English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Plus monthly

Global: Zendy Tools annual

Global: Zendy Tools monthly

Global: Zendy Free Plan

In an autonomous driving system, perception - identification of features andobjects from the environment - is crucial. In autonomous racing, high speedsand 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 adverseweather, deep-learning-based models typically require extensive datasetscaptured in such conditions - the collection of which is a tedious, laborious,and costly process. However, recent developments in CycleGAN architecturesallow the synthesis of highly realistic scenes in multiple weather conditions.To this end, we introduce an approach of using synthesised adverse conditiondatasets in autonomous racing (generated using CycleGAN) to improve theperformance of four out of five state-of-the-art detectors by an average of42.7 and 4.4 mAP percentage points in the presence of night-time conditions anddroplets, respectively. Furthermore, we present a comparative analysis of fiveobject detectors - identifying the optimal pairing of detector and trainingdata for use during autonomous racing in challenging conditions.

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