Prediction based decision making for autonomous highway driving
Autonomous driving decision-making is a challenging task due to the inherent complexity
and uncertainty in traffic. For example, adjacent vehicles may change their lane or overtake …
and uncertainty in traffic. For example, adjacent vehicles may change their lane or overtake …
Safe and rule-aware deep reinforcement learning for autonomous driving at intersections
C Zhang, K Kacem, G Hinz… - 2022 IEEE 25th …, 2022 - ieeexplore.ieee.org
Driving through complex urban environments is a challenging task for autonomous vehicles
(AVs), as they must safely reach their mission goal, and react properly to traffic participants …
(AVs), as they must safely reach their mission goal, and react properly to traffic participants …
An integrated model for autonomous speed and lane change decision-making based on deep reinforcement learning
The implementation of autonomous driving is inseparable from developing intelligent driving
decision-making models, which are facing high scene complexity, poor decision-making …
decision-making models, which are facing high scene complexity, poor decision-making …
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 …
Driving decisions for autonomous vehicles in intersection environments: Deep reinforcement learning approaches with risk assessment
W Yu, Y Qian, J Xu, H Sun, J Wang - World Electric Vehicle Journal, 2023 - mdpi.com
Intersection scenarios are one of the most complex and high-risk traffic scenarios. Therefore,
it is important to propose a vehicle driving decision algorithm for intersection scenarios. Most …
it is important to propose a vehicle driving decision algorithm for intersection scenarios. Most …
A deep reinforcement learning approach for autonomous highway driving
J Zhao, T Qu, F Xu - IFAC-PapersOnLine, 2020 - Elsevier
Autonomous driving has been the trend. In this paper, a Deep Reinforcement Learning
(DRL) method is exploited to model the decision making and interaction between vehicles …
(DRL) method is exploited to model the decision making and interaction between vehicles …
Hybrid decision making for autonomous driving in complex urban scenarios
R Gutiérrez-Moreno, R Barea… - 2023 IEEE Intelligent …, 2023 - ieeexplore.ieee.org
Autonomous driving presents significant challenges due to the variability of behaviours
exhibited by surrounding vehicles and the diversity of scenarios encountered. To address …
exhibited by surrounding vehicles and the diversity of scenarios encountered. To address …
Towards robust decision-making for autonomous driving on highway
Reinforcement learning (RL) methods are commonly regarded as effective solutions for
designing intelligent driving policies. Nonetheless, even if the RL policy is converged after …
designing intelligent driving policies. Nonetheless, even if the RL policy is converged after …
Decision-making strategy on highway for autonomous vehicles using deep reinforcement learning
Autonomous driving is a promising technology to reduce traffic accidents and improve
driving efficiency. In this work, a deep reinforcement learning (DRL)-enabled decision …
driving efficiency. In this work, a deep reinforcement learning (DRL)-enabled decision …
Deep reinforcement learning for autonomous vehicles: lane keep and overtaking scenarios with collision avoidance
SH Ashwin, R Naveen Raj - International Journal of Information …, 2023 - Springer
Numerous accidents and fatalities occur every year across the world as a result of the
reckless driving of drivers and the ever-increasing number of vehicles on the road. Due to …
reckless driving of drivers and the ever-increasing number of vehicles on the road. Due to …