A method for operating a vehicle includes obtaining personal data associated with a user of the vehicle, obtaining operating data for the vehicle, determining a driver behavior recommendation to reduce emissions of the vehicle based at least in part on the personal data and the operating data, and presenting the driver behavior recommendation on an interface.

A method, computer system, and a computer program product for allocating unused computing resources of a plurality autonomous vehicles is provided. The present invention may include receiving at least one computation request from at least one user. The present invention may include identifying the unused computing resources of the plurality of autonomous vehicles. The present invention may include predicting an amount of computational activities performable by the unused computing resources of the plurality of autonomous vehicles. The present invention may include allocating the unused computing resources of the plurality of autonomous vehicles to perform the predicted amount of computational activities, wherein the received at least one computation request is allocated at least a portion of the unused computing resources. The present invention may include distributing a processing load to at least one of the plurality of autonomous vehicles.

A planning system for an autonomous work vehicle system includes a controller having a memory and a processor. The controller is configured to determine an action to be performed by the autonomous work vehicle system and an action location of the action in a work area, determine a transition operation associated with the action that enables the autonomous work vehicle system to perform the action at the action location, and generate a plan for operation of the autonomous work vehicle system. The plan includes the action, the action location, and the transition operation.

A planning system for an autonomous work vehicle system includes a controller having a memory and a processor. The controller is configured to provide an indication of a work area for operation of the autonomous work vehicle system, identify a plurality of zones of the work area, and identify one or more rules for each respective zone of the plurality of zones. The one or more rules define operation of the autonomous work vehicle system in the respective zone. The controller is configured to generate a plan for operation of the autonomous work vehicle system based on the plurality of zones and the one or more rules for each respective zone of the plurality of zones.

To realize an apparatus and a method for receiving a recognition level of an own vehicle from another vehicle and/or an infrastructure facility and performing control to improve a recognition level. A recognition level of the own vehicle calculated by another vehicle or the infrastructure facility is received, and control for improvement of a recognition level of the own vehicle is executed in accordance with the received recognition level. As the control for improvement of a recognition level of the own vehicle, any process is executed among: (a) a process of reducing a traveling speed or stopping; (b) a process of turning on a light; and (c) a process of changing transmission information from the own vehicle. As the process of changing transmission information, a process of adding, to the transmission information, vehicle location information, vehicle speed information, vehicle size information, or reference marker position information is executed.

Embodiments provide extracting information respectively corresponding to predetermined dimensions from environment information corresponding to an unmanned driving environment. In some embodiments, the information respectively corresponding to the dimensions is input into an identification model to obtain a driving feature. Then a risk value representing a driving risk degree of an unmanned device is determined, and a maximum variation of the information corresponding to at least one dimension is determined when a variation of the driving feature is less than a predetermined threshold. A maximum variation of the information corresponding to each dimension is used as a risk contribution feature. A variation representative value of the information corresponding to each dimension is determined from the risk contribution feature. According to the variation representative values of the dimensions, a driving risk factor corresponding to the risk value is determined based on the driving feature.

In a vehicle control device, a switching hyperplane generation unit generates a switching hyperplane based on a travel state of a vehicle and cornering stiffness dependent on a travel surface state as a state of a road surface on which the vehicle travels. A deviation computation unit calculates a deviation between a target trajectory and an actual trajectory of the vehicle. A state estimation unit estimates a state to be controlled of the vehicle based on the deviation calculated by the deviation computation unit. A target steering angle and acceleration/deceleration computation unit calculates a target steering angle and a target acceleration/deceleration rate of the vehicle based on the switching hyperplane generated by the switching hyperplane generation unit and an estimated state as the state estimated by the state estimation unit.

Advanced vehicle control systems are disclosed. Within a vehicle system having several subsystem controllers dedicated to separate tasks in the vehicle, the subsystem controllers may use supplied control parameters. In this context, a centralized optimization unit is configured to receive prediction data, determine, within a prediction horizon, a modification to at least one supplied control parameter using the prediction data; and communicate the modification to the at least one supplied control parameter to at least one subsystem control unit.

A travel control system for a vehicle includes a vehicle speed calculator, a mode continuation determiner, and a mode continuing unit. The vehicle speed calculator evaluates a level of worsening of a traveling environment and calculates a first vehicle speed on the basis of the level of the worsening of the traveling environment. The mode continuation determiner determines whether it is possible to continue with driving assist control in the second driving assist mode by comparing a second vehicle speed in the second driving assist mode with the first vehicle speed. When it is not possible to continue with the driving assist control in the second driving assist mode, the mode continuing unit lowers the second vehicle speed in the second driving assist mode to the first vehicle speed to allow the driving assist control in the second driving assist mode to continue.

A method for supplying data for an at least partially automated vehicle. The method including: detecting the sensor data of an infrastructure; fusing the sensor data to form an environment model; wireless transmission of the environment model data to the at least partially automated vehicle; and wireless transmission of context data, relating to a location of the at least partially automated vehicle, to the at least partially automated vehicle, it being possible to evaluate the environment model data in a defined manner with the aid of the context data.