Abstract: The present disclosure provides a probabilistic decision engine for autonomous vehicles. Briefly described, one embodiment comprises taking a network connection matrix (based on maps and graph theory) and a cost matrix (with entries of the cost's mean values and probability distributions) as input and generates the probability distribution of optimal routes as output. The disclosed probabilistic decision engine comprises a stochastic network standardization module, a stochastic network decomposition module and a probabilistic optimization kernel. A deterministic network reduction method is first used to derive a standard reduced network, augmented by the stochastic network reduction. The standard network is then decomposed into a series of stochastic subnetworks by using the convolution, probability density function (PDF) shifting, and PDF reshaping techniques. A pure-analytical probabilistic solver is finally used to solve the stochastic optimization problem.

Abstract: The present invention relates to a steering control system and method for a vehicle, and more particularly a control system and method for accurately controlling the lateral movement of an autonomous vehicle that has a non-linear steering system, e.g., a hydraulic steering system, which includes measuring wheel angles of the vehicle, calculating the actuation value for the desired wheel angle based on the measured wheel angle, and rotating the steering wheel according to the actuation value; wherein the actuation values are calculated based on a function f( ) representing the nonlinear behavior of the steering ratio depending on the position and movement direction of the steering wheel, and another function g( ) representing a response lag when the steering direction is changed.

Abstract: A device for controlling an industrial system comprises an input block and a reference value predicter. The reference value predicter includes a disturbance predicter, a state predicter, and a model parameter predicter. A model updater updates the model of the industrial system based on the predicted state and the predicted parameters. A one-prediction-step calculator of the reference value predicter calculates a prediction step based on the predicted disturbances and the model of the system. The device further includes a matrix updater and a linear solver that includes a memory structure such that each row of Jacobian and gradient matrices may be processed in parallel, a pivot search block that determines a maximum element in a column of the Jacobian and gradient matrices, and a pivot row reading block. Moreover, the device further includes a solution updater that updates the solution for an iteration step and controls the iteration process and an output block that sends a solution to the industrial system.

Abstract: The present disclosure provides a probabilistic decision engine for autonomous vehicles. Briefly described, one embodiment comprises taking a network connection matrix (based on maps and graph theory) and a cost matrix (with entries of the cost's mean values and probability distributions) as input and generates the probability distribution of optimal routes as output. The disclosed probabilistic decision engine comprises a stochastic network standardization module, a stochastic network decomposition module and a probabilistic optimization kernel. A deterministic network reduction method is first used to derive a standard reduced network, augmented by the stochastic network reduction. The standard network is then decomposed into a series of stochastic subnetworks by using the convolution, probability density function (PDF) shifting, and PDF reshaping techniques. A pure-analytical probabilistic solver is finally used to solve the stochastic optimization problem.

Abstract: Systems and methods for finding the solution to a system of linear equations include use of a reconfigurable hardware based real-time computational solver. The solver apparatus solves systems of linear equations by applying Gauss-Jordan Elimination to an augmented matrix in parallel on reconfigurable hardware consisting of parallel data processing modules, reusable memory blocks and flexible control logic units, which can greatly accelerate the solution procedure.

Abstract: A device for controlling an industrial system comprises an input block and a reference value predicter. The reference value predicter includes a disturbance predicter, a state predicter, and a model parameter predicter. A model updater updates the model of the industrial system based on the predicted state and the predicted parameters. A one-prediction-step calculator of the reference value predicter calculates a prediction step based on the predicted disturbances and the model of the system. The device further includes a matrix updater and a linear solver that includes a memory structure such that each row of Jacobian and gradient matrices may be processed in parallel, a pivot search block that determines a maximum element in a column of the Jacobian and gradient matrices, and a pivot row reading block. Moreover, the device further includes a solution updater that updates the solution for an iteration step and controls the iteration process and an output block that sends a solution to the industrial system.

Abstract: Systems and methods for finding the solution to a system of linear equations include use of a reconfigurable hardware based real-time computational solver. The solver apparatus solves systems of linear equations by applying Gauss-Jordan Elimination to an augmented matrix in parallel on reconfigurable hardware consisting of parallel data processing modules, reusable memory blocks and flexible control logic units, which can greatly accelerate the solution procedure.