  Multi-Sensor Fusion (MSF) based perception systems have been the foundation
in supporting many industrial applications and domains, such as self-driving
cars, robotic arms, and unmanned aerial vehicles. Over the past few years, the
fast progress in data-driven artificial intelligence (AI) has brought a
fast-increasing trend to empower MSF systems by deep learning techniques to
further improve performance, especially on intelligent systems and their
perception systems. Although quite a few AI-enabled MSF perception systems and
techniques have been proposed, up to the present, limited benchmarks that focus
on MSF perception are publicly available. Given that many intelligent systems
such as self-driving cars are operated in safety-critical contexts where
perception systems play an important role, there comes an urgent need for a
more in-depth understanding of the performance and reliability of these MSF
systems. To bridge this gap, we initiate an early step in this direction and
construct a public benchmark of AI-enabled MSF-based perception systems
including three commonly adopted tasks (i.e., object detection, object
tracking, and depth completion). Based on this, to comprehensively understand
MSF systems' robustness and reliability, we design 14 common and realistic
corruption patterns to synthesize large-scale corrupted datasets. We further
perform a systematic evaluation of these systems through our large-scale
evaluation. Our results reveal the vulnerability of the current AI-enabled MSF
perception systems, calling for researchers and practitioners to take
robustness and reliability into account when designing AI-enabled MSF.
