Driving requires reacting to a wide variety of complex environment conditions and agent
behaviors. Explicitly modeling each possible scenario is unrealistic. In contrast, imitation …

Enabling robots to autonomously navigate complex environments is essential for real-world
deployment. Prior methods approach this problem by having the robot maintain an internal …

Prediction, decision-making, and motion planning are essential for autonomous driving. In
most contemporary works, they are considered as individual modules or combined into a …

The utilization of Large Language Models (LLMs) within the realm of reinforcement learning,
particularly as planners, has garnered a significant degree of attention in recent scholarly …

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 …

In this paper, we propose a neural motion planner for learning to drive autonomously in
complex urban scenarios that include traffic-light handling, yielding, and interactions with …

Supervised learning is widely used in training autonomous driving vehicle. However, it is
trained with large amount of supervised labeled data. Reinforcement learning can be trained …

Deep reinforcement learning (DRL) has achieved great success in processing vision-based
driving tasks. However, the end-to-end training manner makes DRL agents suffer from …

Reinforcement Learning (RL) aims at learning an optimal behavior policy from its own
experiments and not rule-based control methods. However, there is no RL algorithm yet …

We develop a deep generative model built on a fully differentiable simulator for multi-agent
trajectory prediction. Agents are modeled with conditional recurrent variational neural …