C Gulino, J Fu, W Luo, G Tucker… - Advances in …, 2024 - proceedings.neurips.cc
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 …
M O'Kelly, H Zheng, D Karthik… - Proceedings of Machine …, 2020 - par.nsf.gov
The deployment and evaluation of learning algorithms on autonomous vehicles (AV) is expensive, slow, and potentially unsafe. This paper details the F1TENTH autonomous …
O Scheel, L Bergamini, M Wolczyk… - … on Robot Learning, 2022 - proceedings.mlr.press
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 …
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 …
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 …
Recent advances in high-fidelity simulators [,,] have enabled closed-loop training of autonomous driving agents, potentially solving the distribution shift in training vs deployment …