Precision Localization and Geofencing Governance System and Method for Light Electric Vehicles

Abstract

A location and governance system and method for light electric vehicles that includes on-board sensors and receivers for providing readings used to compute absolute and relative vehicle position information, and combining the absolute and relative position information to compute a determined vehicle position, and a current surface type being traveled on by the vehicle. Governance commands for the vehicle can be generated based on the current surface type. Positioning system receivers, inertial measuring units, cameras and other sensor can be used. Vibration analysis, image processing, transition detection and other methods can be used to determine vehicle position and surface type, and spatial databases and other resources can be used. Determining a current surface type the vehicle is travelling on can include determining whether the vehicle is traveling on a sidewalk.

We claim: 
     
         1 . A location and governance method for a light electric vehicle, the location and governance method comprising:
 receiving position signals for the vehicle from a position receiver;   computing an absolute position for the vehicle based on the position signals;   receiving vehicle sensor readings from a plurality of sensors on-board the vehicle;   computing position change values for the vehicle based on the vehicle sensor readings;   computing a determined vehicle position using the absolute position and the position change values computed for the vehicle; and   controlling a velocity of the vehicle based on the determined vehicle position.   
     
     
         2 . The location and governance method of  claim 1 , wherein computing the determined vehicle position using the absolute position and the position change values comprises:
 determining one or more most-likely locations for the vehicle based on the absolute-position and the position change values computed for the vehicle; and   computing the determined vehicle position based on the one or more most-likely locations for the vehicle.   
     
     
         3 . The location and governance method of  claim 2 , further comprising:
 determining confidence levels for each of the one or more most-likely locations for the vehicle; and   computing the determined vehicle position based on the one or more most-likely locations for the vehicle and the confidence levels for each of the one or more most-likely locations for the vehicle.   
     
     
         4 . The location and governance method of  claim 2 , further comprising:
 determining a location surface texture for each of the one or more most-likely locations for the vehicle;   determining a current surface texture of a ground surface currently being traveled on by the vehicle based on the one or more most-likely locations for the vehicle and the location surface texture for each of the one or more most-likely locations; and   computing the determined vehicle position based on the one or more most-likely locations for the vehicle and the current surface texture of the ground surface currently being traveled on by the vehicle.   
     
     
         5 . The location and governance method of  claim 4 , further comprising:
 determining confidence levels for each of the one or more most-likely locations for the vehicle;   determining confidence levels for each of the location surface textures for the one or more most-likely locations for the vehicle;   determining the current surface texture of the ground surface currently being traveled on by the vehicle based on the one or more most-likely locations for the vehicle and the confidence levels for each of the one or more most-likely locations for the vehicle, the location surface texture for each of the one or more most-likely locations and the confidence levels for each of the location surface textures for the one or more most-likely locations for the vehicle; and   computing the determined vehicle position based on the one or more most-likely locations for the vehicle and the confidence levels for each of the one or more most-likely locations for the vehicle, the location surface texture for each of the one or more most-likely locations and the confidence levels for each of the location surface textures for the one or more most-likely locations for the vehicle.   
     
     
         6 . The location and governance method of  claim 4 , wherein determining the location surface texture for each of the one or more most-likely locations for the vehicle comprises using a spatial database comprising a plurality of locations in a local area, and a surface texture for each individual location of the plurality of locations in the local area. 
     
     
         7 . The location and governance method of  claim 1 , wherein computing the determined vehicle position using the absolute position and the position change values comprises:
 determining a current surface texture of a ground surface currently being traveled on by the vehicle based on the absolute position and the position change values computed for the vehicle; and   computing the determined vehicle position using the absolute position and the position change values computed for the vehicle and the current surface texture of the ground surface currently being traveled on by the vehicle.   
     
     
         8 . The location and governance method of  claim 7 , wherein receiving the vehicle sensor readings from the plurality of sensors on-board the vehicle comprises:
 receiving inertial measurement unit (IMU) readings from an IMU on-board the vehicle; and   receiving velocity readings from a velocity sensor on-board the vehicle;   receiving visual information from a camera on-board the vehicle; and   wherein computing the position change values for the vehicle based on the vehicle sensor readings comprises computing the position change values for the vehicle based on the IMU readings, the velocity readings and the visual information.   
     
     
         9 . The location and governance method of  claim 8 , further comprising:
 extracting vibration features based on the IMU readings and the velocity readings;   determining image preprocessor surface textures based on the visual information;   determining one or more most-likely surface textures where the vehicle is located based on the vibration features and the image preprocessor surface textures; and   wherein determining the current surface texture of the ground surface currently being traveled on by the vehicle comprises using the one or more most-likely surface textures where the vehicle is located.   
     
     
         10 . The location and governance method of  claim 8 , further comprising:
 detecting surface transitions between location surfaces based on the IMU readings; and   determining a current transition surface texture based on the detected surface transitions; and   wherein determining the current surface texture of the ground surface currently being traveled on by the vehicle comprises using the current transition surface texture.   
     
     
         11 . The location and governance method of  claim 10 , wherein detecting the surface transitions comprises detecting upward transitions and downward transitions of the vehicle; and
 wherein determining the current transition surface texture based on the detected surface transitions comprises determining the current transition surface texture is a sidewalk when an upward transition is detected, and determining the current transition surface texture is a road when a downward transition is detected.   
     
     
         12 . The location and governance method of  claim 8 , further comprising:
 inferring an image classifier surface texture based on the visual information, and   wherein determining the current surface texture of the ground surface currently being traveled on by the vehicle comprises using the image classifier surface texture.   
     
     
         13 . The location and governance method of  claim 8 , further comprising:
 extracting vibration features based on the IMU readings and the velocity readings;   determining image preprocessor surface textures based on the visual information;   determining one or more most-likely surface textures where the vehicle is located based on the vibration features and the image preprocessor surface textures;   detecting surface transitions between location surfaces based on the IMU readings;   determining a current transition surface texture based on the detected surface transitions; and   inferring an image classifier surface texture based on the visual information,   wherein determining the current surface texture of the ground surface currently being traveled on by the vehicle comprises using the one or more most-likely surface textures where the vehicle is located, the current transition surface texture and the image classifier surface texture.   
     
     
         14 . The location and governance method of  claim 13 :
 wherein determining the one or more most-likely surface textures where the vehicle is located based on the vibration features and the image preprocessor surface textures, further comprises determining confidence levels for each of the one or more most-likely surface textures where the vehicle is located;   wherein determining the current transition surface texture based on the detected surface transitions, further comprises determining a confidence level for the current transition surface texture;   wherein inferring the image classifier surface texture based on the visual information, further comprises determining a confidence level for the image classifier surface texture; and   wherein determining the current surface texture of the ground surface currently being traveled on by the vehicle comprises using the one or more most-likely surface textures where the vehicle is located, the current transition surface texture and the image classifier surface texture and the confidence levels for each of the one or more most-likely locations for the vehicle, the one or more most-likely surface textures where the vehicle is located, the current transition surface texture, and the image classifier surface texture.   
     
     
         15 . A location and governance system for a light electric vehicle, the location and governance system comprising:
 an position system receiver located on-board the light electric vehicle, the position system receiver configured to receive position signals from a positioning system;   a plurality of sensors on-board the vehicle, the plurality of sensors configured to provide sensor readings associated with the vehicle;   a processor on-board the vehicle, the processor configured to compute absolute position information for the vehicle based on the position signals, configured to compute position change information for the vehicle based on the sensor readings associated with the vehicle, and configured to combine the absolute position information and the position change information to compute a determined vehicle position; and configured to determine a surface currently being traveled on by the vehicle based on the determined vehicle position.   
     
     
         16 . The location and governance system of  claim 15 , wherein the plurality of sensors comprises:
 an inertial measurement unit (IMU) configured to provide IMU readings to determine inertial movement measurements for the vehicle;   a velocity sensor configured to provide velocity readings to monitor a velocity of the vehicle;   a camera configured to provide visual information to determine relative movement of the vehicle; and   environmental sensors configured to provide environmental readings to determine changes in elevation of the vehicle; and   wherein the IMU readings, the velocity readings, the visual information and the environmental readings are included in the sensor readings used by the processor to compute the position change information for the vehicle.   
     
     
         17 . The location and governance system of  claim 16 , further comprising:
 a spatial database comprising a plurality of polygons in a local area, and for each individual polygon, an absolute location for the individual polygon and a surface type for the individual polygon; and   a surface lookup module configured to determine one or more most-likely polygons of the plurality of polygons in the spatial database where the vehicle is located based on the determined vehicle position and the absolute location for each individual polygon in the spatial database; and   wherein the processor determines the surface currently being traveled on by the vehicle based on the surface types for the one or more most-likely polygons.   
     
     
         18 . The location and governance system of  claim 16 , further comprising:
 an IMU preprocessor configured to extract vibration features based on the IMU readings and the velocity readings;   an image preprocessor configured to determine surface type based on the visual information; and   a texture classifier configured to determine one or more most-likely surface types that the vehicle is located on based on the vibration features from the IMU preprocessor and the surface type from the image preprocessor; and   wherein the processor determines the surface currently being traveled on by the vehicle based on the one or more most-likely surface types from the texture classifier.   
     
     
         19 . The location and governance system of  claim 16 , further comprising:
 a surface transition detector configured to determine transitions between surface types and to determine a current surface type based on the IMU readings and the visual information, and   wherein the processor determines the surface currently being traveled on by the vehicle based on the current surface type from the surface transition detector.   
     
     
         20 . The location and governance system of  claim 15 , the processor is further configured to generate a governance command for the vehicle based on the surface currently being traveled on by the vehicle.