Human-like highway trajectory modeling based on inverse reinforcement learning

R Sun, S Hu, H Zhao, M Moze, F Aioun… - 2019 IEEE Intelligent …, 2019 - ieeexplore.ieee.org
Autonomous driving is one of the current cutting edge technologies. For autonomous cars,
their driving actions and trajectories should not only achieve autonomy and safety, but also …
被引用次数：15 相关文章 所有 2 个版本
[PDF] researchgate.net

Online prediction of lane change with a hierarchical learning-based approach

X Liao, Z Wang, X Zhao, Z Zhao, K Han… - … on Robotics and …, 2022 - ieeexplore.ieee.org
In the foreseeable future, connected and auto-mated vehicles (CAVs) and human-driven
vehicles will share the road networks together. In such a mixed traffic environment, CAVs …
被引用次数：23 相关文章 所有 3 个版本

Incorporating multi-context into the traversability map for urban autonomous driving using deep inverse reinforcement learning

C Jung, DH Shim - IEEE Robotics and Automation Letters, 2021 - ieeexplore.ieee.org
Autonomous driving in an urban environment with surrounding agents remains challenging.
One of the key challenges is to accurately predict the traversability map that probabilistically …
被引用次数：19 相关文章 所有 4 个版本

A Long-Term Actor Network for Human-Like Car-Following Trajectory Planning Guided by Offline Sample-Based Deep Inverse Reinforcement Learning

J Nan, W Deng, R Zhang, Y Wang… - IEEE Transactions on …, 2023 - ieeexplore.ieee.org
Human-like autonomous driving can enhance user acceptance and integration within traffic.
In light of this, this paper presents a planning method for the human-like longitudinal …
相关文章
[PDF] arxiv.org

Integrating deep reinforcement learning with model-based path planners for automated driving

E Yurtsever, L Capito, K Redmill… - 2020 IEEE Intelligent …, 2020 - ieeexplore.ieee.org
Automated driving in urban settings is challenging. Human participant behavior is difficult to
model, and conventional, rule-based Automated Driving Systems (ADSs) tend to fail when …
被引用次数：31 相关文章 所有 10 个版本

Interaction-Aware Planning With Deep Inverse Reinforcement Learning for Human-Like Autonomous Driving in Merge Scenarios

J Nan, W Deng, R Zhang, Y Wang… - IEEE Transactions on …, 2023 - ieeexplore.ieee.org
Merge scenarios on highway are often challenging for autonomous driving, due to its lack of
sufficient tacit understanding on and subtle interaction with human drivers in the traffic flow …
被引用次数：5 相关文章

A deep reinforcement learning approach for long-term short-term planning on frenet frame

M Moghadam, A Alizadeh, E Tekin… - 2021 IEEE 17th …, 2021 - ieeexplore.ieee.org
Tactical decision-making and strategic motion planning for autonomous highway driving are
challenging due to predicting other road users' behaviors, diversity of environments, and …
被引用次数：9 相关文章 所有 2 个版本
[PDF] arxiv.org

Machine Learning-Based Vehicle Intention Trajectory Recognition and Prediction for Autonomous Driving

H Yu, S Huo, M Zhu, Y Gong, Y Xiang - arXiv preprint arXiv:2402.16036, 2024 - arxiv.org
In recent years, the expansion of internet technology and advancements in automation have
brought significant attention to autonomous driving technology. Major automobile …
被引用次数：20 相关文章 所有 2 个版本
[PDF] arxiv.org

Integrating kinematics and environment context into deep inverse reinforcement learning for predicting off-road vehicle trajectories

Y Zhang, W Wang, R Bonatti, D Maturana… - arXiv preprint arXiv …, 2018 - arxiv.org
Predicting the motion of a mobile agent from a third-person perspective is an important
component for many robotics applications, such as autonomous navigation and tracking …
被引用次数：54 相关文章 所有 4 个版本
[PDF] arxiv.org

Probabilistic prediction of interactive driving behavior via hierarchical inverse reinforcement learning

L Sun, W Zhan, M Tomizuka - 2018 21st International …, 2018 - ieeexplore.ieee.org
Autonomous vehicles (AVs) are on the road. To safely and efficiently interact with other road
participants, AVs have to accurately predict the behavior of surrounding vehicles and plan …
被引用次数：131 相关文章 所有 7 个版本