Target point cloud data about a specific road are extracted from perimeter point cloud data acquired by moving a road surface measurement device along a measurement route and scanning the surroundings thereof. A data storage unit stores trajectory point sequence data that represent, as a plurality of trajectory points, the perimeter point cloud data and a trajectory of the movement of the road surface measurement device. A trajectory point sequence setting unit acquires a trajectory point sequence at equal intervals from the trajectory point sequence data. An extraction area setting unit sets, as extraction areas, a column area Ci and a parallelepiped area Hi that are geometric areas disposed at predetermined positions below a trajectory point Xi. An approximate nearest neighbor search processing unit and an extraction processing unit extract, as the target point cloud data, point data that belong to this extraction area of the perimeter point cloud data.

A method including: recording a first image of a first field region; automatically treating a plant within the first region in-situ based on the first image; automatically verifying the plant treatment with a second image of the first region; and automatically treating a second region concurrently with treatment verification.

A method including: recording a first image of a first field region; automatically treating a plant within the first region in-situ based on the first image; automatically verifying the plant treatment with a second image of the first region; and automatically treating a second region concurrently with treatment verification.

An improved system and method for controlling turf grading equipment is described. The system comprises a turf grading device wherein the turf grading device is moved longitudinally over an area and the turf grading device comprises a left side and a right side with each side being able to independently move vertically and in concert provide a motion of pitch and yaw. The system includes a control system wherein the control system controls movement of the left side and the right side independently. The control system comprises a controller interface capable of selecting between a manual override mode and a module selection mode wherein the module selection mode selected from a grade adjust laser mode, a grade adjust autograde mode, a grade adjust autodepth mode and a grade adjust slope mode

An improved system and method for controlling turf grading equipment is described. The system comprises a turf grading device wherein the turf grading device is moved longitudinally over an area and the turf grading device comprises a left side and a right side with each side being able to independently move vertically and in concert provide a motion of pitch and yaw. The system includes a control system wherein the control system controls movement of the left side and the right side independently. The control system comprises a controller interface capable of selecting between a manual override mode and a module selection mode wherein the module selection mode selected from a grade adjust laser mode, a grade adjust autograde mode, a grade adjust autodepth mode and a grade adjust slope mode

A damage quantifier for a vehicle component formed of composite material is determined based on vehicle sensor data. The vehicle is operated based on a mission determined according to determined damage quantifier.

A method of monitoring quality of a road segment from driver data is provided. The method includes receiving, by a server over a network, the driver data for a road segment from one or more sensing units in one or more vehicles. The method also includes calculating, in one or more computing devices, one or more quantitative pavement surface characteristics of the road segment from the driver data using a probabilistic inverse analysis framework. The method also includes identifying, in the one or more computing devices, one or more quantitative vehicle properties of the one or more vehicles from the driver data using the probabilistic inverse analysis framework. The method then includes estimating, in the one or more computing devices, one or more road quality characteristics of the road segment based on at least one of the quantitative pavement surface characteristics of the road segment and the quantitative vehicle properties of the one or more vehicles.

A method of monitoring quality of a road segment from driver data is provided. The method includes receiving, by a server over a network, the driver data for a road segment from one or more sensing units in one or more vehicles. The method also includes calculating, in one or more computing devices, one or more quantitative pavement surface characteristics of the road segment from the driver data using a probabilistic inverse analysis framework. The method also includes identifying, in the one or more computing devices, one or more quantitative vehicle properties of the one or more vehicles from the driver data using the probabilistic inverse analysis framework. The method then includes estimating, in the one or more computing devices, one or more road quality characteristics of the road segment based on at least one of the quantitative pavement surface characteristics of the road segment and the quantitative vehicle properties of the one or more vehicles.

A road surface evaluation apparatus includes a microprocessor and a memory connected to the microprocessor. The microprocessor is configured to perform: acquiring vehicle information including information indicating a motion of a vehicle while traveling and position information of the vehicle and map information including information on a road on which the vehicle is traveling, deriving roughness information indicating a roughness of road surface and a reliability of the roughness information based on the vehicle information, and outputting the roughness information and the reliability in association with the information on the road.

A road surface evaluation apparatus includes a microprocessor and a memory connected to the microprocessor. The microprocessor is configured to perform: acquiring vehicle information including information indicating a motion of a vehicle while traveling and position information of the vehicle and map information including information on a road on which the vehicle is traveling, deriving roughness information indicating a roughness of road surface and a reliability of the roughness information based on the vehicle information, and outputting the roughness information and the reliability in association with the information on the road.