  Autonomous Driving (AD) systems are considered as the future of human
mobility and transportation. Solving computer vision tasks such as image
classification and object detection/segmentation, with high accuracy and low
power/energy consumption, is highly needed to realize AD systems in real life.
These requirements can potentially be satisfied by Spiking Neural Networks
(SNNs). However, the state-of-the-art works in SNN-based AD systems still focus
on proposing network models that can achieve high accuracy, and they have not
systematically studied the roles of SNN parameters when used for learning
event-based automotive data. Therefore, we still lack understanding of how to
effectively develop SNN models for AD systems. Toward this, we propose a novel
methodology to systematically study and analyze the impact of SNN parameters
considering event-based automotive data, then leverage this analysis for
enhancing SNN developments. To do this, we first explore different settings of
SNN parameters that directly affect the learning mechanism (i.e., batch size,
learning rate, neuron threshold potential, and weight decay), then analyze the
accuracy results. Afterward, we propose techniques that jointly improve SNN
accuracy and reduce training time. Experimental results show that our
methodology can improve the SNN models for AD systems than the
state-of-the-art, as it achieves higher accuracy (i.e., 86%) for the NCARS
dataset, and it can also achieve iso-accuracy (i.e., ~85% with standard
deviation less than 0.5%) while speeding up the training time by 1.9x. In this
manner, our research work provides a set of guidelines for SNN parameter
enhancements, thereby enabling the practical developments of SNN-based AD
systems.
