Disclosed is a vehicle that may include a frame to support a power system, such as an engine, and one or more surface supports, such as wheels, to support the frame. The engine may include an internal combustion engine and a fuel supply system therefore. The engine may provide power to drive the wheels.

A driving support ECU performs an inter-vehicle-distance control and a following-travel steering control. When a front vehicle has been specified as both of a inter-vehicle-distance target vehicle and a following-travel steering target vehicle, and there is a potential cutting-in vehicle which is another vehicle having a possibility of cutting in between an own vehicle and a front vehicle, the driving support ECU newly specifies the potential cutting-in vehicle as the inter-vehicle-distance target vehicle and then, newly specifies the potential cutting-in vehicle as the following-travel steering target vehicle, in the cutting-in period.

A device for setting a target vehicle that sets a target vehicle to be subjected to driving assistance control of a host vehicle includes: a detection signal acquisition device capable of acquiring a first detection signal representing an object by an image, and a second detection signal representing the object by a reflection point; and setting control unit, which determines whether to set a forward object as a target vehicle, wherein if a movement history is not associated with the forward object, and a combination history is associated with the forward object, then as a selection threshold of a first determination parameter for determining whether to set the forward object as the target vehicle, a selection threshold is used such that the forward object is less likely to be selected as the target vehicle than with the selection threshold which would be used if a movement history is associated with the forward object.

A braking control apparatus to be installed an electric vehicle includes an acceleration and deceleration operation member, a controller, and a recognizer. The acceleration and deceleration operation member receives an acceleration request in accordance with an operation amount in a first direction from a neutral position, and receive a deceleration request in accordance with an operation amount in a second direction from the neutral position. The controller controls an amount of power regenerated by a rotary electric machine driven by wheels in accordance with the operation amount in the second direction. The recognizer recognizes a preceding vehicle traveling ahead of the electric vehicle. Upon detection of the preceding vehicle at a relative distance from the electric vehicle that is equal to or less than a threshold, the controller performs braking suppression control to decrease the amount of power regenerated in accordance with the operation amount in the second direction.

A braking control method for a braking system of a vehicle is provided according to an exemplary embodiment of the disclosure. The braking control method comprises: obtaining a total braking distance and a first speed of the vehicle; obtaining braking delay information related to the braking system, wherein the braking delay information includes first time information and second time information, the first time information reflects a delay time of a braking signal, and the second time information reflects a preparation time for performing a braking operation according to the braking signal by the braking system; obtaining deceleration information according to the total braking distance, the first speed and the braking delay information; generating the braking signal according to the deceleration information; and performing the braking operation according to the braking signal.

Systems and methods for implementing one or more autonomous features for autonomous and semi-autonomous control of one or more vehicles are provided. More specifically, image data may be obtained from an image acquisition device and processed utilizing one or more machine learning models to identify, track, and extract one or more features of the image utilized in decision making processes for providing steering angle and/or acceleration/deceleration input to one or more vehicle controllers. In some instances, techniques may be employed such that the autonomous and semi-autonomous control of a vehicle may change between vehicle follow and lane follow modes. In some instances, at least a portion of the machine learning model may be updated based on one or more conditions.

Systems and methods for implementing one or more autonomous features for autonomous and semi-autonomous control of one or more vehicles are provided. More specifically, image data may be obtained from an image acquisition device and processed utilizing one or more machine learning models to identify, track, and extract one or more features of the image utilized in decision making processes for providing steering angle and/or acceleration/deceleration input to one or more vehicle controllers. In some instances, techniques may be employed such that the autonomous and semi-autonomous control of a vehicle may change between vehicle follow and lane follow modes. In some instances, at least a portion of the machine learning model may be updated based on one or more conditions.

Systems and methods for implementing one or more autonomous features for autonomous and semi-autonomous control of one or more vehicles are provided. More specifically, image data may be obtained from an image acquisition device and processed utilizing one or more machine learning models to identify, track, and extract one or more features of the image utilized in decision making processes for providing steering angle and/or acceleration/deceleration input to one or more vehicle controllers. In some instances, techniques may be employed such that the autonomous and semi-autonomous control of a vehicle may change between vehicle follow and lane follow modes. In some instances, at least a portion of the machine learning model may be updated based on one or more conditions.

Systems and methods for implementing one or more autonomous features for autonomous and semi-autonomous control of one or more vehicles are provided. More specifically, image data may be obtained from an image acquisition device and processed utilizing one or more machine learning models to identify, track, and extract one or more features of the image utilized in decision making processes for providing steering angle and/or acceleration/deceleration input to one or more vehicle controllers. In some instances, techniques may be employed such that the autonomous and semi-autonomous control of a vehicle may change between vehicle follow and lane follow modes. In some instances, at least a portion of the machine learning model may be updated based on one or more conditions.

Systems and methods for implementing one or more autonomous features for autonomous and semi-autonomous control of one or more vehicles are provided. More specifically, image data may be obtained from an image acquisition device and processed utilizing one or more machine learning models to identify, track, and extract one or more features of the image utilized in decision making processes for providing steering angle and/or acceleration/deceleration input to one or more vehicle controllers. In some instances, techniques may be employed such that the autonomous and semi-autonomous control of a vehicle may change between vehicle follow and lane follow modes. In some instances, at least a portion of the machine learning model may be updated based on one or more conditions.