Lyft, Inc.

Albert Yuen

James Kevin Murphy

Han Suk Kim

Deeksha Goyal

Barak Michener

Thomas Michael Dedeco

Systems and methods are disclosed for improved mapping data validation using randomness measures. Methods may include receiving a map dataset that includes a plurality of map features. A knowledge graph may be generated based on the map dataset that may include nodes representing or corresponding to the map features. Nodes corresponding to map features of a particular feature type may be identified and edges connected to the identified nodes may be processed to identify a plurality of paths. A randomness measure for the particular feature type may be determined based on the plurality of paths. The randomness measure may indicate a predictability of occurrences of map features of the particular feature type in the map dataset. The randomness measure may then be compared to a second randomness measure determined based on another map dataset.

Map data validation using knowledge graphs and randomness measures

The present disclosure relates to systems, methods, and non-transitory computer readable media for identifying traffic control features based on telemetry patterns within digital image representations of vehicle telemetry information. The disclosed systems can generate a digital image representation based on collected telemetry information to represent the frequency of different speed-location combinations for transportation vehicles passing through a traffic area. The disclosed systems can also apply a convolutional neural network to analyze the digital image representation and generate a predicted classification of a type of traffic control feature that corresponds to the digital image representation of vehicle telemetry information. The disclosed systems further train the convolutional neural network to determine traffic control features based on training data.

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San Francisco, CA, United States of America

Albert Yuen