MICROMOBILITY TRANSIT VEHICLE COCKPIT ASSEMBLIES WITH CAMERAS

    公开(公告)号:US20210192229A1

    公开(公告)日:2021-06-24

    申请号:US16909765

    申请日:2020-06-23

    申请人: Lyft, Inc.

    IPC分类号: G06K9/00 G06T7/73 G08G1/00

    摘要: A cockpit for a micromobility transit vehicle may include a camera and a cockpit housing. The cockpit housing may be configured to couple to a handlebar of the micromobility transit vehicle. The cockpit housing may include a first portion and a second portion where the second portion extends from the first portion. The first portion of the cockpit housing may include a surface configured to wrap at least partially around the handlebar. The second portion of the cockpit housing may be configured to secure the camera disposed therein such that the camera is oriented to have a field of view in front of the micromobility transit vehicle. The camera may be disposed in the second portion and configured to capture a scene in the field of view in front of the micromobility transit vehicle. Related systems and methods are additionally disclosed.

    Free lock detection of a micromobility transit vehicle systems and methods

    公开(公告)号:US10783784B1

    公开(公告)日:2020-09-22

    申请号:US16835796

    申请日:2020-03-31

    申请人: Lyft, Inc.

    IPC分类号: G08G1/123 G07C5/08 G07C5/00

    摘要: Techniques are disclosed for systems and methods associated with free lock detection of a micromobility vehicle. Data from one or more sensors of the micromobility vehicle may be received and compared to a threshold stored or determined for the micromobility vehicle. Based on the comparing, an indication of free locking the micromobility vehicle may be determined and one or more notifications of the indication may be generated and sent for display on a mobile device. A parking condition of the micromobility vehicle may also be determined, such as utilizing image data of the micromobility vehicle. The image data may be analyzed to determine whether the micromobility vehicle is parked within a designated parking area distinguished through distinct coloring, patterns, signs, placards, markers, or images.

    CAMERA-SENSOR FUSION MODULE FOR SURFACE DETECTION AND FLEET VEHICLE CONTROL SYSTEMS AND METHODS

    公开(公告)号:US20210191424A1

    公开(公告)日:2021-06-24

    申请号:US16726156

    申请日:2019-12-23

    申请人: Lyft, Inc

    摘要: Techniques are disclosed for systems and methods for surface detection and fleet vehicle control. A method for controlling operations for a plurality of fleet vehicles, the method includes receiving, by a fleet vehicle of the plurality of fleet vehicles using camera-sensor fusion module, first image data of an operational surface in a field of view of the camera-sensor fusion module and inertial data of the fleet vehicle, determining, by the fleet vehicle using a fusion algorithm based on a neural network model, a surface classification of the operational surface based on the first image data and the inertial data, determining an operational parameter for the fleet vehicle based, at least in part, on the surface classification, and controlling an operation of the fleet vehicle based, at least in part, on the operational parameter.

    Tandem Riding Detection on Personal Mobility Vehicles

    公开(公告)号:US20240288272A1

    公开(公告)日:2024-08-29

    申请号:US18174229

    申请日:2023-02-24

    申请人: Lyft, Inc.

    IPC分类号: G01C21/34

    CPC分类号: G01C21/3438 G01C21/3484

    摘要: In particular embodiments, a computing system may collect, for each ride of a plurality of rides, first ride data associated with a user riding a personal mobility vehicle during a certain time period. The system may calculate, for each ride, an approximate load on the personal mobility vehicle based on at least the first ride data. The system may calculate a user profile of the user based on the approximate loads. The system may collect, for a subsequent ride, second ride data associated with the user riding a current personal mobility vehicle after the certain time period. The system may calculate, for the subsequent ride, a second approximate load on the current personal mobility vehicle based on at least the second ride data. The system may compare the second approximate load to the user profile and classify the subsequent ride as individual or tandem based on the comparison.