Amr Ramadan

- Implemented A-SPICE: requirements (SWE.1), design (SWE.3), unit verification (SWE.4), integration tests (SWE.5) - Enhanced traffic rule models (SWE.3/SWE.4) - Improved safety and resolved 1000+ warnings (SWE.4) - Developed road design libraries and improved GUI (Python, Qt) - Tech: C++, Python, ISO26262, MISRA, GTests, Azure DevOps, GitHub, Linux, Qt - Analyzed requirements (SWE.1) - Developed detection algorithm/simulation (SWE.3/SWE.4, 80% accuracy) - Tech: MATLAB/SIMULINK, Machine Learning, Jira

- Taught third and fourth year mechatronics engineering courses - Supervised bachelor thesis in the field of autonomous driving

- Throttle and steering control boxes on a small-scale car. - Drivable Area Extraction based on Shadow Corrected Images - Reinforcement Learning Based Approach for Multi-Vehicle Platooning Problem - Heterogeneous Platooning Decision Making Algorithm based on Optimization and Deep Learning - Design and Validation of a Novel Adaptive Cruise Control Law for a Platoon of Vehicles - Dynamics Platooning Model and Protocols for Self-Driving Vehicles

• Developed ROS-based maneuvering protocols for a platoon of self-driving cars (ROS, C++).

Utilization of lane and vehicle detection, cooperative localization, and vehicle control using model based and model free controllers as well as reinforcement learning, while using optimization techniques like genetic algorithm and deep learning to optimize energy for self-driving vehicles.

Developing ROS-based manoeuvring protocols for a platoon of self-driving cars