  Neural networks has been successfully used in the processing of Lidar data,
especially in the scenario of autonomous driving. However, existing methods
heavily rely on pre-processing of the pulse signals derived from Lidar sensors
and therefore result in high computational overhead and considerable latency.
In this paper, we proposed an approach utilizing Spiking Neural Network (SNN)
to address the object recognition problem directly with raw temporal pulses. To
help with the evaluation and benchmarking, a comprehensive temporal pulses
data-set was created to simulate Lidar reflection in different road scenarios.
Being tested with regard to recognition accuracy and time efficiency under
different noise conditions, our proposed method shows remarkable performance
with the inference accuracy up to 99.83% (with 10% noise) and the average
recognition delay as low as 265 ns. It highlights the potential of SNN in
autonomous driving and some related applications. In particular, to our best
knowledge, this is the first attempt to use SNN to directly perform object
recognition on raw Lidar temporal pulses.
