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.

System, methods, and other embodiments described herein relate to coordinating and optimizing prediction by a model using intermediate data and vehicle-to-vehicle (V2V) communications that improves bandwidth utilization. In one embodiment, a method includes processing acquired data, by a subject vehicle using a prediction model, to execute a vehicle task associated with navigating a driving scene. The method also includes extracting processed data according to the acquired data from intermediate activations of layers within the prediction model according to a cooperation model. The method also includes quantifying relevancy of the processed data, for a target vehicle, to select a subset using the cooperation model. The method also includes communicating the subset to a selected vehicle having the prediction model for additional prediction according to the relevancy and available resources to transmit the subset.

A system and method for adaptive cruise control for defensive driving are disclosed. A particular embodiment includes: receiving input object data from a subsystem of an autonomous vehicle, the input object data including distance data and velocity data relative to a lead vehicle; generating a weighted distance differential corresponding to a weighted difference between an actual distance between the autonomous vehicle and the lead vehicle and a desired distance between the autonomous vehicle and the lead vehicle; generating a weighted velocity differential corresponding to a weighted difference between a velocity of the autonomous vehicle and a velocity of the lead vehicle; combining the weighted distance differential and the weighted velocity differential with the velocity of the lead vehicle to produce a velocity command for the autonomous vehicle; and controlling the autonomous vehicle to conform to the velocity command.

A method of predicting actions of one or more actor agent in a scenario is implemented by an ego agent in the scenario. A plurality of agent models are used to generate a set of candidate futures, each candidate future providing an expected action of the actor agent. A weighting function is applied to each candidate future to indicate its relevance in the scenario. A group of candidate futures is selected for each actor agent based on the indicated relevance, wherein the plurality of agent models comprises a first model representing a rational goal directed behaviour inferable from the vehicular scene, and at least one second model representing an alternate behaviour not inferable from the vehicular scene.

A system for planning motion of a vehicle includes a controller having a processor and tangible, non-transitory memory on which instructions are recorded. The vehicle is capable of an automated driving operation. A planning module is executable by the controller to generate a vehicle trajectory constrained by a set of planning parameters. During a degraded driving situation, an adjustment module is executable by the controller to generate at least one adjusted parameter in the set of planning parameters. The controller is adapted to register occurrence of the degraded driving situation when the planning module is unable to generate a feasible path for the vehicle constrained by the set of planning parameters. The planning module is adapted to generate a modified trajectory plan based on the at least one adjusted parameter, with the automated driving operation being based on the modified trajectory plan during the degraded driving situation.

A method includes receiving sensed vehicle-state data, actuation-command data, and surface-coefficient data from a plurality of remote vehicles, inputting the sensed vehicle-state data, the actuation-command data, and the surface-coefficient data into a self-supervised recurrent neural network (RNN) to predict vehicle states of a host vehicle in a plurality of driving scenarios, and commanding the host vehicle to move autonomously according to a trajectory determined using the vehicle states predicted using the self-supervised RNN.

The invention relates to a method and arrangement for determining a current road inclination, specifically taking into account a quality measure for the determination. The invention also relates to a corresponding computer program product. The method comprises the steps of: measuring (S1), a first vehicle operating parameter; receiving (S2) the first vehicle operating parameter; determining (S3) an indication of a quality level for the first vehicle operating parameter, and determining (S4) an estimated value of the current road inclination based on the first vehicle operating parameter and the indication of the quality level for the first vehicle operating parameter.

In one embodiment, a system determines a target lane for an autonomous driving vehicle (ADV) to change lanes from a current lane to the target lane. The system determines obstacles information for one or more obstacles surrounding the ADV from sensor data. The system determines vehicle information of the ADV including a speed of the ADV. The system applies a reinforcement learning (RL) model to the obstacles and vehicle information of the ADV to generate an action for the ADV, where the action includes an acceleration/deceleration value and a steering angle value. The system controls the ADV to perform the lane change from the current lane to the target lane by executing the action.

An automated driving system (ADS) of an autonomous vehicle includes a communication module, a misbehavior detection module, and a processor. The communication module is configured to receive a vehicle-to-vehicle (V2V) message including source vehicle data and receive a fusion data message including fusion data from a mobile edge computing (MEC) system including a roadside unit (RSU). The source vehicle data includes a source vehicle location. The fusion data is based on RSU sensed data and on vehicle sensed data received at the RSU from at least one vehicle. The misbehavior detection module is configured to determine whether a source vehicle is disposed at the source vehicle location based on the fusion data. The processor is configured to manage performance of the autonomous vehicle in accordance with the source vehicle data based at least in part on the determination. Other embodiments are described and claimed.

This vehicle turning control device controls the turning characteristic of a vehicle having braking/driving sources capable of independently controlling a braking/driving torque for each wheel. The vehicle turning control device includes a yaw moment control device for controlling a yaw moment occurring in the vehicle, and a slip determination device for determining a road surface state from the angular velocity and the angular acceleration of the wheel and the vehicle speed. The yaw moment control device includes a control gain calculator for calculating a control gain, a target yaw rate calculator for calculating a target yaw rate from the vehicle speed, the steering angle, and the control gain, and a yaw moment calculator for calculating the braking/driving torque for each wheel in accordance with the target yaw rate. The control gain calculator calculates the control gain on the basis of a determination result of the slip determination device.