Developing a deep Q-learning and neural network framework for trajectory planning
VSR Kosuru, AK Venkitaraman - European Journal of Engineering and …, 2022 - ej-eng.org
Autonomy field, every vehicle is occupied with some kind or alter driver assist features in
order to compensate driver comfort. Expansion further to fully Autonomy is extremely …
order to compensate driver comfort. Expansion further to fully Autonomy is extremely …
Deep Q-network based decision making for autonomous driving
MP Ronecker, Y Zhu - 2019 3rd international conference on …, 2019 - ieeexplore.ieee.org
Currently decision making is one of the biggest challenges in autonomous driving. This
paper introduces a method for safely navigating an autonomous vehicle in highway …
paper introduces a method for safely navigating an autonomous vehicle in highway …
Conditional DQN-based motion planning with fuzzy logic for autonomous driving
Motion planning is one of the most significant part in autonomous driving. Learning-based
motion planning methods attract many researchers' attention due to the abilities of learning …
motion planning methods attract many researchers' attention due to the abilities of learning …
Adaptive speed planning for unmanned vehicle based on deep reinforcement learning
In order to solve the problem of frequent deceleration of unmanned vehicles when
approaching obstacles, this article uses a Deep Q-Network (DQN) and its extension, the …
approaching obstacles, this article uses a Deep Q-Network (DQN) and its extension, the …
Trajectory planning for autonomous vehicles using hierarchical reinforcement learning
Planning safe trajectories under uncertain and dynamic conditions makes the autonomous
driving problem significantly complex. Current heuristic-based algorithms such as the slot …
driving problem significantly complex. Current heuristic-based algorithms such as the slot …
ES-DQN: A learning method for vehicle intelligent speed control strategy under uncertain cut-in scenario
Q Chen, W Zhao, L Li, C Wang… - IEEE Transactions on …, 2022 - ieeexplore.ieee.org
Uncertain cut-in maneuver of vehicles from adjacent lanes makes it difficult for vehicle's
automatic speed control strategy to make judgments and effective control decisions. In this …
automatic speed control strategy to make judgments and effective control decisions. In this …
An improved DQN path planning algorithm
J Li, Y Chen, XN Zhao, J Huang - The Journal of Supercomputing, 2022 - Springer
Aiming at the problem of vehicle model tracking error and overdependence in traditional
path planning of intelligent driving vehicles, a path planning method of intelligent driving …
path planning of intelligent driving vehicles, a path planning method of intelligent driving …
End-to-end autonomous driving through dueling double deep Q-network
Recent years have seen the rapid development of autonomous driving systems, which are
typically designed in a hierarchical architecture or an end-to-end architecture. The …
typically designed in a hierarchical architecture or an end-to-end architecture. The …
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 …
An improved dueling deep double-q network based on prioritized experience replay for path planning of unmanned surface vehicles
Unmanned Surface Vehicle (USV) has a broad application prospect and autonomous path
planning as its crucial technology has developed into a hot research direction in the field of …
planning as its crucial technology has developed into a hot research direction in the field of …