Here Global B.v.

Voxis, Inc.

Topcon Positioning Systems, Inc.

Dimsdale Engineering, LLC

Andrew Lewis

Jerry Samuel Dimsdale

Davide Pietrobon

Jane MacFarlane

Joseph Newhall West

Thomas Rahjit Singh Gill

William Chen

Matei Stroila

Bo Xu

Jeffrey Minoru Adachi

Antonio Haro

Jane F Macfarlane

Tiffany Barkley

Evan Smothers

Gavin Heverly-Coulson

Robert Berry

Stacey Matthew Mott

A method, apparatus and computer program product are provided to localize data from at least one of two or more data sets a based upon the registration of synthetic images representative of the two or more data sets. In the context of a method, first and second synthetic images are created from first and second data sets, respectively. In creating the first and second synthetic images, representations of one or more features from the first and second data sets are rasterized. The method also determines a transformation based upon a phase correlation between the first and second synthetic images. The transformation provides for improved localization of the data from at least one of the first or second data sets. The method also generates a report that defines the transformation.

Method and apparatus for localizing a data set based upon synthetic image registration

An approach is provided for detecting road closures programmatically by monitoring probe activity. The approach, for example, involves generating a historical model of expected probe activity on the road segment based on historical probe data. The approach also involves monitoring observed probe activity from the road segment. The approach further involves calculating a probability of the observed probe activity based on the historical model of the expected probe activity. The approach further involves determining the closure status of the road segment based on the probability.

Method, apparatus, and system for detecting road closures based on probe activity

An approach is provided for next token prediction based on previously observed tokens. The approach involves receiving an observed time series of tokens, wherein each of the tokens represents an observed data pattern. The approach also involves adding a most recent token from the observed time series of tokens into a variable token set. The approach further involves processing a historical token set to determine a historical token sequence comprising the variable token set followed by a next token. The approach further involves recursively adding a next most recent token from the observed time series of tokens into the variable token set for processing until the next token following the variable token set in the determined historical token sequence is unique or meets a target number of possible predictions. The approach further involves presenting the next token as a predicted next token of the observed time series of tokens.

Method and apparatus for next token prediction based on previously observed tokens

An approach is provided for semantic-free traffic prediction. The approach involves dividing a travel-speed data stream into a plurality of travel-speed patterns. The travel-speed data stream represents vehicle travel speeds occurring in a road network. The approach also involves representing each of the plurality of travel-speed patterns by a respective token. The respective token is selected from a dictionary of tokens representing a plurality of travel-speed templates determined from historical travel-speed data. The approach further involves matching a sequence of the respective tokens corresponding to said each of the plurality of travel-speed patterns to a best-fit sequence of tokens determined from the historical travel-speed data. The approach further involves determining a predicted sequence of tokens based on the best-fit sequence of tokens, and generating a traffic prediction for the road network based on the predicted sequence of tokens.

Method and apparatus for providing semantic-free traffic prediction

An approach is provided for a localized link-centric metric for directional traffic propagation. The approach, for instance, involves designating a base link of the road network. The approach also involves determining a plurality of vehicle trajectories that pass through the base link. The plurality of vehicle trajectories is based on probe data collected from one or more sensors of a plurality of vehicles travelling in the road network. The approach further involves determining a frequency at which the plurality of vehicle trajectories passes through the base link to each of one or more other links in the plurality of vehicle trajectories within a proximity threshold. The approach further involves computing a link-centric metric for said each of the one or more other links relative to the base link based on the determined frequency.

Method and apparatus for providing a localized link-centric metric for directional traffic propagation

An approach is provided for classifying a traffic jam from probe data. The approach involves receiving the probe data that is map-matched to a roadway on which the traffic jam is detected. The probe data is collected from one or more vehicles traveling the roadway. The approach also involves determining a jam area of the roadway based on the probe data. The jam area corresponds to one or more segments of the roadway affected by the traffic jam. The approach further involves determining a set of features indicated by the probe data from a portion of the probe data collected from the jam area. The approach further involves classifying, using a machine learning classifier, the traffic jam as either a recurring traffic jam or a non-recurring traffic jam based on the set of features.

Method and apparatus for classifying a traffic jam from probe data

A method comprising receiving probe data indicative of a set of navigational signal measurements that is matched to a link segment, determining a set of measurement errors such that each measurement error of the set of measurement errors is a difference between a location indicated by the link segment and a location indicated by a navigational signal measurement of the set of navigational signal measurements, determining at least one statistical attribute of the set of measurement errors, and storing an indication of the statistical attribute in map information associated with the link segment is disclosed.

Determination of a statistical attribute of a set of measurement errors

Method, systems, and devices are described for determining traffic volume of one or more path segments. A computing device may receive probe data associated with a road segment from one or more sources. The computing device selects either a free flow algorithm or a congestion algorithm for the probe data, and calculates an estimated probe quantity from historical data using either the free flow algorithm or the congestion algorithm. A traffic volume may be estimated from the estimate probe quantity.

Traffic volume estimation

A time differential is estimated between a plurality of signals by determining a filter response of a first electrical signal with a first filter array, determining a filter response of a second electrical signal with a second filter array, and determining, based at least on the filter response of the first electrical signal and the filter response of the second electrical signal, a time differential between the first electrical signal and the second electrical signal. A first optical signal is converted into the first electrical signal and a second optical signal is converted into the second electrical signal. The filter response of the first electrical signal and the filter response of the second electrical signal are sampled and the time differential between the first electrical signal and the second electrical signal is determined based at least on the sampled filter response of the first electrical signal and the sampled filter response of the second electrical signal.

Time delay estimation

A system and method for imaging a three-dimensional scene having one or more objects. The system includes a light source, a detector array, a timing circuit, an inertial guidance system and a processor connected to the timing circuit and the inertial guidance system. The light source generates an optical pulse and projects the optical pulse on an object so that it is reflected as a reflected pulse. The detector array includes a plurality of detectors, wherein the detectors are oriented to receive the reflected pulse. The timing circuit determines when the reflected pulse reached detectors on the detector array. The inertial guidance system measures angular velocity and acceleration. The processor forms a composite image of the three-dimensional scene as a function of camera position and range to objects in the three-dimensional scene.

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Berkeley, CA, United States of America

Andrew Lewis