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Brian Ferris
Researcher at University of Washington
Publications - 16
Citations - 2423
Brian Ferris is an academic researcher from University of Washington. The author has contributed to research in topics: Public transport & Traffic congestion. The author has an hindex of 12, co-authored 16 publications receiving 2320 citations. Previous affiliations of Brian Ferris include Google.
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Proceedings Article
WiFi-SLAM using Gaussian process latent variable models
TL;DR: This paper proposes a novel technique for solving the WiFi SLAM problem using the Gaussian Process Latent Variable Model (GPLVM) to determine the latent-space locations of unlabeled signal strength data and shows how GPLVM, in combination with an appropriate motion dynamics model, can be used to reconstruct a topological connectivity graph from a signal strength sequence.
Proceedings Article
WiFi-SLAM Using G aussian Process Latent Variable Models
TL;DR: In this paper, the Gaussian Process Latent Variable Model (GPLVM) is used to reconstruct a topological connectivity graph from a signal strength sequence, which can be used to perform efficient WiFi SLAM.
Proceedings ArticleDOI
Gaussian Processes for Signal Strength-Based Location Estimation
TL;DR: Gaussian processes can be used to generate a likelihood model for signal strength measurements and parameters of the model, such as signal noise and spatial correlation between measurements, can be learned from data via hyperparameter estimation.
Journal ArticleDOI
Where Is My Bus? Impact of mobile real-time information on the perceived and actual wait time of transit riders
TL;DR: The OneBusAway transit traveler information system provides real-time next bus countdown information for riders of King County Metro via website, telephone, text-messaging, and smart phone applications.
Proceedings ArticleDOI
Learning to navigate through crowded environments
TL;DR: This paper uses inverse reinforcement learning (IRL) to learn human-like navigation behavior based on example paths and shows that the planner learned to guide the robot along the flow of people when the environment is crowded, and along the shortest path if no people are around.