Systems and methods for automated trade-in with limited human interaction

1 . One or more non-transitory computer-readable medium storing computer instructions that, when executed by one or more processors, cause the one or more processors to perform steps comprising:

continually monitoring, based on feedback from one or more image sensors, for a presence of a vehicle associated with a user within a predetermined area;

determining, based on the feedback, that the vehicle is within the predetermined area;

receiving, from the one or more image sensors, first data comprising multimedia content showing a first aspect of damage to the vehicle associated with the user;

creating a feature vector comprising the first data;

identifying a machine learning algorithm corresponding to the vehicle associated with the user; and

inputting the feature vector into the identified machine learning algorithm to determine a damage estimate to the first aspect of damage.

2 . The non-transitory computer-readable medium of claim 1 , wherein the computer instructions are further configured to:

receiving a request to begin a determination of the first aspect of damage;

receiving a second data comprising multimedia content showing a second aspect of the vehicle associated with the user; and

determining an initial damage estimate for the vehicle associated with the user.

3 . The non-transitory computer-readable medium of claim 2 , wherein the feature vector further comprises the second data.

4 . The non-transitory computer-readable medium of claim 1 , wherein the computer instructions are further configured to:

prior to the inputting the feature vector into the machine learning algorithm, identifying the machine learning algorithm based on vehicle-specific identifying information for the vehicle associated with the user.

5 . The non-transitory computer-readable medium of claim 1 , wherein the computer instructions are further configured to:

prior to the inputting the feature vector into the machine learning algorithm, training the machine learning algorithm using reference data of one or more aspects of a plurality of reference vehicles that are not associated with the user.

6 . The non-transitory computer-readable medium of claim 5 , wherein the computer instructions are further configured to train the machine learning algorithm by:

receiving, for each of the plurality of reference vehicles that are not associated with the user, reference vehicle-specific identifying information and reference data of the first aspect of a given reference vehicle of the plurality of reference vehicles;

receiving, for each of the plurality of reference vehicles, an actual value of the given reference vehicle;

creating, for each of the plurality of reference vehicles, a reference feature vector comprising the reference vehicle-specific identifying information and the reference data;

associating, for each of the plurality of reference vehicles, the reference feature vector to the actual value of the given reference vehicle; and

training the machine learning algorithm using the associated reference feature vectors to predict the actual value of the vehicle associated with the user based on the vehicle and the first data.

7 . The non-transitory computer-readable medium of claim 6 , wherein the computer instructions are further configured to:

determining a predictability for each of the one or more aspects of the reference vehicle for estimating the actual value of the given reference vehicle; and

assigning, based on the determined predictability, a first weight to the first data.

8 . The non-transitory computer-readable medium of claim 1 , wherein the computer instructions are further configured to calibrate the one or more image sensors to produce the multimedia content based on a degree of illumination or a time within a diurnal cycle.

9 . The non-transitory computer-readable medium of claim 1 , wherein the first aspect comprises one or more of:

an exterior region of the vehicle associated with the user, or

an interior region of the vehicle associated with the user.

10 . A method comprising:

continually monitoring, based on feedback from one or more image sensors, for a presence of a vehicle associated with a user within a predetermined area;

determining, based on the feedback, that the vehicle is within the predetermined area;

determining identifying information of the vehicle associated with the user

receiving, from the one or more image sensors, first data comprising multimedia content showing a first aspect of damage to the vehicle associated with the user;

creating a feature vector comprising the first data;

identifying a machine learning algorithm corresponding to the vehicle associated with the user; and

inputting the feature vector into the identified machine learning algorithm to determine a damage estimate to the first aspect of damage.

11 . The method of claim 10 , further comprising:

receiving a request to begin a determination of the first aspect of damage;

receiving a second data comprising multimedia content showing a second aspect of the vehicle associated with the user; and

determining an initial damage estimate for the vehicle associated with the user.

12 . The method of claim 11 , wherein the feature vector further comprises the second data.

13 . The method of claim 10 , further comprising:

prior to the inputting the feature vector into the machine learning algorithm, identifying the machine learning algorithm based on the identifying information of the vehicle associated with the user.

14 . The method of claim 10 , further comprising:

prior to the inputting the feature vector into the machine learning algorithm, training the machine learning algorithm using reference data of one or more aspects of a plurality of reference vehicles that are not associated with the user.

15 . The method of claim 14 , further comprising training the machine learning algorithm by:

receiving, for each of the plurality of reference vehicles that are not associated with the user, reference identifying information and reference data of the first aspect of a given reference vehicle of the plurality of reference vehicles;

receiving, for each of the plurality of reference vehicles, an actual value of the given reference vehicle;

creating, for each of the plurality of reference vehicles, a reference feature vector comprising the reference identifying information and the reference data;

associating, for each of the plurality of reference vehicles, the reference feature vector to the actual value of the given reference vehicle; and

training the machine learning algorithm using the associated reference feature vectors to predict the actual value of the vehicle associated with the user based on the vehicle and the first data.

16 . The method of claim 15 , further comprising:

determining a predictability for each of the one or more aspects of the reference vehicle for estimating the actual value of the given reference vehicle; and

assigning, based on the determined predictability, a first weight to the first data.

17 . The method of claim 10 , further comprising calibrating the one or more image sensors to produce the multimedia content based on a degree of illumination or a time within a diurnal cycle.

18 . The method of claim 10 , wherein the first aspect comprises one or more of:

an exterior region of the vehicle associated with the user, or an interior region of the vehicle associated with the user.

19 . An apparatus comprising:

one or more processors;

one or more image sensors; and

a memory storing instructions that, when executed by the one or more processors, cause the apparatus to:

continually monitor, based on feedback from the one or more image sensors, for a presence of a vehicle associated with a user within a predetermined area;

determine, based on the feedback, that the vehicle is within the predetermined area;

receive, from the one or more image sensors, first data comprising multimedia content showing a first aspect of damage to the vehicle associated with the user;

create a feature vector comprising the first data;

identify a machine learning algorithm corresponding to the vehicle associated with the user; and

input the feature vector into the identified machine learning algorithm to determine a damage estimate to the first aspect of damage.

20 . The apparatus of claim 19 , wherein the instructions, when executed by the one or more processers, further cause the apparatus to:

receive a request to begin a determination of the first aspect of damage;

receive a second data comprising multimedia content showing a second aspect of the vehicle associated with the user; and

determine an initial damage estimate for the vehicle associated with the user.