  Drones are currently being explored for safety-critical applications where
human agents are expected to evolve in their vicinity. In such applications,
robust people avoidance must be provided by fusing a number of sensing
modalities in order to avoid collisions. Currently however, people detection
systems used on drones are solely based on standard cameras besides an emerging
number of works discussing the fusion of imaging and event-based cameras. On
the other hand, radar-based systems provide up-most robustness towards
environmental conditions but do not provide complete information on their own
and have mainly been investigated in automotive contexts, not for drones. In
order to enable the fusion of radars with both event-based and standard
cameras, we present KUL-UAVSAFE, a first-of-its-kind dataset for the study of
safety-critical people detection by drones. In addition, we propose a baseline
CNN architecture with cross-fusion highways and introduce a curriculum learning
strategy for multi-modal data termed SAUL, which greatly enhances the
robustness of the system towards hard RGB failures and provides a significant
gain of 15% in peak F1 score compared to the use of BlackIn, previously
proposed for cross-fusion networks. We demonstrate the real-time performance
and feasibility of the approach by implementing the system in an edge-computing
unit. We release our dataset and additional material in the project home page.
