Project Abstract

The “Autonomous Toy Car Racing” project is driven by a desire to investigate the use of self-driving technology in a tough and dynamic environment, pushing the limits of present capabilities. By creating an autonomous toy vehicle capable of racing around a track, the project hopes to demonstrate the power of machine learning and image processing techniques in allowing accurate and precise driving behavior. The multidisciplinary method, which combines powerful machine learning algorithms with real-time control systems, allows for the rapid, precise, and dependable navigation of complex tracks. The primary goal is to create a working prototype that not only proves the possibility of autonomous driving in small-scale robots, but also encourages future innovation and research in the field. Through this project, the world gains insights into the practicality and potential impact of autonomous systems beyond traditional applications, leading to improvements in academia and industry, and inspiring future innovators. The study took a multidisciplinary approach, combining machine learning, image processing, and real-time control systems to create an autonomous toy vehicle that can race around a track. The project successfully advanced autonomous driving technology through iterative data collecting, pre-processing, model creation, training, and assessment. The efficiency of machine learning methodologies such as transfer learning and reinforcement learning in training the autonomous system is one of the most significant results. The project created a working prototype that demonstrated the feasibility and usefulness of incorporating sophisticated technology for autonomous driving into small-scale robotics. Overall, the project contributes to pushing the boundaries of current autonomous driving technology and inspires further research and innovation in the field.

Keywords: Machine Learning, image processing potential, Effective transfer & reinforcement learning, Robotics, driving technology innovation

 Conference Details

Session: Presentation Stream 17 at Presentation Slot 8

Location: GH011 at Wednesday 8th 09:00 – 12:30

Markers: Arnold Beckmann, Alex Warren (GTA)

Course: MSc Computer Science, Masters PG

Future Plans: I’m looking for work