Lyft, Inc.

Jens Paul Windau

Garrett Korda Drayna

Gary Shambat

Griffin Samuel Valentine Thomson

Jacqueline Benesch Tandler

Christopher Paul Merrill

Kelly Erin Johnson

Yunjie Zhao

Thien Bao Nguyen

Amber L Chen

Edgar-Alison Vasquez Japitana

Martin Douglas Kess

Embodiments described herein provide a localization mechanism that blends location data collected from hardware sensors equipped with a personal mobile vehicle and location and/or status data collected from external location source other than the personal mobile vehicle to generate adjusted location data for the personal mobile vehicle.

Systems and methods for personal mobile vehicle localization in a fleet management system

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.

Free lock detection of a micromobility transit vehicle systems and methods

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.

Camera-sensor fusion module for surface detection and fleet vehicle control systems and methods

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.

Micromobility transit vehicle cockpit assemblies with cameras

Growing community of inventors

San Francisco, CA, United States of America

Jens Paul Windau