Abstract:Accurate model development and efficient representations of multivariate trajectories are crucial to understanding the behavioral patterns of pedestrian motion. Most of the existing algorithms use offline learning approaches to learn such motion behaviors. However, these approaches cannot take advantage of the streams of data that are available after training has concluded, and typically are not generalizable to data that they have not seen before. To solve this problem, this paper proposes two algorithms for … Show more
“…Also, the driver's seat may change, with past drivers working on laptops, eating meals, reading books, watching movies, and/or making safe phone calls to pals. [5]…”
“…Also, the driver's seat may change, with past drivers working on laptops, eating meals, reading books, watching movies, and/or making safe phone calls to pals. [5]…”
“…[5] Also, the driver's seat may change, with past drivers working on laptops, eating meals, reading books, watching movies, and/or making safe phone calls to pals. [6]…”
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