  Besides performance, efficiency is a key design driver of technologies
supporting vehicular perception. Indeed, a well-balanced trade-off between
performance and energy consumption is crucial for the sustainability of
autonomous vehicles. In this context, the diversity of real-world contexts in
which autonomous vehicles can operate motivates the need for empowering
perception models with the capability to detect, characterize and identify
newly appearing objects by themselves. In this manuscript we elaborate on this
threefold conundrum (performance, efficiency and open-world learning) for
object detection modeling tasks over image data collected from vehicular
scenarios. Specifically, we show that well-performing and efficient models can
be realized by virtue of Spiking Neural Networks (SNNs), reaching competitive
levels of detection performance when compared to their non-spiking counterparts
at dramatic energy consumption savings (up to 85%) and a slightly improved
robustness against image noise. Our experiments herein offered also expose
qualitatively the complexity of detecting new objects based on the preliminary
results of a simple approach to discriminate potential object proposals in the
captured image.
