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• W2938768079 abstract "Autonomous driving on highways and freeways, as afeature, is already available in quite a few high-end commercialvehicles being sold today. Autonomous driving in urbanenvironments, on the other hand, is still an active area ofacademic and industrial research [6], because of its relativelycomplex nature. Urban driving requires the self-driving vehicle tointeract with not just other vehicles, but also other moving agentssuch as cyclists and pedestrians. Pedestrian trajectory predictionis challenging because of the relatively higher number of degreesof freedom in pedestrian movement and absence of uniform rulesacross different cities and different scenarios within a city.Furthermore, in scenarios such as intersections, context, such aspedestrian traffic lights, stop signs and sidewalk geometry,significantly influences pedestrian movement. The objective of thisthesis is to present a general, context-aware, long term (order offew seconds) trajectory prediction model for pedestrians in urbanintersections. To meet this objective, first, the Augmented SemiNonnegative Sparse Coding (ASNSC) [13] framework, for trajectoryprediction, is extended to embed context, and build theContext-aware Augmented Semi Nonnegative Sparse Coding (CASNSC)algorithm. For prediction in new, unseen intersections withdifferent curbside geometries (orthogonal versus skewed), CASNSC isfurther extended to build the Transferable Augmented SemiNonnegative Sparse Coding (TASNSC) algorithm. Urban intersectionscan at times vary significantly in the type of pedestrian behaviorsencountered, even across intersections with similar geometries. Forinstance, faster, rule breaking students near a college campusversus slower pedestrians in a residential area. While TASNSC iscapable of successfully transferring knowledge from oneintersection to another, it lacks the ability to update itsprediction model as, and when, new intersections are visited andnovel behaviors are encountered. An online model, based on TASNSC,is also presented in this thesis to account for this particularlimitation." @default.
• W2938768079 created "2019-04-25" @default.
• W2938768079 creator A5027703630 @default.
• W2938768079 date "2018-01-01" @default.
• W2938768079 modified "2023-09-26" @default.
• W2938768079 title "A general, context-aware pedestrian trajectory prediction model" @default.
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• W2938768079 hasPublicationYear "2018" @default.
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