Autonomous Driving using Spiking Neural Networks on Dynamic Vision Sensor Data: A Case Study of Traffic Light Change Detection

Xuelei Chen, Sotirios Spanogianopoulos
9/27/2023
cs.CVcs.NEcs.RO

No official code found. Best implementation: xueleichen/snn-dvs-carla (11 stars, MIT, last commit 2023-11-19).

Abstract

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 computational 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 paper 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.

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Code Implementations(1)

xueleichen/snn-dvs-carlaCommunity100%
MIT

SNNs for CARLA Event Camera Data

111PythonNov 19, 20232 years agoMIT
dynamic-vision-sensorsevent-camerasspiking-neural-networks

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Cite this paper

@article{chen2023autonomous,
  title  = {Autonomous Driving using Spiking Neural Networks on Dynamic Vision Sensor Data: A Case Study of Traffic Light Change Detection},
  author = {Xuelei Chen and Sotirios Spanogianopoulos},
  year   = {2023},
  eprint = {2311.09225},
  archivePrefix = {arXiv},
  url    = {http://arxiv.org/abs/2311.09225v2}
}

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