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Autonomous Driving (AD) related features represent important elements for thenext generation of mobile robots and autonomous vehicles focused onincreasingly intelligent, autonomous, and interconnected systems. Theapplications 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, toincrease the lifetime and autonomy of battery-driven systems. These challengescan be addressed through efficient implementations of Spiking Neural Networks(SNNs) on Neuromorphic Chips and the use of event-based cameras instead oftraditional frame-based cameras.  In this paper, we present a new SNN-based approach, called LaneSNN, fordetecting the lanes marked on the streets using the event-based camera input.We develop four novel SNN models characterized by low complexity and fastresponse, and train them using an offline supervised learning rule. Afterward,we implement and map the learned SNNs models onto the Intel Loihi NeuromorphicResearch Chip. For the loss function, we develop a novel method based on thelinear composition of Weighted binary Cross Entropy (WCE) and Mean SquaredError (MSE) measures. Our experimental results show a maximum Intersection overUnion (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 36neurocores on the Loihi processor while providing a low latency of less than 8ms to recognize an image, thereby enabling real-time performance. The IoUmeasures provided by our networks are comparable with the state-of-the-art, butat a much low power consumption of 1 W.

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