Abstract: Developing autonomous vehicles is a complex challenge. It involves training and testing AI drivers by using supervised data collected on a diversity of driving episodes. We could say that data is the driver in autonomous driving, especially in the deep learning era. In this context, the talk focuses on the efforts conducted at CVC to minimize data labelling efforts. This includes the use of simulated data to support the training of visual models, the development of self-labeling procedures, as well as exploring non-standard paradigms for autonomous driving such as end-to-end driving by imitation learning.
Speaker: Antonio M López has a long trajectory carrying research at the intersection of computer vision, simulation, machine learning, driver assistance, and autonomous driving. Antonio has been deeply involved in the creation of the SYNTHIA dataset and the CARLA open-source simulator, both for democratizing autonomous driving research. He is actively working hand-on-hand with industry partners to bring state-of-the-art techniques to the field of autonomous driving.
Recording: Autonomous Driving Research at CVC/UAB