Predicting real-time crash risk on urban expressways using recurrent neural network
Real-time crash risk prediction is an important area of research that focuses on identifying
hazardous traffic conditions as part of proactive traffic safety management. Although there is …
hazardous traffic conditions as part of proactive traffic safety management. Although there is …
Fast prototype framework for deep reinforcement learning-based trajectory planner
Reinforcement Learning, as one of the main approaches of machine learning, has been
gaining high popularity in recent years, which also affects the vehicle industry and research …
gaining high popularity in recent years, which also affects the vehicle industry and research …
Sim-to-real reinforcement learning applied to end-to-end vehicle control
A Kalapos, C Gór, R Moni… - 2020 23rd International …, 2020 - ieeexplore.ieee.org
In this work, we study vision-based end-to-end reinforcement learning on vehicle control
problems, such as lane following and collision avoidance. Our controller policy is able to …
problems, such as lane following and collision avoidance. Our controller policy is able to …
Planning on the fast lane: Learning to interact using attention mechanisms in path integral inverse reinforcement learning
General-purpose trajectory planning algorithms for automated driving utilize complex reward
functions to perform a combined optimization of strategic, behavioral, and kinematic …
functions to perform a combined optimization of strategic, behavioral, and kinematic …
Spatiotemporal costmap inference for MPC via deep inverse reinforcement learning
It can be difficult to autonomously produce driver behavior so that it appears natural to other
traffic participants. Through Inverse Reinforcement Learning (IRL), we can automate this …
traffic participants. Through Inverse Reinforcement Learning (IRL), we can automate this …
Simulation-based reinforcement learning for autonomous driving
C Galias, A Jakubowski, H Michalewski, B Osiński… - 2019 - openreview.net
Simulation-based reinforcement learning for autonomous driving Page 1 Simulation-based
reinforcement learning for autonomous driving Christopher Galias *12 Adam Jakubowski * 1 …
reinforcement learning for autonomous driving Christopher Galias *12 Adam Jakubowski * 1 …
Inverse reinforcement learning with hybrid-weight trust-region optimization and curriculum learning for autonomous maneuvering
Despite significant advancements, collision-free navigation in autonomous driving is still
challenging, considering the navigation module needs to balance learning and planning to …
challenging, considering the navigation module needs to balance learning and planning to …
Emergency Collision Avoidance Decision-making for Autonomous Vehicles: A Model-based Reinforcement Learning Approach
The challenging task of “intelligent vehicles” opens up a new frontier to enhancing traffic
safety. However, how to determine driving behavior timely and effectively is one of the most …
safety. However, how to determine driving behavior timely and effectively is one of the most …
Interaction-aware trajectory prediction of surrounding vehicles based on hierarchical framework in highway scenarios
This paper presents a hierarchical framework combining a machine learning (ML)-based
approach with a model-based approach to predict the behavior and trajectory of surrounding …
approach with a model-based approach to predict the behavior and trajectory of surrounding …
Using reinforcement learning and simulation to develop autonomous vehicle control strategies
While machine learning in autonomous vehicles development has increased significantly in
the past few years, the use of reinforcement learning (RL) methods has only recently been …
the past few years, the use of reinforcement learning (RL) methods has only recently been …