Parallel training: An ACP-based training framework for iterative learning in uncertain driving spaces
The traffic environment and driving behaviors are of great complexity and uncertainty in our
physical world. Therefore, training in the digital world with low cost and diverse complexities …
physical world. Therefore, training in the digital world with low cost and diverse complexities …
Vision-based autonomous driving: A hierarchical reinforcement learning approach
J Wang, H Sun, C Zhu - IEEE Transactions on Vehicular …, 2023 - ieeexplore.ieee.org
Human drivers have excellent perception and reaction abilities in complex environments
such as dangerous highways, busy intersections, and harsh weather conditions. To achieve …
such as dangerous highways, busy intersections, and harsh weather conditions. To achieve …
Vision-based autonomous car racing using deep imitative reinforcement learning
Autonomous car racing is a challenging task in the robotic control area. Traditional modular
methods require accurate mapping, localization and planning, which makes them …
methods require accurate mapping, localization and planning, which makes them …
Uncertainty-aware model-based offline reinforcement learning for automated driving
Offline reinforcement learning (RL) provides a framework for learning decision-making from
offline data and therefore constitutes a promising approach for real-world applications such …
offline data and therefore constitutes a promising approach for real-world applications such …
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 …
One of the key challenges is to accurately predict the traversability map that probabilistically …
Deductive reinforcement learning for visual autonomous urban driving navigation
Existing deep reinforcement learning (RL) are devoted to research applications on video
games, eg, The Open Racing Car Simulator (TORCS) and Atari games. However, it remains …
games, eg, The Open Racing Car Simulator (TORCS) and Atari games. However, it remains …
A reinforcement learning benchmark for autonomous driving in general urban scenarios
Reinforcement learning (RL) has gained significant interest for its potential to improve
decision and control in autonomous driving. However, current approaches have yet to …
decision and control in autonomous driving. However, current approaches have yet to …
A learning-based framework for handling dilemmas in urban automated driving
SH Lee, SW Seo - 2017 IEEE International Conference on …, 2017 - ieeexplore.ieee.org
Over the last decade, automated vehicles have been widely researched and their massive
potential has been verified through several milestone demonstrations. However, there are …
potential has been verified through several milestone demonstrations. However, there are …
Generalizing decision making for automated driving with an invariant environment representation using deep reinforcement learning
Data driven approaches for decision making applied to automated driving require
appropriate generalization strategies, to ensure applicability to the world's variability …
appropriate generalization strategies, to ensure applicability to the world's variability …
Policy-based reinforcement learning for training autonomous driving agents in urban areas with affordance learning
Learning to drive in urban areas is an open challenge for autonomous vehicles (AVs), as
complex decision making requirements are needed in multi-task co-ordinations …
complex decision making requirements are needed in multi-task co-ordinations …