METHOD AND APPARATUS FOR DETERMINING VEHICLE CLASS BASED UPON AUDIO DATA

    公开(公告)号:US20210375305A1

    公开(公告)日:2021-12-02

    申请号:US17129477

    申请日:2020-12-21

    Abstract: A method, apparatus and computer program product are provided to identify the class of vehicle driving over a road surface based upon audio data collected as the vehicle drives thereover. With respect to predicting a class of a vehicle, audio data is obtained that is created by the vehicle while driving over the road surface. The audio data includes one or more audio frequency features and/or one or more audio amplitude features. The audio data including the one or more audio frequency features and/or the one or more audio amplitude features is provided to a machine learning model and the class of the vehicle that created the audio data is predicted utilizing the machine learning model. A method, apparatus and computer program product are also provided for training the machine learning model to predict the class of the vehicle driving over the road surface.

    METHODS AND SYSTEMS FOR DETECTING A SPEED FUNNEL IN A REGION

    公开(公告)号:US20210335132A1

    公开(公告)日:2021-10-28

    申请号:US16859578

    申请日:2020-04-27

    Abstract: The disclosure provides a method, a system, and a computer program product for detecting a speed funnel in a region. The method comprises obtaining a plurality of traffic object observations for the region and determining at least one first learned traffic object, based on feature-based clustering of the plurality of traffic object observations. The method also includes generating at least one candidate traffic object group by grouping the at least one first learned traffic object and a second learned traffic object and performing validation of the at least one candidate traffic object group based on a statistical model. The method further includes generating at least one validated candidate traffic object group as a result of the validation of the at least one candidate traffic object group and merging the at least one validated candidate traffic object group with a second validated candidate traffic object group to detect the speed funnel.

    METHODS AND SYSTEMS FOR CLASSIFYING A SPEED SIGN

    公开(公告)号:US20210287538A1

    公开(公告)日:2021-09-16

    申请号:US16817229

    申请日:2020-03-12

    Abstract: A system, method and computer program product are provided for classifying at least one speed sign associated with a region. In an example embodiment, the method may include obtaining sensor data comprising speed limit data associated with the at least one speed sign. The method may further include obtaining map data associated with a segment of the region, wherein the map data comprises conditional speed limit data associated with a conditional speed limit sign linked with the segment. The method may further include comparing the speed limit data with the conditional speed limit data and classifying the at least one speed sign as one of a conditional speed sign or a non-conditional speed sign based on the comparison.

    SYSTEM AND METHOD FOR IDENTIFICATION OF A ROADWORK ZONE

    公开(公告)号:US20200300658A1

    公开(公告)日:2020-09-24

    申请号:US16358367

    申请日:2019-03-19

    Abstract: Various aspects of a system, a method, and a computer program product for generation of roadwork extension data of a roadwork zone are disclosed herein. In accordance with an embodiment, the system includes a memory and a processor. The processor may be configured to obtain speed funnel data of one or more speed funnels. The processor may be configured to determine a plurality of candidate roadwork links, based on the speed funnel data. The processor may be configured to obtain vehicular trajectory data corresponding to the plurality of candidate roadwork links. The processor may be further configured to determine at least one qualified roadwork link from the plurality of candidate roadwork links, based on the vehicular trajectory data and speed threshold data of the plurality of candidate roadwork links. The processor may be further configured to generate the roadwork extension data based on the at least one qualified roadwork link.

    SYSTEMS, METHODS, AND COMPUTER PROGRAM PRODUCT FOR ROUTE VALIDATION

    公开(公告)号:US20200240801A1

    公开(公告)日:2020-07-30

    申请号:US16262481

    申请日:2019-01-30

    Abstract: A method, a system, and a computer program product are provided for validating one or more routes between at least a first road object and a second road object. The system, for example, comprises at least one non-transitory memory configured to store computer program code instructions; and at least one processor configured to execute the computer program code instructions. The processor may be configured to obtain at least a first map-matched link corresponding to the first road object and a second map-matched link corresponding to the second road object, and search for one or more downstream links, based on a plurality of first link attributes of the first map-matched link. The processor may be further configured to determine whether the second map-matched link is one among the one or more searched downstream links to obtain a result, and validate the one or more routes based on the result.

    METHOD AND SYSTEM OF A MACHINE LEARNING MODEL FOR DETECTION OF PHYSICAL DIVIDERS

    公开(公告)号:US20200167575A1

    公开(公告)日:2020-05-28

    申请号:US16203239

    申请日:2018-11-28

    Abstract: A method is provided for prediction of a physical divider on a road. The method comprises retrieving vehicular sensor data, map data, or a combination thereof captured over a predefined period of time for at least one segment of the road. The method further comprises aggregating the respective vehicular sensor data, map data, or the combination thereof for the at least one segment of the road to generate one or more aggregated values. The method further comprises generating output data corresponding to presence of the physical divider in the at least one segment of the road by a machine learning model based on the one or more aggregated values as an input to the machine learning model.

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