One of the challenges to reduce the gap between the machine and the human level driving
is how to endow the system with the learning capacity to deal with the coupled complexity of …

Recent research on automotive driving has developed an efficient end-to-end learning
mode that directly maps visual input to control commands. However, it models distinct …

The feasibility of collecting a large amount of expert demonstrations has inspired growing
research interests in learning-to-drive settings, where models learn by imitating the driving …

Learning-based driving solution, a new branch for autonomous driving, is expected to
simplify the modeling of driving by learning the underlying mechanisms from data. To …

We present a learning-based planner that aims to robustly drive a vehicle by mimicking
human drivers' driving behavior. We leverage a mid-to-mid approach that allows us to …

Learning-based approaches have achieved remarkable performance in the domain of
autonomous driving. Leveraging the impressive ability of neural networks and large …

When autonomous vehicles still struggle to solve challenging situations during on-road
driving, humans have long mastered the essence of driving with efficient transferable and …

Conditional Imitation learning is a common and effective approach to train autonomous
driving agents. However, two issues limit the full potential of this approach:(i) the inertia …

Imitation learning is a powerful approach for learning autonomous driving policy by
leveraging data from expert driver demonstrations. However, driving policies trained via …

Deep networks trained on demonstrations of human driving have learned to follow roads
and avoid obstacles. However, driving policies trained via imitation learning cannot be …