Use Case
The Berkeley Autonomous Race Car (BARC) is an autonomous remote control car that handles complex maneuvers such as drifting and obstacle avoidance with high precision and at high speed.
The BARC engineering team selected Julia and the Julia for Mathematical Programming (JuMP) optimization package for path planning in autonomous race cars because Julia provides a fast, syntactically clean way to construct optimization problems that can be solved using open source tools.
In the case of autonomous race cars, sensors on the vehicle collect new data in real time (e.g. speed, terrain, obstacles), and Julia facilitates real-time optimization for safe, effective autonomous race car control.
Engineer Jon Gonzales presented a brief demo at JuliaCon 2016:
According to Francesco Borrelli, Professor of Mechanical Engineering and co-director of the Hyundai Center of Excellence in Integrated Vehicle Safety Systems and Control at UC Berkeley,
Julia has some amazing new features for our research. The port to ARM has made it easy for us to translate our research codes into real world applications.
