| CPC G06N 3/08 (2013.01) [B60W 2050/0083 (2013.01); B60W 60/001 (2020.02); B60W 2556/50 (2020.02); B60W 2556/55 (2020.02); H04L 67/12 (2013.01)] | 20 Claims |
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1. 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 internal 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;
wherein 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.
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