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 …

In this work we are the first to present an offline policy gradient method for learning imitative
policies for complex urban driving from a large corpus of real-world demonstrations. This is …

Urban autonomous driving decision making is challenging due to complex road geometry
and multi-agent interactions. Current decision making methods are mostly manually …

Unlike popular modularized framework, end-to-end autonomous driving seeks to solve the
perception, decision and control problems in an integrated way, which can be more …

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 …

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 …

Data aggregation techniques can significantly improve vision-based policy learning within a
training environment, eg, learning to drive in a specific simulation condition. However, as on …

In this work, we present a data-driven simulation and training engine capable of learning
end-to-end autonomous vehicle control policies using only sparse rewards. By leveraging …

We introduce CARLA, an open-source simulator for autonomous driving research. CARLA
has been developed from the ground up to support development, training, and validation of …

Reinforcement learning is considered to be a strong AI paradigm which can be used to
teach machines through interaction with the environment and learning from their mistakes …