Systems and methods are disclosed for reducing second order dynamics delays in a control subsystem (e.g. throttle, braking, or steering) in an autonomous driving vehicle (ADV). A control input is received from an ADV perception and planning system. The control input is translated in a control command to a control subsystem of the ADV. A reference actuation output is obtained from a storage of the ADV. The reference actuation output is a smoothed output that accounts for second order actuation dynamic delays attributable to the control subsystem actuator. Based on a difference between the control input and the reference actuation output, adaptive gains are determined and applied to the input control signal to reduce error between the control output and the reference actuation output.

Disclosed herein an artificial intelligence apparatus for determining inattention of a driver including a vibration sensor or a gyro sensor configured to sense movement of a driver's seat of a vehicle, a camera configured to receive image data including a face of a driver, a communication modem configured to receive vehicle status information from an ECU (Electronic Control Unit) of the vehicle, and a processor configured to generate movement information of the driver's seat using vibration sensor information received from the vibration sensor or gyro sensor information received from the gyro sensor, generate driver status information corresponding to the driver from the received image data, determine whether the driver is in an inattention status based on the movement information of the driver's seat, the driver status information and the vehicle status information, and output an inattention alarm if the driver is in the inattention status.

An artificial neural network-based projection information recognition apparatus for a vehicle is capable of learning information (projection information) projected on a road surface by a neighboring vehicle based on an artificial neural network and also recognizing information projected on a region of interest (ROI) determined based on a driving direction of the vehicle. The apparatus includes: an object detecting device to detect an object in an image based on a first Convolution Neural Network (CNN), a projection information classifying device to classify projection information located on a road surface among objects detected by the object detecting device, and a controller that recognizes the projection information located in a Region Of Interest (ROI).

Methods and systems are provided for cruise control velocity tracking. In one example, the method or system may generate a torque command output via a velocity controller that allows for an error within bounds to reduce a fuel consumption amount, the torque command output selected from outcomes of a leader and follower game.

There are provided systems and methods for vehicle use and performance restrictions based on detected users. A user may check-in to a vehicle so that the vehicle identifies the user, such as through providing identification to the vehicle using biometrics, logins, or other information. Using the identification, the vehicle may determine parameters and restrictions on use of the vehicle by the user. Parameters may include information about the user, such as age, health, or other statistic stored with the identification for the user, and may be utilized to determine restrictions on use of the vehicle by the user, such as a speed of the vehicle and passengers allowed in the vehicle. Restrictions may also be set for the user, including speeds of travel, routes of travel, and usage of media players in the vehicle. The usage of the vehicle may be monitored and enforced using the restrictions.

A method for adjusting a controller of a transportation vehicle includes receiving transportation vehicle state information and information about a current value of at least one variable controller parameter of the controller, calculating a setpoint value for the variable controller parameter, and outputting the setpoint value for the variable controller parameter. The calculating the setpoint value includes using an artificial neural network based on the transportation vehicle state information and the information about the current value of the variable controller parameter.

A control method for a vehicle is disclosed. The vehicle includes an in-vehicle controller, the in-vehicle controller pre-stores an instruction relationship, and the instruction relationship is used to represent an execution selection that is made by the in-vehicle controller from contrary instructions of at least two controllers. The method includes: receiving, by the in-vehicle controller, a first instruction and a second instruction (S410); and determining, by the in-vehicle controller, a vehicle control instruction according to the instruction relationship, the first instruction, and the second instruction (S420).

A method, a computer program comprising instructions, and a device for processing sensor data in a motor vehicle. An assistance system for a motor vehicle in which a method is implemented, and a motor vehicle having such a system. Sensor data is received. At least one algorithm is applied to the sensor data to determine a first intermediate result. At least one rule is applied to the first intermediate result to determine a second intermediate result. The second intermediate result is evaluated with regard to plausibility. If the second intermediate result is implausible, one or more of the rules applied to the first intermediate result is reversed until the second intermediate result is plausible. The second intermediate result thereby obtained is finally output as an end result.

Embodiments herein can determine an optimal route for an autonomous electric vehicle. The system may score viable routes between the start and end locations of a trip using a numeric or other scale that denotes how viable the route is for autonomy. The score is adjusted using a variety of factors where a learning process leverages both offline and online data. The scored routes are not based simply on the shortest distance between the start and end points but determine the best route based on the driving context for the vehicle and the user.

A working vehicle includes a steering device including a steering handle, a vehicle body to travel with either manual steering by the steering handle or automatic steering of the steering handle based on a traveling reference traveling line, and a controller to permit automatic steering based on steering angles of the steering device obtained when the vehicle body travels a predetermined distance while being steered by the manual steering.