US2023252514A1PendingUtilityA1

Reward system for autonomous rideshare vehicles

Assignee: GM CRUISE HOLDINGS LLCPriority: May 13, 2021Filed: Apr 12, 2023Published: Aug 10, 2023
Est. expiryMay 13, 2041(~14.8 yrs left)· nominal 20-yr term from priority
G06Q 30/0224G06Q 30/0226
68
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Claims

Abstract

A fleet management system implements a rewards-based feedback system to receive feedback from users of a rideshare service that provides rides using autonomous vehicles (AVs). The fleet management system maintains user accounts associated with each user and provides reward points to each user account. When the user is riding in an AV, the user can access a user interface to select a portion of the reward points and reward them to the AV. The fleet management system may analyze the point rewards to identify individual user preferences, to identify preferences across users, or to identify AVs for maintenance or other types of modification.

Claims

exact text as granted — not AI-modified
What is claimed is: 
     
         1 . A method for fleet maintenance, the method comprising:
 receiving a plurality of rewards from users of a ride service, each of the plurality of rewards comprising a number of points, and each of the plurality of rewards associated with an autonomous vehicle (AV) of an AV fleet;   maintaining a plurality of AV accounts, each AV account associated with a respective AV in the AV fleet, each of the plurality of AV accounts comprising a total number of points rewarded to the AV by users of the ride service;   performing a normalization of the total number of points in each of at least a first subset of the plurality of AV accounts;   selecting, from the first subset, a second subset of the AVs in the AV fleet for maintenance, the second subset selected based on the normalized number of points for each of the AVs in the second subset being lower than the normalized number of points for each of the AVs in the first subset but not the second subset; and   directing at least a portion of AVs of the second subset of AVs to autonomously drive to a maintenance facility to receive vehicle maintenance.   
     
     
         2 . The method of  claim 1 , further comprising:
 retrieving data from at least one sensor of a particular AV of the second subset of AVs; and   identifying a feature of the AV requiring maintenance.   
     
     
         3 . The method of  claim 1 , further comprising:
 identifying an in-cabin feature of at least a portion of AVs in the second subset of AVs; and   determining that the identified in-cabin feature is associated with lower normalized numbers of points;   wherein the vehicle maintenance comprises modifying the in-cabin feature.   
     
     
         4 . The method of  claim 1 , wherein the normalization for a particular AV comprises dividing the total number of points rewarded to the AV by a number of rides serviced by the AV. 
     
     
         5 . The method of  claim 1 , wherein the normalization for a particular AV comprises dividing the total number of points rewarded to the AV by a number of miles driven by the AV with a passenger in the AV. 
     
     
         6 . The method of  claim 1 , wherein the normalization for a particular AV comprises selecting a subset of the total number of points rewarded to the AV, the subset of the total number of points received during a particular period of time. 
     
     
         7 . The method of  claim 1 , further comprising:
 identifying, from the first subset, a third subset of the AVs in the AV fleet, the third subset selected based on the normalized number of points for each of the AVs in the third subset being higher than the normalized number of points for each of the AVs in the first subset but not the third subset;   identifying a high-reward feature that is common to at least a portion of the third subset of AVs; and   directing an AV of the second subset of AVs to autonomously drive to a maintenance facility to be modified based on the identified high-reward feature.   
     
     
         8 . A non-transitory computer-readable medium storing instructions that, when executed by a processor, cause the processor:
 receive a plurality of rewards from users of a ride service, each of the plurality of rewards comprising a number of points, and each of the plurality of rewards associated with an autonomous vehicle (AV) of an AV fleet;   maintain a plurality of AV accounts, each AV account associated with a respective AV in the AV fleet, each of the plurality of AV accounts comprising a total number of points rewarded to the AV by users of the ride service;   perform a normalization of the total number of points in each of at least a first subset of the plurality of AV accounts;   select, from the first subset, a second subset of the AVs in the AV fleet for maintenance, the second subset selected based on the normalized number of points for each of the AVs in the second subset being lower than the normalized number of points for each of the AVs in the first subset but not the second subset; and   direct each AV of the second subset of AVs to autonomously drive to a maintenance facility to receive vehicle maintenance.   
     
     
         9 . The non-transitory computer-readable medium of  claim 8 , the instructions further to:
 retrieve data from at least one sensor of a particular AV of the second subset of AVs; and   identify a feature of the AV requiring maintenance.   
     
     
         10 . The non-transitory computer-readable medium of  claim 8 , the instructions further to:
 identify an in-cabin feature of at least a portion of AVs in the second subset of AVs; and   determine that the identified in-cabin feature is associated with lower normalized numbers of points;   wherein the vehicle maintenance comprises modifying the in-cabin feature.   
     
     
         11 . The non-transitory computer-readable medium of  claim 8 , wherein the normalization for a particular AV comprises dividing the total number of points rewarded to the AV by a number of rides serviced by the AV. 
     
     
         12 . The non-transitory computer-readable medium of  claim 8 , wherein the normalization for a particular AV comprises dividing the total number of points rewarded to the AV by a number of miles driven by the AV with a passenger in the AV. 
     
     
         13 . The non-transitory computer-readable medium of  claim 8 , wherein the normalization for a particular AV comprises selecting a subset of the total number of points rewarded to the AV, the subset of the total number of points received during a particular period of time. 
     
     
         14 . The non-transitory computer-readable medium of  claim 8 , the instructions further to:
 identify, from the first subset, a third subset of the AVs in the AV fleet, the third subset selected based on the normalized number of points for each of the AVs in the third subset being higher than the normalized number of points for each of the AVs in the first subset but not the third subset;   identify a high-reward feature that is common to at least a portion of the third subset of AVs; and   direct an AV of the second subset of AVs to autonomously drive to a maintenance facility to be modified based on the identified high-reward feature.   
     
     
         15 . A method for fleet modification, the method comprising:
 configuring a first set of autonomous vehicles (AVs) of an AV fleet with a first vehicle feature;   configuring a second set of AVs of the AV fleet with a second vehicle feature different from the first vehicle feature;   receiving a plurality of rewards from users of a ride service, each of the plurality of rewards comprising a number of points, and each of the plurality of rewards associated with an AV of an AV fleet;   maintaining a plurality of AV accounts, each AV account associated with a respective AV in the AV fleet, each of the plurality of AV accounts comprising a total number of points rewarded to the AV by users of the ride service;   comparing a number of points rewarded to AVs in the first set to a number of points rewarded to AVs in the second set;   determining that the first vehicle feature is preferred over the second vehicle feature based on the comparison; and   configuring at least a portion of the second set of AVs with the first vehicle feature.   
     
     
         16 . The method of  claim 15 , further comprising:
 for at least a portion of the rewards, receiving user input indicating that the first vehicle feature led to the reward; and   determining that the first vehicle feature is preferred over the second vehicle feature further based on the user input.   
     
     
         17 . The method of  claim 15 , further comprising:
 instructing an AV of the second set of AVs to autonomously drive to a maintenance facility for configuration of the first vehicle feature.   
     
     
         18 . The method of  claim 15 , wherein the first vehicle feature is a first software feature, the second vehicle feature is a second software feature, and configuring at least a portion of the second set of AVs with the first vehicle feature comprises installing the first software feature on the portion of the second set of AVs. 
     
     
         19 . The method of  claim 18 , wherein the first software feature programs AVs to perform a first autonomous driving behavior, and the second software feature programs AVs to perform a second autonomous driving behavior different from the first autonomous driving behavior. 
     
     
         20 . The method of  claim 15 , wherein the first vehicle feature is a first in-cabin feature, and the second vehicle feature is a second in-cabin feature.

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