METHOD AND SYSTEM FOR CALIBRATING AN ADAS/ADS SYSTEM OF VEHICLES IN A VEHICLE POOL

Abstract

A method is provided for validation and/or calibration of an advanced driver assistance system (ADAS) and/or an automated driving system (ADS) in which the ADAS/ADS can be executed in both a virtual environment and a real-world environment for a vehicle pool that has multiple vehicles. The method includes: inputting a vehicle model that is described by vehicle parameters; inputting test scenarios for which the ADAS/ADS is tested in the virtual environment (141) with the vehicle (143); inputting evaluation criteria (133) with which a performance (151) of the ADAS/ADS is evaluated in a test drive (102); virtual test driving for all vehicles of the vehicle pool; evaluating (111) all results to identify the vehicle having the worst result; selecting the real vehicle corresponding to the worst-case vehicle; and validating the ADAS/ADS by at least one real test drive with this vehicle.

Claims

1. A method for calibrating an advanced driver assistance system (ADAS) and/or an automated driving system (ADS) for a vehicle pool that comprises multiple vehicles, with each vehicle of the vehicle pool having a pre-set ADAS and/or ADS, the method comprising: inputting vehicle parameters for all of the vehicles of the vehicle pool, the vehicle parameters that are inputted including engine specifications and brake specifications; inputting test scenarios for which the pre-set ADAS/ADS of all vehicles in the vehicle pool are to be tested in a virtual driving environment; inputting evaluation criteria with which a performance of the pre-set ADAS/ADS of all vehicles in the vehicle pool are to be evaluated; virtual test driving all of the vehicles of the vehicle pool in the virtual driving environment, and recording results of the evaluation criteria for each virtual test drive; using the evaluation criteria for evaluating all results for the virtual test drives and identifying the vehicle having the worst result as the worst-case vehicle; selecting the real vehicle from the vehicle pool corresponding to the worst-case vehicle; validating the ADAS/ADS by performing at least one real test drive with the worst-case vehicle for assessing the pre-set ADAS/ADS; and calibrating the ADAS/ADS for all of the vehicles in the vehicle pool based on the assessing of the ADAS/ADS for the worst-case vehicle.

2. The method of claim 1, further comprising checking transferability of the calibrated ADAS/ADS on the worst-case vehicle to all other vehicles of the vehicle pool by performing additional virtual test drives.

3. The method of claim 1, wherein the respective vehicle parameters describing a particular vehicle model are associated with different vehicle derivatives and equipment variants.

4. The method of claim 1, wherein the test scenarios for testing the ADAS/ADS in the virtual environment include emergency braking at an end of a traffic jam as a function of different initial speeds.

5. The method of claim 1, wherein the evaluation criteria include at least one evaluation criterion selected from duration until standstill upon full braking and minimum duration until impact.

6. A system for calibrating an advanced driver assistance system (ADAS) and/or an automated driving system (ADS) for a vehicle pool that comprises multiple vehicles, with each vehicle of the vehicle pool having a pre-set ADAS and/or ADS, the system comprises a computing unit and a computer program product, the computer program product being configured to execute the ADAS/ADS with a virtual vehicle in a virtual environment, the ADAS/ADS also being executable in a real environment; the vehicle pool comprises multiple real vehicles, and each real vehicle of the vehicle pool can be mapped onto the virtual environment by vehicle parameters, and the system being configured to carry out the following: inputting a vehicle model that is described by the vehicle parameters; inputting test scenarios for which the ADAS/ADS is tested in the virtual environment with the respective vehicle; inputting evaluation criteria with which a performance of the ADAS/ADS is evaluated in a test drive; virtual test driving all vehicles of the vehicle pool, and recording results of the evaluation criteria for each of the test drives; evaluating all results and identifying the vehicle having the worst result as the worst-case vehicle; selecting the real vehicle from the vehicle pool corresponding to the worst-case vehicle; and validating the ADAS/ADS by performing at least one real test drive with this vehicle.

7. The system of claim 6, wherein the system further is configured to check a transferability of the evaluation results for the performance of the ADAS/ADS on the worst-case vehicle to all other vehicles of the vehicle pool by additional virtual test drives.

8. The system of claim 7, wherein the vehicle parameters describing a particular vehicle model are associated with different vehicle derivatives and equipment variants.

9. The system of claim 8, wherein at least one scenario for testing the ADAS/ADS in the virtual environment is emergency braking at the end of a traffic jam as a function of different initial speeds.

10. The system of claim 9, wherein at least one evaluation criterion is selected from duration until standstill upon full braking and minimum duration until impact.

11. A computer program product having a computer-readable medium, on which is stored a program code that can be executed for calibrating an advanced driver assistance system (ADAS) and/or an automated driving system (ADS) for a vehicle pool comprising multiple real vehicles, with each of the vehicles of the vehicle pool having a pre-set ADAS and/or ADS, and wherein the program code, when executed on a computing unit, prompts the computing unit to carry out the following steps: inputting vehicle parameters for all of the vehicles of the vehicle pool, the vehicle parameters that are inputted including engine specifications and brake specifications; inputting test scenarios for which the pre-set ADAS/ADS of all vehicles in the vehicle pool are to be tested in a virtual driving environment; inputting evaluation criteria with which a performance of the pre-set ADAS/ADS of all vehicles in the vehicle pool are to be evaluated; virtual test driving all of the vehicles of the vehicle pool in the virtual driving environment, and recording results of the evaluation criteria for each virtual test drive; using the evaluation criteria for evaluating all results for the virtual test drives and identifying the vehicle having the worst result as the worst-case vehicle; selecting the real vehicle from the vehicle pool corresponding to the worst-case vehicle; validating the ADAS/ADS by performing at least one real test drive with the worst-case vehicle for assessing the pre-set ADAS/ADS; and calibrating the ADAS/ADS for all of the vehicles in the vehicle pool based on the assessing of the ADAS/ADS for the worst-case vehicle.

Description

BRIEF DESCRIPTION OF THE DRAWINGS

[0025] FIG. 1 Is a block diagram of a configuration of a system according to the invention.

DETAILED DESCRIPTION

[0026] FIG. 1 is a block diagram 100 of a configuration of the system according to the invention. The block diagram 100 illustrates a workflow structure for a scenario-based or vehicle-based simulation environment with which an ADAS/ADS is validated. For this purpose, a test agent 120 creates a test case that is specified by a test agent strategy 121. The test agent strategy varies scenario and/or vehicle parameters in such a way that the different vehicle derivatives or vehicle configurations can be tested in relevant, specific scenarios. For the validation of the ADAS/ADS, approaching the end of a traffic jam is an example of a test case. The test agent 120 takes the required information for this purpose from a calibration parameter database 131, a scenario database 132, and an evaluation database 133. For example, so-called criticality metrics are stored in the evaluation database 133, with which a performance of the ADAS/ADS can be quantified in the respective test cases, for example duration until standstill upon full braking or minimum duration until impact. A created test case is stored in a dynamic test database 110, on which the results achieved or the visualization 111 thereof also are deposited after simulation of the respective test case. The test case 102 then is transferred to a simulation unit 140. The simulation unit 140 contains multiple models, such as an environment model 141, a driver model 142, and a vehicle model 143. As a result, specific properties, such as the respective vehicle model 143, are provided for the simulation in which the ADAS/ADS then acts, and the properties are available to a simulation function 149 (system under test, abbreviated SUT) via X-in-the-loop simulation algorithms 145. The simulation results 104 are evaluated by an evaluation unit 150 in terms of performance 151 and simulation quality and are transmitted as an evaluation result 105 to the test agent 120. The results visualization 111 can then be used to compare on which vehicle the ADAS/ADS was the worst performing vehicle following the criticality metrics stored in the evaluation database 133. This vehicle thus identified as the worst-case vehicle and is selected for use in real tests. This vehicle then is used for a real test drive to validate the ADAS/ADS.

LIST OF REFERENCE NUMBERS

[0027] 100 Block diagram system [0028] 102 Test case [0029] 104 Simulation results [0030] 105 Evaluation results [0031] 110 Dynamic test database [0032] 111 Visualization of results [0033] 120 Test agent [0034] 121 Test agent strategy [0035] 131 Calibration parameter database [0036] 132 Scenario database [0037] 133 Evaluation database [0038] 140 Simulation unit [0039] 141 Environment model [0040] 142 Driver model [0041] 143 Vehicle model [0042] 145 X-in-the-loop integration [0043] 149 Simulation function [0044] 150 Evaluation unit [0045] 151 Evaluation of performance [0046] 152 Evaluation of simulation quality