A method for operating a control device of a motor vehicle driving by automation. The method includes determining a location of the motor vehicle, and acquiring driving-environment data of the motor vehicle, a control characteristic of the control device of the motor vehicle being formed in such a way that a driving behavior of at least one other road user is influenced in defined manner.

A simulating method for an electric vehicle (EV) includes creating a simulation model associating a plurality of target behaviors of a target vehicle with the EV, obtaining a plurality of vehicle parameters of the EV to generate a set of EV control parameters, obtaining a plurality of configuration parameters of the target vehicle, based on the set of EV control parameters and the plurality of configuration parameters of the target vehicle, using the simulation model to provide a set of simulated target-vehicle controls, where the simulation model is a neural network trained to reflect a relationship between the set of EV control parameters and the set of simulated target-vehicle controls, and outputting the set of simulated target-vehicle controls to the EV, such that the EV is controlled to achieve the plurality of target behaviors of the target vehicle based on the set of simulated target-vehicle controls.

A method of operating a vehicle, such as an autonomous vehicle, includes the steps of receiving a message from roadside infrastructure via an electronic receiver and providing, by a computer system in communication with said electronic receiver, instructions based on the message to automatically implement countermeasure behavior by a vehicle system. Additionally or alternatively, the method may include the steps of receiving a message from another vehicle via an electronic receiver and providing, by a computer system in communication with said electronic receiver, instructions based on the message to automatically implement countermeasure behavior by a vehicle system.

A message can be received from a first object. The message can include information about a position of the first object. An electromagnetic energy can be caused to be transmitted in a direction of the first object at a time in which the first object is in motion. A reflection of the electromagnetic energy can be received from the direction of the first object. A first possible position of a second object can be determined based on the reflection of the electromagnetic energy having traveled entirely along a path defined by a line formed by the first object and the vehicle. A second possible position of the second object can be determined based on the reflection of the electromagnetic energy having traveled along a path outside of the line. An actual position of the second object being the second possible position of the second object can be determined.

Vehicle behavioral monitoring includes determining a measure of distraction of the operator of a target vehicle, characterizing the type or category of distraction, determining level of risk that the target vehicle poses, and invoking various responses including host vehicle notifications and evasive actions and external notification and information sharing.

An information processing device (20) includes a route acquisition unit (202) and an automatic driving section determination unit (204). The route acquisition unit (202) acquires route information indicating a moving route of a mobile body. The automatic driving section determination unit (204) acquires adaptation coefficients for a plurality of sections included in the moving route indicated by the route information, with reference to an adaptation coefficient storage unit (206) that stores automatic driving adaptation coefficients set for the respective sections. Further, the automatic driving section determination unit (204) determines the automatic driving sections of the mobile body in the moving route, based on the acquired adaptation coefficients.

An information processing system, including: a surveillance camera that detects a plurality of obstacles in the vicinity of a specific vehicle; a first determiner that determines whether an unidentified obstacle, which is included in the plurality of obstacles and is not visible from the specific vehicle, is present based on first information regarding the plurality of obstacles detected by the surveillance camera and vehicle information indicating the specific vehicle; and a first communicator that outputs information indicating the unidentified obstacle to the specific vehicle when the first determiner determines that the unidentified obstacle is present.

Techniques to predict object behavior in an environment are discussed herein. For example, such techniques may include determining a trajectory of the object, determining an intent of the trajectory, and sending the trajectory and the intent to a vehicle computing system to control an autonomous vehicle. The vehicle computing system may implement a machine learned model to process data such as sensor data and map data. The machine learned model can associate different intentions of an object in an environment with different trajectories. A vehicle, such as an autonomous vehicle, can be controlled to traverse an environment based on object's intentions and trajectories.

An electronic control system for vehicle includes a center device that manages a program update of a vehicle, and a vehicular master device that is communicable with the center device. The center device, responsive to a user giving approval for program update by using a device not being a possession owned by the user, receives approval information of the user, and stores and manages the approval information in association with vehicle information of the user. The center device transmits the approval information to the user's vehicle side. When the vehicular master device receives the approval information, the vehicular master device performs rewriting of the program.

Embodiments herein can determine an optimal route for an autonomous electric vehicle. The system may score viable routes between the start and end locations of a trip using a numeric or other scale that denotes how viable the route is for autonomy. The score is adjusted using a variety of factors where a learning process leverages both offline and online data. The scored routes are not based simply on the shortest distance between the start and end points but determine the best route based on the driving context for the vehicle and the user.