27 citations · 37 across the 7 of their papers we have counts for
7 papers
Real-time safety assessment of trajectories for autonomous driving
Hoang Tung Dinh, Danilo Romano, Patrick Abrahao Menani +5
Autonomous vehicles (AVs) must always have a safe motion to guarantee that they are not causing any accidents. In an AV system, the motion of the vehicle is represented as a trajec…
FuseMODNet: Real-Time Camera and LiDAR based Moving Object Detection for robust low-light Autonomous Driving
Hazem Rashed, Mohamed Ramzy, Victor Vaquero +3
Moving object detection is a critical task for autonomous vehicles. As dynamic objects represent higher collision risk than static ones, our own ego-trajectories have to be planned…
Improving Map Re-localization with Deep 'Movable' Objects Segmentation on 3D LiDAR Point Clouds
Victor Vaquero, Kai Fischer, Francesc Moreno-Noguer +2
Localization and Mapping is an essential component to enable Autonomous Vehicles navigation, and requires an accuracy exceeding that of commercial GPS-based systems. Current odomet…
Hallucinating Dense Optical Flow from Sparse Lidar for Autonomous Vehicles
Victor Vaquero, Alberto Sanfeliu, Francesc Moreno-Noguer
In this paper we propose a novel approach to estimate dense optical flow from sparse lidar data acquired on an autonomous vehicle. This is intended to be used as a drop-in replacem…
Deep Lidar CNN to Understand the Dynamics of Moving Vehicles
Victor Vaquero, Alberto Sanfeliu, Francesc Moreno-Noguer
Perception technologies in Autonomous Driving are experiencing their golden age due to the advances in Deep Learning. Yet, most of these systems rely on the semantically rich infor…
Deconvolutional Networks for Point-Cloud Vehicle Detection and Tracking in Driving Scenarios
Victor Vaquero, Ivan del Pino, Francesc Moreno-Noguer +3
Vehicle detection and tracking is a core ingredient for developing autonomous driving applications in urban scenarios. Recent image-based Deep Learning (DL) techniques are obtainin…
Joint Coarse-And-Fine Reasoning for Deep Optical Flow
Victor Vaquero, German Ros, Francesc Moreno-Noguer +2
We propose a novel representation for dense pixel-wise estimation tasks using CNNs that boosts accuracy and reduces training time, by explicitly exploiting joint coarse-and-fine re…