Abstract: A system for the detection and management of behavioral disorders, including but not limited to anxiety and panic attacks, is disclosed. The system incorporates wearable body sensors that measure physiological and behavioral variables, an algorithm for detection of panic attack and other behavioral disorders based on the physiological variables are measured and paired with an application module that can display said variables and detected behavioral disorders to the user, an application module that can instruct a user in real-time management techniques including biofeedback and neurofeedback for a detected disorder, an application module that learns users psychological and physiological patterns for the purpose of self-control and correction. Allows for simultaneous monitoring by health care professionals with real-time intervention.

Abstract: This application discloses a computing system to self-diagnosis of faults for an assisted or automated driving system of a vehicle. The computing system can populate measurement data collected by sensors mounted in the vehicle into an environmental model, and detect an object proximate to the vehicle based on the measurement data in the environmental model. The computing system can identify a fault in at least one of the sensors by performing one or more safety cross-checks including one or more of comparing the environmental modal against one or more data models populated with sensor measurements or objects detected by different sensors, analyzing estimated motion of the vehicle or the object for aberrant movement, identifying missing measurement data, identifying divergent object classifications, or identifying operational sensor characteristics. A control system for the vehicle can configured to control operation of the vehicle based, at least in part, on the identified fault.

Abstract: This application discloses a computing system to implement event-driven region of interest management in an assisted or automated driving system of a vehicle. The computing system can identify a portion of an environmental model for a vehicle that corresponds to a region of interest for a driving functionality system. The computing system can detect at least one event associated with the region of interest in the environmental model, and provide data corresponding to the detected event to the driving functionality system. The driving functionality system can utilize the data corresponding to the detected event to control operation of the vehicle.

Abstract: This application discloses a computing system to detect a location of a vehicle relative to map data based on correlations between the map data and an environmental model populated with measurement data captured by sensors mounted in the vehicle. The computing system can identify which landmarks in the map data were correlated to the measurement data and utilized to identify the location of the vehicle. The computing system can select a reduced subset of the measurement data in the environmental model expected to correlate to the identified landmarks based on previous correlations to the identified landmarks in the map data over time, other available sources of localization information, or a configuration of the sensors mounted in the vehicle. The computing system can detect a subsequent location of the vehicle by comparing the map data having the identified landmarks with the reduced subset of the measurement data.

Abstract: This application discloses training of a classification system for an assisted or automated driving system of a vehicle. A processing system can label sensor measurement data collected by sensors mounted in the vehicle with classifications, which can include a type of an object associated with the sensor measurement data and a confidence level of the classification. A training system can utilize the classifications labeled to the sensor measurement data to train a classification graph utilized by the classification system. The training system can select a node in a classification graph based, at least in part, on a classification labeled to sensor measurement data. The training system can compare the sensor measurement data to matchable data in the selected node, and modify the classification graph based, at least in part, on differences between the sensor measurement data and the matchable data in the selected node.

Abstract: This application discloses a computing system to implement sensor event detection and fusion system in an assisted or automated driving system of a vehicle. The computing system can monitor an environmental model to identify spatial locations in the environmental model populated with temporally-aligned measurement data. The computing system can analyze, on a per-sensor basis, the temporally-aligned measurement data at the spatial locations in the environmental model to detect one or more sensor measurement events. The computing system can utilize the sensor measurement events to identify at least one detection event indicative of an object proximate to the vehicle. The computing system can combine the detection event with at least one of another detection event, a sensor measurement event, or other measurement data to generate a fused detection event. A control system for the vehicle can control operation of the vehicle based, at least in part, on the detection event.

Abstract: This application discloses degradation of an assisted or automated driving system for a vehicle based on detected faults. A computing system implementing autonomous driving functionality can detect a fault in an autonomous driving system of the vehicle based on measurement data collected by the vehicle, determine an impact of the fault on object perception in an environment around the vehicle, and prompt a control system in the vehicle to degrade operation of the vehicle based, at least in part, on the impact of the fault on object perception in the environment around the vehicle. The computing system may prompt the control system in the vehicle to reduce a speed of the vehicle, alter a driving strategy for the vehicle, or to have the vehicle enter a safe state.

Abstract: This application discloses a computing system to implement vehicle localization in an assisted or automated driving system. The computing system can receive an environmental model populated with measurement data captured by sensors mounted in a vehicle. The computing system can detect a location of the vehicle relative to the map data based on a correlation between the measurement data and the map data. The computing system can detect landmarks in the map data and switch to sparsely-populated map data from higher-definition map data for subsequent location detections. When the computing system does not detect a vehicle location, the computing system can track movement of the vehicle based on subsequent measurement data in the environmental model. After reacquiring a vehicle location, the computing can realign the tracked movement of the vehicle and measured data to the map data or modify the map data to include the tracked data.

Abstract: This application discloses a computing system to implement map building in an assisted or automated driving system. The computing system can track movement of a vehicle based on sensor measurement data populated in an environmental model and vehicle movement measurements. The computing system can correlate the tracked movement of the vehicle to map data based on a previously detected location of the vehicle relative to the map data. The computing system can modify the map data to include the sensor measurement data utilized to track the movement of the vehicle based on the correlation of the tracked movement of the vehicle to map data. The computing system can modify the map data by building a map of the sensor measurement data and the tracked movement of the vehicle, and populating the map data with the built map.

Abstract: This application discloses self-diagnosis of faults for an assisted or automated driving system of a vehicle. A primary computing system can generate an environmental model populated with objects detected from measurement data from sensors. A secondary computing system can generate a secondary system data structure configured to identify objects located around the vehicle that were detected from measurement data from the sensors. The secondary computing system can identify a fault in the sensors based on a comparison of the secondary system data structure with the environmental model. The secondary computing system also can estimate an amount of time before the vehicle crashes from a vehicle velocity vector and free space in the environment around the vehicle, and identify the fault in the sensors based on the estimated amount of time before the vehicle crashes.

Abstract: This application discloses a computing system to implement object tracking in an assisted or automated driving system of a vehicle. The computing system can assign a pre-classification to a detection event in an environmental model, update the environmental model with new sensor measurements and corresponding detection events over time, and track the detection event in the updated environmental model. The computing system can track the detection event by predicting a future state of the detection event with a state change model selected based on the assigned pre-classification, comparing the predicted future state to an actual future state of the detection event in an update to the environmental model, and determining the detection event corresponds to an object proximate to the vehicle based on the comparison. A control system for the vehicle can control operation of the vehicle based, at least in part, on the tracked detection event.

Abstract: This application discloses a computing system to self-diagnosis of faults for an assisted or automated driving system of a vehicle. The computing system can populate measurement data collected by sensors mounted in the vehicle into an environmental model, and detect an object proximate to the vehicle based on the measurement data in the environmental model. The computing system can identify a fault in at least one of the sensors by performing one or more safety cross-checks including one or more of comparing the environmental modal against one or more data models populated with sensor measurements or objects detected by different sensors, analyzing estimated motion of the vehicle or the object for aberrant movement, identifying missing measurement data, identifying divergent object classifications, or identifying operational sensor characteristics. A control system for the vehicle can configured to control operation of the vehicle based, at least in part, on the identified fault.

Abstract: This application discloses self-diagnosis of faults for an assisted or automated driving system of a vehicle. A primary computing system can generate an environmental model populated with objects detected from measurement data from sensors. A secondary computing system can generate a secondary system data structure configured to identify objects located around the vehicle that were detected from measurement data from the sensors. The secondary computing system can identify a fault in the sensors based on a comparison of the secondary system data structure with the environmental model. The secondary computing system also can estimate an amount of time before the vehicle crashes from a vehicle velocity vector and free space in the environment around the vehicle, and identify the fault in the sensors based on the estimated amount of time before the vehicle crashes.

Abstract: This application discloses a computing system to implement low-level sensor fusion in an assisted or automated driving system of a vehicle. The low-level sensor fusion can include receiving raw measurement data from sensors in the vehicle and temporally aligning the raw measurement data based on a time of capture. The low-level sensor fusion can include spatially aligning measurement coordinate fields of the sensors into an environmental coordinate field based, at least in part, on where the sensors are mounted in the vehicle, and then populating the environmental coordinate field with raw measurement data captured by the sensors based on the spatial alignment of the measurement coordinate fields to the environmental coordinate field. The low-level sensor fusion can detect at least one detection event or object based, at least in part, on the raw measurement data from multiple sensors as populated in the environmental coordinate field.

Abstract: This application discloses training of a classification system for an assisted or automated driving system of a vehicle. A processing system can label sensor measurement data collected by sensors mounted in the vehicle with classifications, which can include a type of an object associated with the sensor measurement data and a confidence level of the classification. A training system can utilize the classifications labeled to the sensor measurement data to train a classification graph utilized by the classification system. The training system can select a node in a classification graph based, at least in part, on a classification labeled to sensor measurement data. The training system can compare the sensor measurement data to matchable data in the selected node, and modify the classification graph based, at least in part, on differences between the sensor measurement data and the matchable data in the selected node.

Abstract: This application discloses a computing system to implement perception in sensor data for an assisted or automated driving system of a vehicle. The computing system can generate a matchable representation of sensor measurement data collected by sensors mounted in a vehicle, and compare the matchable representation of the sensor measurement data to an object model describing a type of an object capable of being located proximate to the vehicle. Based on the comparison, the computing system can classify the sensor measurement data as corresponding to the type of the object based, at least in part, on the comparison of the matchable representation of the sensor measurement data to the object model. A control system for the vehicle can configured to control operation of the vehicle based, at least in part, on the classified type of the object for the sensor measurement data.

Abstract: This application discloses a computing system to implement sensor event detection and fusion system in an assisted or automated driving system of a vehicle. The computing system can monitor an environmental model to identify spatial locations in the environmental model populated with temporally-aligned measurement data. The computing system can analyze, on a per-sensor basis, the temporally-aligned measurement data at the spatial locations in the environmental model to detect one or more sensor measurement events. The computing system can utilize the sensor measurement events to identify at least one detection event indicative of an object proximate to the vehicle. The computing system can combine the detection event with at least one of another detection event, a sensor measurement event, or other measurement data to generate a fused detection event. A control system for the vehicle can control operation of the vehicle based, at least in part, on the detection event.

Abstract: This application discloses a computing system to implement event-driven region of interest management in an assisted or automated driving system of a vehicle. The computing system can identify a portion of an environmental model for a vehicle that corresponds to a region of interest for a driving functionality system. The computing system can detect at least one event associated with the region of interest in the environmental model, and provide data corresponding to the detected event to the driving functionality system. The driving functionality system can utilize the data corresponding to the detected event to control operation of the vehicle.

Abstract: This application discloses a computing system to implement map building in an assisted or automated driving system. The computing system can track movement of a vehicle based on sensor measurement data populated in an environmental model and vehicle movement measurements. The computing system can correlate the tracked movement of the vehicle to map data based on a previously detected location of the vehicle relative to the map data. The computing system can modify the map data to include the sensor measurement data utilized to track the movement of the vehicle based on the correlation of the tracked movement of the vehicle to map data. The computing system can modify the map data by building a map of the sensor measurement data and the tracked movement of the vehicle, and populating the map data with the built map.

Abstract: This application discloses a computing system to implement vehicle localization in an assisted or automated driving system. The computing system can receive an environmental model populated with measurement data captured by sensors mounted in a vehicle. The computing system can detect a location of the vehicle relative to the map data based on a correlation between the measurement data and the map data. The computing system can detect landmarks in the map data and switch to sparsely-populated map data from higher-definition map data for subsequent location detections. When the computing system does not detect a vehicle location, the computing system can track movement of the vehicle based on subsequent measurement data in the environmental model. After reacquiring a vehicle location, the computing can realign the tracked movement of the vehicle and measured data to the map data or modify the map data to include the tracked data.