Lyft, Inc.

Michael Jonathan DiCarlo

Romain Clément

Mikael Kjell Anders Thor

Alya Abbott

Renee Hei-Kyung Park

James Kevin Murphy

Ashivni Shekhawat

Preet Singh Anand

Alexander Wesley Contryman

Akash Gaurav Shah

Julien Van Hout

Desmond Mawuko Torkornoo

Devjit Chakravarti

Zhan Zhang

Louis John Zell

Paul Alfred Dickinson

The disclosed computer-implemented method may include identifying a transportation task within a dynamic transportation network, accessing a database of door closing event locations, wherein the database is populated from observed door closing events within the dynamic transportation network, determining location information specifying at least one of a pickup location and a drop-off location for the transportation task based at least in part on the database of door closing event locations, and providing the location information to a transportation provider computing device, wherein a transportation provider performs the transportation task that comprises at least one of picking up a transportation requestor at the pickup location and dropping off the transportation requestor at the drop-off location. Other methods, systems, and computer-readable media are disclosed.

Systems and methods for routing decisions based on door usage data

This disclosure describes a vehicle-motion-analysis system that can align axes for a provider device and a corresponding transportation vehicle based on the provider device's location and motion data as a basis for generating driving-event scores for particular driving events. In particular, the disclosed systems can generate axes-rotation parameters that align axes of a provider device with axes of a transportation vehicle. In addition, the disclosed systems can identify motion paths, motion patterns, or other driving behaviors that occur during particular driving events. Further, the disclosed systems can generate driving-event scores for such driving events and can customize a graphical user interface based on the driving-event scores to reflect a provider rating and/or a location rating.

Aligning provider-device axes with transportation-vehicle axes to generate driving-event scores

Methods and systems for processing telematics data are provided. In one embodiment, a method is provided that includes obtaining telematics information from a first device associated with a vehicle. The telematics information may indicate operations of the vehicle. An anomalous operation of the vehicle may then be determined based on the telematics information. The method may further include determining whether the anomalous operation of the vehicle is a vehicle collision. If the anomalous operation of the vehicle is a vehicle collision, routing information for the vehicle may be received that identifies previous locations of the vehicle. The routing information may then be compared with the telematics information to identify a location of the vehicle collision.

Telematics data processing for collision detection

The disclosed computer-implemented method may include receiving sensor data associated with a mobile device associated with a vehicle, wherein the sensor data includes at least one of an angular velocity vector, a linear acceleration vector, and a rotational acceleration vector recorded over a period of time, determining an event signature based on the sensor data, and detecting a door closing event associated with the vehicle based at least in part on the event signature. Other methods, systems, and computer-readable media are disclosed.

Systems and methods for detecting vehicle door closing events

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El Dorado Hills, CA, United States of America

Michael Jonathan DiCarlo