Abstract: A vehicle system includes a lidar system that obtains an initial point cloud and obtains a dual density point cloud by implementing a first neural network and based on the initial point cloud. The dual density point cloud results from reducing point density of the initial point cloud outside a region of interest (ROI). Processing the dual density point cloud results in a detection result that indicates any objects in a field of view (FOV) of the lidar system. A controller obtains the detection result from the lidar system and controls an operation of the vehicle based on the detection result.

Abstract: A system includes first and second sensors and a controller. The first sensor is of a first type and is configured to sense objects around a vehicle and to capture first data about the objects in a frame. The second sensor is of a second type and is configured to sense the objects around the vehicle and to capture second data about the objects in the frame. The controller is configured to down-sample the first and second data to generate down-sampled first and second data having a lower resolution than the first and second data. The controller is configured to identify a first set of the objects by processing the down-sampled first and second data having the lower resolution. The controller is configured to identify a second set of the objects by selectively processing the first and second data from the frame.

Abstract: A vehicle system includes a lidar system that obtains an initial point cloud and obtains a dual density point cloud by implementing a first neural network and based on the initial point cloud. The dual density point cloud results from reducing point density of the initial point cloud outside a region of interest (ROI). Processing the dual density point cloud results in a detection result that indicates any objects in a field of view (FOV) of the lidar system. A controller obtains the detection result from the lidar system and controls an operation of the vehicle based on the detection result.

Abstract: A system and method for personalization of adjustable features of a vehicle. The system includes a processor and an actuator. The processor detectors key points of a person from a two-dimensional image and predicts a pose of the person. The processor translates a respective position of the key points from a two-dimensional coordinate system to a three-dimensional coordinate system based in part on the pose and measurements of distances between the key points. The processor determines a baseline configuration of an adjustable feature of the vehicle based in part on measurements between the key points in the three-dimensional coordinate system. The processor causes an actuator to adjust the adjustable feature to conform to the baseline configuration.

Abstract: A vehicle control system for automated driver-assistance includes a controller that generates a control signal to alter operation of one or more actuators of a vehicle based on a heading of a target. Generating the control signal includes determining a first heading of the target based on sensor data. Further, a probability (pa) of the first heading being accurate is computed based on a number of heading flips encountered in a duration-window of a predetermined length. Further, a map-probability (pm) that the target is traveling according to data from a navigation map is computed. Further, a posterior probability (pf) of the first heading being accurate is computed based on the probability (pa) and the map-probability (pm). Generating the control signal further includes, in response to the posterior probability being less than a predetermined threshold, correcting the first heading, and generating the control signal based on the first heading.

Abstract: A system includes first and second sensors and a controller. The first sensor is of a first type and is configured to sense objects around a vehicle and to capture first data about the objects in a frame. The second sensor is of a second type and is configured to sense the objects around the vehicle and to capture second data about the objects in the frame. The controller is configured to down-sample the first and second data to generate down-sampled first and second data having a lower resolution than the first and second data. The controller is configured to identify a first set of the objects by processing the down-sampled first and second data having the lower resolution. The controller is configured to identify a second set of the objects by selectively processing the first and second data from the frame.

Abstract: A vehicle including an advanced driver-assistance system (ADAS) and operator-adjustable devices is described. Controlling the vehicle includes identifying a vehicle operator, and capturing a plurality of operator-selectable settings associated with the plurality of operator-adjustable devices for the vehicle operator. A base profile and a second profile are determined for the vehicle operator based upon the first subset associated with non-autonomous operation of the vehicle and the second subset associated with ADAS, respectively. The plurality of operator-adjustable devices are controlled to the operator-selectable settings associated with the base profile when the vehicle is operating in the non-autonomous mode, and the plurality of operator-adjustable devices are controlled to the operator-selectable settings associated with the second profile when the vehicle is being controlled at least in part by one or more of the subsystems of the ADAS.

Abstract: A system and method to resolve ambiguity in a radar system involve detecting one or more objects with the radar system. The detecting includes obtaining range, azimuth, and an ambiguous range rate of a first object of the one or more objects. A plurality of Kalman filters are generated with state variables that include parameters based on the range, the azimuth, and the ambiguous range rate. Each of the plurality of Kalman filters provides a different estimate for an unambiguous range rate. The method also includes updating the plurality of Kalman filters using additional detections by the radar system, selecting a selected Kalman filter from among the plurality of Kalman filters that exhibits a highest probability mass among a plurality of probability mass corresponding with and derived from the plurality of Kalman filters, and determining the unambiguous range rate of the object using the selected Kalman filter.

Abstract: A system and method for personalization of security features of a vehicle. The system includes a memory having computer readable instructions and one or more processors for executing the computer readable instructions, the computer readable instructions controlling the one or more processors to perform operations. The operations include detecting a person in a cabin of a vehicle. At least one adjustable vehicle setting is detected in relation to the person in the cabin of the vehicle. The adjustable vehicle setting is compared to a stored adjustable vehicle setting in a first user profile. Operation of the vehicle by the person in the cabin of the vehicle is enabled based on the comparison and a permitted level of access from the first user profile.

Abstract: A system and method for personalization of adjustable features of a vehicle. The system includes a processor and an actuator. The processor detectors key points of a person from a two-dimensional image and predicts a pose of the person. The processor translates a respective position of the key points from a two-dimensional coordinate system to a three-dimensional coordinate system based in part on the pose and measurements of distances between the key points. The processor determines a baseline configuration of an adjustable feature of the vehicle based in part on measurements between the key points in the three-dimensional coordinate system. The processor causes an actuator to adjust the adjustable feature to conform to the baseline configuration.

Abstract: A vehicle including an advanced driver-assistance system (ADAS) and operator-adjustable devices is described. Controlling the vehicle includes identifying a vehicle operator, and capturing a plurality of operator-selectable settings associated with the plurality of operator-adjustable devices for the vehicle operator. A base profile and a second profile are determined for the vehicle operator based upon the first subset associated with non-autonomous operation of the vehicle and the second subset associated with ADAS, respectively. The plurality of operator-adjustable devices are controlled to the operator-selectable settings associated with the base profile when the vehicle is operating in the non-autonomous mode, and the plurality of operator-adjustable devices are controlled to the operator-selectable settings associated with the second profile when the vehicle is being controlled at least in part by one or more of the subsystems of the ADAS.

Abstract: A method of updating an identification algorithm of a vehicle includes sensing an image and drawing boundary boxes in the image. The algorithm attempts to identify an object-of-interest within each respective boundary box. The algorithm also attempts to identify a component of the object-of-interest within each respective boundary box, and if component is identified, calculates an excluded amount of a component boundary that is outside an object boundary. When the excluded amount is greater than a coverage threshold, the algorithm communicates the image to a processing center, which may identify a previously un-identified the object-of-interest in the image. The processing center may add the image to a training set of images to define a revised training set of images, and retrain the identification algorithm using the revised training set of images. The updated identification algorithm may then be uploaded onto the vehicle.

Abstract: A system and method to perform dynamic bandwidth adjustment among two or more vehicle sensors includes receiving input. The input includes data from each of the two or more vehicle sensors. The two or more vehicle sensors include a camera, an audio detector, a radar system, or a lidar system. The method also includes determining a bandwidth at which each of the two or more vehicle sensors should provide the data, and providing a control signal to each of the two or more vehicle sensors to adjust the bandwidth based on the determining.

Abstract: A system and method to resolve ambiguity in a radar system involve detecting one or more objects with the radar system. The detecting includes obtaining range, azimuth, and an ambiguous range rate of a first object of the one or more objects. A plurality of Kalman filters are generated with state variables that include parameters based on the range, the azimuth, and the ambiguous range rate. Each of the plurality of Kalman filters provides a different estimate for an unambiguous range rate. The method also includes updating the plurality of Kalman filters using additional detections by the radar system, selecting a selected Kalman filter from among the plurality of Kalman filters that exhibits a highest probability mass among a plurality of probability mass corresponding with and derived from the plurality of Kalman filters, and determining the unambiguous range rate of the object using the selected Kalman filter.

Abstract: A method of updating an identification algorithm of a vehicle includes sensing an image and drawing boundary boxes in the image. The algorithm attempts to identify an object-of-interest within each respective boundary box. The algorithm also attempts to identify a component of the object-of-interest within each respective boundary box, and if component is identified, calculates an excluded amount of a component boundary that is outside an object boundary. When the excluded amount is greater than a coverage threshold, the algorithm communicates the image to a processing center, which may identify a previously un-identified the object-of-interest in the image. The processing center may add the image to a training set of images to define a revised training set of images, and retrain the identification algorithm using the revised training set of images. The updated identification algorithm may then be uploaded onto the vehicle.

Abstract: A method and apparatus for detecting motion of a slow-moving vehicle are provided. The method includes detecting a wheel of the vehicle in a plurality of frames of a video, generating bounding boxes around portions of the frames including the wheel of the vehicle, scaling the portions of the frames including the wheel of the vehicle to a predetermined constant size, determining whether the wheel of the vehicle is moving by analyzing the scaled portions of the image, and outputting information indicating that the vehicle is moving if the determining determines that the wheel of the vehicle is moving.

Abstract: A method for controlling an operating vehicle includes: (a) determining, via a controller, a confidence level that the light of the other vehicle is ON based on images captured by a camera of the operating vehicle; and (b) controlling, via the controller, an alarm of the operating vehicle based on the confidence level that the light of the other vehicle is ON.

Abstract: A system and method to perform detection based on sensor fusion includes obtaining data from two or more sensors of different types. The method also includes extracting features from the data from the two or more sensors and processing the features to obtain a vector associated with each of the two or more sensors. The method further includes concatenating the two or more vectors obtained from the two or more sensors to obtain a fused vector, and performing the detection based on the fused vector.

Abstract: A method for controlling an operating vehicle includes: (a) determining, via a controller, a confidence level that the light of the other vehicle is ON based on images captured by a camera of the operating vehicle; and (b) controlling, via the controller, an alarm of the operating vehicle based on the confidence level that the light of the other vehicle is ON.

Abstract: A system and method to perform detection based on sensor fusion includes obtaining data from two or more sensors of different types. The method also includes extracting features from the data from the two or more sensors and processing the features to obtain a vector associated with each of the two or more sensors. The method further includes concatenating the two or more vectors obtained from the two or more sensors to obtain a fused vector, and performing the detection based on the fused vector.