US 11,971,958 B1
Autonomous vehicle model training and validation using low-discrepancy sequences
Volkmar Uhlig, Cupertino, CA (US); and Par Botes, Atherton, CA (US)
Assigned to GHOST AUTONOMY INC., Mountain View, CA (US)
Filed by GHOST AUTONOMY INC., Mountain View, CA (US)
Filed on Jun. 23, 2023, as Appl. No. 18/340,788.
Application 18/340,788 is a continuation of application No. 18/189,781, filed on Mar. 24, 2023.
Application 18/189,781 is a continuation in part of application No. 17/740,888, filed on May 10, 2022.
This patent is subject to a terminal disclaimer.
Int. Cl. G06F 18/232 (2023.01); G06N 3/08 (2023.01); G06N 20/00 (2019.01)
CPC G06F 18/232 (2023.01) [G06N 3/08 (2013.01); G06N 20/00 (2019.01)] 20 Claims
OG exemplary drawing
 
20. An apparatus comprising at least one processor and memory storing instructions that, when executed, cause the at least one processor to perform steps comprising:
receiving a request to generate a synthetic data set comprising a number of samples, wherein each sample of the synthetic data set is encoded as record comprising a plurality of field-value pairs, wherein the plurality of field-value pairs comprise one or more environmental descriptors for an environment relative to a vehicle and one or more state descriptors describing a state of the vehicle;
generating, based on the number of samples, a low-discrepancy sequence in a multidimensional space comprising a plurality of multidimensional points having a number of dimensions equal to a number of continuous fields in the synthetic data set;
generating the synthetic data set based on the low-discrepancy sequence by generating, for each entry in the low-discrepancy sequence, one or more samples of the synthetic data set; and
validating one or more autonomous driving models using a plurality of simulations each initialized using a corresponding sample in the synthetic data set.