Developing an autonomous driving car has long been a passion of mine. My initial goal is to implement fundamental features such as lane detection, traffic sign recognition, and obstacle avoidance. To achieve this, I plan to start with a small robotic car platform, allowing for practical experimentation and iterative learning in a controlled environment.

I have completed several courses focused on training machine learning models for lane detection and traffic sign recognition. These courses have equipped me with the skills to develop and implement models capable of accurately identifying road lanes and interpreting various traffic signs.

Once the small robotic car is operational, I plan to scale up the project by integrating the developed autonomous driving features—such as lane detection, sign recognition, and obstacle avoidance—into a remote-controlled mobility scooter that I have constructed. This progression will allow for testing and refining the system’s performance on a larger, more robust platform, bridging the gap between small-scale prototypes and full-sized autonomous vehicles.

Concurrently, I aim to apply this technology to my electric G-Wiz car. By equipping the vehicle with sensors to collect data on speed, road conditions, GPS coordinates, and steering angles, I can gather real-world driving information. This data will be instrumental in training the aforementioned models to navigate UK roads effectively.