The present invention is to obtain a control device and a control method capable of appropriately assisting driving of a straddle-type vehicle by a rider.

In a control device (12) and a control method of the present invention, an acquisition section of the control device (12) configured to control an operation of a straddle-type vehicle (10) acquires prediction information about a future lane change by a preceding vehicle that travels ahead of the straddle-type vehicle (10), and a control section of the control device (12) causes the straddle-type vehicle (10) to execute a safety operation (for example, causes a notification device (15) to issue a warning of the lane change to the rider), when the prediction information satisfies a determination criterion during a slipping-through traveling of the straddle-type vehicle (10).

A method for modifying a queue of vehicles. In one embodiment, the method includes at least one computer processor determining respective distance values between a first vehicle and one or more adjacent vehicles within a queue of vehicles. The method further includes determining a threshold distance value that corresponds to a distance required to extract the first vehicle from within the queue of vehicles. The method further includes determining a change of position corresponding to at least one adjacent vehicle to the first vehicle within the queue of vehicles based on the determined respective distance values, wherein the determined change in position moves the at least one adjacent vehicle to a distance value from the first vehicle that exceed the threshold distance value. The method further includes transmitting respective requests to the at least one adjacent vehicle to move to the determined change of position.

To obtain a rider-assistance system capable of providing a rider of a straddle-type vehicle with a sense of comfort and safety during a turn, and a control method for such a rider-assistance system.

The present invention provides the rider-assistance system that assists with driving by the rider of the straddle-type vehicle and includes a controller. The controller includes: an object identification section that identifies an object approaching a side of the straddle-type vehicle on the basis of output of a communication device that wirelessly receives information output from infrastructure equipment or another vehicle; a body position information acquisition section that acquires position information of at least a part of a body of the rider on the turning straddle-type vehicle; a collision possibility determination section that determines a collision possibility of the rider with the object identified by the object identification section on the basis of the position information acquired by the body position information acquisition section; and a safety operation performing section that causes the rider-assistance system to perform safety operation in the case where the collision possibility determination section determines that the collision possibility is high.

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 vehicle is operable in a first operating mode in which the vehicle travels autonomously inside the traffic lane based on a detection of lane markings of a traffic lane and in a second operating mode in which the vehicle autonomously follows a vehicle driving in front while ignoring lane markings, and a method of its operation includes operating the vehicle in a first of the two operating modes, detecting a vehicle environment, and switching from the first operating mode to the other of the two operating modes as a function of the detected vehicle environment. A device can execute the method and a computer program can be executed by a device for performing the method.

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.

A system for navigating a vehicle automatically from a current location to a destination location without a human operator is provided. The system of the vehicle includes a global positioning system (GPS) for identifying a vehicle location and a communications system for communicating with a server of a cloud system. The server is configured to identify that the vehicle location is near or at a parking location. The communications system is configured to receive mapping data for the parking location from the server, and the mapping data is at least in part used to find a path at the parking location to avoid a collision of the vehicle with at least one physical object when the vehicle is automatically moved at the parking location. The mapping data is processed by electronics of the vehicle so that when the vehicle is automatically moved collision with the at least one physical object is avoided and the electronics of the vehicle is configured to process a combination of sensor data obtained by sensors of the vehicle. The processing of the sensor data uses image data obtained from one or more cameras and light data obtained from one or more optical sensors.

Vehicles may have the capability to navigate according to various levels of autonomous capabilities, the vehicle having a different set of autonomous competencies at each level. In certain situations, the vehicle may shift from one level of autonomous capability to another. The shift may require more or less driving responsibility from a human operator. Sensors inside the vehicle collect human operator parameters to determine an alertness level of the human operator. An alertness level is determined based on the human operator parameters and other data including historical data or human operator-specific data. Notifications are presented to the user based on the determined alertness level that are more or less intrusive based on the alertness level of the human operator and on the urgency of an impending change to autonomous capabilities. Notifications may be tailored to specific human operators based on human operator preference and historical performance.

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.

A vehicle driving assistance apparatus predicts (i) a first consumed energy amount corresponding to a consumed energy amount consumed by a driving apparatus of an own vehicle when executing a first following control and (ii) a second consumed energy amount corresponding to the consumed energy amount consumed by the driving apparatus of the own vehicle when executing the second following control. The apparatus executes the second following control when the second consumed energy amount is smaller than the first consumed energy amount. On the other hand, the apparatus executes the first following control when the second consumed energy amount is equal to or greater than the first consumed energy amount.