  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.
