![]() ![]() I cloned the ROAR Github and studied the PID Controller code. Therefore I decided to learn the existing PID control method from the baseline and hopefully improve it a little. Originally, I wanted to attempt the machine learning way, however, later I realized that it was too difficult to finish it in time. Before this competition, I was familiar with python coding and some machine learning concepts. ![]() The map used in this competition is the Berkely Major map which has many sharp turns, bumps, and hilly areas likely to cause crashes. During the competition and the study in ROAR academy, I learned the AI and autonomous car driving that inspired me to participate in this event and also introduced it to my high school’s machine learning club. In the future, the learnings from this competition could be beneficial in real life to control electric cars like Teslas to automatically drive in the streets. The goal of the competition is to learn and race these cars for maximum speed without collisions. ![]() The ROAR Competition S1/S2 is an autonomous racing competition hosted by University of California, Berkeley where high school and college students build and code simulated car models in a virtual Berkeley campus track built by the Berkeley FHL Vive Center for Enhanced Reality. This report summarizes the experience of my work in the Berkeley Robot Open Autonomous Racing (ROAR) competition.
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