Intelligent messaging framework for vehicle ecosystem communication

A system, comprising:

• a first vehicle ecosystem module, wherein the first vehicle ecosystem module comprises a processor, a memory, a first communication system, and a first vehicle ecosystem unit, wherein the first vehicle ecosystem unit comprises a local environment matrix, wherein the local environment matrix comprises a local objective function, information related to an environment of the system, and information related to the first vehicle ecosystem unit stored to a first database;
• and
• a global governance module comprising a global environment matrix, a learning agent powered by a machine learning technique and a second communication system comprising a protocol unit, wherein the protocol unit comprises a predefined meta structure configured for generating a payload content, wherein the system is configured for autonomous communication between the first vehicle ecosystem module and the global governance module that is external to the system, and wherein the global environment matrix comprises information related to a plurality of vehicle ecosystem units, a global objective function, and a communication catalogue corresponding to each of the plurality of vehicle ecosystem units stored to a second database;
• wherein the system is configured for autonomous communication, wherein the global governance module, via the protocol unit, orchestrates the autonomous communication between the first vehicle ecosystem unit using a first communication protocol and a second vehicle ecosystem unit using a second communication protocol;
• wherein the learning agent comprises a deep reinforcement learning module to assess a scenario, wherein an outcome of the first vehicle ecosystem unit is determined based on a decision made by picking the outcome from all possible outcomes after assessing the scenario with the learning agent, wherein the learning agent learns continuously and updates rules based on feedback from the outcome, and by receiving a reward based on a relevance of the outcome to the local objective function and the global objective function;
• and
• wherein the system is configured to enable a function to modify a state of the first vehicle ecosystem unit in an autonomous mode;
• and
• wherein a communication between the first vehicle ecosystem unit and the second vehicle ecosystem unit is independent of a fixed protocol;
• and
• wherein the first vehicle ecosystem unit which is in a first vehicle configured for autonomously communicating with the second vehicle ecosystem unit which is in at least one of a second vehicle and an infrastructure unit in the scenario.

A non-transitory computer-readable medium having stored thereon instructions executable by a computer system to perform a method comprising:

• receiving communication from a first vehicle ecosystem module comprising a first vehicle ecosystem unit and a first communication system, wherein the first vehicle ecosystem unit comprises a local environment matrix, wherein the local environment matrix comprises a local objective function, information related to an environment, and information related to the first vehicle ecosystem unit stored to a first database;
• assessing a scenario for all possible outcomes by a global governance module comprising a global environment matrix, a learning agent powered by a machine learning technique, and a second communication system, wherein the learning agent comprises a deep reinforcement learning module, and the second communication system comprises a protocol unit, wherein the protocol unit comprises a predefined meta structure configured for generating a payload content, and wherein the global environment matrix comprises information related to a plurality of vehicle ecosystem units, a global objective function, and a communication catalogue corresponding to each of the plurality of vehicle ecosystem units stored to a second database;
• identifying a target system by the global governance module, wherein the target system is a second vehicle ecosystem unit;
• dynamically synchronizing the local environment matrix of the first vehicle ecosystem module by communicating with the global governance module and the second vehicle ecosystem unit, wherein the global governance module via the protocol unit, orchestrates a communication between the first vehicle ecosystem unit using a first communication protocol and the second vehicle ecosystem unit is using a second communication protocol;
• determining an outcome of the first vehicle ecosystem unit based on a decision made by picking the outcome from all the possible outcomes after assessing the scenario with the learning agent, wherein the learning agent is configured to learn continuously and update rules for the outcome based on feedback from the outcome, and by receiving a reward based on a relevance of the outcome to the local objective function and the global objective function and
• executing the outcome;
• wherein the method is configured to enable a function to modify a state of the first vehicle ecosystem unit in an autonomous mode;
• and
• wherein the communication between the first vehicle ecosystem unit and the second vehicle ecosystem unit is independent of a fixed protocol;
• and
• wherein the first vehicle ecosystem unit which is in a first vehicle is configured for autonomously communicating with the second vehicle ecosystem unit which is in at least one of a second vehicle and an infrastructure unit in the scenario;
• and wherein the environment comprises surroundings and a space in which the first vehicle is navigating.