Valeo.ai

Valeo.ai x ICCV’21
Valeo.ai participates to ICCV, the premier computer vision conference, in October 2021, presenting six papers on the challenge of understanding complex scenes with cameras, radar or lidar. 

Valeo.ai x CVPR’21
Valeo.ai participates to CVPR , the premier computer vision conference, in June 2021, presenting three papers, contributing to a tutorial on self-supervised learning, co-organizing the workshop on omnidirectional computer vision and presenting keynotes to SafeAI4AutonomouDriving and Vision4AllSeason workshops.

Woodscape dataset
Four Valeo teams collaborate to release Woodscape, the first public multi-sensor driving dataset with fisheye cameras, named after Robert Wood who invented the fisheye camera in 1906, featuring 9 perception tasks such as 2D and 3D object detection, semantic segmentation and depth estimation.

Carrada dataset
In collaboration with researchers at Télécom Paris, Valeo.ai releases the Camera and Automotive Radar with Range-Angle-Doppler Annotations (Carrada) dataset, the first public automotive radar dataset with cars, cyclists and pedestrians precisely annotated in the raw signals.

Can we make driving systems explainable?
Valeo.ai researchers release a comprehensive survey on the explainability of vision-based driving systems, presenting a wide range of existing techniques for post-hoc or by-design explainability, analysing their current limitation and outlining future research avenues toward a better interpretation of self-driving AI models.

Paying attention to vulnerable road users
Vulnerable road users such as pedestrians must be reliably analysed by ADAS and AD systems; Valeo.ai shows that synthetic people in real scenes help to train better detectors (collaboration with CTU Prague) and that a multi-task model can recognize up to 32 attributes, including action and attention (collaboration with EPFL).