Human-like decision making for autonomous vehicles at the intersection using inverse reinforcement learning
Z Wu, F Qu, L Yang, J Gong - Sensors, 2022 - mdpi.com
With the rapid development of autonomous driving technology, both self-driven and human-
driven vehicles will share roads in the future and complex information exchange among …
driven vehicles will share roads in the future and complex information exchange among …
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 …
Tactical decision-making in autonomous driving by reinforcement learning with uncertainty estimation
Reinforcement learning (RL) can be used to create a tactical decision-making agent for
autonomous driving. However, previous approaches only output decisions and do not …
autonomous driving. However, previous approaches only output decisions and do not …
A cascaded supervised learning approach to inverse reinforcement learning
This paper considers the Inverse Reinforcement Learning (IRL) problem, that is inferring a
reward function for which a demonstrated expert policy is optimal. We propose to break the …
reward function for which a demonstrated expert policy is optimal. We propose to break the …
Studies on drivers' driving styles based on inverse reinforcement learning
Although advanced driver assistance systems (ADAS) have been widely introduced in
automotive industry to enhance driving safety and comfort, and to reduce drivers' driving …
automotive industry to enhance driving safety and comfort, and to reduce drivers' driving …
Learning driving styles for autonomous vehicles from demonstration
It is expected that autonomous vehicles capable of driving without human supervision will be
released to market within the next decade. For user acceptance, such vehicles should not …
released to market within the next decade. For user acceptance, such vehicles should not …
Deep reinforcement learning framework for autonomous driving
Reinforcement learning is considered to be a strong AI paradigm which can be used to
teach machines through interaction with the environment and learning from their mistakes …
teach machines through interaction with the environment and learning from their mistakes …
Estimation of personal driving style via deep inverse reinforcement learning
D Kishikawa, S Arai - Artificial Life and Robotics, 2021 - Springer
When applying autonomous driving technology in human-crewed vehicles, it is essential to
consider the personal driving style with ensuring not only safety but also the driver's …
consider the personal driving style with ensuring not only safety but also the driver's …
Safe trajectory planning using reinforcement learning for self driving
Self-driving vehicles must be able to act intelligently in diverse and difficult environments,
marked by high-dimensional state spaces, a myriad of optimization objectives and complex …
marked by high-dimensional state spaces, a myriad of optimization objectives and complex …
Deep reinforcement learning for predictive longitudinal control of automated vehicles
This paper presents a predictive controller for longitudinal motion of automated vehicles
based on Deep Reinforcement Learning. It uses advance information about future speed …
based on Deep Reinforcement Learning. It uses advance information about future speed …