Vision in adverse weather: Augmentation using CycleGANs with various object detectors for robust perception in autonomous racing

Understand

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.