An accurate model of the environment and the dynamic agents acting in it offers great
potential for improving motion planning. We present MILE: a Model-based Imitation …

Existing research on autonomous driving primarily focuses on urban driving, which is
insufficient for characterising the complex driving behaviour underlying high-speed racing …

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 …

In this paper, we propose an end-to-end self-driving network featuring a sparse attention
module that learns to automatically attend to important regions of the input. The attention …

The future of transportation is tightly connected to Autonomous Driving (AD). While a lot of
progress has been made in recent years, there are still obstacles to overcome. One of the …

Conditional imitation learning (CIL) trains deep neural networks, in an end-to-end manner,
to mimic human driving. This approach has demonstrated suitable vehicle control when …

In this paper, we address the issue of increasing the performance of reinforcement learning
(RL) solutions for autonomous racing cars when navigating under conditions where practical …

Designing a safe and human-like decision-making system for an autonomous vehicle is a
challenging task. Generative imitation learning is one possible approach for automating …

Imitation learning holds great promise for addressing the complex task of autonomous urban
driving, as experienced human drivers can navigate highly challenging scenarios with ease …

Imitation learning trains control policies by mimicking pre-recorded expert demonstrations. In
partially observable settings, imitation policies must rely on observation histories, but many …