISTA
BRN Price at posting:
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