In recent years, great efforts have been devoted to deep imitation learning for autonomous
driving control, where raw sensory inputs are directly mapped to control actions. However …

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 …

The decision and planning system for autonomous driving in urban environments is hard to
design. Most current methods manually design the driving policy, which can be expensive to …

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 …

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 …

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 …

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 …

Deep reinforcement learning (DRL) provides a promising way for learning navigation in
complex autonomous driving scenarios. However, identifying the subtle cues that can …

Most existing approaches to autonomous driving fall into one of two categories: modular
pipelines, that build an extensive model of the environment, and imitation learning …

Learning-based approaches, such as reinforcement learning (RL) and imitation learning
(IL), have indicated superiority over rule-based approaches in complex urban autonomous …