Road network optimization based on vehicle telematics information
Methods, systems and apparatus, including computer programs encoded on computer storage media for determining asset efficiency. Unmanned Aerial Vehicles (UAVs) may be used to obtain aerial images of locations, property or structures. The aerial images may be geo-rectified, and a ortho-mosaic, digital surface model, or a point cloud may be created. In the context of an operation where mobile assets are used, such as construction or earth moving equipment, location-based event information may be obtained. The location-based event information may be used to determine road segment conditions or road topology where problematic road conditions likely exist.
1. A computer-implemented method comprising:
obtaining aerial images representing real-world locations;
generating a road network based on the aerial images;
dividing the road network into one or more road segments;
determining a vehicle traversal criteria for each of the one or more road segments;
obtaining historical event information for one or more vehicles, wherein the one or more vehicles have traversed the one or more road segments, and the one or more vehicles have obtained operational data, the operational data comprising at least geo-spatial positions, time, and event data of respective ones of the one or more vehicles;
determining from the historical event information whether the one or more vehicles traversed the one or more road segments at a range or value other than the determined vehicle traversal criteria;
presenting, via a user interface, the one or more road segments; and
for a particular road segment of the one or more road segments, displaying, via the user interface, the road segment as a segment where vehicles have traversed the road segment at a range or value other than the determined vehicle traversal criteria.
2. The method of claim 1 , further comprising:
determining the vehicle traversal criteria as a predetermined traversal time for a particular type of vehicle, each type of vehicle has a particular traversal time for a segment.
3. The method of claim 2 , wherein the historical event information includes a unique identifier for each vehicle of the one or more vehicles.
4. The method of claim 1 , wherein the historical event information for a vehicle includes one or more of the following: time of an event, geo-spatial location of the event, duration of the event, speed of the vehicle, fuel consumption of the vehicle, gear position of the vehicle, brake application, vehicle load information, shock absorber data, acceleration data, deceleration data.
5. The method of claim 1 , further comprising:
determining the vehicle traversal criteria based on road geometry for a particular road segment.
6. The method of claim 5 , wherein the road geometry for a particular road segment is defined by one or more parameters including: cross-falls, center line, superelevation, road width, grade, radius, switch backs, 2-ways, n-ways, or rimpull curves.
7. The method of claim 1 , further comprising:
generating a flight plan for a UAV, the flight plan based upon an inspection area associated with a road segment or with a location of a determined road condition; and
providing the flight plan to the UAV or to a ground control station operable to control the UAV and perform the flight plan.
8. The method of claim 1 , further comprising:
generating instructions for the one or more vehicles to obtain sensor data describing actual physical road conditions;
transmitting the instructions to the one or more vehicles;
receiving sensor data obtained from the one or more vehicles, wherein the sensor data is digital images, or LIDAR data; and
updating portions of the road network from the received sensor data.
9. The method of claim 1 , further comprising:
training a visual classifier with a data set of images of actual physical road conditions;
receiving images different from the data set of images, wherein the different images depict a portion of a road associated with a road network; and
identifying, with the visual classifier, a type of road condition found in the different images, wherein the type of road condition is at least one of: pot holes, wash boarding, ruts, ditches, alligator cracking, block cracking, transverse cracking, edge cracks, joint reflection cracks, slippage cracks, depressions, shoving, upheaval, raveling, water pooling, snow, large rocks, obstacles, erosion, plant or foliage overgrowth, uneven surfaces, or protrusions.
10. A system, comprising:
one or more computer processors; and
a non-transitory storage device storing instructions operable to cause the one or more computer processors to perform operations comprising:
obtaining aerial images representing real-world locations;
generating a road network based on the aerial images;
dividing the road network into one or more road segments;
determining a vehicle traversal criteria for each of the one or more road segments;
obtaining historical event information for one or more vehicles;
determining from the historical event information whether the one or more vehicles traversed the one or more road segments at a range or value other than the determined vehicle traversal criteria;
presenting, via a user interface, the one or more road segments; and
for a particular road segment of the one or more road segments, displaying via the user interface, the road segment as a segment where vehicles have traversed the road segment at a range or value other than the determined vehicle traversal criteria.
11. The system of claim 10 , the operations further comprising:
determining the vehicle traversal criteria as a predetermined traversal time for a particular type of vehicle, each type of vehicle has a particular traversal time for a segment.
12. The system of claim 11 , wherein the historical event information includes a unique identifier for each vehicle of the one or more vehicles.
13. The system of claim 10 , wherein the historical event information for a vehicle includes one or more of the following: time of an event, geo-spatial location of the event, duration of the event, speed of the vehicle, fuel consumption of the vehicle, gear position of the vehicle, brake application, vehicle load information, shock absorber data, acceleration data, or deceleration data.
14. The system of claim 10 , the operations further comprising:
determining the vehicle traversal criteria based on road geometry for a particular road segment.
15. The system of claim 14 , wherein the road geometry for a particular road segment is defined by one or more parameters including: cross-falls, center line, superelevation, road width, grade, radius, switch backs, 2-ways, n-ways, or rimpull curves.
16. The system of claim 10 , the operations further comprising:
generating a flight plan for a UAV, the flight plan based upon an inspection area associated with a road segment or with a location of a determined road condition; and
providing the flight plan to the UAV or to a ground control station operable to control the UAV and perform the flight plan.
17. The system of claim 10 , the operations further comprising:
generating instructions for the one or more vehicles to obtain sensor data describing actual physical road conditions;
transmitting the instructions to the one or more vehicles;
receiving sensor data obtained from the one or more vehicles, wherein the sensor data is digital images, or LIDAR data; and
updating portions of the road network from the received sensor data.
18. The system of claim 10 , the operations further comprising:
training a visual classifier with a data set of images of actual physical road conditions;
receiving images different from the data set of images, wherein the different images depict a portion of a road associated with a road network; and
identifying with the visual classifier, a type of road condition found in the different images, wherein the type of road condition is at least one of: pot holes, wash boarding, ruts, ditches, alligator cracking, block cracking, transverse cracking, edge cracks, joint reflection cracks, slippage cracks, depressions, shoving, upheaval, raveling, water pooling, snow, large rocks, obstacles, erosion, plant or foliage overgrowth, uneven surfaces, or protrusions.
19. A non-transitory storage device storing instructions operable to cause one or more computer processors to perform operations comprising:
obtaining aerial images representing real-world locations;
generating a road network based on the aerial images;
dividing the road network into one or more road segments;
determining a vehicle traversal criteria for each of the one or more road segments;
obtaining historical event information for one or more vehicles, wherein the one or more vehicles have traversed the one or more road segments, and the one or more vehicles have obtained operational data, the operational data comprising at least geo-spatial positions, time, and event data of respective ones of the one or more vehicles;
determining from the historical event information whether the vehicles traversed the one or more road segments at a range or value other than the determined vehicle traversal criteria;
presenting, via a user interface, the one or more road segments; and
for a particular road segment of the one or more road segments, displaying via the user interface, the road segment as a segment where vehicles have traversed the road segment at a range or value other than the determined vehicle traversal criteria.
20. The non-transitory storage device of claim 19 , the operations further comprising:
determining the vehicle traversal criteria as a predetermined traversal time for a particular type of vehicle, each type of vehicle has a particular traversal time for a segment.
21. The non-transitory storage device of claim 20 , wherein the historical event information includes a unique identifier for each vehicle of the one or more vehicles.
22. The non-transitory storage device of claim 19 , wherein the historical event information for a vehicle includes one or more of the following: time of an event, geo-spatial location of the event, duration of the event, speed of the vehicle, fuel consumption of the vehicle, gear position of the vehicle, brake application, vehicle load information, shock absorber data, acceleration data, or deceleration data.
23. The non-transitory storage device of claim 19 , the operations further comprising:
determining the vehicle traversal criteria based on road geometry for a particular road segment.
24. The non-transitory storage device of claim 23 , wherein the road geometry for a particular road segment is defined by one or more parameters including: cross-falls, center line, superelevation, road width, grade, radius, switch backs, 2-ways, n-ways, or rimpull curves.
25. The non-transitory storage device of claim 19 , the operations further comprising:
generating a flight plan for a UA V, the flight plan based upon an inspection area associated with a road segment or with a location of a determined road condition; and
providing the flight plan to the UA V or to a ground control station operable to control the UAV and perform the flight plan.
26. The non-transitory storage device of claim 19 , the operations further comprising:
generating instructions for the one or more vehicles to obtain sensor data describing actual physical road conditions;
transmitting the instructions to the one or more vehicles;
receiving sensor data obtained from the one or more vehicles, wherein the sensor data is digital images, or LIDAR data; and
updating portions of the road network from the received sensor data.
27. The non-transitory storage device of claim 19 , the operations further comprising:
training a visual classifier with a data set of images of actual physical road conditions;
receiving images different from the data set of images, wherein the images depict a portion of a road; and
identifying with the visual classifier, a type of road condition found in the different images, wherein the road condition is at least one of: pot holes, wash boarding, ruts, ditches, alligator cracking, block cracking, transverse cracking, edge cracks, joint reflection cracks, slippage cracks, depressions, shoving, upheaval, raveling, water pooling, snow, large rocks, obstacles, erosion, plant or foliage overgrowth, uneven surfaces, or protrusions.