When Route Selection Meets a Hill: Route Planning With Terrain Change

2012 ◽  
Author(s):  
Qi Wang ◽  
Holly A. Taylor ◽  
Tad T. Brunye
2018 ◽  
Vol 71 (5) ◽  
pp. 1045-1056 ◽  
Author(s):  
Tad T Brunyé ◽  
Shaina B Martis ◽  
Holly A Taylor

Planning routes from maps involves perceiving the symbolic environment, identifying alternate routes and applying explicit strategies and implicit heuristics to select an option. Two implicit heuristics have received considerable attention, the southern route preference and initial segment strategy. This study tested a prediction from decision-making theory that increasing cognitive load during route planning will increase reliance on these heuristics. In two experiments, participants planned routes while under conditions of minimal (0-back) or high (2-back) working memory load. In Experiment 1, we examined how memory load impacts the southern route heuristic. In Experiment 2, we examined how memory load impacts the initial segment heuristic. Results replicated earlier results demonstrating a southern route preference (Experiment 1) and initial segment strategy (Experiment 2) and further demonstrated that evidence for heuristic reliance is more likely under conditions of concurrent working memory load. Furthermore, the extent to which participants maintained efficient route selection latencies in the 2-back condition predicted the magnitude of this effect. Together, results demonstrate that working memory load increases the application of heuristics during spatial decision making, particularly when participants attempt to maintain quick decisions while managing concurrent task demands.


Author(s):  
Julia L. Wright ◽  
Jessie Y.C. Chen ◽  
Michael J. Barnes ◽  
Peter A. Hancock

We examined how varying the transparency of agent reasoning affected operator workload in a route selection task, and how the differing measures of workload compared in assessing and understanding cognitive workload. Participants guided a three-vehicle convoy safely through a simulated environment of which they had a limited amount of information, while maintaining communication with command and monitoring their surroundings for threats. The intelligent route-planning agent assessed potential threats and suggested changes to the convoy route as needed. Each participant was assigned to one of three agent reasoning transparency conditions. Contrary to our hypothesis, NASA-TLX Global workload measures indicated that workload decreased slightly as access to agent reasoning increased. However, psychophysical measures of workload disagreed with NASA-TLX global results. Comparison of individual NASA-TLX workload factors with the psychophysical measures indicated that performance satisfaction was highest in the intermediary transparency condition, and the addition of ambiguous information in the highest transparency condition increased effort and resulted in increased complacent behavior. Recommendations for future workload analysis are offered.


2021 ◽  
Vol 9 (5) ◽  
pp. 502
Author(s):  
Andrea Orlandi ◽  
Andrea Cappugi ◽  
Riccardo Mari ◽  
Francesco Pasi ◽  
Alberto Ortolani

In the complex processes of route planning, voyage monitoring, and post-voyage analysis, a key element is the capability of merging metocean forecast data with the available knowledge of ship responses in the encountered environmental conditions. In this context, a prototype system has been implemented capable of integrating metocean models forecasts with ship specific performance data and models. The work is based on the exploitation of an open source ECDIS-like system originally developed in the e-Navigation framework. The resulting prototype system allows the uploading and visualization of metocean data, the consequent computation of fuel consumption along each analyzed route, and the evaluation of the encountered meteo-marine conditions on each route way point. This allows us to “effectively and deeply dig inside” the various layers of available metocean forecast data regarding atmospheric and marine conditions and evaluating their effects on ship performance indicators. The system could also be used to trigger route optimization algorithms and subsequently evaluate the results. All these functionalities are tailored in order to facilitate the “what-if” analysis in the route selection process performed by deck officers. Many of the added functionalities can be utilized also in a shore-based fleet monitoring and management center. A description is given of the modeling and visualization approaches that have been implemented. Their potentialities are illustrated through the discussion of some examples in Mediterranean navigation.


Author(s):  
Julia L. Wright ◽  
Jessie Y.C. Chen ◽  
Michael J. Barnes ◽  
Peter A. Hancock

We examined how varying the transparency of agent reasoning affected complacent behavior, in the form of incorrect acceptances of an agent’s recommendations, in a route selection task. We were particularly interested in how participants’ eye movements might disambiguate whether the incorrect acceptances were due to complacency or incorrect information processing. Participants guided a threevehicle convoy safely through a simulated environment of which they had a limited amount of information, while maintaining communication with command and monitoring their surroundings for threats. The intelligent route-planning agent assessed potential threats and suggested changes to the convoy route as needed. Each participant was assigned to one of three agent reasoning transparency conditions. While access to agent reasoning did appear to reduce complacent behavior in one condition, performance in the other conditions indicated potential complacent behavior. An area of interest analysis, reviewed in conjunction with the performance data, indicated the reason behind the participants’ behavior was different between these two conditions. While in the non-transparent condition participants were likely engaging in complacent behavior, in the highly transparent condition it is more likely they were overwhelmed by the amount and/or type of information, resulting in difficulty assimilating the information to support their decision-making task.


2019 ◽  
Vol 2019 ◽  
pp. 1-9 ◽  
Author(s):  
Sha-lei Zhan ◽  
Liang Chen ◽  
Ping-Kuo Chen ◽  
Yong Ye

Large-scale crowd evacuation is an important measure guaranteeing the safety of disaster-stricken victims in typhoon relief activities. Decision-making related to antityphoon crowd evacuation must take full consideration of the destructive effect of typhoons and their secondary disasters, time urgency, and resource limitation. To give full play to limited vehicle resources, the influence of a typhoon and its secondary disasters on antityphoon evacuation are mainly manifested during the execution of evacuation tasks in this article. The shortest time spent in completing all evacuation tasks was taken as the objective. Then, a vehicle route selection model for two-phase large-scale antityphoon crowd evacuation was built under an uncertain environment, and a matrix encoding–based genetic algorithm was designed to solve the model. Under the background of Super Typhoon Meranti in 2016, the model and algorithm were applied to crowd evacuation in a typhoon in Xiamen for a simulated analysis. Results indicate that in typhoon relief activities, emergency decision makers can use the proposed method to acquire a scientific and reasonable route selection scheme for antityphoon crowd evacuation according to related typhoon disaster data.


Systems ◽  
2021 ◽  
Vol 9 (4) ◽  
pp. 82
Author(s):  
Eugene Boon Kien Lee ◽  
Douglas L. Van Bossuyt ◽  
Jason F. Bickford

This article presents a Model-Based Systems Engineering (MBSE) methodology for the development of a Digital Twin (DT) for an Unmanned Aerial System (UAS) with the ability to demonstrate route selection capability with a Mission Engineering (ME) focus. It reviews the concept of ME and integrates ME with a MBSE framework for the development of the DT. The methodology is demonstrated through a case study where the UAS is deployed for a Last Mile Delivery (LMD) mission in a military context where adversaries are present, and a route optimization module recommends an optimal route to the user based on a variety of inputs including potential damage or destruction of the UAS by adversary action. The optimization module is based on Multiple Attribute Utility Theory (MAUT) which analyzes predefined criteria which the user assessed would enable the successful conduct of the UAS mission. The article demonstrates that the methodology can execute a ME analysis for route selection to support a user’s decision-making process. The discussion section highlights the key MBSE artifacts and also highlights the benefits of the methodology which standardizes the decision-making process thereby reducing the negative impact of human factors which may deviate from the predefined criteria.


2018 ◽  
Vol 7 (4.35) ◽  
pp. 912
Author(s):  
M. S. Zulkarnain ◽  
R. C. Omar ◽  
I. N.Z. Baharuddin ◽  
R. Roslan ◽  
S. A. Kamarudin

Transmission route selection needs to determine in order to have the best route for the transmission line. This paper presents a few alternative transmission line routes together by using cost path analysis application, minimizing and avoiding propose new located tower in landslide hazard area. The conventional route planning method usually only take into considerations of topographical such as gradient and curvature. This method unable to sustain the environment. This study can take into consideration of many factors that related to Environmental Sensitive Area (ESA) such as prone to landslide area, forestry area, land cover and land use. Different influence factor that assign to the weightage will resulted to different output of the suitability map. This study will have used nine Environmental Sensitive Area (ESA) factors, and using three difference influence factors that will optimize the result of the suitability map.  


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