SYSTEMS AND METHODS FOR ONBOARD ENFORCEMENT OF ALLOWABLE BEHAVIOR BASED ON PROBABILISTIC MODEL OF AUTOMATED FUNCTIONAL COMPONENTS
Systems and methods comprising: obtaining a probabilistic machine learning model encoded with at least one of following categories of questions for an automated system—a situational question, a behavioral question and an operational constraint relevant question; receiving behavior information specifying a manner in which the automated system was to theoretically behave or actually behaved in response to detected environmental circumstances, and/or perception information indicating errors in a perception of a surrounding environment made by the automated system; performing an inference algorithm using the probabilistic machine learning model to obtain at least one inferred probability that a certain outcome will result based on at least one of the behavior information and the perception information; and causing the automated system to perform a given behavior to satisfy a pre-defined behavioral policy, in response to the at least one inferred probability being a threshold probability.
1 . A method for operating an automated system, comprising:
obtaining, by a computing device, a probabilistic machine learning model encoded with at least one of the following categories of questions for the automated system: a situational question, a behavioral question and an operational constraint relevant question;
receiving, by the computing device, (i) behavior information specifying a manner in which the automated system was to theoretically behave or actually behaved in response to detected environmental circumstances or (ii) perception information indicating errors in a perception of a surrounding environment made by the automated system;
performing, by the computing device, an inference algorithm using the probabilistic machine learning model to obtain at least one inferred probability that a certain outcome will result based on at least one of the behavior information and the perception information; and
in response to the at least one inferred probability being a threshold probability, causing the automated system to perform a given behavior to satisfy a pre-defined behavioral policy.
2 . The method according to claim 1 , wherein the probabilistic machine learning model comprises a Bayesian network model with a pre-defined structure or a neural network with learned structure and explicit semantics.
3 . The method according to claim 1 , further comprising updating the probabilistic machine learning model based on at least one of the behavior information and the perception information.
4 . The method according to claim 1 , wherein the automated system comprises an autonomous vehicle.
5 . The method according to claim 4 , wherein the given behavior is a driving maneuver.
6 . The method according to claim 1 , wherein the given behavior comprises capturing data.
7 . The method according to claim 1 , wherein the computing device is external to the automated system.
8 . The method according to claim 7 , further comprising performing operations by the computing device to control behaviors of a plurality of automated systems in a fleet.
9 . The method according to claim 7 , further comprising performing operations by the computing device to control the automated system by (i) exchanging the probabilistic machine learning model between a plurality of automated systems and the computing device and (ii) having the computing device issue driving commands.
10 . A system, comprising:
a processor;
a non-transitory computer-readable storage medium comprising programming instructions that are configured to cause the processor to implement a method for operating an automated system, wherein the programming instructions comprise instructions to:
obtain a probabilistic machine learning model encoded with at least one of the following categories of questions for the automated system: a situational question, a behavioral questions and an operational constraint relevant question;
obtain (i) behavior information specifying a manner in which the automated system was to theoretically behave or actually behaved in response to detected environmental circumstances or (ii) perception information indicating errors in a perception of a surrounding environment made by the automated system;
perform an inference algorithm using the probabilistic machine learning model to obtain at least one inferred probability that a certain outcome will result based on at least one of the behavior information and the perception information; and
cause the automated system to perform a given behavior to satisfy a pre-defined behavioral policy, responsive to the at least one inferred probability being a threshold probability.
11 . The system according to claim 10 , wherein the probabilistic machine learning model comprises a Bayesian network model with a pre-defined structure or a neural network with learned structure and explicit semantics.
12 . The system according to claim 10 , wherein the programming instructions comprise instructions to update the probabilistic machine learning model based on at least one of the behavior information and the perception information.
13 . The system according to claim 10 , wherein the automated system comprises an autonomous vehicle.
14 . The system according to claim 13 , wherein the given behavior is a driving maneuver.
15 . The system according to claim 10 , wherein the given behavior is a performance of a data collection operation.
16 . The system according to claim 10 , wherein the inference algorithm comprises a junction tree algorithm.
17 . A computer program product comprising a memory and programming instructions that are configured to cause a processor to:
obtain a probabilistic machine learning model encoded with at least one of the following categories of questions for the automated system: a situational question, a behavioral question and an operational constraint relevant question;
obtain (i) behavior information specifying a manner in which the automated system was to theoretically behave or actually behaved in response to detected environmental circumstances or (ii) perception information indicating errors in a perception of a surrounding environment made by the automated system;
perform an inference algorithm using the probabilistic machine learning model to obtain at least one inferred probability that a certain outcome will result based on at least one of the behavior information and the perception information; and
cause the automated system to perform a given behavior to satisfy a pre-defined behavioral policy, responsive to the at least one inferred probability being a threshold probability.
18 . The computer program product according to claim 17 , wherein the probabilistic machine learning model comprises a Bayesian network model with a pre-defined structure or a neural network with learned structure and explicit semantics.
19 . The computer program product according to claim 17 , wherein the programming instructions comprise instructions to update the probabilistic machine learning model based on at least one of the behavior information and the perception information.
20 . The computer program product according to claim 17 , wherein the automated system comprises an autonomous vehicle.