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.

A transport facilitation system can receive a pick-up request from a computing device of a user of a transportation arrangement service, the pick-up request comprising a unique identifier and a pick-up location. Using the unique identifier, the system can perform a lookup in a database for a profile indicating vehicle setup preferences for the user, and select a service vehicle to service the pick-up request. The system can further determine a seat assignment within the service vehicle for the user, and based on the vehicle setup preferences indicated in the profile, the system transmit a set of configuration instructions to the service vehicle, the set of configuration instructions to configure one or more adjustable components of the service vehicle for the user.

Systems and methods are provided and include a communication gateway of a control module. The communication gateway establishes wireless communication connections with a plurality of user devices. A plurality of sensors are configured to, in response to the plurality of user devices being connected to the communication gateway, communicate signal information about the wireless communication connections to the control module. The signal information indicates characteristics of the wireless communication connections. The control module (i) determines a location of each user device of the plurality of user devices based on the signal information and (ii) generates a plurality of entries based on the signal information. Each entry of the plurality of entries corresponds to each of the plurality of user devices. A user settings activation module (i) determines user profiles corresponding to each entry of the plurality of entries and (ii) activates at least one vehicle function based on the user profiles.

A system for a vehicle includes a powertrain configured to propel the vehicle, and a controller configured to, during a first lap of the vehicle around a track, identify a portion of the track corresponding to a correlation of velocity, throttle position, and steering angle values indicative of a maximum power threshold, and, during a second lap, responsive to approaching the portion, limit power output by the powertrain causing temperature of the powertrain to fall and, upon entering the portion, increase power output to the maximum power threshold causing the temperature to rise, such that a difference in temperature between initiation of the limiting and exiting of the portion approaches zero.

Methods and devices for generating data for controlling a Self-Driving Car (SDC) are disclosed. The method includes: (i) acquiring a predicted object trajectory for an object, (ii) acquiring a set of anchor points along the lane for the SDC, (iii) for each one of the set of anchor points, determining a series of future moments in time when the SDC is potentially located at the respective one of the set of anchor points, thereby generating a matrix structure including future position-time pairs, (iv) for each future position-time pair in the matrix structure, using the predicted object trajectory for determining a distance between a closest object to the SDC as if the SDC is located at the respective future position-time pair, and (v) storing the distance between the closest object to the SDC in association with the respective future position-time pair in the matrix structure.

A vehicle, system and method for operating the vehicle is disclose. The system includes a radar system and a processor. The radar system locates a gap between targets in a second lane adjoining a first lane, with the host vehicle residing in the first lane. The processor is configured to determine a viability value of the gap for a lane change, select the gap based on the viability value, align the host vehicle with the selected gap, and merge the host vehicle from the first lane into the selected gap in the second lane.

In one embodiment, a method by a computing system of a vehicle includes determining an environment of the vehicle. The method includes generating, based on the environment, multiple proposed vehicle actions with associated operational data. The method includes determining a comfort level for each proposed vehicle action by processing the environment and operational data using a model for predicting comfort levels of vehicle actions. The model is trained using records of performed vehicle actions. The record for each performed vehicle action includes environment data, operational data, and a perceived passenger comfort level for the performed vehicle action. The method includes selecting a vehicle action from the proposed vehicle actions based on the determined comfort level. The method includes causing the vehicle to perform the selected vehicle action.

Implementations described herein relate to leveraging corresponding streams of speed readings of a vehicle generated by different speed sensors of different computing devices to automatically determine an updated tire size of tires of the vehicle. For example, while a user of the vehicle is driving, a first stream of speed readings can be generated by a vehicle speed sensor of an in-vehicle computing device of the vehicle and a second stream of speed readings can be generated by a mobile speed sensor of a mobile computing device of the user of the vehicle. Processor(s) can obtain the different streams of speed readings from the different computing devices and process the different streams using various operations to determine the update tire size of the tires of the vehicle. The updated tire size can be subsequently utilized to update operational parameter(s) of the vehicle that influence how the vehicle operates.

A parking assist system includes: a control device configured to execute a driving process for autonomously moving a vehicle to a target position; a steering operation member configured to receive a steering operation performed by an occupant; a vehicle state detecting device; and a notification device configured to make a notification to the occupant. In the driving process, the control device executes vehicle speed control and steering control. When, during execution of the driving process, the control device determines that the vehicle is a suspension state in which the driving process should be temporarily suspended, the control device causes the notification device to output a prescribed notification and executes a suspension process. In the suspension process, the control device executes the vehicle speed control to stop the vehicle and stops the steering control.

A control system of a vehicle includes: a target speed module configured to, using a parametric driver model and based on first driver parameters, second driver parameters, and vehicle parameters, determine a target vehicle speed trajectory for a future predetermined period; a driver parameters module configured to determine the first driver parameters based on conditions within a predetermined distance in front of the vehicle; and a control module configured to adjust at least one actuator of the vehicle based on the target vehicle speed trajectory and a present vehicle speed.