  Safe road crossing by autonomous wheelchairs can be affected by several
environmental factors such as adverse weather conditions influencing the
accuracy of artificial vision. Previous studies have addressed experimental
evaluation of multi-sensor information fusion to support road-crossing
decisions in autonomous wheelchairs. In this study, we focus on the fine-tuning
of tracking performance and on its experimental evaluation against outdoor
environmental factors such as fog, rain, darkness, etc. It is rather intuitive
that those factors can negatively affect the tracking performance; therefore
our aim is to provide an approach to quantify their effects in the reference
scenario, in order to detect conditions of unacceptable accuracy. In those
cases, warnings can be issued and system can be possibly reconfigured to reduce
the reputation of less accurate sensors, and thus improve overall safety.
Critical situations can be detected by the main sensors or by additional
sensors, e.g., light sensors, rain sensors, etc. Results have been achieved by
using an available laboratory dataset and by applying appropriate software
filters; they show that the approach can be adopted to evaluate video tracking
and event detection robustness against outdoor environmental factors in
relevant operational scenarios.
