Cooperative driving controllers are becoming interesting subjects for future research in automated driving with the increase in connectivity. Using true-scale
autonomous vehicles to properly test new control algorithms can be a challenge
due to three main factors: costs, safety, and space requirements. To overcome
these problems, scaled-down vehicle platforms or so-called Remote Control (RC)
car platforms can be used to test algorithms in a safe environment without the
large costs involved with true-scale platforms. At present, existing RC platforms
are unnecessarily expensive or not able to drive autonomously.

This thesis presents the design of a low-cost 1/12 scale RC platform, consisting
of modified JetRacer Pro AI’s called R2C Car’s. The sensor suite consists of
a 160FoV 8MP camera and an added 2D LiDAR. These are used in a sensor
fusion and filtering framework to detect and locate other R2C Cars. Furthermore, wheel encoders are added to retrieve a velocity estimate of the R2C Car.
To showcase the possibilities of the R2C platform, a longitudinal cooperative
controller is implemented. This controller is based upon a Distributed Model
Predictive Control algorithm, whereby 5Ghz WiFi is used for the communication between R2C Cars. Evaluation of the R2C platform together with the
cooperative controller demonstrates that the platform is suitable for cooperative
driving experiments. Furthermore, information is presented on the performance
of the distributed algorithm.