Machine learning-based universal software component identification
US-12175241-B1 · Dec 24, 2024 · US
US2021188314A1 · US · A1
| Field | Value |
|---|---|
| Publication number | US-2021188314-A1 |
| Application number | US-202017125904-A |
| Country | US |
| Kind code | A1 |
| Filing date | Dec 17, 2020 |
| Priority date | Dec 19, 2019 |
| Publication date | Jun 24, 2021 |
| Grant date | — |
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The invention relates to a method for the dynamic, context-based distribution of program codes in a control system of a vehicle. The control system has a plurality of control apparatuses for executing the program codes. The program codes are assigned to the corresponding control apparatuses of the control system by a global placement graph. In doing so, the global placement graph is calculated in a computing unit that is located outside of the control system. The data of the global placement graph are transmitted to the control system. It is provided for the global placement graph to be calculated based on the current configuration as well as known reconfigurations, wherein the selection of the next node of the global placement graph to be calculated is calculated using the probability of occurrence of an error as well as the effect of the error.
Opening claim text (preview).
What is claimed is: 1 . A method for the dynamic, context-based distribution of program codes in a control system of a vehicle, wherein the control system has a plurality of control units, wherein the program codes are assigned to the corresponding control apparatuses of the control system by a global placement graph, wherein the global placement graph is calculated in a computing unit that is located outside of the control system, and the data of the global placement graph are transmitted to the control system, comprising: calculating the global placement graph based on the current configuration as well as known reconfigurations; and calculating the selection of the next node of the global placement graph to be calculated using a probability of occurrence of an error as well as an effect of the error. 2 . The method of claim 1 , wherein several classes are formed for the probability of occurrence and the effect of the error, wherein the error is assigned to a probability of occurrence and an effect. 3 . The method of claim 2 , wherein the probability of occurrence and the effect of the error are weighted and specified, wherein depending on the weighting, the node of the global placement graph is calculated that has the highest weighting factor. 4 . The method of claim 3 , wherein the weighting factor is determined as a multiplication of the probability of occurrence and the effect. 5 . The method of claim 2 , wherein four different classes are provided for the error effect. 6 . The method of claim 5 , wherein the error effect is subdivided into errors without a risk of injury, errors with a slight risk of injury, errors with serious injuries, and errors with life-threatening injuries. 7 . The method of claim 1 , wherein the probability of occurrence is subdivided into at least four different categories. 8 . The method of claim 1 , wherein a monitoring element is provided that monitors the running of the program code and/or the functioning of the control apparatuses, wherein in the event of a failure of a control apparatus or an error in a program code, the program codes are redistributed to the control units based on the global placement graph. 9 . The method of claim 1 , wherein it is checked whether an assignment plan is already available for the control system for assigning the remaining active program codes to the available control units in the event of a failure of a control apparatus or an error in executing a program code. 10 . The method of claim 1 , wherein the control system is operated in an emergency mode, and an assignment plan is created when a sufficient number of redundant program codes needed to perform an autonomous driving mode is executed. 11 . The method of claim 1 , wherein the motor vehicle is brought to a standstill by a failsafe system and/or is guided to the closest safe stopping point when the minimum number of redundantly executed program codes has been undershot. 12 . The method of claim 1 , wherein the global placement graph is ascertained based on different control systems in similar motor vehicles, information of a server, information of a mobile phone, information from road site units and/or other traffic information systems. 13 . A control system comprising a plurality of control apparatuses, wherein the control system is configured to perform a method of claim 1 when a plurality of machine-readable program codes is executed by at least two control apparatuses of the control system. 14 . A motor vehicle having a control system configured to perform a method of claim 1 . 15 . A computer program with a program code for performing the steps of a method of claim 1 when the program code is executed on a computer. 16 . The method of claim 3 , wherein four different classes are provided for the error effect. 17 . The method of claim 4 , wherein four different classes are provided for the error effect. 18 . The method of claim 2 , wherein the probability of occurrence is subdivided into at least four different categories. 19 . The method of claim 3 , wherein the probability of occurrence is subdivided into at least four different categories. 20 . The method of claim 4 , wherein the probability of occurrence is subdivided into at least four different categories.
Version control (security arrangements therefor G06F21/57); Configuration management · CPC title
specially adapted for safety · CPC title
External transmission of data to or from the vehicle · CPC title
Avoiding failures by using redundant parts · CPC title
Emergency override (Handing over between remote control and on-board control or handing over between remote control arrangements G05D1/227) · CPC title
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