End-to-end visual-based imitation learning has been widely applied in autonomous driving.
When deploying the trained visual-based driving policy, a deterministic command is usually …

Learning an optimal policy for autonomous driving task to confront with complex
environment is a long-studied challenge. Imitative reinforcement learning is accepted as a …

All-day and all-weather navigation is a critical capability for autonomous driving, which
requires proper reaction to varied environmental conditions and complex agent behaviors …

It is well known that semantic segmentation can be used as an effective intermediate
representation for learning driving policies. However, the task of street scene semantic …

Decision-making in dense traffic scenarios is challenging for automated vehicles (AVs) due
to potentially stochastic behaviors of other traffic participants and perception uncertainties …

Existing deep reinforcement learning (RL) are devoted to research applications on video
games, eg, The Open Racing Car Simulator (TORCS) and Atari games. However, it remains …

A safe and robust on-road navigation system is a crucial component of achieving fully
automated vehicles. NVIDIA recently proposed an End-to-End algorithm that can directly …

End-to-end autonomous driving provides a simple and efficient framework for autonomous
driving systems, which can directly obtain control commands from raw perception data …

Deep learning has revolutionized the ability to learn “end-to-end” autonomous vehicle
control directly from raw sensory data. While there have been recent extensions to handle …

State-of-the-art autonomous driving systems rely heavily on detailed and highly accurate
prior maps. However, outside of small urban areas, it is very challenging to build, store, and …