Method for supporting the operation of a vehicle with a sensor unit, computer program product and system
AssigneeVolkswagen Aktiengesellschaft
InventorsSerin Varghese, Jan David Schneider
Life of this patent
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Abstract
The invention relates to a method (100) for supporting the operation of a vehicle (2) with a sensor unit (4) for acquiring sensor data (200) for an evaluation in a trained, artificial neural network (10) with a plurality of network elements (11) for intermediate evaluations (210) of the sensor data (200), the method comprising the following steps: providing (102) the sensor data (200) for the neural network (10), evaluating (103) the sensor data (200) by means of the neural network (10) in view of a result. The invention also relates to a computer program product and to a system (1).
Claims15
A method (100) for supporting the operation of a vehicle (2) with a sensor unit (4) for acquiring sensor data (200) for analysis in a trained, artificial neural network (10) comprising a plurality of network elements (11) for interim analyses (210) of the sensor data (200), comprising the following steps:
- Providing (102) the sensor data (200) for the neural network (10),
- Analyzing (103) the sensor data (200) for detecting at least one event by the neural network (10),
- Monitoring (104) an analysis behavior of the interim analyses (210) when analyzing (103) the sensor data (200),
- Evaluating (105) the sensor data (200) for detecting a sensor interference as a function of the analysis behavior,
- Carrying out a reaction measure (106) as a function of evaluating (105) the sensor data (200).
The method (100) according to any of the preceding claims, characterized in that the network elements (11) comprise network layers, network filters and/or weightings of the neural network (10).
The method (100) according to any of the preceding claims, characterized in that the method (100) comprises the following step:
- identifying (101) key elements (11.1) of the plurality of network elements (11), wherein, when monitoring (104) the analysis behavior, the interim analyses (210) of the key elements (11.1) are monitored.
The method (100) according to any of the preceding claims, characterized in that, for identifying (101) the key elements (11.1) and/or for evaluating (105) the sensor data (200), a calibration process (101.1) is executed, in which interference-affected reference data (201) and interference-free reference data (202) are analyzed by the neural network (10), wherein behavioral deviations of the interim analyses (210) are detected during the calibration process (101.1) when analyzing (103) the interference-affected and interference-free reference data (201, 202).
The method (100) according to any of the preceding claims, characterized in that the calibration process (101.1) is executed by a server (5), wherein analyzing (103) the sensor data (200), monitoring (104) the analysis behavior and evaluating (105) the sensor data (200) is carried out by the vehicle (2).
The method (100) according to any of the preceding claims, characterized in that the key elements (11.1) are weighted when identifying (101) the key elements (11.1), wherein the weighting is taken into account when evaluating (105) the sensor data (200).
The method (100) according to any of the preceding claims, characterized in that an averaging for several interim analyses (210) is performed when evaluating (105) the sensor data (200) and/or when monitoring (104) an analysis behavior of the interim analyses (210).
The method (100) according to any of the preceding claims, characterized in that a comparison of the analysis behavior with a reference behavior of the interim analyses (210) is performed when evaluating (105) the sensor data (200), wherein at least one limit value (212) for a deviation of the analysis behavior from the reference behavior for detecting the sensor interference is specified for the evaluation of the sensor data (200).
The method (100) according to any of the preceding claims, characterized in that the sensor data (200) are classified with regard to an interference classification (213) when evaluating (105) the sensor data (200), wherein the reaction measure (106) is carried out as a function of the interference classification (213).
The method (100) according to any of the preceding claims, characterized in that the reaction measure (106) comprises a validation process (106.1) for validating the evaluation and/or the analysis of the sensor data (200).
The method (100) according to any of the preceding claims, characterized in that the validation process (106.1) comprises loading the sensor data (200) into a further, artificial neural network (10.1) which is trained for analyzing (103) sensor data (200) of the interference classification (213) for detecting the event.
The method (100) according to any of the preceding claims, characterized in that the validation process (106.1) comprises loading the sensor data (200) into several further, trained artificial neural networks (10.2), wherein a consolidation process is executed to obtain an overall evaluation of the sensor data (200) with regard to the event and/or with regard to the sensor interference.
The method (100) according to any of the preceding claims, characterized in that the reaction measure (106) comprises an automatic triggering of a driving maneuver of the vehicle (2).
A computer program product, comprising commands which, when executed by the computing unit (3), cause the computing unit (3) to execute the method (100) according to any of the preceding claims.
A system (1), comprising a vehicle (2) comprising a sensor unit (4) for acquiring sensor data (200) and a computing unit (3) for executing a method (100) according to any of the preceding claims.
Dependencies are inferred from the claim text.