Search Results for author: Fawzi Nashashibi

Found 7 papers, 0 papers with code

Fast maneuver recovery from aerial observation: trajectory clustering and outliers rejection

no code implementations3 Jul 2024 Nelson de Moura, Augustin Gervreau-Mercier, Fernando Garrido, Fawzi Nashashibi

The implementation of road user models that realistically reproduce a credible behavior in a multi-agentsimulation is still an open problem.

Clustering Trajectory Clustering

Interpretable Long Term Waypoint-Based Trajectory Prediction Model

no code implementations11 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.

Autonomous Driving Trajectory Prediction

Hierarchical Attention and Graph Neural Networks: Toward Drift-Free Pose Estimation

no code implementations18 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.

Pose Estimation

Interpretable Goal-Based model for Vehicle Trajectory Prediction in Interactive Scenarios

no code implementations8 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.

Autonomous Driving Trajectory Prediction

Trajectory Prediction for Autonomous Driving based on Multi-Head Attention with Joint Agent-Map Representation

no code implementations6 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.

Autonomous Driving Trajectory Prediction

End-to-End Race Driving with Deep Reinforcement Learning

no code implementations6 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).

Domain Adaptation Object Recognition +3

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