Abstract: A device receives quantitative data from sensors associated with an autonomous vehicle traveling on a road with other vehicles and from other sensors, and processes the quantitative data, with a first model, to determine distances between the autonomous vehicle and the other vehicles. The device processes the quantitative data, with a second model, to determine relative orientations between the autonomous vehicle and the other vehicles, and generates a conceptual neighborhood graph, based on a third model. The device identifies a subset of the conceptual neighborhood graph, and determines relative trajectories between the autonomous vehicle and the other vehicles based on the distances, the relative orientations, and the subset of the conceptual neighborhood graph.

Abstract: A device identifies training data and scoring data for a model, and removes bias from the training data to generate unbiased training data. The device trains the model with the unbiased training data to generate trained models, and processes the trained models, with the scoring data, to generate scores for the trained models. The device selects a trained model, from the trained models, based on model metrics and the scores, and processes a training sample, with the trained model, to generate first results, wherein the training sample is created based on the unbiased training data and production data. The device processes a production sample, with the trained model, to generate second results, wherein the production sample is created based on the production data and the training sample. The device provides the trained model for use in a production environment based on the first results and the second results.

Abstract: A device receives quantitative data from sensors associated with an autonomous vehicle traveling on a road with other vehicles and from other sensors, and processes the quantitative data, with a first model, to determine distances between the autonomous vehicle and the other vehicles. The device processes the quantitative data, with a second model, to determine relative orientations between the autonomous vehicle and the other vehicles, and generates a conceptual neighborhood graph, based on a third model. The device identifies a subset of the conceptual neighborhood graph, and determines relative trajectories between the autonomous vehicle and the other vehicles based on the distances, the relative orientations, and the subset of the conceptual neighborhood graph.

Abstract: A system and method for qualitative analysis of time progressive signals, comprising: a qualitative signal analysis module, comprising at least a processor, a memory, and a long term storage device; and an output processor module comprising at least a processor a memory and a network interface has been devised. The qualitative signal analysis module retrieves signal data over time and applies pre-programmed protocols to compare multiple aspects of the signal data to derive meaningful data. The output processor module encodes data generated by the qualitative signal analysis module for use in subsequent analytical steps such as further manipulation, classification or long term storage.

Abstract: A device identifies training data and scoring data for a model, and removes bias from the training data to generate unbiased training data. The device trains the model with the unbiased training data to generate trained models, and processes the trained models, with the scoring data, to generate scores for the trained models. The device selects a trained model, from the trained models, based on model metrics and the scores, and processes a training sample, with the trained model, to generate first results, wherein the training sample is created based on the unbiased training data and production data. The device processes a production sample, with the trained model, to generate second results, wherein the production sample is created based on the production data and the training sample. The device provides the trained model for use in a production environment based on the first results and the second results.

Abstract: A system and method for qualitative analysis of time progressive signals, comprising: a qualitative signal analysis module, comprising at least a processor, a memory, and a long term storage device; and an output processor module comprising at least a processor a memory and a network interface has been devised. The qualitative signal analysis module retrieves signal data over time and applies pre-programmed protocols to compare multiple aspects of the signal data to derive meaningful data. The output processor module encodes data generated by the qualitative signal analysis module for use in subsequent analytical steps such as further manipulation, classification or long term storage.

Abstract: This invention relates generally to robotics, and more specifically, to systems and methods for establishing an environmental representation. In one embodiment, the invention includes a method of operations including determining quantitative data relating to one or more landmarks; determining qualitative data relating to the one or more landmarks; and establishing at least a portion of an environmental representation using the quantitative data and/or the qualitative data.