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In the modern information society, accurate prediction of human mobility becomes increasingly essential in various areas such as city planning and resource management. With users' historical trajectories, the inherent patterns of their movements can be extracted and utilized to accurately predict the future movements. In this paper, based on a dataset of 100,000 individuals' actively uploaded location...
Robot Reinforcement Learning (RL) algorithms return a policy that maximizes a global cumulative reward signal but typically do not create diverse behaviors. Hence, the policy will typically only capture a single solution of a task. However, many motor tasks have a large variety of solutions and the knowledge about these solutions can have several advantages. For example, in an adversarial setting...
In this paper, we present a novel method to recognize the types of crowd movement from crowd trajectories using agent-based motion models (AMMs). Our idea is to apply a number of AMMs, referred to as exemplar-AMMs, to describe the crowd movement. Specifically, we propose an optimization framework that filters out the unknown noise in the crowd trajectories and measures their similarity to the exemplar-AMMs...
This paper assesses the predictability of individual vehicle's mobility in Beijing expressway system using Electronic Toll Collection (ETC) data records. By examining the uncertainties of movements using entropy, considering both the frequencies and sequential correlations of vehicles' trajectories, we draw to the conclusion that the average limit of predictability of expressway vehicles mobility...
This paper reports on a data-driven motion planning approach for interaction-aware, socially-compliant robot navigation among human agents. Autonomous mobile robots navigating in workspaces shared with human agents require motion planning techniques providing seamless integration and smooth navigation in such. Smooth integration in mixed scenarios calls for two abilities of the robot: predicting actions...
With the rapid increase of the mobile users, mobility prediction has attracted more and more attention. For the moment, a lot of location prediction methods have shown that humans are highly predictable in their movements. However, the method of predicting locations when the user's behavior unexpectedly transits from routine to irregular pattern is undiscovered. In this paper, we propose a practical...
As technology to connect people across the world is advancing, there should be corresponding advancement in taking advantage of data that is generated out of such connection. To that end, next place prediction is an important problem for mobility data. In this paper we propose several models using dynamic Bayesian network (DBN). Idea behind development of these models come from typical daily mobility...
GPS-based activity recognition is extremely important for high-level analysis and location based services. Trajectories of people are highly imbalanced from spatial and temporal perspectives. Many existing researches achieve good results on recognizing activities with lots of GPS logs, such as working and staying at home. However, these approaches usually fail at activities with few trajectory records...
This paper uses Bayesian networks to investigate the impact of three different kind of inputs, namely, physiological, cognitive and affect features, on workload estimation, from a computational point of view. The ability of the proposed models to infer the workload variation of subjects involved in successive tasks demanding different levels of cognitive resources is discussed, in term of two criteria...
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