A Survey and Tutorial of EEG-Based Brain Monitoring for Driver State Analysis

Zhang, Ce; Eskandarian, Azim

doi:10.1109/jas.2020.1003450

Cited by 33 publications

(17 citation statements)

References 124 publications

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“…Moreover, it is known that understanding the cognitive/physiological state of a driver is crucial to building effective driver assistance systems and autonomous vehicles. Hence, interactions based on brain signals have also been proposed to detect, analyse and monitor driver state [328]. Additionally, AR-based interactions can be used to increase driver awareness by highlighting elements present in a natural environment, e.g.…”

Section: Forms Of Interaction Input Devices Outputmentioning

confidence: 99%

The Roles and Modes of Human Interactions with Automated Machine Learning Systems

Khuat¹,

Kedziora²,

Gabrys³

2022

Preprint

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As automated machine learning (AutoML) systems continue to progress in both sophistication and performance, it becomes important to understand the 'how' and 'why' of human-computer interaction (HCI) within these frameworks, both current and expected. Such a discussion is necessary for optimal system design, leveraging advanced data-processing capabilities to support decision-making involving humans, but it is also key to identifying the opportunities and risks presented by ever-increasing levels of machine autonomy. Within this context, we focus on the following questions: (i) How does HCI currently look like for state-of-the-art AutoML algorithms, especially during the stages of development, deployment, and maintenance? (ii) Do the expectations of HCI within AutoML frameworks vary for different types of users and stakeholders? (iii) How can HCI be managed so that AutoML solutions acquire human trust and broad acceptance? (iv) As AutoML systems become more autonomous and capable of learning from complex open-ended environments, will the fundamental nature of HCI evolve? To consider these questions, we project existing literature in HCI into the space of AutoML; this connection has, to date, largely been unexplored. In so doing, we review topics including user-interface design, human-bias mitigation, and trust in artificial intelligence (AI). Additionally, to rigorously gauge the future of HCI, we contemplate how AutoML may manifest in effectively open-ended environments. This discussion necessarily reviews projected developmental pathways for AutoML, such as the incorporation of reasoning, although the focus remains on how and why HCI may occur in such a framework rather than on any implementational details. Ultimately, this review serves to identify key research directions aimed at better facilitating the roles and modes of human interactions with both current and future AutoML systems.

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Section: Forms Of Interaction Input Devices Outputmentioning

confidence: 99%

The Roles and Modes of Human Interactions with Automated Machine Learning Systems

Khuat¹,

Kedziora²,

Gabrys³

2022

Preprint

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“…Medical data constitute a paramount component in this sense. It may take the form of unidimensional/bidimensional signals [6,7], an image stack [8], or a large amount of data [9]. Furthermore, multimodal data sources can also be leveraged [10].…”

Section: Introductionmentioning

confidence: 99%

COVID-19 Detection in CT/X-ray Imagery Using Vision Transformers

Rahhal

Bazi

Jomaa

et al. 2022

JPM

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The steady spread of the 2019 Coronavirus disease has brought about human and economic losses, imposing a new lifestyle across the world. On this point, medical imaging tests such as computed tomography (CT) and X-ray have demonstrated a sound screening potential. Deep learning methodologies have evidenced superior image analysis capabilities with respect to prior handcrafted counterparts. In this paper, we propose a novel deep learning framework for Coronavirus detection using CT and X-ray images. In particular, a Vision Transformer architecture is adopted as a backbone in the proposed network, in which a Siamese encoder is utilized. The latter is composed of two branches: one for processing the original image and another for processing an augmented view of the original image. The input images are divided into patches and fed through the encoder. The proposed framework is evaluated on public CT and X-ray datasets. The proposed system confirms its superiority over state-of-the-art methods on CT and X-ray data in terms of accuracy, precision, recall, specificity, and F1 score. Furthermore, the proposed system also exhibits good robustness when a small portion of training data is allocated.

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“…A UTONOMOUS vehicle development has grow rapidly in recent years because of increasing computing power and more robust decision making algorithms [1]. Perception is a fundamental but vital step for autonomous driving [2], [3].…”

Section: Introductionmentioning

confidence: 99%

A Quality Index Metric and Method for Online Self-Assessment of Autonomous Vehicles Sensory Perception

Zhang¹,

Eskandarian²

2022

Preprint

Self Cite

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Perception is critical to autonomous driving safety. Camera-based object detection is one of the most important methods for autonomous vehicle perception. Current camerabased object detection solutions for autonomous driving cannot provide feedback on the detection performance for each frame. We propose an evaluation metric, namely the perception quality index (PQI), to assess the camera-based object detection algorithm performance and provide the perception quality feedback frame by frame. The method of the PQI generation is by combining the fine-grained saliency map intensity with the object detection algorithm's output results. Furthermore, we developed a superpixel-based attention network (SPA-NET) to predict the proposed PQI evaluation metric by using raw image pixels and superpixels as input. The proposed evaluation metric and prediction network are tested on three open-source datasets . The proposed evaluation metric can correctly assess the camera-based perception quality under the autonomous driving environment according to the experiment results. The network regression Rsquare values determine the comparison among models. It is shown that a Perception Quality Index is useful in self-evaluating a camera's visual scene perception.

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A Survey and Tutorial of EEG-Based Brain Monitoring for Driver State Analysis

Cited by 33 publications

References 124 publications

The Roles and Modes of Human Interactions with Automated Machine Learning Systems

The Roles and Modes of Human Interactions with Automated Machine Learning Systems

COVID-19 Detection in CT/X-ray Imagery Using Vision Transformers

A Quality Index Metric and Method for Online Self-Assessment of Autonomous Vehicles Sensory Perception

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