• Project Title: Image Processing Through Water-Related Weather and Obstacles

  • BASIS Advisor: Ms. Jefferson

  • Internship Location: BYTON

  • Onsite Mentor: Dr. Xinhua Xiao

Here’s a scenario: You’re on a drive. It’s cloudy outside, and you see the droplets begin to accumulate on your car window. Based off the unusual sight impairments that follow, it’s easy to infer it is raining. After you process this, you turn on your windshield wipers. Still, it’s hard to see, and it takes you one or two seconds longer than usual to brake for a traffic light. As a result, you drive carefully and more slowly on your way home. To humans, perceptions and reactions such as these are almost automatic, but it poses a major challenge for those in the machine learning field readying the self-driving car for the world. Water presents many problems for self-driving cars; for instance, water reflects light differently, which can throw off most of our current algorithms which heavily rely on lidar (light-based radar). In more extreme cases, it nearly impossible to gauge the depth of a body of water during very heavy rain. Byton is a company looking to tackle this problem, and I will be working with them to research ways to improve our AI (Artificial Intelligence) algorithms to better include water-related factors. I will be changing and testing the algorithms to better suit this purpose, as well as to choose and label good test data. Along the way I hope to better understand an array of different algorithms and their development process.