Abstract: Various embodiments for systems and methods of evaluating perception systems for autonomous vehicles using a quality temporal logic are disclosed herein.

Abstract: Embodiments of systems and methods for gray-box adversarial testing for control systems with machine learning components are disclosed.

Abstract: Methods of and systems for adaptive cruise control (ACC) of a vehicle include a controller for the vehicle that is configured to execute a computer-implemented model predictive control and a safe spacing policy that reduces collision risk between the vehicle and both a leading vehicle and a following vehicle. The methods include sensing a speed of a leader vehicle in front of the ego vehicle and a distance of the leader vehicle from the ego vehicle, sensing a speed of a follower vehicle behind the ego vehicle and a distance of the follower vehicle from the ego vehicle, and controlling the speed of the ego vehicle to avoid collision with the leader vehicle while reducing risk of the ego vehicle being hit by the follower vehicle.

Abstract: Various embodiments for systems and methods of evaluating perception systems for autonomous vehicles using a quality temporal logic are disclosed herein.

Abstract: One embodiment is a methodology for model verification. An embodiment obtaining, by a processor, a model for a system; identifying, by the processor, at least one block within the model that has a branching structure; identifying, by the processor, at least one model variable affecting a switching condition of the identified at least one block; generating, by the processor, an extended finite state machine modeling a switching behavior of the identified at least one block by using the at least one model variable; combining, by the processor, at least one output variable of the extended finite state machine with at least one of a first output port and a second output port of the system included in the model; and performing, by the processor, model verification and coverage of the model that utilizes outputs from the first output port and the second output port to verify the model.

Abstract: Methods of and systems for adaptive cruise control (ACC) of a vehicle include a controller for the vehicle that is configured to execute a computer-implemented model predictive control and a safe spacing policy that reduces collision risk between the vehicle and both a leading vehicle and a following vehicle. The methods include sensing a speed of a leader vehicle in front of the ego vehicle and a distance of the leader vehicle from the ego vehicle, sensing a speed of a follower vehicle behind the ego vehicle and a distance of the follower vehicle from the ego vehicle, and controlling the speed of the ego vehicle to avoid collision with the leader vehicle while reducing risk of the ego vehicle being hit by the follower vehicle.

Abstract: One embodiment is a methodology for model verification. An embodiment obtaining, by a processor, a model for a system; identifying, by the processor, at least one block within the model that has a branching structure; identifying, by the processor, at least one model variable affecting a switching condition of the identified at least one block; generating, by the processor, an extended finite state machine modeling a switching behavior of the identified at least one block by using the at least one model variable; combining, by the processor, at least one output variable of the extended finite state machine with at least one of a first output port and a second output port of the system included in the model; and performing, by the processor, model verification and coverage of the model that utilizes outputs from the first output port and the second output port to verify the model.

Abstract: Embodiments of model-based system design with model verification are disclosed. An embodiment includes receiving a model for a system and at least one specification for the system. In some embodiments, the system determines at least one of a minimum expected robustness value and a maximum expected robustness value for a region of a search space of the model with respect to the at least one specification. The model may be modified based on the determined minimum or maximum expected robust ness value.

Abstract: A system and method for testing robustness of a simulation model of a cyber-physical system includes computing a set of symbolic simulation traces for a simulation model for a continuous time system stored in memory, based on a discrete time simulation of given test inputs stored in memory. Simulation errors are accounted for due to at least one of numerical instabilities and numeric computations. The set of symbolic simulation traces are validated with respect to validation properties in the simulation model. Portions of the simulation model description are identified that are sources of the simulation errors.