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 …

We demonstrate the first large-scale application of model-based generative adversarial
imitation learning (MGAIL) to the task of dense urban self-driving. We augment standard …

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 …

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 …

Reliable trajectory planning like human drivers in real-world dynamic urban environments is
a critical capability for autonomous driving. To this end, we develop a vision and imitation …

End-to-end autonomous driving aims to build a fully differentiable system that takes raw
sensor data as inputs and directly outputs the planned trajectory or control signals of the ego …

This paper investigates how end-to-end driving models can be improved to drive more
accurately and human-like. To tackle the first issue we exploit semantic and visual maps …

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 …

Achieving a proper balance between planning quality, safety and efficiency is a major
challenge for autonomous driving. Optimisation-based motion planners are capable of …

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 …