Motion planning for autonomous driving: The state of the art and future perspectives
Intelligent vehicles (IVs) have gained worldwide attention due to their increased
convenience, safety advantages, and potential commercial value. Despite predictions of …
convenience, safety advantages, and potential commercial value. Despite predictions of …
[HTML][HTML] Leveraging reinforcement learning for dynamic traffic control: A survey and challenges for field implementation
In recent years, the advancement of artificial intelligence techniques has led to significant
interest in reinforcement learning (RL) within the traffic and transportation community …
interest in reinforcement learning (RL) within the traffic and transportation community …
Is ego status all you need for open-loop end-to-end autonomous driving?
End-to-end autonomous driving recently emerged as a promising research direction to
target autonomy from a full-stack perspective. Along this line many of the latest works follow …
target autonomy from a full-stack perspective. Along this line many of the latest works follow …
A survey of zero-shot generalisation in deep reinforcement learning
The study of zero-shot generalisation (ZSG) in deep Reinforcement Learning (RL) aims to
produce RL algorithms whose policies generalise well to novel unseen situations at …
produce RL algorithms whose policies generalise well to novel unseen situations at …
End-to-end autonomous driving: Challenges and frontiers
The autonomous driving community has witnessed a rapid growth in approaches that
embrace an end-to-end algorithm framework, utilizing raw sensor input to generate vehicle …
embrace an end-to-end algorithm framework, utilizing raw sensor input to generate vehicle …
A survey on safety-critical driving scenario generation—A methodological perspective
Autonomous driving systems have witnessed significant development during the past years
thanks to the advance in machine learning-enabled sensing and decision-making …
thanks to the advance in machine learning-enabled sensing and decision-making …
Freecontrol: Training-free spatial control of any text-to-image diffusion model with any condition
Recent approaches such as ControlNet offer users fine-grained spatial control over text-to-
image (T2I) diffusion models. However auxiliary modules have to be trained for each spatial …
image (T2I) diffusion models. However auxiliary modules have to be trained for each spatial …
Waymax: An accelerated, data-driven simulator for large-scale autonomous driving research
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 …
software in a safe and cost-effective manner. However, realistic simulation requires accurate …
ScenarioNet: Open-source platform for large-scale traffic scenario simulation and modeling
Large-scale driving datasets such as Waymo Open Dataset and nuScenes substantially
accelerate autonomous driving research, especially for perception tasks such as 3D …
accelerate autonomous driving research, especially for perception tasks such as 3D …
Imitation is not enough: Robustifying imitation with reinforcement learning for challenging driving scenarios
Imitation learning (IL) is a simple and powerful way to use high-quality human driving data,
which can be collected at scale, to produce human-like behavior. However, policies based …
which can be collected at scale, to produce human-like behavior. However, policies based …