Connected and automated vehicles: Infrastructure, applications, security, critical challenges, and future aspects
Autonomous vehicles (AV) are game-changing innovations that promise a safer, more
convenient, and environmentally friendly mode of transportation than traditional vehicles …
convenient, and environmentally friendly mode of transportation than traditional vehicles …
Lmdrive: Closed-loop end-to-end driving with large language models
Despite significant recent progress in the field of autonomous driving modern methods still
struggle and can incur serious accidents when encountering long-tail unforeseen events …
struggle and can incur serious accidents when encountering long-tail unforeseen events …
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 …
Open-sourced data ecosystem in autonomous driving: the present and future
With the continuous maturation and application of autonomous driving technology, a
systematic examination of open-source autonomous driving datasets becomes instrumental …
systematic examination of open-source autonomous driving datasets becomes instrumental …
SmartRefine: A Scenario-Adaptive Refinement Framework for Efficient Motion Prediction
Predicting the future motion of surrounding agents is essential for autonomous vehicles
(AVs) to operate safely in dynamic human-robot-mixed environments. Context information …
(AVs) to operate safely in dynamic human-robot-mixed environments. Context information …
A Perspective of Q-value Estimation on Offline-to-Online Reinforcement Learning
Offline-to-online Reinforcement Learning (O2O RL) aims to improve the performance of
offline pretrained policy using only a few online samples. Built on offline RL algorithms, most …
offline pretrained policy using only a few online samples. Built on offline RL algorithms, most …
Integrating Expert Guidance for Efficient Learning of Safe Overtaking in Autonomous Driving Using Deep Reinforcement Learning
Overtaking on two-lane roads is a great challenge for autonomous vehicles, as oncoming
traffic appearing on the opposite lane may require the vehicle to change its decision and …
traffic appearing on the opposite lane may require the vehicle to change its decision and …
[HTML][HTML] Changes in Learning From Social Feedback After Web-Based Interpretation Bias Modification: Secondary Analysis of a Digital Mental Health Intervention …
Background Biases in social reinforcement learning, or the process of learning to predict
and optimize behavior based on rewards and punishments in the social environment, may …
and optimize behavior based on rewards and punishments in the social environment, may …
A Review of Reward Functions for Reinforcement Learning in the context of Autonomous Driving
A Abouelazm, J Michel, JM Zoellner - arXiv preprint arXiv:2405.01440, 2024 - arxiv.org
Reinforcement learning has emerged as an important approach for autonomous driving. A
reward function is used in reinforcement learning to establish the learned skill objectives …
reward function is used in reinforcement learning to establish the learned skill objectives …
Imagination-augmented Hierarchical Reinforcement Learning for Safe and Interactive Autonomous Driving in Urban Environments
SH Lee, Y Jung, SW Seo - arXiv preprint arXiv:2311.10309, 2023 - arxiv.org
Hierarchical reinforcement learning (HRL) has led to remarkable achievements in diverse
fields. However, existing HRL algorithms still cannot be applied to real-world navigation …
fields. However, existing HRL algorithms still cannot be applied to real-world navigation …