Intention prediction has become a relevant field of research in Human-Machine
and Human-Robot Interaction. Indeed, any artificial system (co)-operating with
and along humans, designed to assist and coordinate its actions with a human
partner, would benefit from first inferring the human's current intention. To
spare the user the cognitive burden of explicitly uttering their goals, this
inference relies mostly on behavioral cues deemed indicative of the current
action. It has been long known that eye movements are highly anticipatory of
the single steps unfolding during a task, hence they can serve as a very early
and reliable behavioural cue for intention recognition. This review aims to
draw a line between insights in the psychological literature on visuomotor
control and relevant applications of gaze-based intention recognition in
technical domains, with a focus on teleoperated and assistive robotic systems.
Starting from the cognitive principles underlying the relationship between
intentions, eye movements, and action, the use of eye tracking and gaze-based
models for intent recognition in Human-Robot Interaction is considered, with
prevalent methodologies and their diverse applications. Finally, special
consideration is given to relevant human factors issues and current limitations
to be factored in when designing such systems.

该论文综述了视觉注意力、眼动追踪及基于注视点的意图识别在智能机器人、电信系统和辅助机器人系统中的应用，并强调了设计这些系统时需要考虑的重要人因素问题和当前的限制。

凝视意图估计：在 HRI 中的原理、方法和应用

Gaze-based intention estimation: principles, methodologies, and applications in HRI

Lane detection algorithms have been the key enablers for a fully-assistive
and autonomous navigation systems. In this paper, a novel and pragmatic
approach for lane detection is proposed using a convolutional neural network
(CNN) model based on SegNet encoder-decoder architecture. The encoder block
renders low-resolution feature maps of the input and the decoder block provides
pixel-wise classification from the feature maps. The proposed model has been
trained over 2000 image data-set and tested against their corresponding
ground-truth provided in the data-set for evaluation. To enable real-time
navigation, we extend our model's predictions interfacing it with the existing
Google APIs evaluating the metrics of the model tuning the hyper-parameters.
The novelty of this approach lies in the integration of existing segNet
architecture with google APIs. This interface makes it handy for assistive
robotic systems. The observed results show that the proposed method is robust
under challenging occlusion conditions due to pre-processing involved and gives
superior performance when compared to the existing methods.

本文提出了一种基于卷积神经网络和 SegNet encoder-decoder 架构的新型车道检测方法，并通过与 Google API 的接口实现了实时导航，该方法针对挑战性遮挡条件具有鲁棒性，且性能优于现有方法。