Parallel Parking Week 5

We are currently working on parallel parking. Check out our progress!

Week 2 Progress

Week 2 was a productive week for our group. We were able to successfully install the LejosJ firmware on the NXT brick and begin to learn about the vast library of commands that Lejos affords us. In addition, the preliminary construction of the robot and environment were completed.

One of the design constraints of the robot is that it must drive like a real car. To achieve this, we will use two motors to drive the rear wheels, and a third motor to turn the front wheels. This way, the car can be driven without varying the speed of the driving wheels. This design does not come without problems, however, as the mechanical construction is a lot more complicated and unforeseen physical interference with moving parts will be a problem. That being said, it is something that we hope to work on in the coming weeks so that we can begin to focus on the programming of the robot.

What is out there?

Society is becoming ever more automated, and every day, engineers develop new ways to make our lives easier and safer. Parallel parking is considered by many to be one of the most difficult skills associated with driving, and is the second most common reason for failing a driving exam. 

Self-park cars fifteen year ago sounded unrealistic, but today there are companies that are developing this technology. Ford created an Active Car Assistant where the driver has control over the acceleration of the vehicle, while the wheel is controlled by the computer. 

Audi has the Audi Connect, which without human directions can search for a parking space and park itself. It can also go back to the original position and pick up the driver. 

Other companies that works towards this technology are BMW, with the BMW ConnectedDrive. This car searches for a suitable parking space and parks itself.

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.