Navigating occluded intersections with autonomous vehicles using deep reinforcement learning
Providing an efficient strategy to navigate safely through unsignaled intersections is a
difficult task that requires determining the intent of other drivers. We explore the …
difficult task that requires determining the intent of other drivers. We explore the …
Safe reinforcement learning with scene decomposition for navigating complex urban environments
Navigating urban environments represents a complex task for automated vehicles. They
must reach their goal safely and efficiently while considering a multitude of traffic …
must reach their goal safely and efficiently while considering a multitude of traffic …
Reinforcement learning for autonomous driving with latent state inference and spatial-temporal relationships
Deep reinforcement learning (DRL) provides a promising way for learning navigation in
complex autonomous driving scenarios. However, identifying the subtle cues that can …
complex autonomous driving scenarios. However, identifying the subtle cues that can …
High-level decision making for safe and reasonable autonomous lane changing using reinforcement learning
B Mirchevska, C Pek, M Werling… - 2018 21st …, 2018 - ieeexplore.ieee.org
Machine learning techniques have been shown to outperform many rule-based systems for
the decision-making of autonomous vehicles. However, applying machine learning is …
the decision-making of autonomous vehicles. However, applying machine learning is …
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 …
Learning highway ramp merging via reinforcement learning with temporally-extended actions
Several key scenarios, such as intersection navigation, lane changing, and ramp merging,
are active areas of research in autonomous driving. In order to properly navigate these …
are active areas of research in autonomous driving. In order to properly navigate these …
Driving in dense traffic with model-free reinforcement learning
Traditional planning and control methods could fail to find a feasible trajectory for an
autonomous vehicle to execute amongst dense traffic on roads. This is because the obstacle …
autonomous vehicle to execute amongst dense traffic on roads. This is because the obstacle …
Safe reinforcement learning on autonomous vehicles
There have been numerous advances in reinforcement learning, but the typically
unconstrained exploration of the learning process prevents the adoption of these methods in …
unconstrained exploration of the learning process prevents the adoption of these methods in …
Overtaking maneuvers in simulated highway driving using deep reinforcement learning
Most methods that attempt to tackle the problem of Autonomous Driving and overtaking
usually try to either directly minimize an objective function or iteratively in a Reinforcement …
usually try to either directly minimize an objective function or iteratively in a Reinforcement …
Attention-based hierarchical deep reinforcement learning for lane change behaviors in autonomous driving
Y Chen, C Dong, P Palanisamy… - Proceedings of the …, 2019 - openaccess.thecvf.com
Performing safe and efficient lane changes is a crucial feature for creating fully autonomous
vehicles. Recent advances have demonstrated successful lane following behavior using …
vehicles. Recent advances have demonstrated successful lane following behavior using …
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