[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 …
Real-time safety optimization of connected vehicle trajectories using reinforcement learning
T Ghoul, T Sayed - Sensors, 2021 - mdpi.com
Speed advisories are used on highways to inform vehicles of upcoming changes in traffic
conditions and apply a variable speed limit to reduce traffic conflicts and delays. This study …
conditions and apply a variable speed limit to reduce traffic conflicts and delays. This study …
Implicit Sensing in Traffic Optimization: Advanced Deep Reinforcement Learning Techniques
A sudden roadblock on highways due to many reasons such as road maintenance,
accidents, and car repair is a common situation we encounter almost daily. Autonomous …
accidents, and car repair is a common situation we encounter almost daily. Autonomous …
A deep reinforcement learning-based intelligent intervention framework for real-time proactive road safety management
We propose a variable speed limit (VSL) system for improving the safety of urban
expressways in real time. The system has two main functions: monitoring traffic data and …
expressways in real time. The system has two main functions: monitoring traffic data and …
[PDF][PDF] LongiControl: A Reinforcement Learning Environment for Longitudinal Vehicle Control.
Reinforcement Learning (RL) might be very promising for solving a variety of challenges in
the field of autonomous driving due to its ability to find long-term oriented solutions in …
the field of autonomous driving due to its ability to find long-term oriented solutions in …
Collision Avoidance of Autonomous Vehicles with E-bike at Un-signalized Occluded Intersections Based on Reinforcement Learning
Un-signalized occluded intersections are residential road intersections with narrow lanes
and surrounding buildings, which are prone to traffic accidents. This work uses deep …
and surrounding buildings, which are prone to traffic accidents. This work uses deep …
Chebyshev Transform-Based Robust Trajectory Prediction Using Recurrent Neural Network
S Kwag, B Kang, W Kim, Y Hwang - IEEE Access, 2022 - ieeexplore.ieee.org
Trajectory prediction is gaining attention as a form of situational awareness because it is an
essential component of the support system of autonomous driving, particularly in urban …
essential component of the support system of autonomous driving, particularly in urban …
Trajectory prediction via learning motion cluster patterns in curvilinear coordinates
A high proportion of crashes happen at or near intersections. To improve intersection safety,
trajectory prediction of vehicles has been studied intensively, mostly for automated vehicle …
trajectory prediction of vehicles has been studied intensively, mostly for automated vehicle …
Enhancing Decision-Making in Highway Overtaking Scenarios with Graph Convolution Reinforcement Learning
MK Sam, W Gee, S Arkhstan, H Khan… - Journal of Computer …, 2024 - jcsis.org
Autonomous vehicles have a number of open challenges, one of which is decision-making
regarding motion, particularly while operating in an environment that is both complex and …
regarding motion, particularly while operating in an environment that is both complex and …
Symbolic regression methods for reinforcement learning
Reinforcement learning algorithms can solve dynamic decision-making and optimal control
problems. With continuous-valued state and input variables, reinforcement learning …
problems. With continuous-valued state and input variables, reinforcement learning …