Systems and methods for computational resource allocation for autonomous vehicles

Patent metadata

Field	Value
Publication number	US-11735045-B2
Application number	US-201916711595-A
Country	US
Kind code	B2
Filing date	Dec 12, 2019
Priority date	Dec 4, 2019
Publication date	Aug 22, 2023
Grant date	Aug 22, 2023

Abstract

Systems and methods are directed to allocating unused or otherwise under-utilized computing resources of autonomous vehicles. In one example, a computer-implemented method obtaining, by a computing system, data describing a computational status of each autonomous vehicle of one or more autonomous vehicles describing a current or forecasted computational load. The method includes determining, by the computing system, an amount of excess computational capacity of each autonomous vehicle of the one or more autonomous vehicles, the amount of excess computational capacity for each autonomous vehicle of the one or more autonomous vehicles based at least in part on the computational status of the autonomous vehicle and a total computational capacity of the autonomous vehicle. The method includes allocating, by the computing system, at least a portion of the amount of excess computational capacity of each autonomous vehicle to processing operations associated with participation in a distributed ledger.

First claim

What is claimed is:

1. A method for variably allocating autonomous vehicle compute power, the method comprising:
obtaining, by a computing system comprising one or more computing devices, data describing a total computational capacity for an autonomous vehicle;
obtaining, by the computing system, a route for the autonomous vehicle;
determining, by the computing system, an amount of computational resources to be used for performing one or more driving maneuvers to be performed to navigate the route;
determining, by the computing system, an amount of excess computational capacity respectively for the autonomous vehicle based at least in part on map data indicating, for one or more locations, a computational complexity of navigating at the one or more locations, the amount of excess computational capacity corresponding to a processing capacity to process additional operations while navigating the route, the amount of excess computational capacity based at least in part on a difference between:
the amount of computational resources to be used for performing the one or more driving maneuvers, and
the total computational capacity; and

initiating, by the computing system, processing of the additional operations using at least a portion of the amount of excess computational capacity while navigating the route, the additional operations associated with participation in a distributed ledger.

2. The method of claim 1 , further comprising:
determining, by the computing system, a base route with an associated base route complexity for navigation by the autonomous vehicle;
determining, by the computing system, one or more alternative routes with one or more associated alternative route complexities for navigation by the autonomous vehicle, the one or more alternative route complexities being less complex than the base route complexity;
evaluating, by the computing system and for each of the one or more alternative routes, a computational load difference between the alternative route and the base route based on a comparison of the alternative route complexity to the base route complexity;
determining, by the computing system and for each of the one or more alternative routes, an estimated gain associated with allocation of the computational load difference to processing operations associated with participation in the distributed ledger; and
selecting, by the computing system, a route from the base route and the one or more alternative routes for navigation by the autonomous vehicle based at least in part on the respective estimated gains associated with each of the one or more alternative routes.

3. The method of claim 2 , wherein selecting, by the computing system, the route from the base route and the one or more alternative routes comprises:
presenting, by the computing system and to a user, route selection information, the route selection information comprising the respective estimated gain associated with each alternative route of the one or more routes; and
selecting, by the computing system and based on a user selection input, a route from the base route and the one or more alternative routes for navigation by the autonomous vehicle.

4. The method of claim 3 , wherein presenting, by the computing system and to a user, route selection information comprises:
determining, by the computing system, a difference in travel time between the base route and each of the one or more alternative routes; and
presenting, by the computing system and to a user, route selection information, the route selection information comprising the respective estimated gain and the respective difference in travel time associated with each alternative route of the one or more routes.

5. The method of claim 2 , wherein selecting, by the computing system, the route from the base route and the one or more alternative routes comprises:
determining, by the computing system, a difference in travel time between the base route and each of the one or more alternative routes; and
selecting, by the computing system, a route from the base route and the one or more alternative routes for navigation by the autonomous vehicle based at least in part on the respective estimated gains associated with the one or more alternative routes and the difference in travel time between the base route and each of the one or more alternative routes.

6. The method of claim 1 , comprising:
pooling, by the computing system, the amount of excess computational capacity of the autonomous vehicle with one or more other autonomous vehicles in a distributed ledger processing pool for processing associated with participation in the distributed ledger.

7. The method of claim 1 , wherein the processing of operations associated with participation in the distributed ledger comprises performing hash operations on a block in a cryptographic blockchain.

8. A computing system, comprising:
one or more processors; and
one or more tangible, non-transitory computer readable media storing computer-readable instructions that are executable to cause the one or more processors to perform operations, the operations comprising:
obtaining data describing a total computational capacity for an autonomous vehicle;
obtaining a route for the autonomous vehicle;
determining an amount of computational resources to be used for performing one or more driving maneuvers to be performed to navigate the route;
determining an amount of excess computational capacity respectively for the autonomous vehicle based at least in part on map data indicating, for one or more locations, a computational complexity of navigating at the one or more locations, the amount of excess computational capacity corresponding to a processing capacity to process additional operations while navigating the route, the amount of excess computational capacity based at least in part on a difference between:
the amount of computational resources to be used for performing the one or more driving maneuvers, and
the total computational capacity; and

initiating processing of the additional operations using at least a portion of the amount of excess computational capacity while navigating the route, the additional operations associated with participation in a distributed ledger.

9. The computing system of claim 8 , wherein the operations further comprise:
determining a base route with an associated base route complexity for navigation by the autonomous vehicle;
determining one or more alternative routes with one or more associated alternative route complexities for navigation by the autonomous vehicle, the one or more alternative route complexities being less complex than the base route complexity;
evaluating, for each of the one or more alternative routes, a computational load difference between the alternative route and the base route based on a comparison of the alternative route complexity to the base route complexity;
determining, for each of the one or more alternative routes, an estimated gain associated with allocation of the computational load difference to processing operations associated with participation in the distributed ledger; and
selecting a route from the base route and the one or more alternative routes for navigation by the autonomous vehicle based at least in part on the respective estimated gains associated with each of the one or more alternative routes.

10. The computing system of claim 9 , wherein selecting the route from the base route and the one or more alternative routes comprises:
presenting, to a user, route selection information comprising the respective estimated gain associated with each alternative route of the one or more routes; and
selecting, based on a user selection input, a r

Assignees

• Uatc Llc

Inventors

• Wright Nigel
• Sethuraman Kailash

Classifications

• G08G1/096838 Primary

  where the user preferences are taken into account or the user selects one route out of a plurality · CPC title

• G06F3/067

  Distributed or networked storage systems, e.g. storage area networks [SAN], network attached storage [NAS] · CPC title

• G06Q30/0207 Primary

  Discounts or incentives, e.g. coupons or rebates · CPC title

• G06Q30/0226

  Incentive systems for frequent usage, e.g. frequent flyer miles programs or point systems · CPC title

• G08G1/096883

  where input information is obtained using a mobile device, e.g. a mobile phone, a PDA · CPC title