The AutoDrive challenge puts undergraduates to the test in designing a fully autonomous vehicle. We receive a Chevy Bolt from GM and our task is to autonomize it. It's to equip it with the sensors and the algorithms that are required to actually make it successfully detect stop signs, stay within lane lines, avoid obstacles such as pedestrians and otherwise navigate the world as a human would. Not only are we developing interesting technologies, but we're saving lives. And that is the promise of autonomy in general. Less accidents, fender benders, tailgating is going to be mitigated by our ability to apply technology. The perception system is in charge of taking any real-world data and converting it into something that the computer can use to drive our autonomous car. We are in charge of interfacing with the sensors whether it be camera, Lidar, GPS, IMU. We need to actually get the data in from the sensors and filter that data so that it's usable. And then we are also are writing algorithms to detect stop signs, detect lane lines and then detect objects in the world that we can add to our cost map, where we can navigate around those objects. Virginia Tech has had a very positive reputation of being a school that generates hands-on applied engineering. We teach the fundamentals, but at the same time, engineers that graduate from Virginia Tech have the ability to go out in the workplace and make a difference when they walk in. They know how to solve problems. One thing we knew we wanted was a camera that had a high dynamic range. That way it could see objects that were both in light and dark areas of the image at the same time, which you can easily get when driving. Another specification we wanted was a fairly high frame rate. You don't want the camera to run too slow. So if you detect a stop sign you're already past it. Image processing now is playing a much larger role. My guess is is that the optical side, the image processing will probably be the primary source of perception. It's gonna end up being cheaper and is probably more robust in the long run. Edmund Optics has been really excellent as they sponsor for us. We managed to work with them to get three really excellent cameras, these Allied Vision Prosillicas. As well as corresponding lenses to go with them. In addition to that, they actually provided support beyond just material support. It was engineering support too. None of us had really selected cameras especially for machine vision applications. So it really helped to talk to one of Edmund's solution engineers and get their feedback on which camera was the best. And his advice with, we were between different brands, he recommended that the Allied Vision had an easier API to work with, which just in, especially in a short, time frame project you know, the easier it is to write code for, it's gonna be, the easier it is to integrate with our system. And that proved true. We were able to get this up and running in the first day we we got camera. It's so important for industry to participate. The linking with various industries that provide hardware, that provide expertise goes to building the next generation of the workforce. The reason I took on the mantle of Business Lead is that you know, my eventual goal is to lead a company of my own and getting to work with engineers and also working to build relationships with sponsors has been a really great experience that I think will prepare me for that. I've always been interested in how computers kind of visualize data or gather data from the real world. So it's been interesting to go to these more expensive cameras and sensors, rather than just a $10 sensor that we've used in past classes. We're using very expensive sensors to solve problems that the industry hasn't even solved yet. In general I can't be more pleased with the engineers that I have around here. These are fantastic young people that are very very capable and are gonna make a difference in the world.