Expose perception to the toughest skies and darkest roads. 

Shalev Cohen ,Noam Hadad

A specialized dataset-generation project dedicated to creating edge-case scenarios for ADAS and autonomous vehicles, focused exclusively on extreme weather and challenging lighting. It produces richly varied scenes involving heavy rain, snow, fog, haze, night driving, harsh glare, low sun angles, sudden illumination shifts, and mixed-weather transitions. Each example is annotated for object detection, segmentation, tracking, and hazard recognition. 

Prepare perception for the unexpected on the asphalt. 

Tomer Atia

A dataset-generation project focused on creating edge-case scenarios for ADAS and autonomous vehicles involving animals and debris on the road. It synthesizes and curates diverse scenes featuring wildlife crossings, sudden animal appearances, fallen branches, cargo spills, tire fragments, construction debris, and miscellaneous obstacles under varied environments and viewpoints. Each sample is annotated for detection, segmentation, tracking, and hazard classification.  

Test safety where humans are most at risk.

Sagi Akshikar

A dataset-generation project dedicated to creating edge-case scenarios for ADAS and autonomous vehicles involving vulnerable road users (VRUs). It produces challenging scenes featuring pedestrians, cyclists, scooter riders, children, elderly individuals, and people with mobility aids under diverse poses, occlusions, traffic contexts, and motion patterns. The dataset includes rare high-risk situations such as sudden crossings, partial visibility, unusual behavior, and interactions near vehicles.