Addressing inherent uncertainty: Risk-sensitive behavior generation for automated driving using distributional reinforcement learning
J Bernhard, S Pollok, A Knoll - 2019 IEEE Intelligent Vehicles …, 2019 - ieeexplore.ieee.org
For highly automated driving above SAE level 3, behavior generation algorithms must
reliably consider the inherent uncertainties of the traffic environment, eg arising from the …
reliably consider the inherent uncertainties of the traffic environment, eg arising from the …
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 …
Deep Reinforcement Learning Based Decision-Making Strategy of Autonomous Vehicle in Highway Uncertain Driving Environments
Uncertain environment on multi-lane highway, eg, the stochastic lane-change maneuver of
surrounding vehicles, is a big challenge for achieving safe automated highway driving. To …
surrounding vehicles, is a big challenge for achieving safe automated highway driving. To …
[HTML][HTML] 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 …
Enhancing System-Level Safety in Mixed-Autonomy Platoon via Safe Reinforcement Learning
Connected and automated vehicles (CAVs) have recently gained prominence in traffic
research due to advances in communication technology and autonomous driving. Various …
research due to advances in communication technology and autonomous driving. Various …
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 …
Reliable and efficient lane changing behaviour for connected autonomous vehicle through deep reinforcement learning
S Alagumuthukrishnan, S Deepajothi, R Vani… - Procedia Computer …, 2023 - Elsevier
The establishment of future intelligent transport systems is dependable on the reliable and
seamless function of Connected and Autonomous Vehicles (CAV). Reinforcement learning …
seamless function of Connected and Autonomous Vehicles (CAV). Reinforcement learning …
Decision-making for connected and automated vehicles in chanllenging traffic conditions using imitation and deep reinforcement learning
Decision-making is the “brain” of connected and automated vehicles (CAVs) and is vitally
critical to the safety of CAVs. The most of driving data used to train the decision-making …
critical to the safety of CAVs. The most of driving data used to train the decision-making …
Evaluation of deep reinforcement learning algorithms for autonomous driving
Once considered futuristic, machine learning is already integrated into our everyday life and
will shape many areas of our daily life in the future: This success is mainly due to the …
will shape many areas of our daily life in the future: This success is mainly due to the …
Deep adaptive control: Deep reinforcement learning-based adaptive vehicle trajectory control algorithms for different risk levels
In this study, we explore the problem of adaptive vehicle trajectory control for different risk
levels. Firstly, we introduce a sliding window-based car-following scenario extraction …
levels. Firstly, we introduce a sliding window-based car-following scenario extraction …