A system for controlling a vehicle by jointly estimating a state of a vehicle and a function of a tire friction of a vehicle traveling on a road uses a particle filter maintaining a set of particles. Each particle includes an estimation of a state of the vehicle, an estimation of probability density function (pdf) of the tire friction function, and a weight indicative of a probability of the particle. The system executes the particle filter to update the particles based on a motion model and a measurement model of the vehicle, control commands moving the vehicle and measurements of the state where the vehicle moved according to the control commands. A control command is generated based on the motion of the vehicle, the weighted combinations of the state of the vehicle and the pdf of the tire friction function weighted according corresponding weights of the particles.

A method for reducing exhaust gas emissions of a drive system of a vehicle including an internal combustion engine, including generating first driving profiles using a computer-implemented machine learning system, the statistical distribution of the first driving profiles being a function of a statistical distribution of second driving profiles measured during real driving operation, calculating respective exhaust gas emissions for the first driving profiles using a computer-implemented modeling of the vehicle or the drive system, adapting the drive system as a function of at least one of the calculated exhaust gas emissions, the adaptation taking place as a function of a level or of a profile of the calculated exhaust gas emissions and of a statistical frequency of the corresponding first driving profile, the statistical frequency of the corresponding first driving profile being ascertained with the aid of the statistical distribution of the first driving profiles.

In various examples, systems and methods are disclosed for generating and/or analyzing candidate paths for a multi-body vehicle—e.g., a tractor trailer truck—based on obstacle avoidance considerations and using an uncertainty representation for the vehicle. The uncertainty representation may correspond to a trailer portion of the multi-body vehicle to account for the variations in rotation of the trailer with respect to the tractor. As such, the uncertainty representation may be indicative of a probability that the trailer of the vehicle occupies locations and/or points in world space. This probability—combined with the probability of locations of actors in the environment—may be used to generate candidate paths that satisfy various constraints—e.g., a minimum stochastic distance—between the vehicle and the actor.

A method for analyzing the distribution of energy expenditures of a motor vehicle from data from a communications network and from parameters of the vehicle includes steps in which the energy expenditures of the vehicle over a journey are calculated, the said energy expenditures are analyzed by comparing them with at least one model of the vehicle simulating the same journey, an energy balance report is formulated on the basis of the analysis of the energy expenditures and of the fuel consumption and the said energy balance report is communicated to an external server.

An information processing device includes a processor to execute a program; and a memory to store the program which, when executed by the processor, performs processes of, calculating braking time of a host vehicle; detecting reaction time of the driver of the host vehicle; specifying longer prediction time as the sum of the braking time and the reaction time becomes longer, the prediction time being a range of a time at which a collision between the host vehicle and a surrounding vehicle is predicted in the future; making a prediction of the position and speed of the host vehicle and the position and speed of the surrounding vehicle at a time point included in the prediction time; and predicting, from a result of the prediction, whether or not the host vehicle and surrounding vehicle will collide.

A vehicle control method in a hybrid electric vehicle including an internal combustion engine, a battery, an electric motor, and a control unit. The method includes estimating an estimated vehicle velocity trajectory, estimating an initial engine power trajectory, simulating state of charge of the battery with the vehicle velocity trajectory and the initial engine power trajectory, estimating an initial terminal co-state value, simulating backward co-state dynamics using the state of charge and vehicle velocity trajectory, to obtain a resulting co-state trajectory. The co-state trajectory is used to solve a minimization control and propagate state of charge dynamics forward in time. The method includes updating control and the co-state trajectory, adjusting the terminal co-state value, and controlling a usage of the battery and the internal combustion engine. The method can be performed to optimize the engine power trajectory to minimize fuel consumption in real time.

A moving machine control program causes a computer to execute: acquiring requested external force regarding an actuator; reading out a reference kinetic model that defines moving machine behavior exhibited when the actuator generates external force corresponding to the requested external force; calculating, as requested moving machine behavior, the moving machine behavior exhibited when the actuator generates the external force corresponding to the requested external force, in accordance with the reference kinetic model; measuring actual moving machine behavior during traveling of the moving machine; correcting the requested external force such that the actual moving machine behavior measured in the measuring step approaches the requested moving machine behavior calculated in the calculating step; and controlling the actuator based on the corrected requested external force.

A vehicle control system for automated driver-assistance includes a model-based controller that generates a first control signal to alter an operation of a plurality of actuators of a vehicle based on a reference trajectory for the vehicle, and a present state of the vehicle and actuators. The vehicle control system further includes a neural network controller that generates a second control signal to alter the operation of the actuators of the vehicle based on a reference trajectory for the vehicle, and the present state of the vehicle and actuators. The vehicle control system further includes a combination module that combines the first control signal and the second control signal to operate the actuators based on a combined signal.

A system for lateral control in-path tracking of an autonomous vehicle includes a lateral controller. The lateral controller controls movement of the autonomous vehicle relative to a path and receives as an input a desired target. An outer control loop of the lateral controller includes a first controller generating an output based on the difference between the desired target and a current position of the autonomous vehicle. An inner control loop of the lateral controller includes a second controller receiving the generated output from the first controller. The inner control loop generates a sideslip angle and a yaw rate, wherein the sideslip angle and the yaw rate are returned to the second controller. The sideslip angle and the yaw rate are used to generate the relative yaw angle and lateral distance, which are returned to the first controller as the current position of the autonomous vehicle.

System, methods, and other embodiments described herein relate to improving controls in a device according to risk. In one embodiment, a method includes, in response to receiving sensor data about a surrounding environment of the device, identifying objects from the sensor data that are present in the surrounding environment. The method includes generating a control sequence for controlling the device according to a risk-sensitivity parameter to navigate toward a destination while considering risk associated with encountering the objects defined by the risk-sensitivity parameter. The method includes controlling the device according to the control sequence.