EP-4440884-B1 - METHOD FOR IDENTIFYING THE DRIVER OF A VEHICLE

Inventors

• PAULY, Reena
• HENZLER, MARKUS

Dates

Publication Date

20260513

Application Date

20221026

Claims (11)

1. Computer-implemented method for identifying the driver of a vehicle based on characteristic or state variables of the vehicle, wherein - in a training phase for one or more drivers forming a reference pool, driver-specific characteristic or state variables (x) of the vehicle are determined during a journey and driver-specific classification parameters of a classification model (f(x)) having training sequences of the characteristic or state variables (x) are determined from the classification model (f(x)), - in a subsequent classification phase, driver-specific characteristic or state variables (x) are determined during a journey and a plurality of subsequences of the characteristic or state variables (x) are supplied to the classification model (f(x)) and a match probability (P driver, subsequence ) is determined for the match between the sequence and the training sequence, wherein the match probabilities (P driver, subsequence ) of a plurality of subsequences are combined to form a total probability (P driver, sequence ) that is used to identify the current driver from the reference pool, - wherein the classification model (f(x)) is designed as a neural network.
2. Method according to Claim 1, characterized in that the match probability (P driver, subsequence ) is associated with the classification model (f(x)) according to P driver , subsequence = f x , where P driver, subsequence means the match probability f(x) means the classification model X means the characteristic or state variables.
3. Method according to either of Claims 1 and 2, characterized in that the classification model (f(x)) is designed as a recurrent network, for example as a long short-term memory network (LSTM).
4. Method according to one of Claims 1 to 3, characterized in that there is a discrete probability (P driver, subsequence ) at the output of the classification model (f(x)) for each subsequence, which probabilities are combined to form the total probability (P driver, sequence ).
5. Method according to Claim 4, characterized in that the total probability (P driver, sequence ) is formed by adding the discrete probabilities (P driver, subsequence ) for each subsequence: P driver , sequence = ∑ n = 12 / l 1 n = 1 P driver , subsequence
6. Method according to one of Claims 1 to 5, characterized in that that driver from the reference pool with whom the current driver has the highest value of the total probability (P driver, sequence ) is identified: driver − argmax driver P driver , sequence
7. Method according to one of Claims 1 to 6, characterized in that , if the current driver does not match a driver from the reference pool, an immobilizer in the vehicle is activated.
8. Method according to one of Claims 1 to 7, characterized in that , after the driver has been identified, driver-specific settings are made in the vehicle.
9. Control unit in a vehicle for carrying out the method according to one of Claims 1 to 8, wherein characteristic or state variables (x) of the vehicle can be supplied to the control unit (3, 7) as input variables and a signal which identifies the current driver from the reference pool can be generated in the control unit (3, 7).
10. Vehicle having a control unit according to Claim 9.
11. Computer program product comprising program code designed to carry out steps of the method according to one of Claims 1 to 8 when the computer program product runs in a control unit (3, 7) according to Claim 9.

Description

The invention relates to a method for identifying the driver of a vehicle based on characteristic or state parameters of the vehicle. State of the art From the DE 10 2016 218 719 A1 A system for identifying the driver of a passenger car or truck based on operating patterns is known. These operating patterns are detected using an accelerometer integrated into a control element operated by the driver. To identify the driver, the detected operating pattern is compared with stored operating patterns. In the US 10 417 838 B2 This describes a system for classifying driving maneuvers such as turning, roundabout maneuvers, or braking maneuvers. The classification is based on various vehicle-related measurements, such as vehicle speed, acceleration, and GPS position. In the US 2010/0209890 A1 The classification of driving skill levels using neural networks is described. From the CN 111 738 337 A It is known to assess the distraction state of a driver in various traffic situations based on eye movements and driving data using a long-short-term memory network. From the EP 2 261 096 A method for identifying the driver of a vehicle is known. Disclosure of the invention The method according to claim 1 can be used to identify the driver of a vehicle, in particular a motorized vehicle. The vehicle may be a single-track vehicle such as a motorcycle or a scooter, or a two-track vehicle, in particular a passenger car or a truck. Using the method according to the invention, the current driver of a vehicle can be identified by comparison with one or more drivers from a reference pool. Both a positive identification is possible if the current driver matches one of the drivers from the reference pool, and a negative identification is possible if the current driver does not match any driver from the reference pool. The process takes place in two steps: first, during a training phase, driver-specific parameters or state variables are determined for one or more drivers, forming a reference pool. The training phase occurs while the vehicle is being driven. During this phase, the current parameters or state variables in the vehicle, which change in response to driver input, are determined, primarily using sensors installed in the vehicle. Using a classification model, driver-specific classification parameters are determined from the characteristic or state variables. The parameters under consideration are primarily driver-specific variables such as transmission shift points, brake pressures, and the like. The state variables considered, which are determined via the vehicle's own sensors, include, for example, translational and rotational state variables in or related to the vehicle's longitudinal state. Vehicle lateral and vertical direction on the position, velocity and/or acceleration plane. In a classification phase following the training phase, the trained classification model is fed with driver-specific parameters or state variables, which are determined in the vehicle during a drive with a current driver. Relatively short subsequences of the parameters or state variables are used for this purpose, and these subsequences are typically shorter than the training sequences used to train the classification model for one or more drivers during the training phase. The training sequences in the training phase typically have a duration of, for example, several minutes, whereas the subsequences from the classification phase are considerably shorter than the training sequences, for example, only a maximum of one-tenth of their length, and typically only a few seconds, for example, a maximum of 10 seconds. The various subsequences can be completely different from one another, for example, by considering only subsequences from different time periods, or by using subsequences that overlap in time. The total time span covered by all the subsequences considered is generally shorter than the total time span of the training sequence. The training phase only needs to be completed once to establish the reference pool. The classification phase can be repeated as often as necessary, particularly after each vehicle restart to identify the current driver. It is also possible to repeat the training phase to expand the reference pool with additional drivers. Furthermore, the training phase can be repeated to improve parameter classification after the current driver has been identified, potentially for the entire duration of the current driving time with that driver. During the classification phase, driver-specific characteristics or parameters are determined across several subsequences. The trained classification model is fed each subsequence, from which a probability of match between that subsequence and a training sequence of a driver from the reference pool is calculated. The probability of match across multiple subsequences can then be aggregated into an overall probability, which is used to identify the current driver from the reference pool. This