Lyft, Inc.

Yuanyuan Malek

Asif Haque

James Kevin Murphy

Limin Shen

Charlie Lin

Thaddeus Insuk Hwang

Ramesh Rangarajan Sarukkai

The present application discloses an improved transportation matching system, and corresponding methods and computer-readable media. According to disclosed embodiments, a transportation matching system receives a session indicator and device-based location. The system utilizes the received request and device-based location to identify and analyze historical transportation matching system information. The system then generates a confidence score based on this information that indicates a level of confidence that the device-based location is a pickup location associated with the request. If the generated confidence score exceeds a predetermined threshold, the system provides display components to a requestor computing device that enable confirmation of the transportation request with a single user interaction.

Optimizing pickup locations for transportation requests based on context information utilizing a continued touch gesture

In one embodiment, a method includes receiving a sequence of location data points associated with a vehicle from a first source and a sequence of motion data points associated with the vehicle from a second source. The method includes determining a first turn angle of the vehicle based on at least one location data point in the sequence of location data points associated with the first source. The method includes determining that an additional location data point in the sequence of location data points is inaccurate. The method includes determining a second turn angle of the vehicle by using at least one motion data point in the sequence of motion data points corresponding to the additional location data point that is inaccurate. The method includes determining a turn trajectory of the vehicle by using at least the first turn angle and the second turn angle.

Localization based on sensor data

Optimizing pickup locations for transportation requests based on a confidence score for a context information

A disclosed method may include receiving geographic coordinates of a location at which two parties are to rendezvous, generating a human-understandable geospatial descriptor for the request location, and sending the descriptor to respective devices of the two parties for presentation to the two parties. Generating the human-understandable geospatial descriptor may include identifying a human-visible feature in the vicinity of the request location that is labeled within available map data, selecting, based on a descriptor generation model, a reference expression relative to the identified feature, and applying a grammar-based constructor to the label and the selected reference expression to form the human-understandable geospatial descriptor. The model may be tuned using machine learning. The two parties may include a ride requestor and a ride provider in a ridesharing service. The identified feature may be a point of interest, landmark, street name, intersection, marker, or structure.

Automatic generation of human-understandable geospatial descriptors

This disclosure covers methods, non-transitory computer readable media, and systems that generate route tiles reflecting both GPS locations and map-matched locations for regions along a route traveled by a client device associated with a transportation vehicle. For example, in some implementations, the disclosed systems use an artificial neural network to analyze the route tiles and determine route-accuracy metrics indicating GPS locations or map-matched locations for particular regions along the route. The disclosed systems can then use the route-accuracy metrics to facilitate transport of requestors by, for example, determining a distance of the route or a location of a client device associated with a transportation vehicle.

Utilizing artificial neural networks to evaluate routes based on generated route tiles

In one embodiment, a method includes receiving a sequence of location points and motion data associated with a mobile computing device. The method further includes generating, based on the motion data, a motion-data trace of a path and calculating, for each location point, a distance between the location point and a point on the motion-data trace of the path. The method further includes determining that the distance associated with at least one location point exceeds a threshold distance. The method further includes generating an estimated path traveled by the mobile computing device using (1) the point on the motion-data trace of the path used for calculating the distance associated with each of the at least one location point and (2) the received location point for each of the sequence of location points whose associated distance is at or within the threshold distance.

Growing community of inventors

San Francisco, CA, United States of America

Yuanyuan Malek