[HTML][HTML] Risk-aware controller for autonomous vehicles using model-based collision prediction and reinforcement learning
Abstract Autonomous Vehicles (AVs) have the potential to save millions of lives and
increase the efficiency of transportation services. However, the successful deployment of …
increase the efficiency of transportation services. However, the successful deployment of …
A reinforcement learning based decision-making system with aggressive driving behavior consideration for autonomous vehicles
With the fast development of autonomous vehicle (AV) technology and possible popularity of
AVs in the near future, a mixed-vehicle type driving environment where both AVs and their …
AVs in the near future, a mixed-vehicle type driving environment where both AVs and their …
Integrated eco-driving automation of intelligent vehicles in multi-lane scenario via model-accelerated reinforcement learning
The development of intelligent driving technologies is expected to have the potential in
energy economics. Some reported studies mainly focused on the economical driving …
energy economics. Some reported studies mainly focused on the economical driving …
A reinforcement learning approach for enacting cautious behaviours in autonomous driving system: Safe speed choice in the interaction with distracted pedestrians
GPR Papini, A Plebe, M Da Lio… - IEEE transactions on …, 2021 - ieeexplore.ieee.org
Driving requires the ability to handle unpredictable situations. Since it is not always possible
to predict an impending danger, a good driver should preventively assess whether a …
to predict an impending danger, a good driver should preventively assess whether a …
Vehicles control: Collision avoidance using federated deep reinforcement learning
In the face of growing urban populations and the escalating number of vehicles on the
roads, managing transportation efficiently and ensuring safety have become critical …
roads, managing transportation efficiently and ensuring safety have become critical …
A reinforcement learning benchmark for autonomous driving in intersection scenarios
In recent years, control under urban intersection scenarios has become an emerging
research topic. In such scenarios, the autonomous vehicle confronts complicated situations …
research topic. In such scenarios, the autonomous vehicle confronts complicated situations …
Safety enhancement for deep reinforcement learning in autonomous separation assurance
The separation assurance task will be extremely challenging for air traffic controllers in a
complex and high-density airspace environment. Deep reinforcement learning (DRL) was …
complex and high-density airspace environment. Deep reinforcement learning (DRL) was …
Driving decision and control for automated lane change behavior based on deep reinforcement learning
To fulfill high-level automation, an automated vehicle needs to learn to make decisions and
control its movement under complex scenarios. Due to the uncertainty and complexity of the …
control its movement under complex scenarios. Due to the uncertainty and complexity of the …
Toward trustworthy decision-making for autonomous vehicles: A robust reinforcement learning approach with safety guarantees
While autonomous vehicles are vital components of intelligent transportation systems,
ensuring the trustworthiness of decision-making remains a substantial challenge in realizing …
ensuring the trustworthiness of decision-making remains a substantial challenge in realizing …
Human-Like Control for Automated Vehicles and Avoiding “Vehicle Face-Off” in Unprotected Left Turn Scenarios
J Chen, D Sun, M Zhao - IEEE transactions on intelligent …, 2022 - ieeexplore.ieee.org
Safely and efficiently completing unprotected left turns at intersections is challenging for both
automated vehicles and human drivers, given that it is hard to predict the intentions of other …
automated vehicles and human drivers, given that it is hard to predict the intentions of other …