  Autonomous vehicles rely heavily upon their perception subsystems to see the
environment in which they operate. Unfortunately, the effect of variable
weather conditions presents a significant challenge to object detection
algorithms, and thus it is imperative to test the vehicle extensively in all
conditions which it may experience. However, development of robust autonomous
vehicle subsystems requires repeatable, controlled testing - while real weather
is unpredictable and cannot be scheduled. Real-world testing in adverse
conditions is an expensive and time-consuming task, often requiring access to
specialist facilities. Simulation is commonly relied upon as a substitute, with
increasingly visually realistic representations of the real-world being
developed. In the context of the complete autonomous vehicle control pipeline,
subsystems downstream of perception need to be tested with accurate recreations
of the perception system output, rather than focusing on subjective visual
realism of the input - whether in simulation or the real world. This study
develops the untapped potential of a lightweight weather augmentation method in
an autonomous racing vehicle - focusing not on visual accuracy, but rather the
effect upon perception subsystem performance in real time. With minimal
adjustment, the prototype developed in this study can replicate the effects of
water droplets on the camera lens, and fading light conditions. This approach
introduces a latency of less than 8 ms using compute hardware well suited to
being carried in the vehicle - rendering it ideal for real-time implementation
that can be run during experiments in simulation, and augmented reality testing
in the real world.
