A method for processing autonomous driving simulation data. The method includes: determining a type of a message transmitted between a simulation system and an auto driving system (ADS); determining a data acquisition mode based on the type of the message; obtaining a data stream transmitted between the simulation system and the ADS based on the data acquisition mode; and determining performance of the ADS based on the data stream.

A method detects unintended acceleration of a motor vehicle during a closed-loop speed control mode by determining external forces on the vehicle via a controller, and then calculating a desired acceleration using a measured vehicle speed and the external forces. The method includes determining an actual acceleration of the vehicle, including filtering a speed signal as a first actual acceleration value and/or measuring a second actual acceleration value using an inertial measurement unit (IMU). During the speed control mode, the method includes calculating an acceleration delta value as a difference between the desired acceleration and the actual acceleration, and then using the acceleration delta value to detect the unintended acceleration during the speed control mode. A powertrain system for the motor vehicle, e.g., an electric vehicle, includes the controller and one or more torque generating devices coupled to road wheels of the vehicle.

A device for recognizing a width of a vehicle, including include a weight selecting device that selects at least one weight among a plurality of weights based on a degree of shaking of a present vehicle in a left and right direction and outputs the selected weight as selected weight information when a following vehicle overtakes the present vehicle, a vehicle width calculation device that calculates a vehicle width of the vehicle that has overtaken the present vehicle based on front region image information containing the vehicle that has overtaken the present vehicle, and outputs the calculation result as image vehicle width calculation information, and a weight applying device that applies the selected weight information to the image vehicle width calculation information and outputs the selected weight information-applied image vehicle width calculation information as vehicle width information.

A control device calculates a first estimate value of a first monitored amount of a mobile object, from sensor information of a controlled object, using a transformed first model obtained by transformation of a first model having a multi-layered neural structure and activation functions of a ReLU structure, and predicting dynamic behavior of the mobile object. The transformed first model is a model for which weight coefficients, biases, and upper and lower limit values of input/output variables of neurons are set based on the first model and in which activation functions of the neurons are transformed using a linear inequality function having a binary variable. The control device calculates a second estimate value of a second monitored amount of the controlled object from a candidate value of an operation amount for controlling the second monitored amount. The control device determines a value of the operation amount based on a target value of the second monitored amount, the second estimate value, and the first estimate value.

In one aspect, an apparatus for control of a driving torque for smooth riding on an uneven road is provided that comprises a pitch motion reduction objective function, a longitudinal acceleration reduction objective function, and a jerk reduction objective function are calculated using an acceleration value and a jerk constraint of a vehicle, and weights are reflected in these objective functions to calculate a final driving torque and applied to the vehicle, thereby reducing pitch motion, longitudinal acceleration, and jerk.

Disclosed are an autonomous driving control apparatus for controlling autonomous driving based on feature points of another vehicle, and a method thereof. The autonomous driving control apparatus may obtain information about a surrounding object, may extract one or more feature points corresponding to the object through the information about the surrounding object, may determine whether there is a risk of collision with the surrounding object based on the extracted feature point, and may control autonomous driving of the autonomous vehicle in consideration of the surrounding object having the risk of collision with the autonomous vehicle.

Techniques for using a set of variables to estimate a vehicle velocity of a vehicle are discussed herein. A system may determine an estimated velocity of the vehicle using a minimization based on an initial estimated velocity, steering angle data and wheel speed data. The system may then control an operation of the vehicle based at least in part on the estimated velocity.

In one embodiment, a control command is generated with an MPC controller, the MPC controller including a cost function with weights associated with cost terms of the cost function. The control command is applied to a dynamic model of an autonomous driving vehicle (ADV) to simulate behavior of the ADV. One or more of the weights are based on evaluation of the dynamic model in response to the control command, resulting in an adjusted cost function of the MPC controller. Another control command is generated with the MPC controller having the adjusted cost function. This second control command can be used to effect movement of the ADV.

In a method for carrying out control procedures in a vehicle, a criticality indicator is calculated from various stability indicators. The criticality indicator is fed to at least two different controllers or at least two different sub-controllers of a controller of the vehicle in order for controller parameters to be established.

A computer includes a processor and a memory, and the memory stores instructions executable by the processor to receive sensor data indicating an obstacle, formulate a control barrier function for a vehicle and the obstacle based on the sensor data, determine a control input based on the control barrier function and a combination function, and actuate a component of the vehicle according to the control input. The combination function is a sum of a first function weighted by a first weight and a second function weighted by a second weight, and the first weight and the second weight are based on a kinematic state of the obstacle.