METHOD FOR CREATING AN EVALUATION MODEL FOR DETECTING THE SEAT OCCUPANCY STATE OF A SEATING ARRANGEMENT

Abstract

In a method for creating an evaluation model for the automated detection of a seat occupancy state of a seating arrangement with at least one seat, a data set for a seat occupancy state is provided, wherein the data set represents measurement data for at least two consecutive time points, wherein the measurement data each represent an associated radar point cloud (305), wherein the radar point cloud (305) was or is obtained on the basis of a radar scan of a spatial area surrounding the seating arrangement (105) at least section by section.Providing the dataset involves generating feature vectors from the measurement data for each time point, where each feature vector represents a predefined number of features of the radar point cloud for that time point, and combining the feature vectors into a single vector to obtain the dataset representing the features of the radar point cloud at each time point. The procedure further includes providing hyperparameters for the evaluation model, configured to be set based on a metric, and setting the hyperparameters, for which a metric is provided and optimized for the dataset using an optimization procedure.The evaluation model is created with the configured hyperparameters for training with training data for a method for automatically detecting the occupancy status of a seating arrangement (105) with at least one seat (105a-e), wherein the data set forms input data for the evaluation model and an output of the evaluation model is an evaluation result that is assigned to the occupancy status of the seating arrangement (105). A method for automatically detecting the occupancy status, in particular seat-related, of a seating arrangement with at least one seat uses the created and appropriately trained evaluation model.

Claims

[1] Method for creating an evaluation model for automated detection of a seat occupancy state of a seating arrangement (105) with at least one seat (105a-e), the method comprising: - Providing a data set for a seat occupancy state, wherein the data set represents measurement data for at least two consecutive points in time, wherein the measurement data each represent an associated radar point cloud (305), wherein the radar point cloud (305) was or is obtained on the basis of a radar scan of a spatial area surrounding the seat arrangement (105) at least in sections, wherein the provision of the data set comprises the following: - generating feature vectors from the measurement data for each of the time points, wherein each of the feature vectors represents a predetermined number of features of the radar point cloud for a time point; and - Combining the feature vectors into an overall tensor to obtain the data set representing the features of the radar point cloud at each of the points in time; - Providing hyperparameters for the evaluation model, wherein the hyperparameters are configured to be set based on a metric; - Setting the hyperparameters, whereby a metric is provided and optimized for the dataset using an optimization procedure; and - Creating the evaluation model with the set hyperparameters for training with training data for a method for the automated detection of a seat occupancy state of a seating arrangement (105) with at least one seat (105a-e), wherein the data set forms input data for the evaluation model and an output of the evaluation model is an evaluation result which is assigned to the seat occupancy state of the seating arrangement (105). [2] The method of claim 1, wherein: the seating arrangement (105) comprises a plurality of seats (105a-e), wherein the seat occupancy state is an individual or cumulative seat occupancy state of the seats (105a-e); and for the radar point cloud (305), the set of its radar points (310, 315) is divided into several clusters, each containing a subset of the radar points (310, 315), by means of cluster formation depending on the respective spatial position of the radar points (310, 315) in relation to the seats (105a-e), to individually assign each of the seats (105a-e) to one of the clusters that is spatially closest to it, where the feature vectors are generated individually for each cluster and for each time point, where all feature vectors are combined to form the overall tensor to thereby obtain the data set. [3] Method according to claim 2, wherein the radar point cloud (305) is segmented into a plurality of clusters by assigning a subset of the radar points (310, 315) of the respective radar point cloud (305) to each of the seats (105a-e) as a cluster depending on their respective position such that the radar points (310, 315) of the cluster are located in a defined closed spatial area in the vicinity of the seat (105a-e). [4] Method according to one of the preceding claims, wherein the evaluation model has a structure of a recurrent neural network (RNN). [5] Method according to one of the preceding claims, wherein the optimization method is a Bayesian optimization. [6] Method according to one of the preceding claims, wherein the optimization method is carried out with a predetermined number of iterations. [7] Method according to one of the preceding claims, wherein the individual radar points (310, 315) of each radar point cloud (305) are each represented by a position of the respective radar point in three-dimensional space and by at least one of the following parameters: - A Doppler shift value of the radar signal at the respective radar point; - A signal-to-noise ratio value of the radar signal to the respective radar point. [8] Method for training an evaluation model created according to a method according to one of the preceding claims for an automated recognition of a seat occupancy state of a seat arrangement (105) with at least one seat (105a-e), the method comprising: - Acquiring a data set for a seat occupancy state, wherein the data set represents measurement data for at least two consecutive points in time, wherein the measurement data each represent an associated radar point cloud (305), wherein the radar point cloud (305) was or is obtained on the basis of a radar scan of a spatial area surrounding the seat arrangement (105) at least in part and is associated with one of several predefined possible seat occupancy states of the seat arrangement (105); and - generating training data from the data set, wherein the training data are made available to the evaluation model as input data in order to obtain as its output an evaluation result which is associated with the seat occupancy state of the seat arrangement (105). [9] The method of claim 8, wherein: the seating arrangement (105) comprises a plurality of seats (105a-e), wherein the seat occupancy state is an individual or cumulative seat occupancy state of the seats (105a-e); and for the radar point cloud (305), the set of its radar points (310, 315) is divided into several clusters, each containing a subset of the points (310, 315), by means of cluster formation depending on the respective spatial position of the points (310, 315) in relation to the seats (105a-e), in order to individually assign to each of the seats (105a-e) a cluster that is spatially closest to it. [10] Method for the automated detection of a seat occupancy state of a seat arrangement (105) with at least one seat (105a-e), the method comprising: - Acquiring a data set for a seat occupancy state, wherein the data set represents measurement data for at least two consecutive points in time, wherein the measurement data each represent an associated radar point cloud (305), wherein each radar point cloud (305) was or is obtained on the basis of a radar scan of a spatial region surrounding the seat arrangement (105) at least in section; - Determining a seat occupancy state of the seat arrangement (105) using an evaluation model that was created according to a method according to one of claims 1 to 7 and trained according to a method according to one of claims 8 or 9 and, depending on the radar point cloud based on the acquired data set, provides one of several predefined possible seat occupancy states of the seat arrangement (105) as an evaluation result; and - Output of information defined depending on the evaluation result. [11] Method according to claim 10, wherein the outputting of the information comprises controlling a signal source in dependence on the information in order to cause the signal source to output a defined signal in dependence on the control, wherein the signal source is controlled in dependence on the information in such a way that it outputs a signal when the information results from an evaluation result, according to which at least one seat of the seat arrangement (105) is occupied and / or a selected predetermined seat occupancy state exists. [12] The method of claim 11, further comprising: Detecting a seat belt fastening state of at least one seat of the seat arrangement (105) or receiving seat belt information characterizing this seat belt fastening state; wherein the signal source is controlled as a function of the seat belt information and the information from the evaluation result such that it outputs a seat belt fastening indication signal if, according to the information, at least one seat of the seat arrangement (105) is occupied and / or a selected predetermined seat occupancy state exists and seat belt information indicates that the associated seat belt of the seat is not fastened. [13] System (115) for the automated detection of a seat occupancy state of a seat arrangement (105) having at least one seat (105a-e), wherein the system (115) comprises a data processing device configured to carry out the method according to one of claims 10 to 12 for detecting the seat occupancy state. [14] A computer program or computer program product comprising instructions which, when executed on the data processing device of the system (115) according to claim 13, cause the system (115) to carry out the method according to any one of claims 10 to 12. [15] Vehicle (100), comprising: a seating arrangement (105) with at least one seat (105a-e); a radar sensor (110) for at least partially scanning the seat arrangement (105); and a system (115) according to claim 13 for the automated detection of a seat occupancy state of the seat arrangement (105) as a function of an at least section-wise radar scan of the seat arrangement (105) carried out by the radar sensor (110).

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