Concept of the comprehension level of situation awareness using an expert system

Nine, Julkar; Manoharan, Shanmugapriyan; Hardt, Wolf‐Dietrich

doi:10.1088/1757-899x/1019/1/012103

Cited by 5 publications

(4 citation statements)

References 2 publications

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“…In other words, SA is related to users' ability to take action based on the perception of information that can predict certain future events. Accordingly, SA is related to how environmental elements are perceived, comprehended and predicted [19,20]. Perception is the first level in SA, which has to do with the observation of information.…”

mentioning

confidence: 99%

The Effectiveness of Online Platforms after the Pandemic: Will Face-to-Face Classes Affect Students’ Perception of Their Behavioural Intention (BIU) to Use Online Platforms?

et al. 2021

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The purpose of this study is to investigate students’ intention to continue using online learning platforms during face-to-face traditional classes in a way that is parallel to their usage during online virtual classes (during the pandemic). This investigation of students’ intention is based on a conceptual model that uses newly used external factors in addition to the technology acceptance model (TAM) contrasts; hence, it takes into consideration users’ satisfaction, the external factor of information richness (IR) and the quality of the educational system and information disseminated. The participants were 768 university students who have experienced the teaching environments of both traditional face-to-face classes and online classes during the pandemic. A structural equation modelling (SEM) test was conducted to analyse the independent variables, including the users’ situation awareness (SA), perceived ease of use, perceived usefulness, satisfaction, IR, education system quality and information quality. An online questionnaire was used to explore students’ perceptions of their intention to use online platforms accessibly in a face-to-face learning environment. The results showed that (a) students prefer online platforms that have a higher level of content richness, to be able to implement the three dimensions of users’ situation awareness (perception, comprehension and projection); (b) there were significant effects of TAM constructs on students’ satisfaction and acceptance; (c) students are in favour of using a learning platform that is characterised by a high level of educational system quality and information quality and (d) students with a higher level of satisfaction have a more positive attitude in their willingness to use the online learning system.

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mentioning

confidence: 99%

The Effectiveness of Online Platforms after the Pandemic: Will Face-to-Face Classes Affect Students’ Perception of Their Behavioural Intention (BIU) to Use Online Platforms?

et al. 2021

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“…The methods suggested in [7], [8], [9] utilize vehicle detection and then search for lights inside the vehicle areas that have been identified by the systems. Cui et al [7] {cui_vision-based_2015} utilized the HSV color space to extract red pixels, followed by the OPTICS method to extract the two biggest clusters, in order to select taillight candidates for their study.…”

Section: A Tailight Detectionmentioning

confidence: 99%

“…Cui et al [7] {cui_vision-based_2015} utilized the HSV color space to extract red pixels, followed by the OPTICS method to extract the two biggest clusters, in order to select taillight candidates for their study. According to [8], the method presented in [6] makes use of the Y-distance test and then chooses pairings by comparing the sizes and forms of the candidate areas that have been obtained.…”

Section: A Tailight Detectionmentioning

confidence: 99%

Traffic Light and Back-light Recognition using Deep Learning and Image Processing with Raspberry Pi

Nine¹,

Mathavan²

2021

ESS

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Traffic light detection and back-light recognition are essential research topics in the area of intelligent vehicles because they avoid vehicle collision and provide driver safety. Improved detection and semantic clarity may aid in the prevention of traffic accidents by self-driving cars at crowded junctions, thus improving overall driving safety. Complex traffic situations, on the other hand, make it more difficult for algorithms to identify and recognize objects. The latest state-of-the-art algorithms based on Deep Learning and Computer Vision are successfully addressing the majority of real-time problems for autonomous driving, such as detecting traffic signals, traffic signs, and pedestrians. We propose a combination of deep learning and image processing methods while using the MobileNetSSD (deep neural network architecture) model with transfer learning for real-time detection and identification of traffic lights and back-light. This inference model is obtained from frameworks such as Tensor-Flow and Tensor-Flow Lite which is trained on the COCO data. This study investigates the feasibility of executing object detection on the Raspberry Pi 3B+, a widely used embedded computing board. The algorithm’s performance is measured in terms of frames per second (FPS), accuracy, and inference time.

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“…On the other hand, in order to cope with the complexity of vehicle behavior, qualitative rules or expert systems based on actual driving experience are developed. Uttendorf et al [12], Maeda [13], and Nine et al [14] developed an expert system for automated driving. In particular, Brown et al [15] presented a method to extract and quantify the driving skills of experts in driving large articulated vehicles from stored dynamic data.…”

Section: Introductionmentioning

confidence: 99%

Path Planning for Perpendicular Parking of Large Articulated Vehicles Based on Qualitative Kinematics and Geometric Methods

Han

2023

Vehicles

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Since large articulated vehicles have uncertainties in trailer articulation angle as well as dynamic complexity, it is not easy to accurately establish a reliable motion plan. In this paper, two geometric path plans constructed based on the empirical rules of driving experts are presented so that articulated vehicles can automatically perform perpendicular parking on a reverse path. By analyzing the empirical parking methods of professional drivers, these path plans were constructed by appropriately combining several standardized simple basic motions to facilitate implementation in real vehicles. In addition, the path plans included appropriate complementary motions to effectively respond to uncertainties arising from articulation angles, etc. The complementary motions developed in this study are based on the results of qualitative analysis on the behavior of articulated vehicles. The usefulness of the proposed articulated vehicle parking method has been proven through hundreds of experimental tests using a scaled model automated vehicle.

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Concept of the comprehension level of situation awareness using an expert system

Cited by 5 publications

References 2 publications

The Effectiveness of Online Platforms after the Pandemic: Will Face-to-Face Classes Affect Students’ Perception of Their Behavioural Intention (BIU) to Use Online Platforms?

The Effectiveness of Online Platforms after the Pandemic: Will Face-to-Face Classes Affect Students’ Perception of Their Behavioural Intention (BIU) to Use Online Platforms?

Traffic Light and Back-light Recognition using Deep Learning and Image Processing with Raspberry Pi

Path Planning for Perpendicular Parking of Large Articulated Vehicles Based on Qualitative Kinematics and Geometric Methods

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