  In an autonomous driving system, perception - identification of features and
objects from the environment - is crucial. In autonomous racing, high speeds
and small margins demand rapid and accurate detection systems. During the race,
the weather can change abruptly, causing significant degradation in perception,
resulting in ineffective manoeuvres. In order to improve detection in adverse
weather, deep-learning-based models typically require extensive datasets
captured in such conditions - the collection of which is a tedious, laborious,
and costly process. However, recent developments in CycleGAN architectures
allow the synthesis of highly realistic scenes in multiple weather conditions.
To this end, we introduce an approach of using synthesised adverse condition
datasets in autonomous racing (generated using CycleGAN) to improve the
performance of four out of five state-of-the-art detectors by an average of
42.7 and 4.4 mAP percentage points in the presence of night-time conditions and
droplets, respectively. Furthermore, we present a comparative analysis of five
object detectors - identifying the optimal pairing of detector and training
data for use during autonomous racing in challenging conditions.
