Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for determining causal models for controlling environments. One of the methods includes obtaining data specifying baseline probability distributions for each of a plurality of controllable elements; maintaining a causal model; repeatedly performing the following: selecting control settings for the environment based on the causal model and values for a particular internal parameter of the control system that are sampled from a range of possible values; selecting control settings for the environment based on the baseline probability distributions; monitoring environment responses to the control settings selected based on the causal model and the control settings selected based on the baseline probability distributions; determining, for each of the possible values, a measure of a difference between a current system performance and a baseline system performance; and updating how frequently each of the possible values is sampled.

A road surface condition prediction system includes a collector which collects pieces of moisture information on moisture on a road surface obtained by detecting the moisture on the road surface of a road on which moving bodies travel, and pieces of position information each indicating a position on the road surface at which the moisture is detected, one or more of the pieces of moisture information and one or more of the pieces of position information being collected from each of the moving bodies; and a predictor which predicts a moisture condition of a target road surface at a time after a time at which moisture on the target road surface is detected, based on moisture information obtained by detecting the moisture on the target road surface, the target road surface being a road surface at a position indicated by at least one of the pieces of position information.

A road surface condition prediction system includes a collector which collects pieces of moisture information on moisture on a road surface obtained by detecting the moisture on the road surface of a road on which moving bodies travel, and pieces of position information each indicating a position on the road surface at which the moisture is detected, one or more of the pieces of moisture information and one or more of the pieces of position information being collected from each of the moving bodies; and a predictor which predicts a moisture condition of a target road surface at a time after a time at which moisture on the target road surface is detected, based on moisture information obtained by detecting the moisture on the target road surface, the target road surface being a road surface at a position indicated by at least one of the pieces of position information.

A system and methods for multivariant learning and optimization repeatedly generate self-organized experimental units (SOEUs) based on the one or more assumptions for a randomized multivariate comparison of process decisions to be provided to users of a system. The SOEUs are injected into the system to generate quantified inferences about the process decisions. Responsive to injecting the SOEUs, at least one confidence interval is identified within the quantified inferences, and the SOEUs are iteratively modified based on the at least one confidence interval to identify at least one causal interaction of the process decisions within the system. The causal interaction can be used for testing, diagnosis, and optimization of the system performance.

A driving apparatus and a driving control method is provided. The driving apparatus includes vehicle body; a plurality of vehicle propulsion bodies connected to the vehicle body and configured to enable the vehicle body to travel; at least one sensor configured to sense surroundings of the vehicle body; and a controller configured to control the vehicle body to travel by referring to at least one sensing result of the at least one sensor, wherein the at least one sensor includes a vision generation sensor configured to generate vision information regarding a travel route of the vehicle body and wherein the controller is further configured to determine a road surface condition of the travel route by referring to the vision information, and adjust a difference in driving speed between the plurality of vehicle propulsion bodies according to the road surface condition.

A driving apparatus and a driving control method is provided. The driving apparatus includes vehicle body; a plurality of vehicle propulsion bodies connected to the vehicle body and configured to enable the vehicle body to travel; at least one sensor configured to sense surroundings of the vehicle body; and a controller configured to control the vehicle body to travel by referring to at least one sensing result of the at least one sensor, wherein the at least one sensor includes a vision generation sensor configured to generate vision information regarding a travel route of the vehicle body and wherein the controller is further configured to determine a road surface condition of the travel route by referring to the vision information, and adjust a difference in driving speed between the plurality of vehicle propulsion bodies according to the road surface condition.

In a method for operating a vehicle which has a communication interface, a position-determining unit and at least one road data set-determining unit, a database road data set is received by the communication interface, which data set is presumably made available by the database which is arranged externally with respect to the vehicle and which is representative of the position-dependent, road-related property. Depending on the database road data set and a vehicle road data set which is assigned thereto in terms of position, a trust characteristic value is determined which is representative of the level of trust in further database road datasets which relate to predefinable positions of the vehicle.

Provided is control apparatus for an electric vehicle, which is capable of suppressing simultaneous slip of front and rear wheels. The control apparatus for an electric vehicle controls a front electric motor and a rear electric motor so that a difference between a torque command value of the front electric motor and a torque command value of the rear electric motor is larger than a predetermined value.

Provided is control apparatus for an electric vehicle, which is capable of suppressing simultaneous slip of front and rear wheels. The control apparatus for an electric vehicle controls a front electric motor and a rear electric motor so that a difference between a torque command value of the front electric motor and a torque command value of the rear electric motor is larger than a predetermined value.

A device for estimating a state of a road surface on which a tire is running, the device including: an acceleration sensor 11 installed in the tire; an acceleration information acquiring means 12, 13, 14 that acquires acceleration information input to the tire from an output of the acceleration sensor 11; a storage means 15 that stores acceleration information of each road surface roughness set in advance; and a road surface state estimating means 16 that compares the acquired acceleration information with the acceleration information of each road surface roughness stored in the storage means 15 so as to estimate the state of the road surface.