Hierarchical reinforcement learning for autonomous decision making and motion planning of intelligent vehicles
Y Lu, X Xu, X Zhang, L Qian, X Zhou - IEEE Access, 2020 - ieeexplore.ieee.org
Autonomous decision making and motion planning in complex dynamic traffic environments,
such as left-turn without traffic signals and multi-lane merging from side-ways, are still …
such as left-turn without traffic signals and multi-lane merging from side-ways, are still …
Hierarchical motion planning and tracking for autonomous vehicles using global heuristic based potential field and reinforcement learning based predictive control
G Du, Y Zou, X Zhang, Z Li, Q Liu - IEEE Transactions on …, 2023 - ieeexplore.ieee.org
The autonomous vehicle is widely applied in various ground operations, in which motion
planning and tracking control are becoming the key technologies to achieve autonomous …
planning and tracking control are becoming the key technologies to achieve autonomous …
A hierarchical motion planning framework for autonomous driving in structured highway environments
This paper presents an efficient hierarchical motion planning framework with a long
planning horizon for autonomous driving in structured environments. A 3D motion planning …
planning horizon for autonomous driving in structured environments. A 3D motion planning …
A hybrid deep reinforcement learning and optimal control architecture for autonomous highway driving
Autonomous vehicles in highway driving scenarios are expected to become a reality in the
next few years. Decision-making and motion planning algorithms, which allow autonomous …
next few years. Decision-making and motion planning algorithms, which allow autonomous …
Integrated eco-driving automation of intelligent vehicles in multi-lane scenario via model-accelerated reinforcement learning
The development of intelligent driving technologies is expected to have the potential in
energy economics. Some reported studies mainly focused on the economical driving …
energy economics. Some reported studies mainly focused on the economical driving …
A reinforcement learning approach to autonomous decision making of intelligent vehicles on highways
X Xu, L Zuo, X Li, L Qian, J Ren… - IEEE Transactions on …, 2018 - ieeexplore.ieee.org
Autonomous decision making is a critical and difficult task for intelligent vehicles in dynamic
transportation environments. In this paper, a reinforcement learning approach with value …
transportation environments. In this paper, a reinforcement learning approach with value …
Hierarchical evasive path planning using reinforcement learning and model predictive control
Motion planning plays an essential role in designing self-driving functions for connected and
autonomous vehicles. The methods need to provide a feasible trajectory for the vehicle to …
autonomous vehicles. The methods need to provide a feasible trajectory for the vehicle to …
Inverse reinforcement learning based: Segmented lane-change trajectory planning with consideration of interactive driving intention
Y Sun, Y Chu, T Xu, J Li, X Ji - IEEE Transactions on Vehicular …, 2022 - ieeexplore.ieee.org
One of the most challenging problems in autonomous driving is trajectory planning for lane
changes. Conventional trajectory planning is generally realized by optimizing a specific cost …
changes. Conventional trajectory planning is generally realized by optimizing a specific cost …
Efficient Lane-changing Behavior Planning via Reinforcement Learning with Imitation Learning Initialization
Robust lane-changing behavior planning is critical to ensuring the safety and comfort of
autonomous vehicles. In this paper, we proposed an efficient and robust vehicle lane …
autonomous vehicles. In this paper, we proposed an efficient and robust vehicle lane …
Motion planner with fixed-horizon constrained reinforcement learning for complex autonomous driving scenarios
In autonomous driving, behavioral decision-making and trajectory planning remain huge
challenges due to the large amount of uncertainty in environments and complex interaction …
challenges due to the large amount of uncertainty in environments and complex interaction …
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