LaneSNNs: Spiking Neural Networks for Lane Detection on the Loihi Neuromorphic Processor

by   Alberto Viale, et al.

Autonomous Driving (AD) related features represent important elements for the next generation of mobile robots and autonomous vehicles focused on increasingly intelligent, autonomous, and interconnected systems. The applications involving the use of these features must provide, by definition, real-time decisions, and this property is key to avoid catastrophic accidents. Moreover, all the decision processes must require low power consumption, to increase the lifetime and autonomy of battery-driven systems. These challenges can be addressed through efficient implementations of Spiking Neural Networks (SNNs) on Neuromorphic Chips and the use of event-based cameras instead of traditional frame-based cameras. In this paper, we present a new SNN-based approach, called LaneSNN, for detecting the lanes marked on the streets using the event-based camera input. We develop four novel SNN models characterized by low complexity and fast response, and train them using an offline supervised learning rule. Afterward, we implement and map the learned SNNs models onto the Intel Loihi Neuromorphic Research Chip. For the loss function, we develop a novel method based on the linear composition of Weighted binary Cross Entropy (WCE) and Mean Squared Error (MSE) measures. Our experimental results show a maximum Intersection over Union (IoU) measure of about 0.62 and very low power consumption of about 1 W. The best IoU is achieved with an SNN implementation that occupies only 36 neurocores on the Loihi processor while providing a low latency of less than 8 ms to recognize an image, thereby enabling real-time performance. The IoU measures provided by our networks are comparable with the state-of-the-art, but at a much low power consumption of 1 W.


page 1

page 2

page 4


CarSNN: An Efficient Spiking Neural Network for Event-Based Autonomous Cars on the Loihi Neuromorphic Research Processor

Autonomous Driving (AD) related features provide new forms of mobility t...

ColibriUAV: An Ultra-Fast, Energy-Efficient Neuromorphic Edge Processing UAV-Platform with Event-Based and Frame-Based Cameras

The interest in dynamic vision sensor (DVS)-powered unmanned aerial vehi...

Spiking neural networks trained with backpropagation for low power neuromorphic implementation of voice activity detection

Recent advances in Voice Activity Detection (VAD) are driven by artifici...

High Speed Cognitive Domain Ontologies for Asset Allocation Using Loihi Spiking Neurons

Cognitive agents are typically utilized in autonomous systems for automa...

StereoSpike: Depth Learning with a Spiking Neural Network

Depth estimation is an important computer vision task, useful in particu...

NET-TEN: a silicon neuromorphic network for low-latency detection of seizures in local field potentials

Therapeutic intervention in neurological disorders still relies heavily ...

Real-time ultra-low power ECG anomaly detection using an event-driven neuromorphic processor

Accurate detection of pathological conditions in human subjects can be a...

Please sign up or login with your details

Forgot password? Click here to reset