Our Goal

As passenger vehicles become ever more sophisticated, processes like automated parallel parking and brake assist are increasingly common in modern cars. In the coming years, automation in vehicles will most likely continue to progress, potentially leading to an age when driving is a completely automated process.

A major hurdle in achieving such progress is enabling vehicles to make logical decisions based on the data collected by its various sensors. As a result, vehicles must not only be able to collect data from their environment, but appropriately process it to make the best decision possible.

Our project will aim to design robotic vehicles capable of parking themselves in a variable environment with preferential parking spot selection. Our goal will be to create an intelligent, preference-driven automated parking system that can accurately map an environment and choose the best possible parking spot.

The most challenging aspect of the project will be to program a system that can map an environment and then make a decision based on that information. The environment must also be designed, as it would in a world of automated vehicles, to facilitate this data collection and decision making.

In the end, we hope to deliver a demonstration of an intelligent parking system that can not only self-park, but also map its environment and choose the best spot available.