An execution device executes an obtaining process that obtains a state of a vehicle, an operating process that operates an electronic device of the vehicle based on the state of the vehicle obtained by the obtaining process and operation data, a performance determining process that determines whether an environmental performance of the vehicle when the electronic device is operated is lower than a determination performance, and a data updating process that updates the operation data so as to increase the environmental performance of the vehicle when the performance determining process determines that the environmental performance of the vehicle is lower than the determination performance.

Methods and systems are presented for improving performance of a vehicle operating in a cruise control mode where a controller adjusts torque output from a vehicle to maintain vehicle speed within a desired range. The methods and systems include adapting a vehicle dynamics model and a vehicle fuel consumption model that provide input to nonlinear model predictive controller.

A method of generating vehicle control data includes: storing, with a storage device, relationship prescription data; operating, with an execution device, an operable portion of an internal combustion engine; acquiring, with the execution device, a detection value from a sensor that detects the state of the vehicle; calculating, with the execution device, a reward; and updating, with the execution device, the relationship prescription data using update mapping determined in advance, the update mapping using the state of the vehicle based on the detection value, an operation amount used to operate the operable portion, and the reward corresponding to the operation as arguments, and returning the relationship prescription data which have been updated such that an expected profit for the reward calculated when the operable portion is operated in accordance with the relationship prescription data increases.

In one embodiment, a computer-implemented method for optimizing a controller of an autonomous driving vehicle (ADV) includes obtaining several samples, each sample having a set of parameters, iteratively performing, until a predetermined condition is satisfied: determining, for each sample, a score according to a configuration of the controller based on the set of parameters of the sample, applying a machine learning model to the samples and corresponding scores to determine a mean function and a variance function, producing a new sample as a minimum of a function of the mean function and the variance function with respect to an input space of the set of parameters, adding the new sample to the several samples, and outputting the new sample as an optimal sample, where parameters of the optimal sample are utilized to configure the controller to autonomously drive the ADV.

An agricultural machine arrangement includes at least one traction machine and at least one attachment device adapted to the traction machine with a driver assistance system optimizing the operation of the traction machine and/or of the respective attachment device. The drive assistance system includes a computing unit and at least one display unit, wherein the computing unit processes information generated by machine-internal sensor systems, external information and information storable in the computing unit. The driver assistance system is structured so that it forms an automatic traction machine adjusting unit and/or an automatic attachment device adjusting unit, wherein the respective automatic adjusting units independently of one another or as a function of one another bring about an “optimization” of the mode of operation of the traction machine and/or of the at least one attachment device.

In various embodiments, methods, systems, and vehicles are provided for executing a lane change for a host vehicle. In various embodiments, one or more sensors obtain sensor data pertaining to target vehicles in proximity to the host vehicle; and a processor at least facilitates: obtaining, using the sensor data, predictions as to future positions and movement of the target vehicle; identifying a plurality of gaps through which the host vehicle may accomplish the lane change, based on the predictions; calculating a cost for each of the plurality of gaps; selecting, a selected gap of the plurality of gaps, having a minimized cost; and executing the lane change for the host vehicle via the selected gap.

A method is for calculating a management setpoint for the consumption of fuel and of electric current by a hybrid motor vehicle including at least one electric motor that is supplied with electric current by a traction battery and an internal combustion engine that runs on fuel. The method includes: dividing a journey into segments; acquiring attributes for each segment; for each segment, acquiring a relationship between the fuel and electrical consumption; determining an optimal consumption point in each acquired relationship to maximize discharging the traction battery over the segments for which use of the internal combustion engine is not authorized, minimize the fuel consumption of the hybrid motor vehicle over the entire journey, and maximize the discharging of the traction battery upon completion of the journey; and developing a setpoint for power management over the entire journey, according to the coordinates of the optimal points.

A device, method, and processor readable storage medium for controlling travel of a vehicle. The device includes a processor and a controller; the processor is configured to set a speed curve of the vehicle according to a relative speed and distance between the vehicle and the front subject, and the controller is configured to control the vehicle to travel according to the set speed curve.

Presented systems and methods facilitate efficient and effective performance of vehicle operations. In one embodiment, a system comprises a user interface, a processor, and a memory. The user interface is configured to convey information associated with operation of a vehicle to and from a user, wherein the information associated with the operation of the vehicle includes information associated with a performance objective. Information associated with the operation of the vehicle can include metric information that expresses a characteristic corresponding to the performance objective. The metric information can be associated with various aspects (e.g., current, future, etc.) of the vehicle operation. Processing by the processor can include comparative analysis of actual performance of the vehicle operation to target values associated with the vehicle operation. The user interface can include a performance indicator. The performance indicator can be configured to convey information associated with the various aspects of vehicle operation.

Systems and methods for autonomous vehicle motion planning are provided. Sensor data describing an environment of an autonomous vehicle and an initial travel path for the autonomous vehicle through the environment can be obtained. A number of trajectories for the autonomous vehicle are generated based on the sensor data and the initial travel path. The trajectories can be evaluated by generating a number of costs for each trajectory. The costs can include a safety cost and a total cost. Each cost is generated by a cost function created in accordance with a number of relational propositions defining desired relationships between the number of costs. A subset of trajectories can be determined from the trajectories based on the safety cost and an optimal trajectory can be determined from the subset of trajectories based on the total cost. The autonomous vehicle can control its motion in accordance with the optimal trajectory.