Learning risk-aware costmaps via inverse reinforcement learning for off-road navigation
The process of designing costmaps for off-road driving tasks is often a challenging and
engineering-intensive task. Recent work in costmap design for off-road driving focuses on …
engineering-intensive task. Recent work in costmap design for off-road driving focuses on …
Compatible reward inverse reinforcement learning
Abstract Inverse Reinforcement Learning (IRL) is an effective approach to recover a reward
function that explains the behavior of an expert by observing a set of demonstrations. This …
function that explains the behavior of an expert by observing a set of demonstrations. This …
Prediction of reward functions for deep reinforcement learning via Gaussian process regression
J Lim, S Ha, J Choi - IEEE/ASME Transactions on Mechatronics, 2020 - ieeexplore.ieee.org
Inverse reinforcement learning (IRL) is a technique for automatic reward acquisition,
however, it is difficult to apply to high-dimensional problems with unknown dynamics. This …
however, it is difficult to apply to high-dimensional problems with unknown dynamics. This …
[PDF][PDF] Risk-sensitive Inverse Reinforcement Learning via Coherent Risk Models.
A Majumdar, S Singh… - … science and systems, 2017 - m.roboticsproceedings.org
The literature on Inverse Reinforcement Learning (IRL) typically assumes that humans take
actions in order to minimize the expected value of a cost function, ie, that humans are risk …
actions in order to minimize the expected value of a cost function, ie, that humans are risk …
Intelligent vehicle decision-making and trajectory planning method based on deep reinforcement learning in the Frenet space
J Wang, L Chu, Y Zhang, Y Mao, C Guo - Sensors, 2023 - mdpi.com
The complexity inherent in navigating intricate traffic environments poses substantial hurdles
for intelligent driving technology. The continual progress in mapping and sensor …
for intelligent driving technology. The continual progress in mapping and sensor …
Modeling driver behavior from demonstrations in dynamic environments using spatiotemporal lattices
One of the most challenging tasks in the development of path planners for intelligent
vehicles is the design of the cost function that models the desired behavior of the vehicle …
vehicles is the design of the cost function that models the desired behavior of the vehicle …
Machine learning for autonomous vehicle's trajectory prediction: A comprehensive survey, challenges, and future research directions
V Bharilya, N Kumar - Vehicular Communications, 2024 - Elsevier
The significant contribution of human errors, accounting for approximately 94%(with a
margin of±2.2%), to road crashes leading to casualties, vehicle damages, and safety …
margin of±2.2%), to road crashes leading to casualties, vehicle damages, and safety …
Inverse reinforcement learning of behavioral models for online-adapting navigation strategies
To increase the acceptance of autonomous systems in populated environments, it is
indispensable to teach them social behavior. We would expect a social robot, which plans its …
indispensable to teach them social behavior. We would expect a social robot, which plans its …
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 …
Car-following method based on inverse reinforcement learning for autonomous vehicle decision-making
There are still some problems need to be solved though there are a lot of achievements in
the fields of automatic driving. One of those problems is the difficulty of designing a car …
the fields of automatic driving. One of those problems is the difficulty of designing a car …