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.

An apparatus of controlling an engine including an electric supercharger includes: an engine to combust fuel to generate power; a drive motor to assist the power of the engine and selectively operate as a generator to generate electrical energy; a battery configured to supply electrical energy to the drive motor and to be charged by the electrical energy generated from the drive motor; a plurality of electric superchargers respectively installed in a plurality of intake lines through which an ambient air flows to be supplied to a combustion chamber of the engine; and a controller that based on a determined driving tendency, adjusts a target speed of the electric superchargers of the plurality of electric superchargers, determine a driving mode of the electric superchargers, limits a maximum output of the engine, and variably adjusts a SOC electricity-generating region where the engine charges the battery.

Systems and methods for operating a driveline of a vehicle that includes an automatic transmission and a torque converter are described. In one example, vehicle launch is controlled according to a linear quadratic regulator that provides feedback control according to torque converter slip error and vehicle speed error. The vehicle launch is also controlled according to feed forward control that is based on requested torque converter slip and requested vehicle speed.

A system for adaptive in-drive updating, for a vehicle travelling on a route, includes a controller having a processor and tangible, non-transitory memory. The vehicle is carrying a load. The controller is adapted to obtain one or more dynamic parameters pertaining to the load. A plurality of adaptive predictors is selectively executable by the controller at a timepoint during the route at which a completed portion of the route has been traversed by the vehicle and a remaining portion remains untraversed. The plurality of adaptive predictors includes a speed predictor configured to generate a global speed profile. The plurality of adaptive predictors includes a driving consumption predictor is configured to predict a driving consumption profile for the remaining portion of the route based in part on the dynamic parameter, the route features, the global speed profile, and a past drive consumption.

An apparatus may include a driving information input unit for receiving driving information generated while a vehicle travels, a steering angle location control unit for receiving a command steering angle for autonomous driving and a current motor steering angle of a driving motor and outputting an autonomous driving command through location control, and a motor-driven power steering control unit for driving the driving motor based on the autonomous driving command in an autonomous driving mode, determining whether a driver intervenes in steering, based on the driving information during the autonomous driving, computing a driver command according to the driver' steering based on a result of the determination, computing a compensation output between the autonomous driving command and the driver command by applying a weighting according to a steering angular speed, and making a mode transition from the autonomous driving mode to a driver mode while driving the driving motor.

A pre-crash control device includes target information acquisition units and an electric control unit configured to update a recognized position based on an acquired position each time target information is newly acquired, to estimate a moving direction based on history of the recognized position, to determine whether a collision probability is high based on the recognized position and the moving direction, and to perform pre-crash control if it is determined that the collision probability is high and a time to collision becomes equal to or smaller than a threshold execution time. The electric control unit is configured to update the recognized position based on a currently predicted position and on the acquired position and to update the recognized position to the acquired position when the time to collision becomes equal to or smaller than a first threshold switching time.

Techniques are disclosed for switching between a manual driving mode and an autonomous driving mode. A system for switching driving modes can include a vehicle control unit in communication with a plurality of sensors and a plurality of vehicle controls of an autonomous vehicle. The vehicle control unit can include a control manager configured to receive a request to switch driving modes from a first mode to a second mode, obtain a driving state using the plurality of sensors, determine the driving state meets a switching standard, enter a pending switching state in which a control input received for the first mode is combined with a control input received for the second mode to generate a vehicle control output, send a message indicating that the driving mode is to switch from the first mode to the second mode, and switch driving modes from the first mode to the second mode.

This application is directed to on-vehicle behavior modeling of vehicles. A vehicle has one or more processors, memory, a plurality of sensors, and a vehicle control system. The vehicle collects training data via the plurality of sensors, and the training data include data for one or more vehicles during a collection period. The vehicle locally applies machine learning to train a vehicle driving behavior model using the collected training data. The vehicle driving behavior model is configured to predict a behavior of one or more vehicles. The vehicle subsequently collecting sensor data from the plurality of sensors and drives the vehicle by applying the vehicle driving behavior model to predict vehicle behavior based on the collected sensor data. The vehicle driving behavior model is configured to predict behavior of an ego vehicle and/or a distinct vehicle that appears near the ego vehicle.

In some examples, a vehicle suspension for supporting, at least in part, a sprung mass, includes a damper connected to the sprung mass, the damper including a movable piston. The vehicle suspension further includes an actuator and a controller. The controller may be configured to determine a frequency of motion associated with the sprung mass. When the frequency of motion is below a first frequency threshold, the controller may send a control signal to cause the actuator to apply a deceleration force to the sprung mass. Further, when the frequency of motion associated with the sprung mass exceeds the first frequency threshold, the controller may send a control signal to cause the actuator to apply a compensatory force to the sprung mass. For instance, a magnitude of the compensatory force may be based on a friction force determined for the damper.

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.