Abstract: A system for representing objects in a surrounding environment of a vehicle using a Frenet coordinate system. The system includes a memory and an electronic processor. The electronic processor is in communication with the memory and configured to, receive, from one or more sensors, input regarding one or more objects in the surrounding environment. The input includes, for each of the one or more objects, a representation of a location of the object in Cartesian coordinates. The electronic processor is further configured to convert the location of each object from Cartesian coordinates to Frenet coordinates and store the location of each object in Frenet coordinates in the memory.

Abstract: System, method, and device for controlling a vehicle. In one example, the system includes an electronic processor configured to capture, via a camera, a first image, determine, within the first image, a road traffic factor, and generate, based on sensor information from one or more sensors of the vehicle, a second image depicting an environment surrounding the vehicle. The second image includes the road traffic factor. The electronic processor is also configured to, determine, based on the detected road traffic factor and the second image, a predicted trajectory of a traffic participant proximate to the vehicle, and generate a steering command for the vehicle based on the predicted trajectory.

Abstract: A system for performing object and activity recognition based on data from a camera and a radar sensor. The system includes a camera, a radar sensor, and an electronic processor. The electronic processor is configured to receive an image from the camera and determine a portion of the image including an object. The electronic processor is also configured to receive radar data from the radar sensor and determine radar data from the radar sensor associated with the object in the image from the camera. The electronic processor is also configured to convert the radar data associated with the object to a time-frequency image and analyze the time-frequency image and the image of the object to classify the object and an activity being performed by the object.

Abstract: A system for representing objects in a surrounding environment of a vehicle using a Frenet coordinate system. The system includes a memory and an electronic processor. The electronic processor is in communication with the memory and configured to, receive, from one or more sensors, input regarding one or more objects in the surrounding environment. The input includes, for each of the one or more objects, a representation of a location of the object in Cartesian coordinates. The electronic processor is further configured to convert the location of each object from Cartesian coordinates to Frenet coordinates and store the location of each object in Frenet coordinates in the memory.

Abstract: A system for performing object and activity recognition based on data from a camera and a radar sensor. The system includes a camera, a radar sensor, and an electronic processor. The electronic processor is configured to receive an image from the camera and determine a portion of the image including an object. The electronic processor is also configured to receive radar data from the radar sensor and determine radar data from the radar sensor associated with the object in the image from the camera. The electronic processor is also configured to convert the radar data associated with the object to a time-frequency image and analyze the time-frequency image and the image of the object to classify the object and an activity being performed by the object.

Abstract: System, method, and device for controlling a vehicle. In one example, the system includes an electronic processor configured to capture, via a camera, a first image, determine, within the first image, a road traffic factor, and generate, based on sensor information from one or more sensors of the vehicle, a second image depicting an environment surrounding the vehicle. The second image includes the road traffic factor. The electronic processor is also configured to, determine, based on the detected road traffic factor and the second image, a predicted trajectory of a traffic participant proximate to the vehicle, and generate a steering command for the vehicle based on the predicted trajectory.

Abstract: Methods of creating trained semantic segmentation network models and operating vehicles using the model. One example method includes an outside view camera configured to capture images that represent an artificial representation of the driver's view, a driver-facing camera configured to capture a driver's eye movements, and an electronic controller. The electronic controller is configured to receive images from the cameras; calibrate the image of the driver's eye movement with the artificial driver view; create a pixel weighted heat map of the calibrated images; create a trained semantic segmentation neural network model and a trained attention neural network model using the pixel weighted heat map and the artificial driver view; and operate the vehicle using the trained semantic segmentation neural network model and the trained attention neural network model.

Abstract: Methods of creating trained semantic segmentation network models and operating vehicles using the model. One example method includes an outside view camera configured to capture images that represent an artificial representation of the driver's view, a driver-facing camera configured to capture a driver's eye movements, and an electronic controller. The electronic controller is configured to receive images from the cameras; calibrate the image of the driver's eye movement with the artificial driver view; create a pixel weighted heat map of the calibrated images; create a trained semantic segmentation neural network model and a trained attention neural network model using the pixel weighted heat map and the artificial driver view; and operate the vehicle using the trained semantic segmentation neural network model and the trained attention neural network model.

Abstract: A system for determining whether image data is within a predetermined range that image analysis software is configured to analyze. The system includes a camera and an electronic processor. The electronic processor is configured to receive the image data from the camera and generate a prediction regarding the image data and a confidence value associated with the prediction. The electronic processor is also configured to perturb the image data using a perturbation value and generate a prediction regarding the perturbed image data and a confidence value associated with the prediction. The electronic processor is further configured to compare the confidence values and disable autonomous functionality of a vehicle when the difference between the confidence value associated with the prediction regarding the image data and the confidence value associated with the prediction regarding the image data is less than a predetermined threshold value.

Abstract: Systems and methods for updating an enrollment template having a plurality of enrollment views of a biometric input object. A determination is made as to whether a new input biometric view is a candidate view for template update based on a match criterion, and a determination is made as to whether the new input biometric view increases coverage of the biometric input object by the enrollment template. The new input biometric view is added to the enrollment template as a new enrollment view in response to determining that the new biometric view i) is a candidate view for template update, and ii) increases coverage of the biometric input object by the enrollment template.

Abstract: This is a method and apparatus for bridge collision alert and monitoring impact analysis, which allows users of the method and apparatus to continuously monitor the integrity of a bridge as well as to detect insult or impact to the bridge structure or both as well as viewing purposes as for forensic purposes. The system is comprised of a series of monitors that gather different pieces of date and integrate that data into a system for view by the operator.