A distracted driving analysis system for identifying distracted driving events is provided. The system includes a processor in communication with a memory device programmed to: (i) receive driving event records, each driving event record including phone usage by a user, wherein a driving event record is labeled as an actual distracted driving event or a passenger event, (ii) divide the driving event records into at least two clusters based at least in part upon common features and the labels of each driving event record by processing the plurality of driving event records with a semi-supervised machine learning algorithm, (iii) generate a trained model based at least in part upon the at least two clusters, (iv) process a new driving event using the trained model, (v) assign the new driving event to one of the clusters using the trained model, and/or (vi) determine whether the new driving event is an actual distracted driving event or a passenger event.

A map information correction method for correcting map information includes information of a lane boundary line, the method further including: detecting a position with respect to an own vehicle of a lane boundary line set in place on a road surface around the own vehicle; estimating an own position on a map of the own vehicle; and correcting, depending on the estimated own position and the detected position of the lane boundary line, a position of the lane boundary line included in the map information by, in a first region comparatively close to the own vehicle, a larger rotational correction amount than in a second region comparatively far from the own vehicle and, in the second region, a larger translational correction amount than in the first region.

Embodiments of the present invention provide a vehicle having different operating modes, and for each such mode a different characteristic of output torque and accelerator pedal position. The rise of output torque in response to a propulsion request is more or less delayed according to the instant operating mode. The invention provides for blending of the response to a propulsion request so that the delay is progressively varied between a source and target operating mode.

Methods and systems are presented for improving performance of a vehicle operating in a cruise control mode where a controller adjusts torque output from a vehicle to maintain vehicle speed within a desired range. The methods and systems include adapting a vehicle dynamics model and a vehicle fuel consumption model that provide input to nonlinear model predictive controller.

A vehicle dynamics control system receives a feedback state signal including values of a roll rate and a roll angle of the motion of the vehicle and updates parameters of a model of roll dynamics of the vehicle by fitting the received values into the roll dynamics model. The roll dynamics model explains the evolution of the roll rate and the roll angle based on the parameters including a center of gravity (CoG) parameter modeling a location of a CoG of the vehicle, and a spring constant and a damping coefficient modeling suspension dynamics of the vehicle. The system determines a control command for controlling at least one actuator of the vehicle using a motion model including the updated CoG parameter and submits the control command to the vehicle controller to control the motion of the vehicle.

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.

One embodiment describes an automation system including a sensor that determines operational parameters of the automation system; one or more actuators that perform control actions during operation of the automation system; and a control system communicatively coupled to the sensor and the one or more actuators. The control system includes memory that stores the operational parameters; determines occurrence of memory errors in data stored in the memory; determines error parameters that indicate characteristics of the memory errors; determines error-corrected data by correcting the memory errors based at least in part on the error parameters; adaptively adjusts a refresh rate used to refresh stored data in the memory based at least in part on the error parameters; and determines control commands instructing the one or more actuators to perform the control actions by processing the error-corrected data.

A computer-implemented method for training a machine learning system for generating driving profiles and/or driving routes of a vehicle including: a generator obtains first random vectors and generates first driving routes and associated first driving profiles related to the first random vectors, driving routes and respectively associated driving profiles recorded in driving mode are stored in a data base, second driving routes and respectively associated second driving profiles recorded in driving mode are selected from the database, a discriminator obtains first pairs made up of first generated driving routes and respectively associated first generated driving profiles and second pairs made up of second driving routes and respectively associated second driving profiles recorded in driving mode, the discriminator calculates outputs that characterize each pair, and a target function is optimized as a function of the outputs of the discriminator.

A method of generating vehicle control data includes: storing, with a storage device, relationship prescription data; operating, with an execution device, an operable portion of an internal combustion engine; acquiring, with the execution device, a detection value from a sensor that detects the state of the vehicle; calculating, with the execution device, a reward; and updating, with the execution device, the relationship prescription data using update mapping determined in advance, the update mapping using the state of the vehicle based on the detection value, an operation amount used to operate the operable portion, and the reward corresponding to the operation as arguments, and returning the relationship prescription data which have been updated such that an expected profit for the reward calculated when the operable portion is operated in accordance with the relationship prescription data increases.

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.