This bachelor degree project presents an extension of a base optimization model for a transit line which can be used to evaluate the efficiency of different configurations of a platoon with different scenarios of berths. Furthermore, different levels of autonomous vehicles are studied, three cases are presented. The first case implies that every vehicle has a driver, the second, semi-autonomous vehicles are used in the platoon which has a leading vehicle with driver. Then, the fully autonomous vehicles represent the last studied case. A new method to compute the service time in the stops which differentiate the time that passengers are boarding or alighting from delays or time lost in queues that may appear with an increasing demand is added to the base model. It is introduced also a two-step non-linear approach to the crowding factor that consider the sharp deterioration when the load factor of the bus is almost one. In this project the bus capacity has been considered as a variable to see if there is an optimum vehicle size that cover different values of demand. Numerical results are provided and the result show that vehicle platooning with equal number of vehicles than stop berths is always competitive in high-demands. Moreover, if semi-autonomous case is found the bus platooning gain effectiveness and is competitive with lower demand values. In the case of fully autonomous vehicles the gain of bus platooning is not as high as in the semiautonomous but has still an improvement and is competitive with medium demand values.

With all the advancements in autonomous and connected cars, there is a developing body of research around the security and robustness of driving automation systems. Attacks and mitigations for said attacks have been explored, but almost always solely in software simulations. For this thesis, I led a team to build the foundation for an open source platoon of scale semi-autonomous vehicles. This work will enable future research into implementing theoretical attacks and mitigations. Our 1/10 scale car leverages an Nvidia Jetson, embedded microcontroller, and sensors. The Jetson manages the computer vision, networking, control logic, and overall system control; the embedded microcontroller directly controls the car. A lidar module is responsible for recording distance to the preceding car, and an inertial measurement unit records the velocity of the car itself. I wrote the software for the networking, interprocess, and serial communications, as well as the control logic and system control.