A method for determining information related to a lane change of a target vehicle includes obtaining information related to an environment of the target vehicle. The information related to the environment relates to a plurality of features of the environment of the target vehicle. The plurality of features are partitioned into two or more groups of features. The method further determines two or more weighting factors for the two or more groups of features. An attention mechanism is used for determining the two or more weighting factors. The method further determines the information related to the lane change of the target vehicle based on the information related to the environment of the target vehicle using a machine-learning network. A weighting of the plurality of features of the environment of the target vehicle within the machine-learning network is based on the two or more weighting factors for the two or more groups of features.

A driving limitation system includes the steps of: identifying a driver by a driver authentication means; extracting information on the identified driver; extracting data relating to vehicle speed limitation according to the driver; extracting data relating to limitation in a vehicle traveling area according to the driver; performing selection or generation of map data regarding a traveling area; generating, on the basis of the extracted data relating to the vehicle speed limitation, vehicle traveling area limitation and the driver, a traveling speed control command within the limitation in the vehicle traveling area in accordance with the physical ability and/or cognitive ability of the driver; transmitting the traveling speed control command to a vehicle speed control means; generating a control command for controlling the vehicle speed on the basis of the safety speed indicated by the vehicle speed control means; and sending the control command to an engine and to a braking system to set the traveling speed of the vehicle to be equal to or lower than the safety speed according to the driver.

A system for recording event data of an autonomous vehicle is disclosed. The present disclosure suggests an EDR system operation scheme suitable for properly investigating the cause of a vehicle accident. Subsystems of an autonomous driving control system may determine whether an event satisfying a preconfigured condition has occurred, and send to an EDR system a trigger signal to trigger recording of data elements related to the event when the occurrence of the event is detected. The EDR system may record data elements that are useful to reconstruct a map of an external environment surrounding an autonomous vehicle at the time of the occurrence of the event or to reconstruct an internal environment of the vehicle.

Provided is an information processing device including: a first acquisition unit configured to acquire a task; a second acquisition unit configured to acquire a processing time of the task; a third acquisition unit configured to acquire a wirelessly communicable period with an external communication device; and a communication control unit configured to control, based on the processing time and the wirelessly communicable period, a wireless communication unit to transmit request information to the external communication device, the request information requesting to process at least a part of the task, in which the communication control unit is configured to control the wireless communication unit to perform direct communication with the external communication device.

In one aspect, an operating method of an intelligence vehicle driving control system is provided that comprises: a collecting step of collecting big data including a wheel torque and a speed for every vehicle type and traffic information; a torque calculating step of learning the big data using a predetermined machine learning model and inputs a specific desired speed profile to the machine learning model to calculate a motor torque of a driving vehicle; and an optimal speed profile deriving step of calculating an energy consumption required to generate the calculated motor torque using a predetermined dynamic programming method and a reverse vehicle dynamic model and deriving an optimal speed profile in which the energy consumption is minimized.

A cooling mechanism apparatus, system, and method for adaptively controlling heat of a vehicle processor, includes a processor configured to control deactivation or activation of an application driving the in-vehicle controller depending on grades of a predetermined functional safety level of the application and to determine an order of the deactivation or the activation of the application when heat is generated in the controller.

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 may include obtaining input information relating to an environment in which an autonomous vehicle (AV) operates, the input information describing at least one of: a state of the AV, an operation of the AV within the environment, a property of the environment, or an object included in the environment. The method may include identifying a first object in the vicinity of the AV based on the obtained input information. The method may include determining a first object rule corresponding to the first object, the first object rule indicating suggested driving behavior for interacting with the first object. The method may include determining a first decision that follows the first object rule and sending an instruction to a control system of the AV, the instruction describing a given operation of the AV responsive to the first object rule according to the first decision.

An ADAS-linked active hood apparatus includes an ADAS device that measures information regarding a driving state of a vehicle and an object and a collision sensor unit that is positioned at a front of the vehicle and measures collision with the object. An active hood lift system (AHLS) raises one end of a hood of the vehicle based on a signal from the collision sensor unit. A controller sets a pedestrian detection threshold (PDT) turn, receives information regarding a plurality of front objects from the ADAS device to compensate for a PDT, compensates for an output reference value of the collision sensor unit based on the compensated PDT, and determines whether collision occurs using the collision sensor unit to adjust pop-up of the AHLS when an output value equal to or greater than the compensated reference value is applied.

A vehicle control system having a subsystem controller for initiating control of a first group of at least one vehicle subsystem in a selected one of a plurality of subsystem control modes each corresponding to one or more different driving conditions; and an estimator module for evaluating at least one driving condition indicator to determine the extent to which each of the subsystem control modes is appropriate and for providing an output indicative of the subsystem control mode that is most appropriate. The estimator module is configured to increase the probability to which the at least one off-road driving mode is determined appropriate in dependence on at least one terrain indicator. In an automatic response mode the subsystem controller selects the most appropriate one of the subsystem control modes for each subsystem of the first group in dependence on the output.