Controlling autonomous vehicle (AV) functions includes receiving occupant identification data from device(s) of an AV and identifying occupant(s) of the AV based on the received occupant identification data, determining a respective prioritization level for each occupant of the occupant(s), the prioritization level for each occupant dictating priority of the AV in performing commands provided to the AV by that occupant to control AV functions, receiving, from an occupant, input of a command for performance by the AV to control a function of the AV, determining whether to perform the command to control the function of the AV based on the prioritization level for the occupant, and performing processing based on the determining whether to perform the command.

A signal processing module is provided. The signal processing module includes a function weight table that stores weights for each first sensor for an autonomous driving mode and an ADAS driving mode and selects and outputs only the weight for each first sensor, a first weight applying device that generates a function weight application signal by applying the weight for each first sensor to sensing information of sensors for sensing an object, a road environment determining device that determines a road environment based on the sensing information of the sensors for sensing the object, a road environment weight table that stores weights for each second sensor for a road environment and selects and outputs an weight for each second sensor, and a second weight applying device that outputs a dataset by applying the weight for each second sensor to the function weight application signal.

A signal processing module is provided. The signal processing module includes a function weight table that stores weights for each first sensor for an autonomous driving mode and an ADAS driving mode and selects and outputs only the weight for each first sensor, a first weight applying device that generates a function weight application signal by applying the weight for each first sensor to sensing information of sensors for sensing an object, a road environment determining device that determines a road environment based on the sensing information of the sensors for sensing the object, a road environment weight table that stores weights for each second sensor for a road environment and selects and outputs an weight for each second sensor, and a second weight applying device that outputs a dataset by applying the weight for each second sensor to the function weight application signal.

The present disclosure discloses a vehicle driving control system including a road information device calculating road information of a curved road on which the vehicle drives; a road surface information device calculating road surface condition of a road; a neighboring vehicle information device calculating location information or movement information of the neighboring vehicle located close to the vehicle; and a computing device computing a target speed based on the road information calculated by the road information device, the road surface condition calculated by the road surface information device, and the location information or movement information of the neighboring vehicle calculated by the neighboring vehicle information device.

Methods and systems for vehicles are provided for trailer steering assistance. The system includes a computer system onboard the vehicle and configured to, by a processor: monitor a front steering angle of the vehicle and a hitch angle of the vehicle as the vehicle and the vehicle trailer move in the reverse direction, dynamically adjust a rear steering angle of the vehicle as the vehicle and the trailer move in the reverse direction based on the front steering angle and the hitch angle to match the rear steering angle to the front steering angle while the hitch angle less than a predetermined hitch angle, and dynamically adjust the rear steering angle of the vehicle as the vehicle and the trailer move in the reverse direction based on the front steering angle and the hitch angle to maintain the hitch angle at the predetermined hitch angle.

An unmanned device acquires sensing data of surrounding obstacles; determines, for each obstacle, at least one predicted track of the obstacle in a future period of time based on the sensing data; determines, for each moment in the future period of time and according to the predicted track corresponding to the obstacle, a collision probability that a collision with the obstacle occurs at each position in a target region at the moment; and determines a global collision probability that the collision with the obstacle occurs in the entire target region at the moment. According to the global collision probability corresponding to each obstacle at each moment, the unmanned device controls the unmanned device in the future period of time.

In an operational mode of a vehicle, a vehicle system can be influenced by a mix of autonomous control inputs and manual control inputs. A first weight can be assigned to manual control inputs, and a second weight can be assigned to autonomous control inputs. While the vehicle is being operated primarily by manual inputs from a human driver, it can be determined whether the human driver of the vehicle has made a driving error and whether a current driving environment of the vehicle is a low complexity driving environment. Responsive to determining that the human driver of the vehicle has made the driving error and to determining that the current driving environment of the vehicle is a low complexity driving environment, the second weight assigned to autonomous control inputs can be automatically increased. Autonomous control inputs can influence the vehicle system in an amount corresponding to the second weight.

Systems and methods for implementing and/or validating a model reference adaptive control (MRAC) for human-in-the-loop control of a vehicle system. A first operator model is applied to a first feedback-loop-based MRAC scheme, wherein the first operator model is configured to adjust a control command provided as an input to the MRAC scheme based at least in part on an actual action of the vehicle system and a reference action for the vehicle system with a time-delay. A stability limit of a first operating parameter is determined for the MRAC scheme based on the application of the first operator model to the first feedback-loop-based MRAC scheme. The MRAC scheme is validated in response to determining that expected operating conditions of the first operating parameter are within the determined stability limit of the first operating parameter.

A method for calculating a lateral position of an ego motor vehicle on a traffic lane includes calculating a first theoretical lateral position of the ego vehicle, calculating a second theoretical lateral position of the ego vehicle, calculating a third theoretical lateral position of the ego vehicle, calculating the lateral position of the ego vehicle using a weighted average of the first lateral position, the second lateral position, and the third lateral position.

This application is directed to augmenting training data used for vehicle driving modelling. A computer system obtains a first image of a road and identifies a drivable area of the road within the first image. The computer system obtains an image of an object and generates a second image from the first image by overlaying the image of the object over the drivable area. The second image is added to a corpus of training images to be used by a machine learning system to generate a model for facilitating driving of a vehicle (e.g., at least partial autonomously). In some embodiments, the computer system applies machine learning to train a model using the corpus of training images and distributes the model to one or more vehicles. In use, the model processes road images captured by the one or more vehicles to facilitate vehicle driving.