Originally, this team was focused on competing in the F1Tenth competition for autonomous racing, but now we have pivoted to working on Autonomous Driving research.
We are currently researching the use of Spiking Neural Networks with Prof. Jason Eshragian, and using Deep Reinforcement Learning and LLMs to create more robust and explainable driving models with Prof. Leilani Gilpin. Currently we are learning to use the nuScenes dataset to train a simple lane-following model which we can then test on our 1/10th scale autonomous car.
Through participating in Cruz Control, students will gain experience with tools that are fundamental to the robotics and autonomous sensing industries, such as ROS and Gazebo, and discover the mathematical underpinnings behind these technologies. Further, members can delve deeper into core autonomous systems concepts such as perception through sensors, path planning, autonomous navigation, control systems, robotics simulation, and applying simulation to hardware projects.