Automated vehicle condition grading

A method for automated vehicle condition grading, the method comprising:

• obtaining, from a client device or a data store, a set of images associated with a vehicle, wherein each image of the set of images is associated with a vehicle perspective of a set of vehicle perspectives;
• processing each image of the set of images according to a set of machine learning models to generate a set of optical vehicle condition grades, wherein each optical vehicle condition grade, of the set of optical vehicle condition grades, indicates a discrete score from a range of discrete scores that are associated with damage to the vehicle that is shown in the set of images, wherein the set of machine learning models comprises:
• a first machine learning model used to generate an optical vehicle condition grade in the set of optical vehicle condition grades;
• and
• a second model associated with the first machine learning model used to generate an optical vehicle condition subgrade in the set of optical vehicle condition grades;
• assigning a plurality of weighted values to the set of images, comprising;
• assigning a first weighted value to a first image of the set of images based on a first section of the vehicle shown in the first image and whether the vehicle includes a feature, wherein the first image is assigned a first value for the first weighted value based on the vehicle including the feature and is assigned a second value for the first weighted value based on the vehicle not including the feature;
• and
• assigning a second weighted value to a second image of the set of images based on a second section of the vehicle shown in the second image, wherein the second image is assigned a second value for the second weighted value regardless of whether the vehicle includes the feature;
• determining, based on applying the plurality of weighted values to the set of optical vehicle condition grades, an aggregated optical vehicle condition grade;
• generating, based at least in part on the aggregated optical vehicle condition grade, a final vehicle condition grade for the vehicle;
• and
• providing an indication of the final vehicle condition grade for the vehicle to a client computing device.

A method for automating vehicle condition grading, the method comprising:

• obtaining, from a client device or a data store, a plurality of sets of images, each set of images corresponding to a different perspective of a vehicle;
• obtaining, from a client device or a data store, values for a predetermined set of known parameters of the vehicle, including at least one of vehicle body type, vehicle door set type, vehicle drive type, and vehicle engine type,
• processing each of the sets of images according to a machine learning model to generate optical vehicle condition grades corresponding to the different perspectives of the vehicle by assigning a discrete value score from a range of discrete value scores associated with the condition of the vehicle shown in the sets of images for each of the different perspectives;
• weighting the value of at least one of the optical vehicle condition grades based upon the value of at least one of the known parameters relating to the corresponding perspective of the vehicle condition grade;
• determining, an aggregated optical vehicle condition grade based upon the optical vehicle condition grades including the weighted optical vehicle condition grade,
• generating, based at least in part on the aggregated optical vehicle condition, a final vehicle condition grade for the vehicle;
• and
• storing the final vehicle condition grade for the vehicle in a vehicle condition grade data store.