System and method for monitoring and managing cognitive load of an occupant of a vehicle

1. A method for monitoring and managing a cognitive load of an occupant of a vehicle, comprising:

analyzing an interaction data between an agent of the vehicle and the at least one occupant, wherein the interaction data includes data relating to an upcoming interaction between the least one occupant of the vehicle and the agent;

determining, based on analysis of a first set of sensory inputs received from a first set of sensors from within a cabin of the vehicle, and based on the analysis of the interaction data, a current cognitive load score of at least one occupant of the vehicle, wherein the cognitive load defines a mental effort required by the occupant to perform at least one task, and wherein the first set of sensory inputs are at least indicative of a current state within the cabin;

determining, based on an analysis of a second set of sensory inputs, a current state of the vehicle;

analyzing the current cognitive load score of the at least one occupant of the vehicle with respect to the current state of the vehicle; and

selecting at least one predetermined plan for execution when a reduction of the current cognitive load score of the at least one occupant is desirable, wherein the determination is based on a result of the analysis of the current cognitive load score of the at least one occupant and the current state of the vehicle, wherein the plan causes execution of an action by the agent of the vehicle.

2. The method of claim 1 , wherein the current state of the vehicle includes at least one of: a current state of an external environment of the vehicle and a current state of an internal environment of the vehicle.

3. The method of claim 2 , wherein the current state of the external environment of the vehicle is determined using at least one machine learning technique.

4. The method of claim 1 , wherein the analysis of the first set of sensory inputs further comprises at least one computer vision technique.

5. The method of claim 1 , wherein the determination of the current cognitive load score of the at least one occupant is achieved using at least one machine learning technique.

6. The method of claim 5 , wherein the at least one machine learning technique includes at least one of: one or more neural networks, recurrent neural networks, decision tree learning, Bayesian networks, and clustering.

7. The method of claim 1 , wherein the analysis of the second set of sensory inputs further comprises at least one computer vision technique.

8. A non-transitory computer readable medium having stored thereon instructions for causing a processing circuitry to perform a process, the process comprising:

analyzing an interaction data between an agent of the vehicle and the at least one occupant, wherein the interaction data includes data relating to an upcoming interaction between the least one occupant of the vehicle and the agent;

determining, based on analysis of a first set of sensory inputs received from a first set of sensors from within a cabin of a vehicle, and based on the analysis of the interaction data, a current cognitive load score of at least one occupant of the vehicle, wherein the cognitive load defines a mental effort required by the occupant to perform at least one task, and wherein the first set of sensory inputs are at least indicative of a current state within the cabin;

determining, based on an analysis of a second set of sensory inputs, a current state of the vehicle;

analyzing the current cognitive load score of the at least one occupant of the vehicle with respect to the current state of the vehicle; and

selecting at least one predetermined plan for execution when a reduction of the current cognitive load score of the at least one occupant is desirable, wherein the determination is based on a result of the analysis of the current cognitive load score of the at least one occupant and the current state of the vehicle, wherein the plan causes execution of an action by the agent of the vehicle.

9. A system for monitoring and managing a cognitive load of an occupant of a vehicle, comprising:

a processing circuitry; and

a memory, the memory containing instructions that, when executed by the processing circuitry, configure the system to:

analyze an interaction data between an agent of the vehicle and the at least one occupant, wherein the interaction data includes data relating to an upcoming interaction between the least one occupant of the vehicle and the agent;

determine, based on analysis of a first set of sensory inputs received from a first set of sensors from within a cabin of the vehicle, and based on the analysis of the interaction data, a current cognitive load score of at least one occupant of the vehicle, wherein the cognitive load defines a mental effort required by the occupant to perform at least one task, and wherein the first set of sensory inputs are at least indicative of a current state within the cabin;

determine, based on an analysis of a second set of sensory inputs, a current state of the vehicle;

analyze the current cognitive load score of the at least one occupant of the vehicle with respect to the current state of the vehicle; and

select at least one predetermined plan for execution when a reduction of the current cognitive load score of the at least one occupant is desirable, wherein the determination is based on a result of the analysis of the current cognitive load score of the at least one occupant and the current state of the vehicle, wherein the plan causes execution of an action by the agent of the vehicle.

10. The system of claim 9 , wherein the current state of the vehicle includes at least one of: a current state of an external environment of the vehicle and a current state of an internal environment of the vehicle.

11. The system of claim 10 , wherein the current state of the external environment of the vehicle is determined using at least one machine learning technique.

12. The system of claim 9 , wherein the analysis of the first set of sensory inputs further comprises at least one computer vision technique.

13. The system of claim 9 , wherein the determination of the current cognitive load score of the at least one occupant is achieved using at least one machine learning technique.

14. The system of claim 13 , wherein the at least one machine learning technique includes at least one of: one or more neural networks, recurrent neural networks, decision tree learning, Bayesian networks, and clustering.

15. The system of claim 9 , wherein the analysis of the second set of sensory inputs further comprises at least one computer vision technique.