Learning-based hierarchical decision-making framework for automatic driving in incompletely connected traffic scenarios
F Yang, X Li, Q Liu, X Li, Z Li - Sensors, 2024 - mdpi.com
The decision-making algorithm serves as a fundamental component for advancing the level
of autonomous driving. The end-to-end decision-making algorithm has a strong ability to …
of autonomous driving. The end-to-end decision-making algorithm has a strong ability to …
Learning automated driving in complex intersection scenarios based on camera sensors: A deep reinforcement learning approach
Making proper decisions at intersections that are one of the most dangerous and
sophisticated driving scenarios is full of challenges, especially for autonomous vehicles …
sophisticated driving scenarios is full of challenges, especially for autonomous vehicles …
Human-like decision making of artificial drivers in intelligent transportation systems: An end-to-end driving behavior prediction approach
Drivers can be either human beings or artificial drivers in future intelligent transportation
systems (ITSs). It is important to learn how people drive so that artificial drivers can be …
systems (ITSs). It is important to learn how people drive so that artificial drivers can be …
FEN-DQN: An End-to-End Autonomous Driving Framework Based on Reinforcement Learning with Explicit Affordance
The slow convergence rate is a thorny problem of current end-to-end autonomous driving
paradigm with various traffic elements and tasks. In this paper, we propose an end-to-end …
paradigm with various traffic elements and tasks. In this paper, we propose an end-to-end …
A bayesian driver agent model for autonomous vehicles system based on knowledge-aware and real-time data
J Ma, H Xie, K Song, H Liu - Sensors, 2021 - mdpi.com
A key research area in autonomous driving is how to model the driver's decision-making
behavior, due to the fact it is significant for a self-driving vehicles considering their traffic …
behavior, due to the fact it is significant for a self-driving vehicles considering their traffic …
Enhancing sensing and decision-making of automated driving systems with multi-access edge computing and machine learning
Emerging self-driving vehicles are now capable of sensing the environment and performing
autonomous operations, paving the way to a more efficient, safer, and greener transportation …
autonomous operations, paving the way to a more efficient, safer, and greener transportation …
MALE-A: Stimuli and Cause Prediction for Maneuver Planning via Graph Neural Networks in Autonomous Driving
P Rama, N Bajcinca - 2023 IEEE 26th International Conference …, 2023 - ieeexplore.ieee.org
The driving behavior inherently involves different tasks, one such task corresponding to
maneuver planning to navigate safely the road network. Yet, the planned maneuver can not …
maneuver planning to navigate safely the road network. Yet, the planned maneuver can not …
Autonomous Driving Landscape
The recent proliferation of computing technologies (eg, sensors, computer vision, machine
learning, and hardware acceleration), and the broad deployment of communication …
learning, and hardware acceleration), and the broad deployment of communication …
Edge-enhanced Graph Attention Network for driving decision-making of autonomous vehicles via Deep Reinforcement Learning
Y Qiang, X Wang, X Liu, Y Wang… - Proceedings of the …, 2024 - journals.sagepub.com
Despite the rapid advancement in the field of autonomous driving vehicles, developing a
safe and sensible decision-making system remains a challenging problem. The driving …
safe and sensible decision-making system remains a challenging problem. The driving …
Convolutional neural network-based intelligent decision-making for automated vehicles
The decision-making module provides proper driving maneuvers for Automated vehicles
(AVs), which is essential to the deployment of AVs. Current research works attempted to …
(AVs), which is essential to the deployment of AVs. Current research works attempted to …