no code implementations • 11 Dec 2023 • Amina Ghoul, Itheri Yahiaoui, Fawzi Nashashibi
In this paper, we study the impact of adding a long-term goal on the performance of a trajectory prediction framework.
no code implementations • 18 Sep 2023 • Kathia Melbouci, Fawzi Nashashibi
The most commonly used method for addressing 3D geometric registration is the iterative closet-point algorithm, this approach is incremental and prone to drift over multiple consecutive frames.
no code implementations • 8 Aug 2023 • Amina Ghoul, Itheri Yahiaoui, Anne Verroust-Blondet, Fawzi Nashashibi
The abilities to understand the social interaction behaviors between a vehicle and its surroundings while predicting its trajectory in an urban environment are critical for road safety in autonomous driving.
no code implementations • 6 May 2020 • Kaouther Messaoud, Nachiket Deo, Mohan M. Trivedi, Fawzi Nashashibi
The future trajectories of agents can be inferred using two important cues: the locations and past motion of agents, and the static scene structure.
no code implementations • 2 Aug 2018 • Maximilian Jaritz, Raoul de Charette, Emilie Wirbel, Xavier Perrotton, Fawzi Nashashibi
Convolutional neural networks are designed for dense data, but vision data is often sparse (stereo depth, point clouds, pen stroke, etc.).
Ranked #9 on Depth Completion on KITTI Depth Completion
no code implementations • 6 Jul 2018 • Maximilian Jaritz, Raoul de Charette, Marin Toromanoff, Etienne Perot, Fawzi Nashashibi
We present research using the latest reinforcement learning algorithm for end-to-end driving without any mediated perception (object recognition, scene understanding).