Autonomous Driving using Spiking Neural Networks on Dynamic Vision
  Sensor Data: A Case Study of Traffic Light Change Detection
Xuelei Chen
Autonomous driving is a challenging task that has gained broad attention from
both academia and industry. Current solutions using convolutional neural
networks require large amounts of computational resources, leading to high
power consumption. Spiking neural networks (SNNs) provide an alternative
computation model to process information and make decisions. This biologically
plausible model has the advantage of low latency and energy efficiency. Recent
work using SNNs for autonomous driving mostly focused on simple tasks like lane
keeping in simplified simulation environments. This project studies SNNs on
photo-realistic driving scenes in the CARLA simulator, which is an important
step toward using SNNs on real vehicles. The efficacy and generalizability of
the method will be investigated.