Method Of Machine-Learned Verification And Advance Notice Oracles For Autonomous Systems

A system for training, verifying, and providing advance notice, the system comprising a non-transitory computer readable medium storing instruction executable by a processor, wherein the instructions comprise instructions to:

• by a machine learning classifier, wherein an adversary vehicle, and a primary vehicle are on the same path;
• creating a path position probability transition matrix, wherein the path position probability transition matrix comprises an acceleration parameters array of one or more acceleration parameters for a potential position, wherein the path position probability transition matrix comprises equal probabilities for creating a samples array, evaluating a sample path, sorting a sample path, and using Ne elite path;
• creating a samples array, wherein the sample array is one or more paths, wherein the one or more paths are a sequence of grid points of a two-dimensional grid, wherein the grid points comprise:
• a position and an acceleration, wherein the position is a sampled position from a probability distribution of the path probability transition matrix, and wherein the acceleration is a sampled acceleration from an acceleration probability distribution associated with the sampled position;
• evaluating a sample path, wherein a custom score function asserts a potential position and a potential acceleration to identify a collision path of the adversary vehicle and the primary vehicle, a no collision path of adversary vehicle and the primary vehicle, a small position and acceleration distance between the adversary vehicle and primary vehicle;
• sorting the sample path, based at least in part a custom score of a custom score function and selecting a subset of the sample paths as Ne elite paths;
• using the Ne elite paths to update the path probability transition matrix and the acceleration parameters array;
• and
• repeating until the Ne elite paths stabilizes.

A Hybrid-Pair-Cross Entropy method of training, verification, and advanced notice for an autonomous system, the method comprising:

• by an adversary vehicle and a primary vehicle, wherein the adversary vehicle, and the primary vehicle are on the same path;
• creating a path position probability transition matrix, wherein the path position probability transition matrix comprises an acceleration parameters array of one or more acceleration parameters for a potential position, wherein the path position probability transition matrix comprises equal probabilities for creating a samples array, evaluating a sample path, sorting a sample path, and using Ne elite path;
• creating a samples array, wherein the sample array is one or more paths, wherein the one or more paths are a sequence of grid points of a two-dimensional grid, wherein the grid points comprise:
• a position and an acceleration, wherein the position is a sampled position from a probability distribution of the path probability transition matrix, and wherein the acceleration is a sampled acceleration from an acceleration probability distribution associated with the sampled position;
• evaluating a sample path, wherein a custom score function asserts a potential position and a potential acceleration to identify a collision path of the perturbation-adversary and the primary vehicle, a no collision path of adversary vehicle and the primary vehicle, a small position and acceleration distance between adversary and primary vehicle, wherein the custom score function prioritizes a high priority sample path as a slight deviation from a non-colliding path that result in a collision in the Ne elite paths, wherein the custom score function prioritizes successively different colliding or non-colliding paths to generate a high-variance dataset, wherein the high-variance data set trains a machine learning model;
• sorting the sample path based on a corresponding score from the custom score function and selecting a subset of the sample paths as Ne elite paths;
• using the Ne elite paths to update the path probability transition matrix and the acceleration parameters array;
• repeating until the Ne elite paths stabilizes for a predetermined number of iterations;
• updating the path probability transition matrix by the number of times the Ne elite paths transitions from location i to location j, divided by N2;
• updating Gaussian parameters pairs of the acceleration parameters array as follows:
• μ
• i
• =
• 1
• N
• e
• ⁢
• ∑
• K
• =
• 0
• N
• e
• ⁢
• accek
• [
• k
• ,
• i
• ]
• ⁢
• σ
• i
• 2
• =
• 1
• N
• e
• ⁢
• ∑
• k
• =
• 0
• N
• e
• ⁢
• (
• μ
• i
• -
• accel
• [
• k
• ,
• i
• ]
• )
• 2
• wherein accel[k, i] is the acceleration in cell i of the two-dimensional grid according to a k elite path;
• wherein the custom score function prioritizes a high priority sample path as a slight deviation from a non-colliding path that result in a collision in the Ne elite paths;
• wherein the custom score function prioritizes successively different colliding or non-colliding paths to generate a high-variance dataset;
• and
• wherein the high-variance data set trains a machine learning model.