Search Results for author: Ahmad El Sallab

Found 13 papers, 4 papers with code

VM-MODNet: Vehicle Motion aware Moving Object Detection for Autonomous Driving

no code implementations22 Apr 2021 Hazem Rashed, Ahmad El Sallab, Senthil Yogamani

In this work, we aim to leverage the vehicle motion information and feed it into the model to have an adaptation mechanism based on ego-motion.

Autonomous Driving Motion Compensation +2

Unsupervised Neural Sensor Models for Synthetic LiDAR Data Augmentation

no code implementations24 Nov 2019 Ahmad El Sallab, Ibrahim Sobh, Mohamed Zahran, Mohamed Shawky

Evaluation is performed on unseen real LiDAR frames from KITTI dataset, with different amounts of simulated data augmentation using the two proposed approaches, showing improvement of 6% mAP for the object detection task, in favor of the augmenting LiDAR point clouds adapted with the proposed neural sensor models over the raw simulated LiDAR.

Data Augmentation Object Detection +1

FuseMODNet: Real-Time Camera and LiDAR based Moving Object Detection for robust low-light Autonomous Driving

no code implementations11 Oct 2019 Hazem Rashed, Mohamed Ramzy, Victor Vaquero, Ahmad El Sallab, Ganesh Sistu, Senthil Yogamani

In this work, we propose a robust and real-time CNN architecture for Moving Object Detection (MOD) under low-light conditions by capturing motion information from both camera and LiDAR sensors.

Autonomous Driving Moving Object Detection +1

RGB and LiDAR fusion based 3D Semantic Segmentation for Autonomous Driving

no code implementations1 Jun 2019 Khaled El Madawy, Hazem Rashed, Ahmad El Sallab, Omar Nasr, Hanan Kamel, Senthil Yogamani

Motivated by the fact that semantic segmentation is a mature algorithm on image data, we explore sensor fusion based 3D segmentation.

3D Semantic Segmentation Autonomous Driving +2

LiDAR Sensor modeling and Data augmentation with GANs for Autonomous driving

1 code implementation17 May 2019 Ahmad El Sallab, Ibrahim Sobh, Mohamed Zahran, Nader Essam

Simulators are often used for data augmentation, which requires realistic sensor models that are hard to formulate and model in closed forms.

Autonomous Driving Data Augmentation +3

YOLO3D: End-to-end real-time 3D Oriented Object Bounding Box Detection from LiDAR Point Cloud

3 code implementations7 Aug 2018 Waleed Ali, Sherif Abdelkarim, Mohamed Zahran, Mahmoud Zidan, Ahmad El Sallab

LiDAR sensors are employed to provide the 3D point cloud reconstruction of the surrounding environment, while the task of 3D object bounding box detection in real time remains a strong algorithmic challenge.

3D Point Cloud Reconstruction Object Detection +1

Meta learning Framework for Automated Driving

no code implementations11 Jun 2017 Ahmad El Sallab, Mahmoud Saeed, Omar Abdel Tawab, Mohammed Abdou

Under the proposed framework, we propose MetaDAgger, a novel algorithm which tackles the generalization issues in traditional imitation learning.

Imitation Learning Meta-Learning

Deep Reinforcement Learning framework for Autonomous Driving

1 code implementation8 Apr 2017 Ahmad El Sallab, Mohammed Abdou, Etienne Perot, Senthil Yogamani

This is of particular relevance as it is difficult to pose autonomous driving as a supervised learning problem due to strong interactions with the environment including other vehicles, pedestrians and roadworks.

Atari Games Autonomous Driving +1

End-to-End Deep Reinforcement Learning for Lane Keeping Assist

no code implementations13 Dec 2016 Ahmad El Sallab, Mohammed Abdou, Etienne Perot, Senthil Yogamani

This is of particular interest as it is difficult to pose autonomous driving as a supervised learning problem as it has a strong interaction with the environment including other vehicles, pedestrians and roadworks.

Autonomous Driving

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