Method and device for configuring a system architecture of an autonomous vehicle

Patent metadata

Field	Value
Publication number	US-2021286631-A1
Application number	US-202117194095-A
Country	US
Kind code	A1
Filing date	Mar 5, 2021
Priority date	Mar 12, 2020
Publication date	Sep 16, 2021
Grant date	—

Abstract

A system architecture of an autonomous vehicle, wherein the system architecture includes a plurality of application instances and nodes. The application instances are distributed and executed on the computation nodes according to a configuration, wherein measured sensor data of at least one sensor is input to at least part of the application instances, and wherein at least part of the application instances creates and provides control signals for controlling the vehicle. At least one context information of a prevailing context is gathered, and wherein the configuration is adapted according to the at least one gathered context information.

First claim

1 - 15 . (canceled).

16 . A method for operating a system architecture of an autonomous vehicle, comprising:
inputting vehicle sensor data to at least a portion of a plurality of application instances distributed and executed across a plurality of computational nodes of the autonomous vehicle according to a configuration; generating control signals via at least the portion of the plurality of application instances to control the autonomous vehicle; gathering at least one context information of a current context; and adapting the configuration according to the gathered context information.

17 . The method of claim 16 , wherein the context information comprises at least one vehicle user context information and/or one vehicle user request.

18 . The method of claim 16 , wherein the context information comprises environment information, wherein the environment information is captured by at least one sensor of the vehicle and/or is received by communicating with at least one backend server.

19 . The method of claim 16 , wherein the context information comprises vehicle information.

20 . The method of claim 16 , wherein adapting the configuration comprises optimizing the configuration according to at least one optimization criterion.

21 . The method of claim 20 , wherein the at least one optimization criterion is selected and/or defined according to the at least one context information

22 . The method of claim 16 , wherein adapting the configuration comprises selecting and/or defining application instance requirements according to the at least one gathered context information.

23 . The method of claim 16 , further comprising defining application instance requirements by using implication rules that are specified and applied using a declarative programming language.

24 . The method of claim 16 , wherein adapting the configuration comprises at least one of:
selecting a set of application instances according to the at least one gathered context information; determining redundancy requirements of the application instances according to the at least one gathered context information; and/or determining hardware and/or software segregation requirements of the application instances according to the at least one gathered context information.

25 . The method of claim 16 , wherein adapting the configuration comprises optimally assigning application instances to the computation nodes.

26 . The method of claim 25 , wherein the assigning comprises applying at least one of integer linear programming, evolutionary game theory, and reinforcement learning to determine the optimal assignment.

27 . The method of claim 16 , wherein adapting the configuration comprises establishing safety validation of the adapted configuration.

28 . The method of claim 16 , further comprising repeating the inputting, generating, gathering, and adapting such that the configuration is repeatedly adapted according to the current context.

29 . A system for operating a system architecture of an autonomous vehicle, comprising:
one or more sensors for producing sensor data; a context gathering device configured to gather context information of a current context of the autonomous vehicle; a reconfiguration device; and a memory for storing a configuration for the autonomous vehicle, wherein the context gathering device, the reconfiguration device, and the memory are configured to
input the vehicle sensor data to at least a portion of a plurality of application instances distributed and executed across a plurality of computational nodes of the autonomous vehicle according to the configuration;
generate control signals via at least the portion of the plurality of application instances to control the autonomous vehicle;
gather at least one context information of a current context; and
adapt the configuration according to the gathered context information.

30 . The system of claim 29 , wherein the context information comprises at least one of
(i) vehicle user context information; (ii) a vehicle user request; (iii) environment information captured by the one or more sensors; (iv) environment information received by communicating with at least one backend server; and (v) vehicle information.

31 . The system of claim 29 , wherein adapting the configuration comprises at least one of
optimizing the configuration according to at least one optimization criterion, selected and/or defined according to the at least one context information; and/or selecting and/or defining application instance requirements according to the at least one gathered context information.

32 . The system of claim 29 , wherein the context gathering device, the reconfiguration device, and the memory are configured to define application instance requirements by using implication rules that are specified and applied using a declarative programming language.

33 . The method of claim 16 , wherein the context gathering device, the reconfiguration device, and the memory are configured to adapt the configuration by at least one of:
selecting a set of application instances according to the at least one gathered context information; determining redundancy requirements of the application instances according to the at least one gathered context information; determining hardware and/or software segregation requirements of the application instances according to the at least one gathered context information; and/or optimally assigning application instances to the computation nodes, wherein the assigning comprises applying at least one of integer linear programming, evolutionary game theory, and reinforcement learning to determine the optimal assignment.

34 . A method for operating a system architecture of an autonomous vehicle, comprising:
inputting vehicle sensor data to at least a portion of a plurality of application instances distributed and executed across a plurality of computational nodes of the autonomous vehicle according to a configuration; generating control signals via at least the portion of the plurality of application instances to control the autonomous vehicle; gathering at least one context information of a current context; and adapting the configuration according to the gathered context information, wherein adapting the configuration comprises optimizing the configuration according to at least one optimization criterion determined from the at least one context information

Assignees

• Volkswagen Ag
• Argo Al GmbH

Inventors

• Kain Tobias
• Müller Julian-Steffen
• Wesche Maximilian
• Decke Hendrik
• Mundhenk Philipp

Classifications

• G06F18/217

  Validation; Performance evaluation; Active pattern learning techniques · CPC title

• B60W60/00

  Drive control systems specially adapted for autonomous road vehicles · CPC title

• B60W50/0098 Primary

  Details of control systems ensuring comfort, safety or stability not otherwise provided for · CPC title

• B60W50/02

  Ensuring safety in case of control system failures, e.g. by diagnosing, circumventing or fixing failures · CPC title

• B60W2050/0082

  for initialising the control system · CPC title