Systems and methods for displaying contextually-sensitive braking information

1. A system for generating contextually-sensitive braking information, the system comprising:

one or more memories; and

one or more processors coupled to the one or more memories and configured to:

detect a braking event of a vehicle, the braking event associated with a braking force;

apply a braking model to the braking force to generate a braking rationale, the braking model trained by a plurality of previous braking forces corresponding to a plurality of previous braking events; and

transmit the braking rationale.

2. The system of claim 1 , wherein the one or more processors are further configured to:

transmit the braking rationale to a third party.

3. The system of claim 1 , wherein the braking model incorporates a weighted prioritization metric including one or more weights corresponding to one or more categories of data related to the vehicle.

4. The system of claim 1 , wherein the one or more processors are further configured to:

determine a current route of the vehicle;

retrieve an upcoming traffic indication from a traffic database based at least in part upon the current route; and

generate the braking rationale based at least in part upon the upcoming traffic indication.

5. The system of claim 1 , wherein the braking rationale includes at least one selected from a group consisting of one or more alphanumeric characters, one or more symbols, one or more images, one or more video images, one or more audio bytes, an audio representation and a visual representation.

6. The system of claim 1 , wherein the braking rationale includes first braking rationale to be displayed for a first time period and a second braking rationale to be displayed for a second time period, wherein the first braking rationale is different from the second braking rationale, wherein the first time period is different from the second time period.

7. The system of claim 1 , wherein the one or more processors are further configured to:

capture a set of digital images corresponding to an upcoming environment of the vehicle;

determine a proximate object within the upcoming environment of the vehicle based at least in part upon the set of digital images; and

generate the braking rationale based at least in part upon the proximate object;

wherein to determine the proximate object within the upcoming environment of the vehicle includes using one or more of (i) object recognition (OR), (ii) object character recognition (OCR), or (iii) facial recognition; and

wherein the one or more processors are further configured to:

capture a set of proximity data indicative of a set of distances from the vehicle to the proximate object; and

wherein the braking rationale includes a distance from the set of distances.

8. A computer-implemented method for generating contextually-sensitive braking information, the method comprising:

detecting, by one or more processors, a braking event of a vehicle, the braking event associated with a braking force;

applying a braking model to the braking force to generate a braking rationale, the braking model trained by a plurality of previous braking forces corresponding to a plurality of previous braking events; and

transmitting the braking rationale.

9. The computer-implemented method of claim 8 , wherein the transmitting the braking rationale comprises transmitting the braking rationale to a third party.

10. The computer-implemented method of claim 8 , wherein the braking model incorporates a weighted prioritization metric including one or more weights corresponding to one or more categories of data related to the vehicle.

11. The computer-implemented method of claim 8 , further comprising:

determining, by the one or more processors, a current route of the vehicle;

retrieving, by the one or more processors, an upcoming traffic indication from a traffic database based at least in part upon the current route; and

generating, by the one or more processors, the braking rationale based at least in part upon the upcoming traffic indication.

12. The computer-implemented method of claim 8 , wherein the braking rationale includes at least one selected from a group consisting of one or more alphanumeric characters, one or more symbols, one or more images, one or more video images, one or more audio bytes, an audio representation and a visual representation.

13. The computer-implemented method of claim 8 , wherein the braking rationale includes a first braking rationale to be displayed for a first time period and a second braking rationale to be displayed for a second time period, wherein the first braking rationale is different from the second braking rationale, wherein the first time period is different from the second time period.

14. The computer-implemented method of claim 8 , further comprising:

capturing, by an image sensor, a set of digital images corresponding to an upcoming environment of the vehicle;

determining, by the one or more processors, a proximate object within the upcoming environment of the vehicle based at least in part upon the set of digital images; and

generating, by the one or more processors, the braking rationale based at least in part upon the proximate object;

wherein the determining the proximate object within the upcoming environment of the vehicle includes using one or more of (i) object recognition (OR), (ii) object character recognition (OCR), or (iii) facial recognition, and

wherein the method further comprising:

capturing, by a proximity sensor, a set of proximity data indicative of a set of distances from the vehicle to the proximate object;

wherein the braking rationale includes a distance from the set of distances.

15. A system for generating contextually-sensitive braking information, the system comprising:

one or more memories; and

one or more processors interfacing with the one or more memories and configured to:

detect a braking event of a vehicle;

receive a set of images collected by one or more sensors;

determine a proximate object within an upcoming environment of the vehicle based at least in part upon the set of images using at least one selected from a group comprising of object recognition (OR), object character recognition (OCR), or facial recognition;

determine a distance from the vehicle to the proximate object based on a set of proximity data; and

generate a braking rationale for the braking event based at least in part upon the proximate object and the determined distance.

16. The system of claim 15 , wherein the one or more processors are further configured to:

transmit the braking rationale to a third party.

17. The system of claim 15 , wherein the one or more processors are further configured to:

determine a current route of the vehicle;

retrieve an upcoming traffic indication from a traffic database based at least in part upon the current route; and

generate the braking rationale based at least in part upon the upcoming traffic indication.

18. The system of claim 15 , wherein the braking rationale includes at least one selected from a group consisting of one or more alphanumeric characters, one or more symbols, one or more images, one or more video images, one or more audio bytes, an audio representation and a visual representation.

19. The system of claim 15 , wherein the braking rationale includes first braking rationale to be displayed for a first time period and a second braking rationale to be displayed for a second time period, wherein the first braking rationale is different from the second braking rationale, wherein the first time period is different from the second time period.

20. The system of claim 15 , wherein the one or more processors are further configured to:

retrieve a set of weather conditions associated with a current location of the vehicle; and

adjust a threshold braking force based at least in part upon the set of weather conditions.

21. A system for generating contextually-sensitive braking information, the system comprising:

means for storing data thereon; and

means for performing operations comprising:

detecting a braking event of a vehicle, the braking event associated with a braking force;

applying a braking model to the braking force to generate a braking rationale, the braking model trained by a plurality of previous braking forces corresponding to a plurality of previous braking events; and

transmitting the braking rationale.