US 12,475,375 B2
Systems and methods for assessing infrastructure
Miguel Dickson, Pittsburgh, PA (US); and Benjamin T. Schmidt, Penn Hills, PA (US)
Assigned to Michelin Mobility Intelligence, Inc., Pittsburgh, PA (US)
Appl. No. 17/769,304
Filed by RoadBotics, Inc., Pittsburgh, PA (US)
PCT Filed Oct. 14, 2020, PCT No. PCT/US2020/055508
§ 371(c)(1), (2) Date Apr. 14, 2022,
PCT Pub. No. WO2021/076573, PCT Pub. Date Apr. 22, 2021.
Claims priority of provisional application 62/915,426, filed on Oct. 15, 2019.
Prior Publication US 2023/0306573 A1, Sep. 28, 2023
Int. Cl. G06V 10/70 (2022.01); E01C 23/01 (2006.01); G06N 3/082 (2023.01); G06T 7/00 (2017.01); G06V 10/82 (2022.01); G06V 20/10 (2022.01); G06V 20/56 (2022.01)
CPC G06N 3/082 (2013.01) [E01C 23/01 (2013.01); G06T 7/0002 (2013.01); G06V 10/70 (2022.01); G06V 10/82 (2022.01); G06V 20/10 (2022.01); G06V 20/56 (2022.01); G06T 2207/10016 (2013.01); G06T 2207/20081 (2013.01); G06T 2207/30184 (2013.01)] 25 Claims
OG exemplary drawing
 
1. A system comprising:
at least one processor; and
at least one non-transitory computer-readable medium having encoded thereon instructions which, when executed, program the at least one processor to:
determine, based on sensor data collected from a first plurality of road segments, an assessment for each road segment of the first plurality of road segments, the assessment being indicative of an overall condition of the road segment;
use one or more selected thresholds to filter the first plurality of road segments, based on the respective assessments, thereby obtaining a second plurality of road segments, wherein:
the at least one processor is programmed to filter out road segments having assessments above a selected threshold indicative of severe damage;
identify, based on the sensor data, at least one road segment from the second plurality of road segments as a candidate for a selected type of maintenance, wherein:
the sensor data comprises an image of the at least one road segment; and
the at least one processor is programmed to use at least one machine learning model to identify, from the image, one or more instances of physical damage exhibited by the at least one road segment; and
construct a candidate batch comprising road segments that are identified as candidates for the selected type of maintenance.