METHOD FOR ESTIMATING COVERAGE OF THE AREA OF TRAFFIC SCENARIOS

Abstract

A computer-implemented method for estimating coverage of the field of traffic scenarios includes providing various traffic scenarios, classifying and/or clustering the traffic scenarios into known or unknown traffic scenarios, using a statistical process on the classified and/or clustered traffic scenarios for estimating predefined classification numbers that describe the coverage of the fields of traffic scenarios, and generating other traffic scenarios or termination of the method, depending on the classification numbers.

Claims

1. A computer-implemented method for estimating coverage of the field of traffic scenarios, the method comprising: providing various traffic scenarios; at least one of classifying or clustering the traffic scenarios into one of at least known or unknown traffic scenarios; using a statistical process on the at least one of the classified or the clustered traffic scenarios for estimating predefined classification numbers that describe coverage of fields of the traffic scenarios; and at least one of generating other traffic scenarios or terminating the method, depending on the classification numbers.

2. The computer-implemented method according to claim 1, further comprising generating the various traffic scenarios using a simulation.

3. The computer-implemented method according to claim 1, further comprising generating the various traffic scenarios from sensor data recorded by at least one of stationary or mobile traffic detection systems.

4. The computer-implemented method according to claim 1, further comprising using a clustering process to classify the traffic scenarios.

5. The computer-implemented method according to claim 1, further comprising using a self-learning system comprising artificial intelligence to classify the traffic scenarios.

6. The computer-implemented method according to claim 5, classifying the traffic scenarios by a trained classifier, wherein the trained classifier is trained to identify distinguishing features.

7. The computer-implemented method according to claim 6, wherein the trained classifier is a deep neural network.

8. The computer-implemented method according to claim 1, further comprising using at least one of an extrapolation process or a core density estimator for the statistical process.

9. The computer-implemented method according to claim 1, further comprising using at least one of a Good-Toulmin estimator or an Efron-Thisted estimator for the statistical process.

10. The computer-implemented method according to claim 1, wherein the classification numbers comprise at least one of a number of unknown traffic scenarios or a statistical distribution of the unknown traffic scenarios.

11. The computer-implemented method according to claim 1, wherein the classification numbers comprise a criticality of the unknown traffic scenarios.

12. The computer-implemented method according to claim 11, further comprising simulating new critical traffic scenarios on a basis of a criticality of the unknown traffic scenarios.

13. The computer-implemented method according to claim 1, further comprising simulating new traffic scenarios on a basis of the identified unknown traffic scenarios.

14. The computer-implemented method according to claim 1, further comprising: clustering the traffic scenarios; and subsequently classifying the clustered traffic scenarios.

15. An apparatus for data processing, comprising a processor that is configured to: provide various traffic scenarios; at least one of classify or cluster the traffic scenarios into one of at least known or unknown traffic scenarios; use a statistical process on the at least one of the classified or the clustered traffic scenarios for estimating predefined classification numbers that describe coverage of fields of the traffic scenarios; and at least one of generate other traffic scenarios or terminate a process, depending on the classification numbers.

16. The apparatus according to claim 15, wherein the processor is further configured to: generate the various traffic scenarios using a simulation.

17. The apparatus according to claim 15, wherein the processor is further configured to: generate the various traffic scenarios from sensor data recorded by at least one of stationary or mobile traffic detection systems.

18. The apparatus according to claim 15, wherein the processor is further configured to: use a clustering process to classify the traffic scenarios.

19. The apparatus according to claim 15, wherein the processor is further configured to: use a self-learning system comprising artificial intelligence to classify the traffic scenarios.

20. The apparatus according to claim 15, wherein the processor is further configured to: use at least one of an extrapolation process or a core density estimator for the statistical process.

Description

[0044] Further features, properties and advantages of the present invention can be derived from the following description in reference to the attached drawings. Therein:

[0045] FIG. 1 shows, schematically, a first embodiment of the method according to the invention;

[0046] FIG. 2 shows, schematically, a second embodiment of the method according to the invention;

[0047] FIG. 3 shows, schematically, a third embodiment of the method according to the invention.

[0048] Numerous traffic scenarios are provided in a first step S1. These can be generated, for example, by recording real traffic scenarios with measurement systems in test vehicles, or through simulation. Real traffic scenarios can be modified in the simulations, for example, in order to generate new traffic scenarios. All of these traffic scenarios relate to routes, i.e. the scenarios all correspond to traffic scenarios in traffic. Alternatively or additionally, cell phone cameras, drones, etc. can be used for generating real traffic scenarios. It is also possible to generate real traffic scenarios with stationary and/or traffic recording systems, e.g. traffic cameras and/or traffic monitoring devices. These form a comprehensive and cost effective source for collecting data.

[0049] Other sources for collecting data could comprise a data base system in which drivers are paid to drive real vehicles along specific routes in order to record traffic scenarios.

[0050] In a second step S2 these traffic scenarios are clustered and classified as known or unknown traffic scenarios. The clustering can take place in a clustering process. The traffic scenarios can be classified as known or unknown traffic scenarios in the clustering process.

[0051] A statistical process is applied to the clustered traffic scenarios in a third step S3 to estimate predefined classification numbers that describe the approximate coverage of the scenario field. The predefined classification numbers contain information regarding the extent to which the previously documented scenario field covers the field of all traffic scenarios.

[0052] By way of example, the number of unknown traffic scenarios estimated through the clustering, the statistical distribution of the unknown traffic scenarios obtained in this manner, and the criticality of the unknown traffic scenarios determined in this manner can be taken into account in the classification numbers.

[0053] The criticality describes the danger posed by a traffic situation (critical traffic situation), in which intervention by a driver assistance system or a driver is necessary. An example of a critical traffic situation is when there is the danger of a collision between the vehicle and another vehicle or obstacle, or when the vehicle is too close to another vehicle or obstacle. Other critical traffic situations are also conceivable.

[0054] The classification numbers are estimated in a fourth step S4, and the method is either terminated or continued on the basis of the estimation. If the scenario field has not yet been sufficiently covered, for example, then further traffic scenarios must be generated. If, however, unknown traffic scenarios are “sufficiently” infrequent, then the scenario field has been sufficiently covered, and the method can be terminated. A sufficient coverage can be determined on an individual basis in this case, e.g. by the manufacturer.

[0055] The method is terminated in a fifth step S5 on the basis of the estimation. In this case, sufficient coverage of the scenario field has been obtained, and the method can be terminated.

[0056] In a sixth step S6, the scenario field coverage is still insufficient, and new traffic scenarios must be generated on the basis of the identified critical and/or unknown traffic scenarios.

[0057] This can take place, for example, by varying the identified critical and/or unknown traffic scenarios. One or more parameters of the identified critical and/or unknown traffic scenarios can be modified for this. By way of example, a critical, previously unknown traffic scenario can be a maneuver with numerous vehicles in a roundabout in which the test vehicle, which generates the sensor data, passes through the roundabout at a speed of 20 kmh. Traffic scenarios can then be simulated, for example, in which the test vehicle passes through the traffic circle at a speed of 50 kmh. Other parameters, such as weather conditions, e.g. rain, snow or fog, can also be modified. The method is then repeated using the newly generated traffic scenarios.

[0058] A statistical prognosis for the future occurrences of “unknown” traffic scenarios can be estimated using the method.

[0059] If the scenario field has been sufficiently covered, a virtual test vehicle can then make a virtual test drive of the traffic scenarios in the scenario field.

[0060] FIG. 2 shows a second embodiment of the method according to the invention. Numerous traffic scenarios are again provided in a first step A1 therein. These traffic scenarios relate to all routes, i.e. the scenario field corresponds to all traffic scenarios in traffic.

[0061] These traffic scenarios are classified by a trained classifier in a second step A2, and divided into known and unknown traffic scenarios. The classifier is a classifier that has been trained to identify distinguishing features. Possible distinguishing features comprise physical variables (position, orientation, speed, acceleration, and time), which describe the movement of objects in traffic in relation to one another.

[0062] A statistical process is applied in a third step A3 to the classified traffic scenarios to estimate the predefined classification numbers with respect to the scenario field coverage. The predefined classification numbers indicate the extent to which the previously documented scenario field covers the field of all traffic scenarios. An extrapolation process can be used for the statistical process. Alternatively, a core density estimator can be used. A Good-Toulmin estimator or Efron-Thisted estimator, or variations thereof, can also be used for the statistical process.

[0063] The classification numbers are estimated in a fourth step A4, and the method is either terminated or continued, based on this estimation.

[0064] Sufficient scenario field coverage is recognized in a fifth step A5, and the method is consequently terminated.

[0065] There is still insufficient scenario field coverage in a sixth step A6, and new traffic scenarios must be generated on the basis of identified critical and/or unknown traffic scenarios. The method is then repeated using the newly generated traffic scenarios.

[0066] FIG. 3 shows a third embodiment of the invention.

[0067] Numerous traffic scenarios are again provided in a first step B1. These traffic scenarios preferably relate to selected routes or a specific, previously selected area.

[0068] These traffic scenarios are first clustered in a second step B2, e.g. with a density-based clustering process. The clusters are subsequently classified with a trained classifier, and divided into known or unknown traffic scenarios. The classifier is a classifier that has been trained to identify distinguishing features. Possible distinguishing features comprise physical variables (position, orientation, speed, acceleration, and time), which describe the movement of objects in traffic in relation to one another.

[0069] A statistical process is applied in a third step B3, e.g. an extrapolation process, to the classified traffic scenarios in order to estimate predefined classification numbers that describe the coverage of the scenario field.

[0070] The classification numbers are then evaluated in a fourth step B4.

[0071] A sufficient scenario field coverage is identified in a fifth step B5, and the method is consequently terminated.

[0072] New traffic scenarios are generated in a sixth step B6 on the basis of identified critical and/or unknown traffic scenarios.

[0073] This can take place, e.g. by varying the identified critical and/or unknown traffic scenarios. One or more parameters of the identified critical traffic scenarios can be modified for this. The method is then repeated with the newly generated traffic scenarios.

REFERENCE SYMBOLS

[0074] S1-S6 method steps

[0075] A1-A6 method steps

[0076] B1-B6 method steps