A control device mounted in a vehicle in which at least one controlled part is controlled based on an output parameter obtained by inputting input parameters to a learned model using a neural network, provided with a parked period predicting part predicting future parked periods of the vehicle and a learning plan preparing part preparing a learning plan for performing relearning of the learned model during the future parked periods based on results of prediction of the future parked periods.

A method for operating a partially autonomous motor vehicle, wherein in a non-parked state of the motor vehicle, sensor data of a sensor device, which detects at least one person in the surroundings of the motor vehicle, are analyzed with respect to a behavior of the person disadvantageously impairing the further driving operation of the motor vehicle and at least one action counteracting the behavior of the person is triggered in dependence on the analysis result.

The systems and methods disclosed herein are configured to determine if a trailer backup assist system is needed to assist a driver with a procedure to backup a trailer that is connected to a vehicle. The estimation of need for assistance may be determined by an assistance model. If assistance is needed, the systems and methods provide an input to initiate a process to enable the trailer backup assist system.

Techniques for determining an uncertainty metric associated with an object in an environment can include determining the object in the environment and a set of candidate trajectories associated with the object. Further, a vehicle, such as an autonomous vehicle, can be controlled based at least in part on the uncertainty metric. The vehicle can determine a traversed trajectory associated with the object and determine a difference between the traversed trajectory and the set of candidate trajectories. Based on the difference, the vehicle can determine an uncertainty metric associated with the object. In some instances, the vehicle can input the traversed trajectory and the set of candidate trajectories to a machine-learned model that can output the uncertainty metric.

Aspects of the disclosure provide for controlling an autonomous vehicle. For instance, a first probability distribution may be generated for the vehicle at a first future point in time using a generative model for predicting expected behaviors of objects and a set of characteristics for the vehicle at an initial time expected to be perceived by an observer. Planning system software of the vehicle may be used to generate a trajectory for the vehicle to follow. A second probability distribution may be generated for a second future point in time using the generative model based on the trajectory and a set of characteristics for the vehicle at the first future point expected to be perceived by the observer. A surprise assessment may be generated by comparing the first probability distribution to the second probability distribution. The vehicle may be controlled based on the surprise assessment.

An apparatus for determining a lane change path of an autonomous vehicle is provided. The apparatus includes a learning device configured to learn lane change paths corresponding to a lane change strategy of the autonomous vehicle, and a controller configured to interwork with the learning device to extract at least two lane change paths corresponding to the lane change strategy among a plurality of lane change paths in a drivable area of the autonomous vehicle and determine a final lane change path based on properties of the extracted lane change paths.

A system and a method for controlling an autonomous driving vehicle. The system includes vehicle sensors and a controller. The controller has a processor and a storage device storing computer executable code. The computer executable code, when executed at the processor, is configured to: receive vehicle parameters from the vehicle sensors; obtain a vehicle dynamic model by adding a dynamics error bound to a state space model, wherein the dynamics error bound is estimated using linear least square; minimize a linear quadratic regulator cost function based on the vehicle dynamic model; and control the vehicle using control input obtained from the minimized cost function.

The present disclosure relates to systems and methods for determining a driving action in autonomous driving. The systems may obtain driving information associated with a vehicle; determine a state of the vehicle; determine one or more candidate driving actions and one or more evaluation values corresponding to the one or more candidate driving actions based on the driving information and the state of the vehicle by using a trained driving-action model; select a target driving action from the one or more candidate driving actions based on the one or more evaluation values; determine a target driving path based on the target driving action; and send signals to a control component of the vehicle to direct the vehicle to take the target driving action to follow the target driving path.

In one embodiment, a computer-implemented method for optimizing a controller of an autonomous driving vehicle (ADV) includes obtaining several samples, each sample having a set of parameters, iteratively performing, until a predetermined condition is satisfied: determining, for each sample, a score according to a configuration of the controller based on the set of parameters of the sample, applying a machine learning model to the samples and corresponding scores to determine a mean function and a variance function, producing a new sample as a minimum of a function of the mean function and the variance function with respect to an input space of the set of parameters, adding the new sample to the several samples, and outputting the new sample as an optimal sample, where parameters of the optimal sample are utilized to configure the controller to autonomously drive the ADV.

Systems and techniques for managing seat haptics are described herein. A system for managing seat haptics may include a seat equipped with an actuator, a processor, and a controller. The processor may receive a signal from a vehicle system. The controller may control the actuator to operate according to a first mode at a first time and operate according to a second mode at a second time based on the signal received from the vehicle system, such as a navigation system or sensor system. The vehicle system may be a vehicle navigation system and the controller may control the actuator to operate according to the first mode, at a first frequency, and according to the second mode, at a second frequency. The first time may be during travel along a route and the second time may be when the vehicle is less than a threshold distance from a destination.