48 citations · 97 across the 7 of their papers we have counts for
8 papers
Adversarial Attacks on Multi-task Visual Perception for Autonomous Driving
Ibrahim Sobh, Ahmed Hamed, Varun Ravi Kumar +1
Deep neural networks (DNNs) have accomplished impressive success in various applications, including autonomous driving perception tasks, in recent years. On the other hand, current…
Deep Reinforcement Learning for Autonomous Driving: A Survey
B Ravi Kiran, Ibrahim Sobh, Victor Talpaert +4
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…
Unsupervised Neural Sensor Models for Synthetic LiDAR Data Augmentation
Ahmad El Sallab, Ibrahim Sobh, Mohamed Zahran +1
Data scarcity is a bottleneck to machine learning-based perception modules, usually tackled by augmenting real data with synthetic data from simulators. Realistic models of the veh…
End-to-End 3D-PointCloud Semantic Segmentation for Autonomous Driving
Mohammed Abdou, Mahmoud Elkhateeb, Ibrahim Sobh +1
3D semantic scene labeling is a fundamental task for Autonomous Driving. Recent work shows the capability of Deep Neural Networks in labeling 3D point sets provided by sensors like…
LiDAR Sensor modeling and Data augmentation with GANs for Autonomous driving
Ahmad El Sallab, Ibrahim Sobh, Mohamed Zahran +1
In the autonomous driving domain, data collection and annotation from real vehicles are expensive and sometimes unsafe. Simulators are often used for data augmentation, which requi…
Yes, we GAN: Applying Adversarial Techniques for Autonomous Driving
Michal Uricar, Pavel Krizek, David Hurych +3
Generative Adversarial Networks (GAN) have gained a lot of popularity from their introduction in 2014 till present. Research on GAN is rapidly growing and there are many variants o…