PREDICTION OF A LIKELY DRIVING BEHAVIOR

Abstract

A method for carrying out a prediction of a driving behavior of a second vehicle by a control unit of a first vehicle. Data of vehicle surroundings of the second vehicle, and/or data of a vehicle driver and/or of a load of the second vehicle being received by the control unit, at least one feature being ascertained based on the data and a likely driving behavior of the second vehicle being calculated by the control unit based on the ascertained feature. A control unit is also described.

Claims

1-11. (canceled)

12. A method for carrying out a prediction of a driving behavior of a second vehicle by a control unit of a first vehicle, the method comprising the following steps: receiving, by the control unit of the first vehicle, data of: (i) vehicle surroundings of the second vehicle, and/or (ii) a vehicle driver of the second vehicle and/or (iii) a load of the second vehicle; ascertaining, by the control unit, at least one feature based on the data; and calculating, by the control unit, a likely driving behavior of the second vehicle based on the ascertained feature.

13. The method as recited in claim 12, wherein the data are received from a database and/or from a sensor of the first vehicle.

14. The method as recited in claim 12, wherein the likely driving behavior of the second vehicle is calculated by a simulation model, and/or by at least one algorithm and/or by an artificial intelligence.

15. The method as recited in claim 12, wherein the at least one feature ascertained by the control unit includes an age of the vehicle driver, and/or a gender of the vehicle driver, and/or a condition of the vehicle driver.

16. The method as recited in claim 12, wherein the at least one feature ascertained by the control unit includes a vehicle class, and/or a vehicle condition, and/or at least one vehicle license plate number and/or a condition of a rotating beacon.

17. The method as recited in claim 12, wherein the at least one feature ascertained by the control unit includes an advertising space on the second vehicle and/or a label on the second vehicle, the driving behavior of the second vehicle being assessed based on the advertising space on the second vehicle and/or the label on the second vehicle.

18. The method as recited in claim 12, wherein a likely trajectory of the second vehicle is calculated by the control unit of the first vehicle based on the ascertained feature.

19. The method as recited in claim 12, wherein a likely driving mode of the second vehicle is ascertained by the control unit of the first vehicle based on the at least one ascertained feature of the vehicle driver of the second vehicle.

20. The method as recited in claim 12, wherein a load condition of the second vehicle is ascertained by the control unit of the first vehicle based on the received measured data, and likely vehicle dynamics of the second vehicle are calculated by the control unit of the first vehicle based on the load condition of the second vehicle.

21. The method as recited in claim 12, wherein likely vehicle dynamics of the second vehicle are calculated by the control unit of the first vehicle based on a number of passengers of the second vehicle.

22. A control unit of a first vehicle configured to carry out a prediction of a driving behavior of a second vehicle, the control unit configured to: receive, by the control unit of the first vehicle, data of: (i) vehicle surroundings of the second vehicle, and/or (ii) a vehicle driver of the second vehicle and/or (iii) a load of the second vehicle; ascertain, by the control unit, at least one feature based on the data; and calculate, by the control unit, a likely driving behavior of the second vehicle based on the ascertained feature.

Description

BRIEF DESCRIPTION OF THE DRAWINGS

[0048] Preferred exemplary embodiments of the present invention are explained in greater detail below based on highly simplified schematic representations.

[0049] FIG. 1 schematically shows a representation of a system including vehicles and an infrastructure unit, in accordance with an example embodiment of the present invention.

[0050] FIG. 2 schematically shows a flowchart for illustrating a method according to one specific embodiment of the present invention.

DETAILED DESCRIPTION OF EXAMPLE EMBODIMENTS

[0051] FIG. 1 schematically shows a representation of a system 1, including a first vehicle 2, a second vehicle 4 and an external database 6. First vehicle 2 is driving behind second vehicle 4.

[0052] First vehicle 2 includes two sensors 8, 10, which are designed as cameras. Camera sensors 8, 10 are connected in a data-transmitting manner to an onboard control unit 12. Control unit 12 is able to receive and evaluate the measured data of sensors 8, 10. For this purpose, control unit 12 includes an artificial intelligence, which has been trained in advance. The detection ranges of sensors 8, 10 are schematically represented.

[0053] Sensors 8, 10 of first vehicle 2 detect second vehicle 4. Based on the measured data of sensors 8, 10, control unit 12 is able to ascertain or detect features of second vehicle 4. According to the exemplary embodiment, a license plate number 14 of second vehicle 4, for example, is detected and a registration district “KA” for Karlsruhe of second vehicle 4 is ascertained by control unit 12.

[0054] Based on license plate number 14, control unit 12 is able to calculate the likely behavior of second vehicle 4 to the extent that second vehicle 4 will with an increased probability take an exit 16 in the direction of Karslruhe and not follow the course of present road 18.

[0055] Control unit 12 of first vehicle 2 is able to draw data from database 6 via a wireless communication link 20. Database 6 may include, in particular, pieces of local and temporal information, which are useful for likely trajectory 22. According to the exemplary embodiment, control unit 12 is able to receive pieces of information about the road courses and the route to Karlsruhe via exit 16. Thus, the likely trajectory may be calculated as a likely driving behavior of second vehicle 4 by the control unit with the aid of the artificial intelligence.

[0056] Without the use of semantic knowledge, the probability of traveling on exit 16 is approximately 50:50 or only a fixed a priori probability may be assumed. With knowledge about other road user 4 and knowledge about the surroundings, this a priori probability may be determined for each road user 4 individually and the prediction may thus be improved.

[0057] FIG. 2 schematically shows a flowchart for illustrating a method 24 according to one specific embodiment of the present invention.

[0058] In a step 25, measured data of vehicle surroundings F are obtained by control unit 12 from off-board database 6.

[0059] Alternatively or in addition, measured data of vehicle surroundings F may be ascertained 26 by vehicle sensors 8, 10.

[0060] Measured data of second vehicle 4, of a vehicle driver and/or of a load of second vehicle 4 is/are ascertained by vehicle sensors 8, 10 of first vehicle 2 and transmitted 27 to control unit 12 subsequent to or in parallel with preceding steps 25, 26.

[0061] The measured data are evaluated by control unit 12 in a further step 28 and features 14 are detected or ascertained.

[0062] At least one feature 14 of vehicle surroundings F, of second vehicle 4, of the vehicle driver of second vehicle 4, of the passengers and/or of the load of second vehicle 4, in particular, is/are ascertained by control unit 12 of first vehicle 2 based on the measured data.

[0063] In a further step 29, a likely driving behavior 22 of second vehicle 4 is calculated by control unit 12 of first vehicle 2 based on the ascertained features.

[0064] An instruction or a notification of a vehicle control system of first vehicle 2 may subsequently take place via control unit 12, as a result (30) of which the driving mode of first vehicle 2 may be adjusted in accordance with likely driving behavior 22 of second vehicle 4.