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.

A driving support ECU performs an inter-vehicle-distance control and a following-travel steering control. When an inter-vehicle-distance target vehicle and a following-travel steering target vehicle are the same specific other vehicle as each other, and there is a potential cutting-in vehicle between an own vehicle and the specific other vehicle, the driving support ECU newly specifies the potential cutting-in vehicle as the inter-vehicle-distance target vehicle at a first time point in a cutting-in period and newly specifies the potential cutting-in vehicle as the following-travel steering target vehicle at a second time point in the cutting-in period.

A vehicle control device includes a travel control processing unit configured to control traveling of a host vehicle in accordance with an automated driving mode, where a travel control for the vehicle is performed at least partially automatically by way of automated driving, or a manual driving mode, where traveling of the vehicle is performed based on an operating device which is operated by a vehicle occupant, and an operation amount acquisition unit for acquiring an operation amount by which the operating device is operated by the vehicle occupant. On the basis of the operation amount acquired by the operation amount acquisition unit when switching from the manual driving mode to the automated driving mode, the travel control processing unit sets a first O/R threshold value for the operation amount at a time of canceling at least a portion of the automated driving mode.

The present invention describes a control system, which is adapted and determined to identify motor vehicles driving in front. The control system is at least adapted and determined to capture other motor vehicles participating in the traffic ahead of the own motor vehicle with the at least one environmental sensor. The control system is at least adapted and determined to determine a respective position of the other motor vehicles with the at least one environmental sensor. The control system is at least adapted and determined to determine a trajectory of the own motor vehicle from a current speed and a current yaw rate of the own motor vehicle. The control system is at least adapted and determined to select a single motor vehicle from the other motor vehicles that has the shortest distance to the trajectory, in order to follow this single motor vehicle with the own motor vehicle.

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.

A driving system for at least automated longitudinal guidance for a motor vehicle is designed to determine or receive a virtual acceleration for the motor vehicle, to determine, for each of at least two further road users in the environment of the motor vehicle, the duration until a virtual collision of the motor vehicle with the road user in question, in each case at least in dependence on the virtual acceleration of the motor vehicle, to select one of the at least two further road users as a target object in dependence on the respective durations until a virtual collision of the motor vehicle with the respective road users, which durations were determined for the at least two further road users, and to determine the longitudinal guidance for the motor vehicle in dependence on the road user selected as the control object.

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.

The present disclosure provides systems and methods for an adaptive cruise control that controls a speed of a host vehicle as a passing vehicle quickly passes through a trajectory of the host vehicle. In one form, a system includes at least one processor that is configured to determine that a passing vehicle is entering a trajectory in which the host vehicle is travelling based on a longitudinal speed, a longitudinal acceleration, a lateral speed, and a lateral acceleration of the passing vehicle; determine to maintain a follow distance of an adaptive cruise control system of the host vehicle to at least one target vehicle in the trajectory in which the host vehicle is traveling prior to the passing vehicle entering the trajectory; and to maintain the follow distance to the at least one target vehicle while the passing vehicle passes through the trajectory in which the host vehicle is traveling.