Search Results for author: Ibrahim Sobh

Found 8 papers, 1 papers with code

Deep Reinforcement Learning for Autonomous Driving: A Survey

no code implementations2 Feb 2020 B Ravi Kiran, Ibrahim Sobh, Victor Talpaert, Patrick Mannion, Ahmad A. Al Sallab, Senthil Yogamani, Patrick Pérez

With the development of deep representation learning, the domain of reinforcement learning (RL) has become a powerful learning framework now capable of learning complex policies in high dimensional environments.

Autonomous Driving Imitation Learning +3

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 +2

End-to-End 3D-PointCloud Semantic Segmentation for Autonomous Driving

no code implementations26 Jun 2019 Mohammed Abdou, Mahmoud Elkhateeb, Ibrahim Sobh, Ahmad El-Sallab

Imbalanced distribution of classes in the dataset is one of the challenges that face 3D semantic scene labeling task.

Autonomous Driving Scene Labeling +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

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