Programmatically identifying a personality of an autonomous vehicle

1. A method comprising:

receiving data from an autonomous vehicle;

generating a vector representing the autonomous vehicle based on the data;

classifying the vector into one or more personalities using a trained model, each of the one or more personalities associated with a plaintext sentiment generated by the trained model;

receiving a search query from a user, the search query comprising a text query;

identifying a sentiment of the search query;

identifying one or more autonomous vehicles responsive to the sentiment based on personalities assigned to the one or more autonomous vehicles, the one or more autonomous vehicles including the autonomous vehicle; and

transmitting the one or more autonomous vehicles to the user.

2. The method of claim 1 , the data comprising data recorded by one or more sensors installed on the autonomous vehicle.

3. The method of claim 2 , the data further comprising log data recorded by monitoring a bus of the autonomous vehicle.

4. The method of claim 1 , the generating the vector comprising calculating frequencies and amounts of repairs and faults included within the data.

5. The method of claim 1 , the generating the vector comprising calculating amounts of rapid accelerations and decelerations included within the data.

6. The method of claim 1 , the generating the vector comprising identifying one or more decision points included within the data.

7. The method of claim 1 , the generating the vector comprising calculating one or more of a routing efficiency or cost efficiency based on routing data included within the data.

8. The method of claim 1 , the generating the vector comprising calculating costs and profits generated by the autonomous vehicle during non-routine activities based on the data.

9. The method of claim 1 , the classifying the vector into one or more personalities comprising analyzing each component of the autonomous vehicle and assigning a personality to the autonomous vehicle if statistics associated with a respective component exceed a pre-defined threshold.

10. The method of claim 1 , further comprising:

selecting a set of autonomous vehicle objects;

clustering the set of autonomous vehicle objects into a set of clusters;

assigning each cluster in the set of clusters a personality identifier using a natural language processing (NLP) algorithm; and

training the trained model using the clusters of autonomous vehicle objects and the assigned personality identifiers, wherein the classifying the vector into one or more personalities comprises inputting the vector into the model.

11. A system comprising:

a processor; and

a storage medium for tangibly storing thereon program logic for execution by the processor, the stored program logic comprising:

logic, executed by the processor, for receiving data from an autonomous vehicle;

logic, executed by the processor, for generating a vector representing the autonomous vehicle based on the data;

logic, executed by the processor, for classifying the vector into one or more personalities using a trained model, each of the one or more personalities associated with a plaintext sentiment generated by the trained model;

logic, executed by the processor, for receiving a search query from a user, the search query comprising a text query;

identifying a sentiment of the search query;

logic, executed by the processor, for identifying one or more autonomous vehicles responsive to the sentiment based on personalities assigned to the one or more autonomous vehicles, the one or more autonomous vehicles including the autonomous vehicle; and

logic, executed by the processor, for transmitting the one or more autonomous vehicles to the user.

12. The system of claim 11 , the data comprising data recorded by one or more sensors installed on the autonomous vehicle.

13. The system of claim 12 , the data further comprising log data recorded by monitoring a bus of the autonomous vehicle.

14. The system of claim 11 , the generating the vector comprising calculating frequencies and amounts of repairs and faults included within the data.

15. The system of claim 11 , the generating the vector comprising calculating amounts of rapid accelerations and decelerations included within the data.

16. The system of claim 11 , the generating the vector comprising identifying one or more decision points included within the data.

17. The system of claim 11 , the generating the vector comprising calculating one or more of a routing efficiency or cost efficiency based on routing data included within the data.

18. The system of claim 11 , the generating the vector comprising calculating costs and profits generated by the autonomous vehicle during non-routine activities based on the data.

19. The system of claim 11 , the classifying the vector into one or more personalities comprising analyzing each component of the autonomous vehicle and assigning a personality to the autonomous vehicle if statistics associated with a respective component exceed a pre-defined threshold.

20. The system of claim 11 , the stored program logic further comprising:

logic, executed by the processor, for selecting a set of autonomous vehicle objects;

logic, executed by the processor, for clustering the set of autonomous vehicle objects into a set of clusters;

logic, executed by the processor, for assigning each cluster in the set of clusters a personality identifier using a natural language processing (NLP) algorithm; and

logic, executed by the processor, for training the trained model using the clusters of autonomous vehicle objects and the assigned personality identifiers, wherein the classifying the vector into one or more personalities comprises inputting the vector into the model.