Ibrahim, 2021
ViewPDF| Publication | Publication Date | Title |
|---|---|---|
| Muhammad et al. | Vision-based semantic segmentation in scene understanding for autonomous driving: Recent achievements, challenges, and outlooks | |
| Haghighat et al. | Applications of deep learning in intelligent transportation systems | |
| US11919545B2 (en) | Scenario identification for validation and training of machine learning based models for autonomous vehicles | |
| Razi et al. | Deep learning serves traffic safety analysis: A forward‐looking review | |
| Wang et al. | A traffic prediction model based on multiple factors | |
| Ibrahim et al. | CyclingNet: Detecting cycling near misses from video streams in complex urban scenes with deep learning | |
| Sathya et al. | A framework for designing unsupervised pothole detection by integrating feature extraction using deep recurrent neural network | |
| Fu et al. | A method in modeling interactive pedestrian crossing and driver yielding decisions during their interactions at intersections | |
| Ibrahim et al. | Cycling near misses: a review of the current methods, challenges and the potential of an AI-embedded system | |
| Yaqoob et al. | Deep transfer learning-based anomaly detection for cycling safety | |
| Kadav et al. | Development of computer vision models for drivable region detection in snow occluded lane lines | |
| Azad et al. | A review on machine learning in Intelligent Transportation Systems Applications | |
| Gawali et al. | Dual-discriminator conditional Giza pyramids construction generative adversarial network based traffic density recognition using road vehicle images | |
| Bouhsissin et al. | SafeSmartDrive: real-time traffic environment detection and driver behavior monitoring with machine and deep learning | |
| Jadhav et al. | Road accident analysis and prediction of accident severity using machine learning | |
| He et al. | Applications of deep learning techniques for pedestrian detection in smart environments: a comprehensive study | |
| Ibrahim | A computer vision system for detecting and analysing critical events in cities | |
| Dabboussi et al. | Data-driven methods and challenges for intelligent transportation systems in smart cities | |
| Rahim et al. | A novel Spatio–Temporal deep learning vehicle turns detection scheme using GPS-Only data | |
| Ji et al. | An expert ensemble for detecting anomalous scenes, interactions, and behaviors in autonomous driving | |
| Penchalaiah et al. | Customized CNN-Based Condition Monitoring of Road Traffic for Intelligent Transportation Routing | |
| Dafrallah et al. | Pedestrian walking direction classification for Moroccan road safety | |
| Aeri et al. | Unveiling Effectiveness: Advanced Vehicle Tracking and Detection Systems in Action | |
| EL MALLAHI et al. | Enhancing Traffic Safety with Advanced Machine Learning Techniques and Intelligent Identification | |
| Alafi et al. | Multi-class real-time crash risk forecasting using convolutional neural network: Istanbul case study |