  Autonomous Driving (AD) related features provide new forms of mobility that
are also beneficial for other kind of intelligent and autonomous systems like
robots, smart transportation, and smart industries. For these applications, the
decisions need to be made fast and in real-time. Moreover, in the quest for
electric mobility, this task must follow low power policy, without affecting
much the autonomy of the mean of transport or the robot. These two challenges
can be tackled using the emerging Spiking Neural Networks (SNNs). When deployed
on a specialized neuromorphic hardware, SNNs can achieve high performance with
low latency and low power consumption. In this paper, we use an SNN connected
to an event-based camera for facing one of the key problems for AD, i.e., the
classification between cars and other objects. To consume less power than
traditional frame-based cameras, we use a Dynamic Vision Sensor (DVS). The
experiments are made following an offline supervised learning rule, followed by
mapping the learnt SNN model on the Intel Loihi Neuromorphic Research Chip. Our
best experiment achieves an accuracy on offline implementation of 86%, that
drops to 83% when it is ported onto the Loihi Chip. The Neuromorphic Hardware
implementation has maximum 0.72 ms of latency for every sample, and consumes
only 310 mW. To the best of our knowledge, this work is the first
implementation of an event-based car classifier on a Neuromorphic Chip.
