There is currently a huge interest around autonomous vehicles from both industry and
academia. This is mainly due to recent advances in machine learning and deep learning …

Driving requires reacting to a wide variety of complex environment conditions and agent
behaviors. Explicitly modeling each possible scenario is unrealistic. In contrast, imitation …

Autonomous driving systems need to handle complex scenarios such as lane following,
avoiding collisions, taking turns, and responding to traffic signals. In recent years …

In this work, we present a lightweight pipeline for robust behavioral cloning of a human
driver using end-to-end imitation learning. The proposed pipeline was employed to train and …

The state-of-the-art decision and planning approaches for autonomous vehicles have
moved away from manually designed systems, instead focusing on the utilisation of large …

Our goal is to train a policy for autonomous driving via imitation learning that is robust
enough to drive a real vehicle. We find that standard behavior cloning is insufficient for …

End-to-end approaches to autonomous driving have high sample complexity and are difficult
to scale to realistic urban driving. Simulation can help end-to-end driving systems by …

Current deep learning based autonomous driving approaches yield impressive results also
leading to in-production deployment in certain controlled scenarios. One of the most popular …

Hand-crafting generalised decision-making rules for real-world urban autonomous driving is
hard. Alternatively, learning behaviour from easy-to-collect human driving demonstrations is …

With the development of deep representation learning, the domain of reinforcement learning
(RL) has become a powerful learning framework now capable of learning complex policies …