scholarly journals Estimation of Driver Lane Change Intention Based on the LSTM and Dempster–Shafer Evidence Theory

2021 ◽  
Vol 2021 ◽  
pp. 1-11
Author(s):  
Zhi-Qiang Liu ◽  
Man-Cai Peng ◽  
Yue-Chen Sun
Keyword(s):  
Decision Making ◽  
Visual Information ◽  
Short Term Memory ◽  
Evidence Theory ◽  
Head Orientation ◽  
Final Decision ◽  
Lane Change ◽  
Driver Workload ◽  
Vehicle Automation ◽  
Correct Estimation

Rapid and correct estimation of driver lane change intention plays an important role in the advanced driver assistance system (ADAS), which could make the driver improve the reliability of the ADAS system and help to decrease driver workload. In this study, a method based on the long short-term memory network (LSTM) and Dempster–Shafer evidence theory is proposed. The model consists of a preliminary decision-making label and a final decision-making label. Driver visual information, head orientation, and vehicle dynamics are collected by preliminary decision-making label. Then, LSTM is used to calculate the initial probability of the driver lane change (left, right, and lane keeping) maneuver intention. The outputs of LSTM are normalized and assigned a basic probability by the Dempster–Shafer evidence theory. The final decision-making label analyzes the information and outputs the probability of each lane change intention and the decision is to identify the driver's current intention. The experimental results show that the accuracy of the model is 90.7% for the intention of changing left and 89.1% for the intention of changing right. The outcome of this work is an essential component for all levels of road vehicle automation.

How multidisciplinary are multidisciplinary case reviews in cancer care? Feasibility analysis of a theory-driven team decision-making fidelity framework

2019 ◽  
Author(s):  
Tayana Soukup ◽  
Ged Murtagh ◽  
Ben W Lamb ◽  
James Green ◽  
Nick Sevdalis
Keyword(s):  
Decision Making ◽  
Cancer Care ◽  
Recent Decade ◽  
Individual Case ◽  
Multidisciplinary Teams ◽  
Final Decision ◽  
Political Climate ◽  
Implementation Framework ◽  
Feasibility Evaluation ◽  
The Uk

Background Multidisciplinary teams (MDTs) are a standard cancer care policy in many countries worldwide. Despite an increase in research in a recent decade on MDTs and their care planning meetings, the implementation of MDT-driven decision-making (fidelity) remains unstudied. We report a feasibility evaluation of a novel method for assessing cancer MDT decision-making fidelity. We used an observational protocol to assess (1) the degree to which MDTs adhere to the stages of group decision-making as per the ‘Orientation-Discussion-Decision-Implementation’ framework, and (2) the degree of multidisciplinarity underpinning individual case reviews in the meetings. MethodsThis is a prospective observational study. Breast, colorectal and gynaecological cancer MDTs in the Greater London and Derbyshire (United Kingdom) areas were video recorded over 12-weekly meetings encompassing 822 case reviews. Data were coded and analysed using frequency counts.Results Eight interaction formats during case reviews were identified. case reviews were not always multi-disciplinary: only 8% of overall reviews involved all five clinical disciplines present, and 38% included four of five. The majority of case reviews (i.e. 54%) took place between two (25%) or three (29%) disciplines only. Surgeons (83%) and oncologists (8%) most consistently engaged in all stages of decision-making. While all patients put forward for MDT review were actually reviewed, a small percentage of them (4%) either bypassed the orientation (case presentation) and went straight into discussing the patient, or they did not articulate the final decision to the entire team (8%). Conclusions Assessing fidelity of MDT decision-making at the point of their weekly meetings is feasible. We found that despite being a set policy, case reviews are not entirely MDT-driven. We discuss implications in relation to the current eco-political climate, and the quality and safety of care. Our findings are in line with the current national initiatives in the UK on streamlining MDT meetings, and could help decide how to re-organise them to be most efficient.

scholarly journals What’s in a (Change of) Name? Much—but Not That Much—and Not What Wiebe Claims

2020 ◽  
Vol 32 (2) ◽  
pp. 159-184 ◽  
Author(s):  
Satoko Fujiwara ◽  
Tim Jensen
Keyword(s):  
Decision Making ◽  
Executive Committee ◽  
International Association ◽  
International Committee ◽  
Academic Standards ◽  
General Assembly ◽  
Final Decision ◽  
Points Of View ◽  
The Difference ◽  
Consultative Body

Abstract Donald Wiebe claims that the IAHR leadership (already before an Extended Executive Committee (EEC) meeting in Delphi) had decided to water down the academic standards of the IAHR with a proposal to change its name to “International Association for the Study of Religions.” His criticism, we argue, is based on a series of misunderstandings as regards: 1) the difference between the consultative body (EEC) and the decision-making body (EC), 2) the difference between the preliminary points of view of individuals and final proposals by the EC, 3) personal conversations, 4) the link between the proposal to change the name and the wish to tighten up the academic profile of the IAHR. Moreover, if the final decision-making bodies, the International Committee and the General Assembly, adopt the proposal, the new name as little as the old can make the IAHR more or less scientific. Tightening up the academic, scientific profile of the IAHR takes more than a change of name.

scholarly journals New dilemmas, old problems: advances in data analysis and its geoethical implications in groundwater management

2021 ◽  
Vol 3 (6) ◽  
Author(s):  
César de Oliveira Ferreira Silva ◽  
Mariana Matulovic ◽  
Rodrigo Lilla Manzione
Keyword(s):  
Decision Making ◽  
Data Analytics ◽  
Data Science ◽  
Final Decision ◽  
Aquifer Recharge ◽  
Groundwater Governance ◽  
Ethical Attitude ◽  
New Information ◽  
Modeling Experiment ◽  
Using Data

Abstract Groundwater governance uses modeling to support decision making. Therefore, data science techniques are essential. Specific difficulties arise because variables must be used that cannot be directly measured, such as aquifer recharge and groundwater flow. However, such techniques involve dealing with (often not very explicitly stated) ethical questions. To support groundwater governance, these ethical questions cannot be solved straightforward. In this study, we propose an approach called “open-minded roadmap” to guide data analytics and modeling for groundwater governance decision making. To frame the ethical questions, we use the concept of geoethical thinking, a method to combine geoscience-expertise and societal responsibility of the geoscientist. We present a case study in groundwater monitoring modeling experiment using data analytics methods in southeast Brazil. A model based on fuzzy logic (with high expert intervention) and three data-driven models (with low expert intervention) are tested and evaluated for aquifer recharge in watersheds. The roadmap approach consists of three issues: (a) data acquisition, (b) modeling and (c) the open-minded (geo)ethical attitude. The level of expert intervention in the modeling stage and model validation are discussed. A search for gaps in the model use is made, anticipating issues through the development of application scenarios, to reach a final decision. When the model is validated in one watershed and then extrapolated to neighboring watersheds, we found large asymmetries in the recharge estimatives. Hence, we can show that more information (data, expertise etc.) is needed to improve the models’ predictability-skill. In the resulting iterative approach, new questions will arise (as new information comes available), and therefore, steady recourse to the open-minded roadmap is recommended. Graphic abstract

scholarly journals A Game Theory-Based Approach for Modeling Autonomous Vehicle Behavior in Congested, Urban Lane-Changing Scenarios

Sensors ◽  
2021 ◽  
Vol 21 (4) ◽  
pp. 1523
Author(s):  
Nikita Smirnov ◽  
Yuzhou Liu ◽  
Aso Validi ◽  
Walter Morales-Alvarez ◽  
Cristina Olaverri-Monreal
Keyword(s):  
Game Theory ◽  
Decision Making ◽  
Autonomous Vehicles ◽  
Autonomous Vehicle ◽  
Urban Traffic ◽  
Road User ◽  
Lane Change ◽  
Traffic Light ◽  
User Interactions ◽  
Lane Changing

Autonomous vehicles are expected to display human-like behavior, at least to the extent that their decisions can be intuitively understood by other road users. If this is not the case, the coexistence of manual and autonomous vehicles in a mixed environment might affect road user interactions negatively and might jeopardize road safety. To this end, it is highly important to design algorithms that are capable of analyzing human decision-making processes and of reproducing them. In this context, lane-change maneuvers have been studied extensively. However, not all potential scenarios have been considered, since most works have focused on highway rather than urban scenarios. We contribute to the field of research by investigating a particular urban traffic scenario in which an autonomous vehicle needs to determine the level of cooperation of the vehicles in the adjacent lane in order to proceed with a lane change. To this end, we present a game theory-based decision-making model for lane changing in congested urban intersections. The model takes as input driving-related parameters related to vehicles in the intersection before they come to a complete stop. We validated the model by relying on the Co-AutoSim simulator. We compared the prediction model outcomes with actual participant decisions, i.e., whether they allowed the autonomous vehicle to drive in front of them. The results are promising, with the prediction accuracy being 100% in all of the cases in which the participants allowed the lane change and 83.3% in the other cases. The false predictions were due to delays in resuming driving after the traffic light turned green.

scholarly journals Developing an approach for decision making units integration using multi objective particle swarm optimization

2014 ◽  
Vol 4 (1) ◽  
pp. 48 ◽  
Author(s):  
Abdorrahman Haeri ◽  
Kamran Rezaie ◽  
Seyed Morteza Hatefi
Keyword(s):  
Decision Making ◽  
Particle Swarm Optimization ◽  
Performance Measures ◽  
Particle Swarm ◽  
Final Decision ◽  
Swarm Optimization ◽  
Multi Objective ◽  
Decision Making Unit ◽  
The Difference ◽  
Decision Making Units

In recent years, integration between companies, suppliers or organizational departments attracted much attention. Decision making about integration encounters with major concerns. One of these concerns is which units should be integrated and what is the effect of integration on performance measures. In this paper the problem of decision making unit (DMU) integration is considered. It is tried to integrate DMUs so that the considered criteria are satisfied. In this research two criteria are considered that are mean of efficiencies of DMUs and the difference between DMUs that have largest and smallest efficiencies. For this purpose multi objective particle swarm optimization (MOPSO) is applied. A case with 17 DMUs is considered. The results show that integration has increased both considered criteria effectively.  Additionally this approach can presents different alternatives for decision maker (DM) that enables DM to select the final decision for integration.

Method for Selecting Storage Enterprises Based on IFHA

2015 ◽  
Vol 713-715 ◽  
pp. 1769-1772
Author(s):  
Jie Wu ◽  
Lei Na Zheng ◽  
Tie Jun Pan
Keyword(s):  
Decision Making ◽  
Group Decision Making ◽  
Group Decision ◽  
Decision Makers ◽  
Decision Problems ◽  
Decision Making Process ◽  
Final Decision ◽  
Multiple Decision Makers ◽  
Intuitionistic Fuzzy ◽  
Averaging Operator

In order to reflect the decision-making more scientific and democratic, modern decision problems often require the participation of multiple decision makers. In group decision making process,require the use of intuitionistic fuzzy hybrid averaging operator (IFHA) to get the final decision result.

scholarly journals Student dropout at university: a phase-orientated view on quitting studies and changing majors

2021 ◽  
Author(s):  
Lisa Bäulke ◽  
Carola Grunschel ◽  
Markus Dresel
Keyword(s):  
Decision Making ◽  
Information Search ◽  
Structural Equation ◽  
Cross Sectional Study ◽  
Final Decision ◽  
Cross Sectional ◽  
Confirmatory Factor Analyses ◽  
Student Dropout ◽  
Regulatory Processes ◽  
Present Cross Sectional Study

AbstractStudent dropout can be conceptualized as a decision-making process, consisting of different phases. Based on previous literature on student dropout, decision-making, and action-phases, we proposed that the process of developing dropout intentions includes the following phases: non-fit perception, thoughts of quitting/changing, deliberation, information search, and a final decision. In the present cross-sectional study, we empirically investigated if the assumed phases can be distinguished from each other, if the phases follow the presumed order, and whether each phase is associated with certain characteristics. Furthermore, we considered a strict separation between quitting studies completely and changing a major. For this purpose, we analyzed data of N = 1005 students (average age of 23.0 years; 53% female; 47% male) from a German University. By using confirmatory factor analyses, we found the supposed factor structure for the different phases concerning both kinds of dropout, quitting studies, and changing majors. In each process, structural equation modelling indicated positive relations between adjoining phases. The factor values correlated to a substantial amount with an assortment of variables associated with student dropout. On a theoretical level, the conception of different phases of student dropout helps to get a better understanding of regulatory processes in the context of student dropout.

scholarly journals Real-Time AI-Based Informational Decision-Making Support System Utilizing Dynamic Text Sources

2021 ◽  
Vol 11 (13) ◽  
pp. 6237
Author(s):  
Azharul Islam ◽  
KyungHi Chang
Keyword(s):  
Machine Learning ◽  
Decision Making ◽  
Random Forest ◽  
Support System ◽  
Classification Accuracy ◽  
Short Term Memory ◽  
Learning Algorithm ◽  
Unstructured Data ◽  
Stochastic Gradient Descent ◽  
Decision Making Support

Unstructured data from the internet constitute large sources of information, which need to be formatted in a user-friendly way. This research develops a model that classifies unstructured data from data mining into labeled data, and builds an informational and decision-making support system (DMSS). We often have assortments of information collected by mining data from various sources, where the key challenge is to extract valuable information. We observe substantial classification accuracy enhancement for our datasets with both machine learning and deep learning algorithms. The highest classification accuracy (99% in training, 96% in testing) was achieved from a Covid corpus which is processed by using a long short-term memory (LSTM). Furthermore, we conducted tests on large datasets relevant to the Disaster corpus, with an LSTM classification accuracy of 98%. In addition, random forest (RF), a machine learning algorithm, provides a reasonable 84% accuracy. This research’s main objective is to increase the application’s robustness by integrating intelligence into the developed DMSS, which provides insight into the user’s intent, despite dealing with a noisy dataset. Our designed model selects the random forest and stochastic gradient descent (SGD) algorithms’ F1 score, where the RF method outperforms by improving accuracy by 2% (to 83% from 81%) compared with a conventional method.

Assessing ventilation strategies in a school with observed indoor air problems

Facilities ◽  
2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Ulrika Uotila ◽  
Arto Saari ◽  
Juha-Matti Kalevi Junnonen ◽  
Lari Eskola
Keyword(s):  
Decision Making ◽  
Air Quality ◽  
Indoor Air Quality ◽  
Indoor Air ◽  
Final Decision ◽  
Target Number ◽  
Content Type ◽  
Existing Building ◽  
Ventilation Strategies

Purpose Poor indoor air quality in schools is a worldwide challenge that poses health risks to pupils and teachers. A possible response to this problem is to modify ventilation. Therefore, the purpose of this paper is to pilot a process of generating alternatives for ventilation redesign, in an early project phase, for a school to be refurbished. Here, severe problems in indoor air quality have been found in the school. Design/methodology/approach Ventilation redesign is investigated in a case study of a school, in which four alternative ventilation strategies are generated and evaluated. The analysis is mainly based on the data gathered from project meetings, site visits and the documents provided by ventilation and condition assessment consultants. Findings Four potential strategies to redesign ventilation in the case school are provided for decision-making in refurbishment in the early project phase. Moreover, the research presents several features to be considered when planning the ventilation strategy of an existing school, including the risk of alterations in air pressure through structures; the target number of pupils in classrooms; implementing and operating costs; and the size of the space that ventilation equipment requires. Research limitations/implications As this study focusses on the early project phase, it provides viewpoints to assist decision-making, but the final decision requires still more accurate calculations and simulations. Originality/value This study demonstrates the decision-making process of ventilation redesign of a school with indoor air problems and provides a set of features to be considered. Hence, it may be beneficial for building owners and municipal authorities who are engaged in planning a refurbishment of an existing building.

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