Abstract: Systems and methods for mitigating certain spoofing of vehicle features are disclosed herein. An example method can include determining input torque values obtained from a steering torque sensor associated with a steering wheel of a vehicle, wherein the input torque values are obtained over a period of time, determining road disturbances using a road disturbance model, applying a driver model that is indicative of human driver hands-on-wheel behaviors, determining when input torque values are indicative of a spoof or human interaction with the steering wheel using the input torque values, the road disturbance model, and the driver model, and executing a remediating measure when the input torque values are indicative of the spoof.

Abstract: The disclosure is generally directed to systems and methods for detecting that the vehicle is in a reverse mode, receiving a road surface topology, determining a feature of interest is within a collected image and outside a displayed field of view (FOV) for a rear-facing camera due to the road surface topology, adjusting the displayed FOV for the rear-facing camera to place the feature of interest within the displayed FOV. Receiving a reverse mode indication may be over a controller area network (CAN) bus in the vehicle. Receiving a road surface topology includes receiving an estimate of the road surface topology via at least one of monocular depth estimation, photogrammetric range imaging using structure from motion (SFM), multi-view stereo, imaging radar, lidar and a sensor system on the vehicle.

Abstract: Hazard-resultant effects to land and buildings are predicted based on various inputs. Hazards may include any appropriate type of hazard (e.g., flood, wildfire, climate-related hazards, or the like). Inputs may include the likelihood that that a specific type of hazard may occur for various scenarios, terrestrial boundaries, property boundaries, census geographies, or the like. Relationships between the inputs are determined and used to quantify parameters pertaining to a specific type of hazard. For example, the depth of flood water may be predicted for a particular terrestrial boundary, a city or town, or a building, for specific climate scenarios. A risk likelihood of the quantified parameter may be determined for a particular period of time and environment. For example, flooding to a building may be determined, broken down by depth threshold and year of annual risk for specific climate scenarios. Economic loss also may be predicted.

Abstract: Hazard-resultant effects to land and buildings are predicted based on various inputs. Hazards may include any appropriate type of hazard (e.g., flood, wildfire, climate-related hazards, or the like). Inputs may include the likelihood that that a specific type of hazard may occur for various scenarios, terrestrial boundaries, property boundaries, census geographies, or the like. Relationships between the inputs are determined and used to quantify parameters pertaining to a specific type of hazard. For example, the depth of flood water may be predicted for a particular terrestrial boundary, a city or town, or a building, for specific climate scenarios. A risk likelihood of the quantified parameter may be determined for a particular period of time and environment. For example, flooding to a building may be determined, broken down by depth threshold and year of annual risk for specific climate scenarios. Economic loss also may be predicted.

Abstract: Hazard-resultant effects to land and buildings are predicted based on various inputs. Hazards may include any appropriate type of hazard (e.g., flood, wildfire, climate-related hazards, or the like). Inputs may include the likelihood that that a specific type of hazard may occur for various scenarios, terrestrial boundaries, property boundaries, census geographies, or the like. Relationships between the inputs are determined and used to quantify parameters pertaining to a specific type of hazard. For example, the depth of flood water may be predicted for a particular terrestrial boundary, a city or town, or a building, for specific climate scenarios. A risk likelihood of the quantified parameter may be determined for a particular period of time and environment. For example, flooding to a building may be determined, broken down by depth threshold and year of annual risk for specific climate scenarios. Economic loss also may be predicted.

Abstract: Hazard-resultant effects to land and buildings are predicted based on various inputs. Hazards may include any appropriate type of hazard (e.g., flood, wildfire, climate-related hazards, or the like). Inputs may include the likelihood that that a specific type of hazard may occur for various scenarios, terrestrial boundaries, property boundaries, census geographies, or the like. Relationships between the inputs are determined and used to quantify parameters pertaining to a specific type of hazard. For example, the depth of flood water may be predicted for a particular terrestrial boundary, a city or town, or a building, for specific climate scenarios. A risk likelihood of the quantified parameter may be determined for a particular period of time and environment. For example, flooding to a building may be determined, broken down by depth threshold and year of annual risk for specific climate scenarios. Economic loss also may be predicted.