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 interfacing with the one or more memories and configured to:

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

compare the braking force to a predetermined threshold braking force to determine whether the braking force exceeds the predetermined threshold braking force; and

in response to determining that the braking force exceeds the predetermined threshold braking force, generate a braking rationale corresponding to the braking event;

wherein the one or more processors are further configured to:

use a braking model that is trained by (i) a plurality of previous braking forces corresponding to a plurality of previous braking events, and (ii) a set of previous braking rationales; and

apply the braking model to generate the braking rationale.

2. 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.

3. 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.

4. 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.

5. 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.

6. The system of claim 5 , 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.

7. The system of claim 1 , 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 the threshold braking force based at least in part upon the set of weather conditions.

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;

comparing, by the one or more processors, the braking force to a predetermined threshold braking force to determine whether the braking force exceeds the predetermined threshold braking force; and

in response to determining that the braking force exceeds the predetermined threshold braking force, generate a braking rationale corresponding to the braking event by at least:

using, by the one or more processors, a braking model that is trained by (i) a plurality of previous braking forces corresponding to a plurality of previous braking events, and (ii) a set of previous braking rationales; and

applying, by the one or more processors, the braking model to generate the braking rationale corresponding to the braking event.

9. 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.

10. 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.

11. 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.

12. 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.

13. The computer-implemented method of claim 12 , wherein the determining a 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 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.

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

retrieving, by the one or more processors, a set of weather conditions associated with a current location of the vehicle; and

adjusting, by the one or more processors, the threshold braking force based at least in part upon the set of weather conditions.

15. A non-transitory computer-readable storage medium comprising instructions stored thereon for generating contextually-sensitive braking information, wherein the instructions when executed on one or more processors cause the one or more processors to:

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

compare the braking force to a predetermined threshold braking force to determine whether the braking force exceeds the predetermined threshold braking force; and

in response to determining that the braking force exceeds the predetermined threshold braking force, generate a braking rationale corresponding to the braking event by at least:

using, by the one or more processors, a braking model that is trained by (i) a plurality of previous braking forces corresponding to a plurality of previous braking events, and (ii) a set of previous braking rationales; and

applying, by the one or more processors, the braking model to generate the braking rationale corresponding to the braking event.

16. The non-transitory computer-readable storage medium of claim 15 , wherein the instructions further cause the one or more processors 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.

17. The non-transitory computer-readable storage medium of claim 16 , 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.

18. The non-transitory computer-readable storage medium of claim 16 , 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.

19. The non-transitory computer-readable storage medium of claim 15 , wherein the instructions further cause the one or more processors 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.

20. The non-transitory computer-readable storage medium of claim 19 , wherein the determining a 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 instructions further cause the one or more processors to:

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

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