Autonomous Vehicle Object Detection

Ampera Racing, a student team from the Federal University of Santa Catarina, is developing an autonomous electric vehicle with a focus on cost-effective object detection, aiming to achieve one of the first autonomous racing vehicles in Latin America by the end of 2022. The software architecture for their self-driving car involves five key systems, including environment perception and motion planning, while utilizing tools such as YOLOv5 for object detection and OpenCV for inverse perspective mapping, which provides a top view of the environment to assist in positioning. The team has participated in international competitions like Formula Student Online, securing high ranks in electric and driverless categories, and plans to compete in Formula Student Germany. To address the challenges of environment perception, they experimented with monocular cameras for position estimation, using both keypoints neural networks and bird's-eye view methods, with the latter offering reduced computational cost. Their path planning algorithm uses Delaunay Triangulation to generate possible trajectories, and for lateral control, methods such as Pure Pursuit and Stanley Controller are employed, with implementations tested in both simulators and real-world miniature vehicles.