Ibrahim Sobh

Orcid: 0000-0002-9414-6267

According to our database1, Ibrahim Sobh authored at least 15 papers between 2019 and 2022.

Collaborative distances:
  • Dijkstra number2 of five.
  • Erdős number3 of four.

Timeline

Legend:

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Links

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Bibliography

2022
Deep Reinforcement Learning for Autonomous Driving: A Survey.
IEEE Trans. Intell. Transp. Syst., 2022

RL_QOptimizer: A Reinforcement Learning Based Query Optimizer.
IEEE Access, 2022

Multimodel System for Driver Distraction Detection and Elimination.
IEEE Access, 2022

Study of LiDAR Segmentation and Model's Uncertainty using Transformer for Different Pre-trainings.
Proceedings of the 17th International Joint Conference on Computer Vision, 2022

ASPICE Applicability on New Automotive Technologies (AI).
Proceedings of the Systems, Software and Services Process Improvement, 2022

Adversarial Attacks on Multi-task Visual Perception for Autonomous Driving.
Proceedings of the Autonomous Vehicles and Machines 2022, online, January 15-26, 2022, 2022

2021
Autonomous driving in the face of unconventional odds.
Commun. ACM, 2021

Post Pandemic Era: Future of the Automotive Online Assessments.
Proceedings of the Systems, Software and Services Process Improvement, 2021

GG-Net: Gaze Guided Network for Self-driving Cars.
Proceedings of the Autonomous Vehicles and Machines 2021, online, January 11-28, 2021, 2021

2020
End-to-End Multitask Learning for Driver Gaze and Head Pose Estimation.
Proceedings of the Autonomous Vehicles and Machines 2020, 2020

2019
Unsupervised Neural Sensor Models for Synthetic LiDAR Data Augmentation.
CoRR, 2019

End-to-End 3D-PointCloud Semantic Segmentation for Autonomous Driving.
CoRR, 2019

LiDAR Sensor modeling and Data augmentation with GANs for Autonomous driving.
CoRR, 2019

Exploring Applications of Deep Reinforcement Learning for Real-world Autonomous Driving Systems.
Proceedings of the 14th International Joint Conference on Computer Vision, 2019

Yes, we GAN: Applying adversarial techniques for autonomous driving.
Proceedings of the Autonomous Vehicles and Machines 2019, 2019


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