US 12,254,415 B2
Computer-implemented method and test unit for approximating a subset of test results
Sebastian Bannenberg, Paderborn (DE); Fabian Lorenz, Paderborn (DE); and Rainer Rasche, Paderborn (DE)
Assigned to dSPACE GMBH, Paderborn (DE)
Filed by dSPACE GmbH, Paderborn (DE)
Filed on Dec. 22, 2021, as Appl. No. 17/559,828.
Application 17/559,828 is a continuation of application No. PCT/EP2020/073062, filed on Aug. 18, 2020.
Claims priority of application No. 10 2019 122 414.4 (DE), filed on Aug. 21, 2019; application No. 19192741 (EP), filed on Aug. 21, 2019; and application No. 19192743 (EP), filed on Aug. 21, 2019.
Prior Publication US 2022/0138094 A1, May 5, 2022
Int. Cl. G06N 3/088 (2023.01); B60W 50/06 (2006.01); B60W 60/00 (2020.01); G05B 13/02 (2006.01); G06F 11/36 (2006.01); G06F 11/3668 (2025.01); G06N 3/045 (2023.01); G06N 3/08 (2023.01)
CPC G06N 3/088 (2013.01) [B60W 50/06 (2013.01); B60W 60/00 (2020.02); G05B 13/027 (2013.01); G06F 11/3664 (2013.01); G06F 11/3688 (2013.01); G06F 11/3692 (2013.01); G06N 3/045 (2023.01); G06N 3/08 (2013.01)] 14 Claims
OG exemplary drawing
 
1. A computer-implemented method for approximating a subset of test results of a virtual test of a device, the method comprising:
providing a data set defining a state space, wherein each state is formed by a parameter set of driving situation parameters for which state one or more actions are feasible to attain another parameter set from the parameter set, wherein each parameter set has at least one environment parameter describing an environment of a motor vehicle and at least one EGO parameter describing a state of the motor vehicle; and
performing an approximation step in which a function value of at least one further parameter set is approximated using an artificial neural network,
wherein the at least one further parameter set lies within a line-shaped edge area of a target range,
wherein if the function value of the approximated at least one further parameter set is greater than or equal to a predetermined threshold value, the at least one further parameter set is identified as associated with the subset of the test results, the subset representing a boundary between critical and non-critical test results,
wherein if the function value of the at least one further parameter set is less than the predetermined threshold value, the artificial neural network carries out at least one further approximation step starting from the respectively last approximated further parameter set until the function value of another parameter set is greater than or equal to the predetermined threshold, and
wherein the subset of the test results is used, at least partially, for an autonomous guidance of the motor vehicle.