The disclosure describes embodiments for modifying a whether an ego vehicle changes lanes to a target lane at a target time based on a payload of a Vehicle-to-Everything (V2X) message originated by a remote vehicle. In some embodiments, a method includes determining, based on the payload, whether the remote vehicle is changing lanes to the target lane at the target time. The method includes determining that the ego vehicle is changing lanes to the target lane at approximately the target time. The method includes estimating that the ego vehicle and the remote vehicle will collide at the target lane at the target time. The method includes modifying an operation of a vehicle component of the ego vehicle so that the ego vehicle does not change lanes to the target lane at the target time.

A method of controlling driving of a vehicle using an in-wheel system includes: calculating a time to collision (TTC) by dividing a distance between the vehicle and an obstacle located in front of the vehicle by relative velocity; determining whether the vehicle enters a braking avoidance section, based on the calculated TTC; and generating, by a motor mounted in each wheel, braking force of a brake by an amount of shortage of braking force of the brake compared with a demanded braking force if the vehicle enters the braking avoidance section.

The present disclosure relates to an autonomous emergency braking system and a method. More specifically, the autonomous emergency braking system according to the present disclosure includes: a sensor that includes a gravity sensor detecting a force of gravity applied to a host vehicle and a vehicle-speed sensor detecting a vehicle speed of the host vehicle; an inclination determiner that determines an inclination of a road surface on which the host vehicle is traveling based on the vehicle speed of the host vehicle and the force of gravity; and a controller that adjusts an AEB warning time based on the determined inclination of the road surface.

A vehicle control device includes a travel environment recognition device configured to recognize travel environment, a vehicle travel state detection device, and an autonomous/manual driving mode controller. The autonomous/manual driving mode controller includes a learning correction unit configured to store at least one of a plurality of control parameters indicating the vehicle travel state by operation of the driver in the autonomous driving mode, and to correct the control parameter in the autonomous driving mode according to the stored control parameter. When the stored control parameter is the control parameter changed by an operation by the driver midway in passing or after passing, the learning correction unit is configured to correct the control parameter in the autonomous driving mode so that an acceleration level or the deceleration level is increased midway in passing or after passing.

An electronic device for stopping a vehicle can be activated automatically in the presence of an emergency stopping instruction. A module calculates at least one automatic movement setpoint of the vehicle in order to follow an emergency stopping trajectory comprising a plurality of successive emergency stopping vectors each associated with a separate segment of a portion of the successive segments of the predefined trajectory, from at least for each emergency stopping vector: a stored last actual movement vector of the autonomous vehicle, the predefined movement trajectory of the autonomous vehicle, and a stored last location datum of the autonomous vehicle, and delivered by at least one sensor of the autonomous vehicle. An emergency steering module is capable of steering the vehicle according to said at least one setpoint.

A controller and a control method are capable of improving safety by automatic emergency deceleration action while suppressing a motorcycle from falling over. One arrangement also obtains a brake system that includes such a controller. In the controller, the control method, and the brake system, a control mode that causes the motorcycle to take the automatic emergency deceleration action is initiated in response to trigger information generated in accordance with peripheral environment of the motorcycle. In the control mode, automatic emergency deceleration that is deceleration of the motorcycle generated by the automatic emergency deceleration action is controlled in accordance with a lean angle of the motorcycle.

Methods, apparatus, systems, and articles of manufacture are disclosed herein that mitigate hard-braking events. An example apparatus includes a world generator to generate a deep learning model to identify and categorize an object in a proximity of a vehicle, a data analyzer to determine a danger level associated with the object, the danger level indicative of a likelihood of a collision between the vehicle and the object, a vehicle response determiner to determine, based on the danger level, a response of the vehicle to avoid a collision with the object, and an instruction generator to transmit instructions to a steering system or a braking system of the vehicle based on the determined vehicle response.

A braking control apparatus for controlling a braking operation for an own vehicle is configured to: acquire information on an object detected around the own vehicle; calculate, when a collision between the own vehicle and the object is predicted based on both an estimated route of the object estimated based on the acquired information on the object and an estimated route of the own vehicle, a collision range in the own vehicle at a collision timing between the own vehicle and the object or a collision range in the object at the collision timing; and control, according to a positional relationship between a predetermined braking-unrequired range in the own vehicle and the calculated collision range in the own vehicle or a positional relationship between a predetermined braking-unrequired range in the object and the calculated collision range in the object, whether to perform the braking operation for the own vehicle.

A method for the automated control of the longitudinal movement of a motor vehicle having an automated positive acceleration process in a longitudinal direction of the vehicle and an automated deceleration in the longitudinal direction of the vehicle. An acceleration variable is determined based on a jerk value and limited in terms of absolute value. And the jerk value is in turn determined in a driving mode in which, starting from a vehicle actual longitudinal speed and a vehicle actual longitudinal acceleration, the motor vehicle is adjusted to a predeterminable vehicle longitudinal speed taking into account a predeterminable maximum positive driving mode vehicle longitudinal acceleration, a predeterminable maximum driving mode vehicle longitudinal deceleration and at least one predeterminable driving operating mode jerk absolute value which limits the jerk.

Systems and methods are disclosed for estimating slipperiness of a road surface. This estimate may be obtained using an image sensor mounted on a vehicle. The estimated road slipperiness may be utilized when calculating a risk index for the road, or for an area including the road. If a predetermined threshold for slipperiness is exceeded, corrective actions may be taken. For instance, warnings may be generated to human drivers that are in control of driving vehicle, and autonomous vehicles may automatically adjust vehicle speed based upon road slipperiness detected.