Prioritized experience replay-based deep q learning: Multiple-reward architecture for highway driving decision making
Decision making is a fundamental component to ensure safe autonomous driving in
highway scenarios. The mainstream architecture for this task is the classical deep Q learning …
highway scenarios. The mainstream architecture for this task is the classical deep Q learning …
An improved Dueling Deep Q-network with optimizing reward functions for driving decision method
J Cao, X Wang, Y Wang, Y Tian - Proceedings of the …, 2023 - journals.sagepub.com
Aiming at poor effects and single consideration factors of traditional driving decision-making
algorithm in high-speed and complex environment, a method based on improved deep …
algorithm in high-speed and complex environment, a method based on improved deep …
Multi-reward architecture based reinforcement learning for highway driving policies
W Yuan, M Yang, Y He, C Wang… - 2019 IEEE Intelligent …, 2019 - ieeexplore.ieee.org
A safe and efficient driving policy is essential for the future autonomous highway driving.
However, driving policies are hard for modeling because of the diversity of scenes and …
However, driving policies are hard for modeling because of the diversity of scenes and …
Decision-making for oncoming traffic overtaking scenario using double DQN
S Mo, X Pei, Z Chen - 2019 3rd Conference on Vehicle Control …, 2019 - ieeexplore.ieee.org
Great progress has been made in the field of machine learning in recent years. And learning-
based methods have been widely utilized for developing highly autonomous vehicle. To this …
based methods have been widely utilized for developing highly autonomous vehicle. To this …
Tactical decision-making for autonomous driving using dueling double deep Q network with double attention
Decision-making is still a significant challenge to realize fully autonomous driving. Using
deep reinforcement learning (DRL) to solve autonomous driving decision-making problems …
deep reinforcement learning (DRL) to solve autonomous driving decision-making problems …
Human-like autonomous vehicle speed control by deep reinforcement learning with double Q-learning
Y Zhang, P Sun, Y Yin, L Lin… - 2018 IEEE intelligent …, 2018 - ieeexplore.ieee.org
Autonomous driving has become a popular research project. How to control vehicle speed is
a core problem in autonomous driving. Automatic decision-making approaches, such as …
a core problem in autonomous driving. Automatic decision-making approaches, such as …
Multi-input autonomous driving based on deep reinforcement learning with double bias experience replay
J Cui, L Yuan, L He, W Xiao, T Ran… - IEEE Sensors …, 2023 - ieeexplore.ieee.org
It is still a challenge to realize safe and fast autonomous driving through deep reinforcement
learning (DRL). Most autonomous driving reinforcement learning models are subject to a …
learning (DRL). Most autonomous driving reinforcement learning models are subject to a …
Risk-aware high-level decisions for automated driving at occluded intersections with reinforcement learning
Reinforcement learning is nowadays a popular framework for solving different decision
making problems in automated driving. However, there are still some remaining crucial …
making problems in automated driving. However, there are still some remaining crucial …
Enhanced decision making in multi-scenarios for autonomous vehicles using alternative bidirectional Q network
MS Rais, K Zouaidia, R Boudour - Neural Computing and Applications, 2022 - Springer
To further enhance decision making in autonomous vehicles field, grant more safety,
comfort, reduce traffic, and accidents, learning approaches were adopted, mainly …
comfort, reduce traffic, and accidents, learning approaches were adopted, mainly …
Urban driving with multi-objective deep reinforcement learning
C Li, K Czarnecki - arXiv preprint arXiv:1811.08586, 2018 - arxiv.org
Autonomous driving is a challenging domain that entails multiple aspects: a vehicle should
be able to drive to its destination as fast as possible while avoiding collision, obeying traffic …
be able to drive to its destination as fast as possible while avoiding collision, obeying traffic …