Vision-based Autonomous Driving by Imitation Learning

Developing autonomous vehicles requires training and testing AI drivers with supervised data gathered from a wide variety of driving scenarios. We could say that data is the driver in autonomous driving. This talk highlights the work carried out at CVC/UAB to reduce the need for manual data labeling, focusing on the use of sensorimotor models trained through imitation learning. Antonio’s team brings nearly seven years of experience to this field, ranging from simulation with CARLA to deploying real-world vehicles in the Catalan Pyrenees and on the UAB campus. Their research also includes comparative studies of human attention and AI driver attention. In this presentation, we will review the team's research journey on this topic from its beginnings to the present, discussing current achievements and open questions