Abstract: A method for testing a vehicular control system includes receiving, at a simulator, a discrete diagram identifying a plurality of tasks to be simulated and connections between the plurality of tasks, and receiving, at the simulator, a requested determinism level selected at a user device from a plurality of determinism levels. An optimal execution policy based on the simulator, the discrete diagram and the requested determinism level is selected from a plurality of execution policies. The plurality of tasks are simulated, by the simulator, using the selected optimal execution policy to simulate operation of the vehicular control system on a vehicle.

Abstract: A method for testing a vehicular control system includes receiving, at a simulator, data including a plurality of tasks and a plurality of channels, where the tasks communicate via the channels. The method includes receiving, at the simulator and from a user device, a system under test (SUT) selection selecting one of the plurality of tasks for testing. The method includes determining, using the graph and the SUT selection, an inverse reachability set of tasks from the plurality of tasks. The inverse reachability set of tasks includes a subset of the plurality of tasks to provide the SUT selection live data during simulation. The inverse reachability set of tasks does not include at least one task from the plurality of tasks. The method also includes simulating, by the simulator, only the tasks of the inverse reachability set of tasks to test the selected one of the plurality of tasks.

Abstract: A solver may generate a system of equations for an acausal model. A partitioning engine may transform at least some of the equations into groups of equations whose inputs/outputs are connected directly. The partitioning engine may transform at least some of the equations into groups of linear equations and/or groups of switched linear equations that are connected through nonlinear functions. The solver may determine input-output relationships of the groups of equations. A simulation model generator that may include a library of types of model elements may construct a causal simulation model.

Abstract: A system is configured to: identify a block of a model; represent the block as a group of sub blocks; and establish connections for the group of sub blocks. The block includes an input signal and two or more output signals. The group of sub blocks include a state block that provides a state, and two or more output blocks for receiving the state.