Intelligent transportation method and system

Abstract

The present disclosure relates to transportation and related methods and systems, including intelligent digital twin systems that create, manage and provide digital twins of transportation systems using vehicle operating conditions, identity management systems, sensor data and other data, quantum computing methods and systems including a set of quantum computing services, and biology-based systems and methods for communicating and / or processing data.

Claims

1. 1. A transportation system comprising: a first data system and a second data system; The first data system comprises: receiving a plurality of data values ​​of a data stream; generating a predictive model for predicting future data values ​​of the data stream based on the received data values, wherein generating the predictive model includes determining a plurality of model parameters; and configured to transmit the plurality of model parameters; The second data system receiving the plurality of model parameters transmitted by the first data system; parameterizing a predictive model using the plurality of model parameters; predicting future data values ​​of the data stream using the parameterized predictive model; and and adjusting an operational state of the transportation system based on the future data value.

2. Adjusting the operational state of the transportation system comprises: predicting, by the second data system, an effect of the operating conditions on the transportation system through analysis of data from social media sources; and 2. The transportation system of claim 1, further comprising adjusting, by the second data system, at least one operating state of the transportation system in response to the predicted effect.

3. Adjusting the operational state of the transportation system comprises: classifying social media data sourced from a plurality of social media sources as impacting the transportation system using a first neural network; predicting at least one operational objective of the transportation system based on the categorized social media data using a second neural network; and 2. The transportation system of claim 1, further comprising: adjusting the operational state of the transportation system to achieve the at least one operational objective of the transportation system using a third neural network.

4. 10. The transportation system of claim 1, wherein receiving the plurality of data values ​​comprises aggregating social media source data regarding a plurality of individuals, the data being sourced from a plurality of social media sources.

5. 2. The transportation system of claim 1, wherein the plurality of data values ​​are received from one or more security cameras, and the data stream includes motion vectors extracted from video data captured by the security cameras.

6. 1. A method for prioritizing predictive model data streams, comprising: receiving, by a first device, social media data sourced from a plurality of social media sources as affecting a transportation system; classifying, by the first device, the social media data based on a set of model parameters for each of a plurality of predictive models, each predictive model being trained to predict future data values ​​for the transportation system; selecting, by the first device, at least one predictive model data stream from the categorized social media data; parameterizing, by the first device, a predictive model using a set of model parameters included in the selected at least one predictive model stream; and and predicting, by the first device, at least one future data value of the transportation system using the parameterized predictive model.

7. 7. The method of claim 6, wherein selecting at least one assignment of a priority by the first device to each of a plurality of predictive model data streams included in the social media data and selecting the at least one predictive model stream are performed based on a priority assigned to each of the plurality of predictive model data streams.

8. 8. The method of claim 7, wherein the selected at least one predictive model data stream is associated with a highest priority among the plurality of predictive model data streams.

9. 8. The method of claim 7, wherein the selecting includes suppressing at least one of the non-selected predictive model data streams based on the priority assigned to each of the at least one non-selected predictive model data stream.

10. The method of claim 6 , further comprising adjusting an operational state of the transportation system based on the future data value of the transportation system.

11. 1. A system for transportation, comprising: a hybrid neural network, the hybrid neural network comprising: a first neural network configured to process a plurality of data values ​​of a data stream to determine an emotional state of a rider of a vehicle, the plurality of data values ​​including sensor data collected from one or more sensors and associated with the rider of the vehicle; and a second neural network configured to generate a predictive model for predicting a future emotional state of the rider of the vehicle based on the received plurality of data values; and a third neural network configured to adjust at least one operating parameter of the vehicle based on an output of the predictive model.

12. 12. The system of claim 11, wherein generating the predictive model includes determining a plurality of model parameters of the predictive model, and the third neural network adjusts the at least one operational parameter of the vehicle based on the plurality of model parameters determined by the predictive model.

13. 12. The system of claim 11, wherein the predictive model includes a behavioral analysis model, and the predicted future emotional state of the rider is based on a predicted behavior of the rider in response to the at least one operating parameter of the vehicle.

14. The hybrid neural network comprises: receiving additional data values ​​of said data stream; and 12. The system of claim 11, further configured to refine the predictive model based on the additional data values, the refinement comprising adjusting one or more model parameters of the predictive model.

15. 12. The system of claim 11, wherein the data stream comprises a video stream received from a camera associated with the vehicle, and the plurality of data values ​​comprises one or more vectors extracted from the video stream received from the camera.

16. 1. A system for transportation, comprising: an expert system for selecting a configuration of a vehicle, the configuration including at least one parameter selected from the group consisting of vehicle parameters, user experience parameters, and combinations thereof; the expert system includes a first data system and a second data system; The first data system comprises: receiving a plurality of data values ​​of a data stream, the data values ​​comprising sensor data collected from one or more sensor devices; generating a predictive model for predicting the at least one parameter based on the received plurality of data values, wherein generating the predictive model includes determining a plurality of model parameters; and transmitting the plurality of model parameters; The second data system receiving the plurality of model parameters; parameterizing a predictive model based on the plurality of model parameters; and selecting the at least one parameter based on the parameterized predictive model.

17. 20. The system of claim 16, wherein the predictive model includes a behavioral analysis model, and the at least one parameter is based on a predicted behavior of a rider of the vehicle.

18. 17. The system of claim 16, wherein the predictive model includes a classification model, and the at least one parameter includes a predicted future state of the vehicle based on classification data received from one or more sensor devices associated with the vehicle.

19. 17. The system of claim 16, wherein the data stream comprises a video stream received from a camera associated with the vehicle, and the plurality of data values ​​comprises one or more motion vectors extracted from the video stream received from the camera.

20. The expert system comprises: a first neural network configured to operate to classify a state of the vehicle through analysis of information about the vehicle captured by an Internet of Things device while the vehicle is being operated; and a second neural network configured to optimize the at least one parameter of the vehicle based on the classified state of the vehicle, information about a state of a rider aboard the vehicle, and information relating to vehicle operation and effects on the rider state.

21. 1. A system for transportation, comprising: a quantization-enabled risk identification module configured to identify a risk associated with the vehicle; and a vehicle parameter selection module configured to adjust at least one vehicle parameter of the vehicle based on the risk to improve a safety margin of the vehicle.

22. The quantization-enabled risk identification module includes: identifying a risk associated with an operational state of the vehicle; assessing the impact of the at least one vehicle parameter on the safety margin of the vehicle; determining a current risk profile associated with the vehicle; determining a potential risk profile associated with the vehicle based on adjusting one or more operating parameters of the vehicle; or determining a risk and a probability of one or more predicted events associated with the vehicle; 22. The system of claim 21, configured to perform one or more of the following:

23. 22. The system of claim 21, wherein the quantization-enabled risk identification module is configured to improve the safety margin by determining a risk type of a risk associated with the vehicle based on a set of risk types.

24. The quantization-enabled risk identification module includes: predicting one or more events associated with the vehicle; determining an effect of the predicted event or events on the safety margin associated with the vehicle; 22. The system of claim 21, further configured to determine an adjustment to the at least one vehicle parameter of the vehicle that improves the safety margin of the vehicle based on the predicted one or more events.

25. 22. The system of claim 21, wherein the quantization-enabled risk identification module is further configured to generate a classical prediction engine that identifies the risk associated with the vehicle, and the vehicle parameter selection module is based on an output of the classical prediction engine.

26. 1. A vehicle routing method comprising: adjusting the quantum continuous learning system based on the representations received from the user; determining, with the quantum continuous learning system, routing preferences for routes of the vehicle; determining at least one vehicle routing parameter used to route the vehicle to reflect said routing preferences; and adjusting, by a vehicle routing system, the route of the vehicle based on the at least one determined vehicle routing parameter.

27. Presenting a gaming activity in a gaming-based interface that influences vehicle routing preferences; and 27. The method of claim 26, further comprising receiving a response of the user to the presented gaming activity via the game-based interface, and wherein the adjusting of the quantum continuous learning system is based on the response of the user to the presented gaming activity.

28. 27. The method of claim 26, wherein the quantum continuous learning system is further configured to receive a continuous stream of real-time data, and the routing preferences are determined by the quantum continuous learning system based on the continuous stream of real-time data.

29. 27. The method of claim 26, wherein said adjusting said quantum continuous learning system comprises continually training said quantum continuous learning system based on said real-time data, said real-time data including said representations received from said users.

30. 27. The method of claim 26, wherein the vehicle is included in a set of vehicles, and adjusting the route of the vehicle includes adjusting a routing parameter of at least one other vehicle in the set of vehicles based on the at least one determined vehicle routing parameter.

31. An artificial intelligence system including a quantum annealing module; The artificial intelligence system Receives the operating status of each rechargeable vehicle from multiple rechargeable vehicles in the target area, forecasting a short-term need to charge each rechargeable vehicle based on the operational status of each rechargeable vehicle; The system is configured to determine, using the quantum annealing module, at least one parameter of a charging plan for a charging infrastructure based on the predicted short-term need to charge each rechargeable vehicle.

32. Determining the at least one parameter of the charging plan comprises: determining, by the quantum annealing module, a set of candidate state changes associated with each candidate parameter of a set of candidate parameters for the charging plan of the charging infrastructure; and 32. The system of claim 31, further comprising applying, by the quantum annealing module, a quantum annealing selection to the set of candidate parameters and determining the at least one parameter of the charging plan based on the set of candidate state changes determined by the quantum annealing module.

33. Determining the at least one parameter of the charging plan comprises: setting, by the quantum annealing module, an initial weight of the state of the charging infrastructure associated with each candidate parameter of a set of candidate parameters for the charging plan; and evolving, by the quantum annealing module, the initial weights of each state to adjusted weights based on a time-dependent formula; 32. The system of claim 31, wherein the at least one parameter is determined based on the adjusted weights of the states associated with each candidate parameter of the set of candidate parameters.

34. 32. The system of claim 31 , wherein the artificial intelligence system is further configured to receive capacity information associated with the charging infrastructure, and wherein determining the at least one parameter is based on the capacity information associated with the charging infrastructure.

35. The artificial intelligence system further comprises a hybrid neural network; The hybrid neural network comprises: a first portion of the hybrid neural network configured to operate on a first portion of the operating conditions of each rechargeable vehicle and associated with path planning for the rechargeable vehicle; 32. The system of claim 31 , further comprising: a second portion of the hybrid neural network configured to operate in a second portion of the operating conditions of each rechargeable vehicle and associated with a charging range of each rechargeable vehicle.

36. 1. A system for transportation, comprising: A cognitive system including a quantum annealing module, the cognitive system comprising: determining, by the quantum annealing module, at least one parameter of a reward to be provided to a rider of the vehicle in response to the rider taking a predetermined action while riding in the vehicle; The system is configured to provide the reward to the rider in response to the rider performing the predetermined action.

37. 37. The system of claim 36, wherein the at least one parameter is based on at least one input received from the rider via a rider interface, and the perception system is configured to present the reward offer to the rider via the rider interface.

38. 37. The system of claim 36, wherein the predetermined action includes a selection of a route for the vehicle by the rider, and parameters of the reward provided to the rider are based on routing preferences of the rider.

39. Determining the at least one parameter of the reward comprises: determining, by the quantum annealing module, an effect of each of a set of predetermined actions that may be performed by the riders on a set of vehicles; and 37. The system of claim 36, further comprising: determining, by the quantum annealing module, the parameters of the reward based on a quantum annealing selection from the set of predetermined actions.

40. 37. The system of claim 36, wherein determining the at least one parameter of the reward includes determining, by the quantum annealing module, a set of candidate state changes associated with the routing of a set of vehicles based on the predetermined behavior of the riders, and wherein the at least one parameter of the reward is based on the set of candidate state changes associated with the routing of the set of vehicles.

41. 1. A system for transportation, comprising: a data capture module configured to capture a data set relating to an interaction between a rider in a vehicle and a user interface of the vehicle; a dual-objective artificial neural network; The dual-purpose artificial neural network comprises: training the vehicle based on the dataset to perform actions on behalf of a rider within the vehicle; retraining based on a dual process learning function applied to the data set to adjust the actions to perform on behalf of the rider; 20. The system of claim 1 , further comprising: a step of: updating the dataset in response to the retraining of the dual-objective artificial neural network.

42. Retraining the dual-objective artificial neural network comprises: Identifying poor performance of the dual-purpose artificial intelligence network in a classification task; updating the dataset to include at least one additional data sample relevant to the classification task; and 42. The system of claim 41, further comprising retraining the dual-objective artificial neural network based on the data set that includes the at least one additional data sample relevant to the classification task.

43. Retraining the dual-objective artificial neural network comprises: updating the data set to include at least one additional data sample based on an additional action performed on behalf of the rider; and 42. The system of claim 41, further comprising retraining the dual-objective artificial neural network based on the data set that includes the at least one additional data sample associated with the additional behavior.

44. Retraining the dual-objective artificial neural network comprises: identifying new problems for which the dual-objective artificial neural network is not currently trained to perform actions on behalf of the rider; updating the dataset to include at least one additional data sample related to the new problem; and 42. The system of claim 41, further comprising: retraining the dual-objective artificial neural network based on the data set that includes the at least one additional data sample related to the new problem.

45. 42. The system of claim 41, wherein the data capture module includes a robotic process automation module configured to capture the data set related to an action related to the vehicle performed by the rider, and the dual-purpose artificial neural network is configured to train based on the data set to perform the action on behalf of the rider performing the action.

46. 1. A system for transportation, comprising: an interface configured to configure a set of expert systems to provide one or more outputs related to a set of parameters, the parameters being selected from a group including at least one vehicle parameter, at least one fleet parameter, or at least one user experience parameter; and a dual-objective artificial neural network; The dual-purpose artificial neural network comprises: training the expert system based on the data set to select one or more parameters of the set; retraining based on a dual process learning function applied to the data set to adjust the one or more parameters selected for the set of expert systems; 20. The system of claim 1 , further comprising: a step of: updating the dataset in response to the retraining of the dual-objective artificial neural network.

47. Retraining the dual-objective artificial neural network comprises: identifying a poor selection of the expert system based on a first set of one or more parameters selected by the dual-objective artificial neural network; updating the data set to include at least one additional data sample related to the one or more parameters; and 47. The system of claim 46, further comprising retraining the dual-objective artificial neural network based on the data set including the at least one additional data sample associated with the one or more parameters.

48. Retraining the dual-objective artificial neural network comprises: updating the data set to include at least one additional data sample based on an additional output of the set of expert systems in response to the one or more parameters; and 47. The system of claim 46, further comprising retraining the dual-objective artificial neural network based on the data set including the at least one additional data sample associated with the additional output.

49. Retraining the dual-objective artificial neural network comprises: identifying a new problem for which the set of expert systems is not currently trained to provide one or more outputs; updating the dataset to include at least one additional data sample related to the new problem; and 47. The system of claim 46, further comprising retraining the dual-objective artificial neural network based on the data set that includes the at least one additional data sample related to the new problem.

50. The dual process learning function receiving at least one additional data sample associated with the new input value; Integrating at least one additional data sample with at least one data sample of the data set; and 47. The system of claim 46, further configured to: update the dataset based on the integration of the at least one additional data sample with the at least one data sample of the dataset.

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