Belief state separated reinforcement learning for autonomous vehicle decision making under uncertainty
In autonomous driving, the ego vehicle and its surrounding traffic environments always have
uncertainties like parameter and structural errors, behavior randomness of road users, etc …
uncertainties like parameter and structural errors, behavior randomness of road users, etc …
A multi-task fusion strategy-based decision-making and planning method for autonomous driving vehicles
W Liu, Z Xiang, H Fang, K Huo, Z Wang - Sensors, 2023 - mdpi.com
The autonomous driving technology based on deep reinforcement learning (DRL) has been
confirmed as one of the most cutting-edge research fields worldwide. The agent is enabled …
confirmed as one of the most cutting-edge research fields worldwide. The agent is enabled …
Human-Guided Deep Reinforcement Learning for Optimal Decision Making of Autonomous Vehicles
Although deep reinforcement learning (DRL) methods are promising for making behavioral
decisions in autonomous vehicles (AVs), their low training efficiency and difficulty to adapt to …
decisions in autonomous vehicles (AVs), their low training efficiency and difficulty to adapt to …
Reinforcement learning based safe decision making for highway autonomous driving
In this paper, we develop a safe decision-making method for self-driving cars in a multi-lane,
single-agent setting. The proposed approach utilizes deep reinforcement learning (RL) to …
single-agent setting. The proposed approach utilizes deep reinforcement learning (RL) to …
Combining decision making and trajectory planning for lane changing using deep reinforcement learning
S Li, C Wei, Y Wang - IEEE Transactions on Intelligent …, 2022 - ieeexplore.ieee.org
In the context of Automated Vehicles, the Automated Lane Change system, is fundamentally
based upon the separate constructs of Perception, Decision making, Trajectory Planning …
based upon the separate constructs of Perception, Decision making, Trajectory Planning …
Applications and prospect of reinforcement learning in unmanned ground vehicles
L Yuanzhe, H Jibin - Information and Control, 2022 - xk.sia.cn
Unmanned ground vehicle (UGV) can replace human to conduct civilian and military
missions, which is of great strategic significance to the construction of intelligent …
missions, which is of great strategic significance to the construction of intelligent …
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 …
Deep Reinforcement Learning for Autonomous Vehicle Intersection Navigation
BB Elallid, H El Alaoui… - … Conference on Innovation …, 2023 - ieeexplore.ieee.org
In this paper, we explore the challenges associated with navigating complex T-intersections
in dense traffic scenarios for autonomous vehicles (AVs). Reinforcement learning algorithms …
in dense traffic scenarios for autonomous vehicles (AVs). Reinforcement learning algorithms …
Enhancing High-Speed Cruising Performance of Autonomous Vehicles through Integrated Deep Reinforcement Learning Framework
High-speed cruising scenarios with mixed traffic greatly challenge the road safety of
autonomous vehicles (AVs). Unlike existing works that only look at fundamental modules in …
autonomous vehicles (AVs). Unlike existing works that only look at fundamental modules in …
[HTML][HTML] Safe reinforcement learning with mixture density network, with application to autonomous driving
A Baheri - Results in Control and Optimization, 2022 - Elsevier
This paper presents a safe reinforcement learning system for automated driving that benefits
from multimodal future trajectory predictions. We propose a safety system that consists of two …
from multimodal future trajectory predictions. We propose a safety system that consists of two …