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 …

Simulation is an essential tool to develop and benchmark autonomous vehicle planning
software in a safe and cost-effective manner. However, realistic simulation requires accurate …

Data-driven simulation has become a favorable way to train and test autonomous driving
algorithms. The idea of replacing the actual environment with a learned simulator has also …

Nowadays, autonomous cars can drive smoothly in ordinary cases, and it is widely
recognized that realistic sensor simulation will play a critical role in solving remaining corner …

The deployment and evaluation of learning algorithms on autonomous vehicles (AV) is
expensive, slow, and potentially unsafe. This paper details the F1TENTH autonomous …

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 …

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 …

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 …

We present a system that enables an autonomous small-scale RC car to drive aggressively
from visual observations using reinforcement learning (RL). Our system, FastRLAP, trains …

Recent advances in high-fidelity simulators [,,] have enabled closed-loop training of
autonomous driving agents, potentially solving the distribution shift in training vs deployment …