Abstract: A method for calibrating a radar system comprising one or more radar sensors includes defining, for a given calibration iteration from among a plurality of calibration iterations, one or more operational characteristics of the one or more radar sensors and generating a range-azimuth map based on radar data generated by the one or more radar sensors. The method includes identifying a reference set of pixels from among the plurality of pixels, where the reference set of pixels is associated with a reference object, determining whether a set of intensity values associated with the reference set of pixels satisfies an intensity condition, and adjusting the one or more operational characteristics of the one or more radar sensors in response to the set of intensity values not satisfying the intensity condition.

Abstract: System, methods, and other embodiments described herein relate to improving the synchronizing of systems in a vehicle without relying on external controls or hardware. In one embodiment, a method includes detecting a stop of a vehicle according to data acquired from two or more sensors. The method also includes identifying motion from the stop according to the data and corresponding timestamps from the two or more sensors, wherein the data and the corresponding timestamps are internal to the two or more sensors. The method also includes synchronizing the two or more sensors according to the corresponding timestamps. The method also includes controlling the motion of the vehicle using the two or more sensors.

Abstract: A camera control system is provided for controlling operation of a camera mounted on a vehicle. The system includes a processor and a memory communicably coupled to the processor. The memory stores a camera control module configured to compare a recognition confidence level associated with a feature to a predetermined threshold and, responsive to the recognition confidence level being below the predetermined threshold and using location information associated with the feature, control operation of the camera to capture at least one image of the feature during movement of the vehicle.

Abstract: A system for producing a data set to train a neural network for object detection can include a processor and a memory. The memory can store an object detection module and a data set determination module. The object detection module can include instructions that cause the processor to: (1) cause the neural network to determine, from data, produced by a ranging sensor system, a first set of objects and (2) cause another object detection system to determine, from the data, a second set of objects. The data set determination module can include instructions that cause the processor to: (2) compare a first count, of objects in the first set, and a second count, of objects in the second set and (2) designate, in response to the first count being different from the second count, the second set as the data set to train the neural network for object detection.

Abstract: A method includes generating a radar-based intensity map and a lidar-based intensity map and performing one or more augmentation routines on the radar-based intensity map and the lidar-based intensity map to generate a radar input and a lidar input. The method includes generating a plurality of teacher-based bounding boxes and a plurality of student-based bounding boxes based on the radar input and the lidar input. The method includes determining a loss value of the plurality of student-based bounding boxes based on the plurality of teacher-based bounding boxes and a plurality of ground truth bounding boxes, updating one or more weights of the student neural network based on the loss value, and updating one or more weights of the teacher neural network based on a moving average associated with the one or more weights of the student neural network.

Abstract: A method of generating virtual sensor data of a virtual single-photon avalanche diode (SPAD) lidar sensor includes generating a two-dimensional (2D) lidar array having a plurality of cells. The method further includes interpolating image data from a virtual camera with the 2D lidar array to define auxiliary image data, generating a virtual ambient image based on a red-channel (R-channel) data of the auxiliary image data, identifying a plurality of virtual echoes of the virtual SPAD lidar sensor based on the R-channel data and a defined photon threshold, defining a virtual point cloud indicative of virtual photon measurements of the virtual SPAD lidar sensor based on the plurality of virtual echoes, and outputting data indicative of the virtual ambient image, the virtual photon measurements, the virtual point cloud, or a combination thereof, as the virtual sensor data of the virtual SPAD lidar sensor.

Abstract: A method includes generating a plurality of lidar inputs based on the lidar data, where each lidar input from among the plurality of lidar inputs comprises an image-based portion and a geometric-based portion, and where each lidar input from among the plurality of lidar inputs defines a position coordinate of the one or more objects. The method includes performing, for each lidar input from among the plurality of lidar inputs, a convolutional neural network (CNN) routine based on the image-based portion to generate one or more image-based outputs and assigning the plurality of lidar inputs to a plurality of echo groups based on the geometric-based portion. The method includes concatenating the one or more image-based outputs and the plurality of echo groups to generate a plurality of fused outputs and identifying the one or more objects based on the plurality of fused outputs.

Abstract: A method of calibrating a camera sensor and a SPAD LiDAR includes extracting identified features in each of a selected camera image and an ambient-intensity (A-I) image, generating a set of keypoints based on the identified features extracted for each of the images to provide a set of 2D camera keypoint locations and a set of 2D A-I keypoint locations, determining matched keypoints based on the set of 2D A-I keypoint locations and the set of 2D camera keypoint locations to provide a set of 2D A-I matched pixel locations and a set of 2D camera matched pixel locations, interpolating a 3D point cloud data with the set of 2D A-I matched pixel locations to obtain a set of 3D LiDAR matched pixel locations, and determining and storing extrinsic parameters to transform the set of 3D LiDAR matched pixel locations with the set of 2D camera matched pixel locations.

Abstract: A system for generating synthetic training data may include one or processors and a memory in communication with the one or more processors and having a receiving module, a duplication module, and an insertion module. The modules have instructions that when executed by the one or more processors cause the one or more processors to receive original training data being in the form of a three-dimensional point cloud and having one or more original objects formed by at least a portion of the three-dimensional point cloud and annotated with original annotation data, duplicate one of the one or more original objects to generate a synthetic object, and insert the synthetic object within the original training data to generate the synthetic training data.

Abstract: A system for classifying an object may include one or more processors, a sensor and a memory device. The memory device may include a data collection module, a starburst module, and an object classifying module. The modules have instructions that when executed by the one or more processors cause the one or more processors to obtain three dimensional point cloud data from the sensor, identify at least one cluster of points representing the object within the three dimensional point cloud data, identify a center point of the at least one cluster of points, project a plurality of rays from the center point to points of the at least one cluster of points to generate a shape, compare the shape to a plurality of candidate shapes, and classify the object when the shape matches at least one of the plurality of candidate shapes.

Abstract: A system for utilizing object tracking algorithms for tracking objects external to a vehicle includes a processor and a memory in communication with the processor. The object tracking algorithms include a lower computationally demanding object tracking algorithm and a higher computationally demanding object tracking algorithm. The memory has one or more modules that have instructions that cause the processor to determine if the vehicle is located in an urban setting or a non-urban setting and utilize the lower computationally demanding object tracking algorithm by an object tracking system when the vehicle is located in the non-urban setting. If it is determined that the vehicle is in an urban setting, the instructions cause the processor to determine available computational resources of the object tracking system and utilizing either the lower computationally demanding object tracking algorithm or the higher computationally demanding object tracking algorithm based on the available computational resources.

Abstract: A camera control system is provided for controlling operation of a camera mounted on a vehicle. The system includes a processor and a memory communicably coupled to the processor. The memory stores a camera control module configured to compare a recognition confidence level associated with a feature to a predetermined threshold and, responsive to the recognition confidence level being below the predetermined threshold and using location information associated with the feature, control operation of the camera to capture at least one image of the feature during movement of the vehicle.

Abstract: System, methods, and other embodiments described herein relate to improving the synchronizing of systems in a vehicle without relying on external controls or hardware. In one embodiment, a method includes detecting a stop of a vehicle according to data acquired from two or more sensors. The method also includes identifying motion from the stop according to the data and corresponding timestamps from the two or more sensors, wherein the data and the corresponding timestamps are internal to the two or more sensors. The method also includes synchronizing the two or more sensors according to the corresponding timestamps. The method also includes controlling the motion of the vehicle using the two or more sensors.

Abstract: A system includes a processor and a memory in communication with the processor having one or more modules. The modules include instructions that cause the processor to receive first sensor information from a first sensor and second sensor information of a shared field of view. The instructions cause the processor to detect one or more objects within the shared field of view using the first sensor information and the second sensor information. When one or more discrepancies are identified between the one or more objects detected using the first sensor information and the one or more objects detected using the second sensor information are detected, the instructions that cause the processor to determine that the first sensor or the second sensor needs cleaning.

Abstract: A system for utilizing object tracking algorithms for tracking objects external to a vehicle includes a processor and a memory in communication with the processor. The object tracking algorithms include a lower computationally demanding object tracking algorithm and a higher computationally demanding object tracking algorithm. The memory has one or more modules that have instructions that cause the processor to determine if the vehicle is located in an urban setting or a non-urban setting and utilize the lower computationally demanding object tracking algorithm by an object tracking system when the vehicle is located in the non-urban setting. If it is determined that the vehicle is in an urban setting, the instructions cause the processor to determine available computational resources of the object tracking system and utilizing either the lower computationally demanding object tracking algorithm or the higher computationally demanding object tracking algorithm based on the available computational resources.

Abstract: Systems, methods, and other embodiments described herein relate to selectively adapting settings of a sensor according to a driving context. In one embodiment, a method includes determining a driving context for an ego vehicle according to sensor data about a surrounding environment. The method includes, in response to identifying that the driving context satisfies a sensor threshold, adjusting a parameter associated with a camera in the ego vehicle.

Abstract: A system includes a processor and a memory in communication with the processor having one or more modules. The modules include instructions that cause the processor to receive first sensor information from a first sensor and second sensor information of a shared field of view. The instructions cause the processor to detect one or more objects within the shared field of view using the first sensor information and the second sensor information. When one or more discrepancies are identified between the one or more objects detected using the first sensor information and the one or more objects detected using the second sensor information are detected, the instructions that cause the processor to determine that the first sensor or the second sensor needs cleaning.

Abstract: A system for generating synthetic training data may include one or processors and a memory in communication with the one or more processors and having a receiving module, a duplication module, and an insertion module. The modules have instructions that when executed by the one or more processors cause the one or more processors to receive original training data being in the form of a three-dimensional point cloud and having one or more original objects formed by at least a portion of the three-dimensional point cloud and annotated with original annotation data, duplicate one of the one or more original objects to generate a synthetic object, and insert the synthetic object within the original training data to generate the synthetic training data.

Abstract: A system for improving inspection systems is provided. The system generally includes a camera in communication with a processor. The camera is configured to capture an image for inspection of an object A to be inspected. The camera transfers the image to the processor for comparison with a reference image. If the comparison is not within a predetermined range, then the camera settings are adjusted and a new image is taken until the current image matches the reference image. Alternatively, or in addition to, the system is capable of learning based on a plurality of data and creating a model image for comparison prior to capturing the first image.

Abstract: A system for classifying an object may include one or more processors, a sensor and a memory device. The memory device may include a data collection module, a starburst module, and an object classifying module. The modules have instructions that when executed by the one or more processors cause the one or more processors to obtain three dimensional point cloud data from the sensor, identify at least one cluster of points representing the object within the three dimensional point cloud data, identify a center point of the at least one cluster of points, project a plurality of rays from the center point to points of the at least one cluster of points to generate a shape, compare the shape to a plurality of candidate shapes, and classify the object when the shape matches at least one of the plurality of candidate shapes.