A method 20 and system 10 monitor road condition, by providing, for portions (14.1-14.m) of a road 12, an approximation 210 of a roughness figure in accordance with a roughness index. The method 10 includes receiving speed data 208 of a first vehicle 16 travelling along each of the portions of the road 12 and receiving, from a measuring device 18 carried on the first vehicle 16, measured acceleration data 204 of the device 18 perpendicular to the road 12 surface. The acceleration data 204 is processed to provide a parameter value 206 relating to the acceleration data 204 for each of the portions of the road 12. A first speed-based conversion equation and the speed data 208 is utilized to convert the parameter 206 into the approximation 210 of a roughness figure for each of the portions of the road 12, in accordance with the roughness index.

A method for estimating a probability distribution of the maximum coefficient of friction (μ) at a current and/or future waypoint (s, s*) of a vehicle. According to the method, a first probability distribution (WV1) for the maximum coefficient of friction (μ) at the waypoint (s) of the vehicle is determined by a Bayesian network from a first data set, which is, or was determined, for one, in particular current, waypoint (s) of the vehicle and which characterizes the maximum coefficient of friction (μ) at the waypoint (s) of the vehicle.

A control device, system, and method for a vehicle to determine a driver's comfort level, configured to receive a first sensor output of a first physiological sensor measuring at least one first physiological feature of the driver, and receive a second sensor output of a second physiological sensor measuring at least one second physiological feature of the driver, create at least one reference data set by recording the first sensor output over a first predetermined reference time period and recording the second sensor output over a second predetermined reference time period being different than the first predetermined reference time period, determine at least one reference index for the comfort level of the driver based on the reference data set, and determine a comfort level index value of the driver, the index value determined as a function of the reference index and the current first and/or second sensor output.

In one embodiment, a system determines a difference in time between a local time source and a time of a GPS sensor. The system determines a max limit in difference and a max recovery increment or max recovery time interval for a smooth time source recovery. The system determines that the difference between the local time source and a time of the GPS sensor to be less than the max limit. The system plans a smooth recovery of the time source to converge the local time source to a time of the GPS sensor within the max recovery time interval. The system generates a timestamp based on the recovered time source to timestamp sensor data for a sensor unit of the ADV.

A road surface condition estimation apparatus (1) is provided with a collecting device (111) for collecting, from vehicle (2), behavior information relating to a behavior of the vehicle; a determining device (112) for determining on the basis of the behavior information whether or not an abnormality condition is satisfied, the abnormality condition being set on the basis of a specific behavior that is expected to be taken by the vehicle when the vehicle encounters a road surface abnormality; and an estimating device (112) for estimating a condition of the road surface on the basis of a determined result of the determining device.

A memory embodies instructions, and a processor is coupled to the memory and is operative by the instructions to facilitate: accessing a source of information regarding farm cultivation techniques; constructing a cultivation knowledge graph by parsing the source of information regarding farm cultivation techniques, using natural language processing; identifying cultivation quality assessment factors by applying machine learning to the cultivation knowledge graph; estimating quality of a farm cultivation task by comparing a stream of real-time data to the cultivation quality assessment factors, wherein the stream of real-time data is related to performance of the farm cultivation task; identifying from the stream of real-time data, using the cultivation knowledge graph, a controllable variable that affects the quality of the farm cultivation task; and improving the quality of the farm cultivation task by facilitating a change in the controllable variable. The controllable variable may be the identity of a tractor operator.

A vehicle control device includes a sensor, a transfer case and a controller. The sensor detects a yaw rate of a vehicle. The transfer case distributes a drive force from a motive power source to front wheels and rear wheels. The controller determines a road surface friction coefficient to be low upon detecting an absolute value of the yaw rate during forward travel of the vehicle to be equal to or greater than a prescribed value other than zero, determines the road surface friction coefficient to be high upon detecting the absolute value is less than prescribed value, and controls a distribution amount of the transfer case based on a determination result of the road surface friction coefficient.

A method 20 and system 10 monitor road condition, by providing, for portions (14.1-14.m) of a road 12, an approximation 210 of a roughness figure in accordance with a roughness index. The method 10 includes receiving speed data 208 of a first vehicle 16 travelling along each of the portions of the road 12 and receiving, from a measuring device 18 carried on the first vehicle 16, measured acceleration data 204 of the device 18 perpendicular to the road 12 surface. The acceleration data 204 is processed to provide a parameter value 206 relating to the acceleration data 204 for each of the portions of the road 12. A first speed-based conversion equation and the speed data 208 is utilized to convert the parameter 206 into the approximation 210 of a roughness figure for each of the portions of the road 12, in accordance with the roughness index.

A vehicle control device includes sensors that detect pulse signals corresponding to rotation of front wheels and of rear wheels of a vehicle, and a controller that increases a count at a rise and a fall of the pulse signals. The controller estimates a road surface friction coefficient based on a time rate of change of a difference between a value counted up using the front wheels and a value counted up using the rear wheels.

A vehicle control device includes sensors that detect pulse signals corresponding to rotation of front wheels and of rear wheels of a vehicle, and a controller that increases a count at a rise and a fall of the pulse signals. The controller estimates a road surface friction coefficient based on a time rate of change of a difference between a value counted up using the front wheels and a value counted up using the rear wheels.