The invention relates to a control System (2) for a motor vehicle (1), comprising a central vehicle Controller (3) for Controlling vehicle functions (4) and a plurality of driver assistance Systems (5). The driver assistance Systems (5) are set up to transmit a task key describing the particular assistance function thereof to the vehicle Controller (3) and to transmit a security key assigned to the particular assistance function to the vehicle Controller (3). The central vehicle Controller (3) is set up to identify a particular task of the driver assistance Systems (5) on the basis of the transmitted task keys, to carry out a security check by virtue of the vehicle Controller (3) checking, on the basis of the security keys transmitted in each case for the respectively identified tasks, whether the particular security key complies with at least one security level assigned to the particular task, and, if the security check was successful, to control the vehicle functions (4) according to respective control commands transmitted by the driver assistance System (5) in Order to perform the respective assistance functions. The invention also comprises a method for operating such a control System (2) and a motor vehicle (1) having such a control System (2).

A computer, including a processor and a memory, the memory including instructions to be executed by the processor to receive a vehicle path from a server computer and verify the vehicle path based on vehicle dynamics and vehicle constraints. The instruction can include further instructions to, when the vehicle path is verified as correct, operating the vehicle on the vehicle path and, when the vehicle path is verified as incorrect, stopping the vehicle.

In one embodiment, in addition to detecting or recognizing an actual lane, a virtual lane is determined based on the current state or motion prediction of an ADV. A virtual lane may or may not be identical or similar to the actual lane. A virtual lane may represent the likely movement of the ADV in a next time period given the current speed and heading direction of the vehicle. If an object is detected that may cross a lane line of the virtual lane and is a closest object to the ADV, the object is considered as a CIPO, and an emergency operation may be activated. That is, even though an object may not be in the path of an actual lane, if the object is in the path of a virtual lane of an ADV, the object may be considered as a CIPO and subject to a special operation.

A computer, including a processor and a memory, the memory including instructions to be executed by the processor to generate a first color image of a road environment, determine one or more value decompositions of one or more of the red, green, and blue channels of the first color image, obtain one or more modified singular value decompositions by modifying respective ones of the singular value decompositions by a non-linear equation and reconstruct a second color image based on the modified one or more singular value decompositions. The instructions can include further instructions to train a deep neural network based on the second color image and operate a vehicle based on the deep neural network.

Systems and methods are provided for controlling a vehicle. In one embodiment, a method includes: determining, by a processor, that a lane change is desired; determining, by the processor, a lane change action based on a reinforcement learning method and a rule-based method, wherein each of the methods evaluates lane data, vehicle data, map data, and actor data; and controlling, by the processor, the vehicle to perform the lane change based on the lane action.

A system or method implemented by an autonomous vehicle involves determining a path plan to reach a destination from an origin. The path plan includes two or more path steps indicating tasks to be completed to reach the destination. The method includes, during traversal of the path plan by the autonomous vehicle, evaluating one or more of the two or more path steps of a planning horizon to determine a behavior plan for the planning horizon. The planning horizon is based on a current position of the autonomous vehicle, the behavior plan includes a speed and a trajectory, and the evaluating includes performing a cost analysis using a parallelized tree-based decision scheme at each of two or more simulation intervals within the planning horizon. The evaluating and the determining the behavior plan is repeated at two or more positions of the autonomous vehicle from the origin to the destination.

A machine automation system for controlling and operating an automated machine. The system includes a controller and sensor bus including a central processing core and a multi-medium transmission intranet for implementing a dynamic burst to broadcast transmission scheme where messages are burst from nodes to the central processing core and broadcast from the central processing core to all of the nodes.

A vehicle includes an actuator, a drivetrain configured to receive mechanical power from the actuator, an accelerator pedal position sensor configured to output a driver-demanded torque, and a controller in electric communication with the sensor and the actuator. The controller is programmed to receive the driver-demanded torque and output a shaped torque command to mitigate driveline disturbances caused by backlash and shaft compliance.

In one embodiment, in addition to detecting or recognizing an actual lane, a virtual lane is determined based on the current state or motion prediction of an ADV. A virtual lane may or may not be identical or similar to the actual lane. A virtual lane may represent the likely movement of the ADV in a next time period given the current speed and heading direction of the vehicle. If an object is detected that may cross a lane line of the virtual lane and is a closest object to the ADV, the object is considered as a CIPO, and an emergency operation may be activated. That is, even though an object may not be in the path of an actual lane, if the object is in the path of a virtual lane of an ADV, the object may be considered as a CIPO and subject to a special operation.

The present invention discloses a method for constructing linear luenberger observer for vehicle control. The method for constructing linear luenberger observer for vehicle control comprises the following steps: step 1: building a state-space equation of a driving system of a vehicle to judge observability of the driving system; step 2: dividing the state of the driving system into blocks, and reconstructing state components of the driving system to obtain an rewritten state observation equation of the driving system; step 3: introducing transformation into the rewritten state equation of the driving system to obtain an expression equation and an error equation of the Luenberger observer. The linear luenberger observer constructed by the present invention has low implementation difficulty. High-frequency noise in an output signal of a rotational speed sensor is reduced.