Abstract: A method for agent and scenario modeling is described. The method includes analyzing, through a model-based systems engineering (MBSE) model, high-level events extracted from driving log data to identify a dataset of interest from the driving log data. The method also includes extracting an episode of interest from the dataset of interest according to an MBSE state transition diagram. The method further includes generating a driving scenario of interest based on the episode of interest. The method also includes utilizing the driving scenario of interest to parameterize agent and/or scenario models used for autonomous operation of an ego vehicle.

Abstract: Arrangements described herein can facilitate the development of autonomous driving applications by enabling a user to modify existing parameters and/or define new parameters in scenario files and/or to create driving evaluation tools. A first user interface based on a schema can be generated. A first input, such as a new parameter or a modification to an existing parameter defined by a schema, can be provided on the first user interface. A second user interface can be generated using the first input provided on the first user interface. The second user interface can include the new parameter or the modification to the existing parameter. A second input relating to the new parameter or the modification to the existing parameter can be received on the second user interface. Valid data can be generated based on the second input and that conforms with the schema.

Abstract: Systems and methods are provided for requirements engineering, and may include: receiving as input, time series data from at least one of a simulation of a vehicle run on a simulation system, or from the vehicle in operation; a requirements monitoring system checking to determine whether a plurality of requirements for operation of the vehicle are met, wherein the requirements are expressed in signal temporal logic form and a requirement includes at least an associated minimal sampling rate and a filtering policy applicable to the requirement; determining a quantitative conformance for each of selected requirements of the plurality of requirements; and add requirements to a verified requirements set based on the qualitative conformance of the requirements.

Abstract: Systems and methods are provided for requirements engineering, and may include: receiving as input, time series data from at least one of a simulation of a vehicle run on a simulation system, or from the vehicle in operation; a requirements monitoring system checking to determine whether a plurality of requirements for operation of the vehicle are met, wherein the requirements are expressed in signal temporal logic form and a requirement includes at least an associated minimal sampling rate and a filtering policy applicable to the requirement; determining a quantitative conformance for each of selected requirements of the plurality of requirements; and add requirements to a verified requirements set based on the qualitative conformance of the requirements.

Abstract: System, methods, and other embodiments described herein relate to modeling dynamic agents in a surrounding environment of an ego vehicle. In one embodiment, a method includes, in response to receiving sensor data including present observations of a road agent of the dynamic agents in the surrounding environment, identifying previous observations of the road agent from an electronic data store. The method includes estimating a future state of the road agent using at least the present observations and the previous observations of the road agent to compute the future state according to a probabilistic model comprised of a transition model that accounts for dynamic behaviors of the road agent to characterize transitions between states, and an agent model that accounts for actions of the road agent. The method includes controlling one or more vehicle systems of the ego vehicle according to the future state of the road agent.

Abstract: System, methods, and other embodiments described herein relate to modeling dynamic agents in a surrounding environment of an ego vehicle. In one embodiment, a method includes, in response to receiving sensor data including present observations of a road agent of the dynamic agents in the surrounding environment, identifying previous observations of the road agent from an electronic data store. The method includes estimating a future state of the road agent using at least the present observations and the previous observations of the road agent to compute the future state according to a probabilistic model comprised of a transition model that accounts for dynamic behaviors of the road agent to characterize transitions between states, and an agent model that accounts for actions of the road agent. The method includes controlling one or more vehicle systems of the ego vehicle according to the future state of the road agent.