Autonomous Driving using Spiking Neural Networks on Dynamic Vision Sensor Data: A Case Study of Traffic Light Change Detection
This work addresses energy efficiency in autonomous driving systems, but it is incremental as it extends SNN applications to more realistic simulations without major breakthroughs.
The paper tackled the problem of high computational resource and power consumption in autonomous driving by applying spiking neural networks (SNNs) to dynamic vision sensor data for traffic light change detection, achieving results in a photo-realistic CARLA simulator environment as a step toward real-world use.
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.