Autonomous vehicle model training and validation using low-discrepancy sequences

An apparatus comprising at least one processor and memory storing instructions that, when executed, cause the at least one processor to perform steps comprising:

• generating a low-discrepancy sequence in a multidimensional space comprising a plurality of multidimensional points;
• mapping each sample of a plurality of samples of a data corpus to a corresponding entry in the low-discrepancy sequence, wherein each sample of the plurality of samples is encoded as a record comprising a plurality of field-value pairs, wherein the plurality of field-value pairs comprises one or more environmental descriptors for an environment relative to a corresponding vehicle and one or more state descriptors describing a state of the corresponding vehicle, wherein each sample is generated by and received from the corresponding vehicle;
• selecting, from the data corpus, a training data set by selecting, for each multidimensional point of the low-discrepancy sequence having one or more mapped samples, a mapped sample for inclusion in the training data set;
• and
• training one or more models used to generate autonomous driving decisions of an autonomous vehicle based on the selected training data set.