  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 successfully applied in this area, most existing
methods still heavily rely on the pre-processing of the pulse signals derived
from LiDAR sensors, and therefore introduce additional computational overhead
and considerable latency. In this paper, we propose an approach to address the
object recognition problem directly with raw temporal pulses utilizing the
spiking neural network (SNN). Being evaluated on various datasets (including
Sim LiDAR, KITTI and DVS-barrel) derived from LiDAR and dynamic vision sensor
(DVS), our proposed method has shown comparable performance as the
state-of-the-art methods, while achieving remarkable time efficiency. It
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 directly perform object recognition on raw temporal pulses.
