Abstract: A system and method includes determining uncertainty estimation in an object detection deep neural network (DNN) by retrieving a calibration dataset from a validation dataset that includes scores associated with all classes in an image, including a background (BG) class, determining background ground truth boxes in the calibration dataset by comparing ground truth boxes with detection boxes generated by the object detection DNN using an intersection over union (IoU) threshold, correcting for class imbalance between ground truth boxes and background ground truth boxes in a ground truth class by updating the ground truth class to include a number of background ground truth boxes based on a number of ground truth boxes in the ground truth class, estimating uncertainty of the object detection DNN based on the class imbalance correction, and updating output data sets of the object detection DNN based on the class imbalance correction.

Abstract: Image data are input to a machine learning program. The machine learning program is trained with a virtual boundary model based on a distance between a host vehicle and a target object and a loss function based on a real-world physical model. An identification of a threat object is output from the machine learning program. A subsystem of the host vehicle is actuated based on the identification of the threat object.

Abstract: A system comprising a computer including a processor and a memory, the memory including instructions such that the processor is programmed to: process vehicle sensor data with a deep neural network to generate a prediction indicative of one or more objects based on the data and determine an object uncertainty corresponding to the prediction and when the object uncertainty is greater than an uncertainty threshold, segment the vehicle sensor data into a foreground portion and a background portion. Classify the foreground portion as including an unseen object class when a foreground uncertainty is greater than a foreground uncertainty threshold; classify the background portion as including unseen background when a background uncertainty is greater than a background uncertainty threshold; and transmit the data and a data classification to a server.

Abstract: A computer, including a processor and a memory, the memory including instructions to be executed by the processor to determine a second convolutional neural network (CNN) training dataset by determining an underrepresented object configuration and an underrepresented noise factor corresponding to an object in a first CNN training dataset, generate one or more simulated images including the object corresponding to the underrepresented object configuration in the first CNN training dataset by inputting ground truth data corresponding to the object into a photorealistic rendering engine and generate one or more synthetic images including the object corresponding to the underrepresented noise factor in the first CNN training dataset by processing the simulated images with a generative adversarial network (GAN) to determine a second CNN training dataset. The instructions can include further instructions to train a CNN to using the first and the second CNN training datasets.

Abstract: Image data are input to a machine learning program. The machine learning program is trained with a virtual boundary model based on a distance between a host vehicle and a target object and a loss function based on a real-world physical model. An identification of a threat object is output from the machine learning program. A subsystem of the host vehicle is actuated based on the identification of the threat object.

Abstract: A first computer can operate a first instance of a neural network, receive a first data set input to the first instance of the neural network, determine a first calibration parameter for the neural network in the first instance of the neural network based on the first data set, and send the first calibration parameter to a server computer. A second computer can operate a second instance of the neural network, receive a second data set input to the second instance of the neural network, determine a second calibration parameter for the neural network in the second instance of the neural network based on the second data set, and send the second calibration parameter to the server computer. A server computer can aggregate the first and second calibration parameters to update a model of the neural network and update the neural network model for the first and second instances of the neural network at the first and second computers based on the aggregated first and second calibration parameters.

Abstract: Original sensor data is received from one or more sensors of a vehicle. Free space around the vehicle is identified according to the sensor data, such as by identifying regions where data points have a height below a threshold. A location for an object model is selected from the free space. A plane is fitted to sensor data around the location and the object model is oriented according to an orientation of the plane. Sensing of the object model by a sensor of the vehicle is simulated to obtain simulated data, which is then added to the original sensor data. Sensor data corresponding to objects that would have been obscured by the object model is removed from the original sensor data. Augmented sensor data may be used to validate a control algorithm or train a machine learning model.

Abstract: Original sensor data is received from one or more sensors of a vehicle. Free space around the vehicle is identified according to the sensor data, such as by identifying regions where data points have a height below a threshold. A location for an object model is selected from the free space. A plane is fitted to sensor data around the location and the object model is oriented according to an orientation of the plane. Sensing of the object model by a sensor of the vehicle is simulated to obtain simulated data, which is then added to the original sensor data. Sensor data corresponding to objects that would have been obscured by the object model is removed from the original sensor data. Augmented sensor data may be used to validate a control algorithm or train a machine learning model.

Abstract: A system comprising a computer including a processor and a memory, the memory including instructions such that the processor is programmed to: process vehicle sensor data with a deep neural network to generate a prediction indicative of one or more objects based on the data and determine an object uncertainty corresponding to the prediction and when the object uncertainty is greater than an uncertainty threshold, segment the vehicle sensor data into a foreground portion and a background portion. Classify the foreground portion as including an unseen object class when a foreground uncertainty is greater than a foreground uncertainty threshold; classify the background portion as including unseen background when a background uncertainty is greater than a background uncertainty threshold; and transmit the data and a data classification to a server.

Abstract: A system comprising a computer including a processor and a memory, the memory including instructions such that the processor is programmed to: determine whether a difference between a friction coefficient label and a determined friction coefficient corresponding to an image depicting a surface is greater than a label threshold; modify the determined friction coefficient to equal the friction coefficient label when the difference is greater than the label threshold; and retrain a neural network using the image and the friction coefficient label.

Abstract: A computer, including a processor and a memory, the memory including instructions to be executed by the processor to determine a second convolutional neural network (CNN) training dataset by determining an underrepresented object configuration and an underrepresented noise factor corresponding to an object in a first CNN training dataset, generate one or more simulated images including the object corresponding to the underrepresented object configuration in the first CNN training dataset by inputting ground truth data corresponding to the object into a photorealistic rendering engine and generate one or more synthetic images including the object corresponding to the underrepresented noise factor in the first CNN training dataset by processing the simulated images with a generative adversarial network (GAN) to determine a second CNN training dataset.

Abstract: A vehicle system includes a processor and a memory. The memory stores instructions executable by the processor to identify an area of interest from a plurality of areas on a map, to determine that a detected sound is received in a vehicle audio sensor upon determining that a source of the sound is within the area of interest and not another area in the plurality of areas, and to operate the vehicle based at least in part on the detected sound.

Abstract: Systems, methods, and devices for determining a location of a vehicle or other device are disclosed. A method includes receiving sensor data from a sensor and determining a prior map comprising LIDAR intensity values. The method includes extracting a sub-region of the prior map around a hypothesis position of the sensor. The method includes extracting a Gaussian Mixture Model (GMM) distribution of intensity values for a region of the sensor data by expectation-maximization and calculating a log-likelihood for the sub-region of the prior map based on the GMM distribution of intensity values for the sensor data.

Abstract: A computer, including a processor and a memory, the memory including instructions to be executed by the processor to, based on sensor data in a vehicle, determine a database that includes object data for a plurality of objects, including, for each object, an object identification, a measurement of one or more object attributes, and an uncertainty specifying a probability of correct object identification, for the object identification and the object attributes determined based on the sensor data, wherein the object attributes include an object size, an object shape and an object location. The instructions include further instructions to determine a map based on the database including the respective locations and corresponding uncertainties for the vehicle type and download the map to a vehicle based on the vehicle location and the vehicle type.

Abstract: A method for autonomous vehicle localization. The method may include receiving, by an autonomous vehicle, millimeter-wave signals from at least two 5G transmission points. Bearing measurements may be calculated relative to each of the 5G transmission points based on the signals. A vehicle velocity may be determined by observing characteristics of the signals. Sensory data, including the bearing measurements and the vehicle velocity, may then be fused to localize the autonomous vehicle. A corresponding system and computer program product are also disclosed and claimed herein.

Abstract: Data from sensors of a vehicle is captured along with data tracking a driver's gaze. The route traveled by the vehicle may also be captured. The driver's gaze is evaluated with respect to the sensor data to determine a feature the driver was focused on. A focus record is created for the feature. Focus records for many drivers may be aggregated to determine a frequency of observation of the feature. A machine learning model may be trained using the focus records to identify a region of interest for a given scenario in order to more quickly identify relevant hazards.

Abstract: A vehicle system includes a processor and a memory. The memory stores instructions executable by the processor to identify an area of interest from a plurality of areas on a map, to determine that a detected sound is received in a vehicle audio sensor upon determining that a source of the sound is within the area of interest and not another area in the plurality of areas, and to operate the vehicle based at least in part on the detected sound.

Abstract: Systems, methods, and devices for determining a location of a vehicle or other device are disclosed. A method includes receiving sensor data from a sensor and determining a prior map comprising LIDAR intensity values. The method includes extracting a sub-region of the prior map around a hypothesis position of the sensor. The method includes extracting a Gaussian Mixture Model (GMM) distribution of intensity values for a region of the sensor data by expectation-maximization and calculating a log-likelihood for the sub-region of the prior map based on the GMM distribution of intensity values for the sensor data.

Abstract: Data from sensors of a vehicle is captured along with data tracking a driver's gaze. The route traveled by the vehicle may also be captured. The driver's gaze is evaluated with respect to the sensor data to determine a feature the driver was focused on. A focus record is created for the feature. Focus records for many drivers may be aggregated to determine a frequency of observation of the feature. A machine learning model may be trained using the focus records to identify a region of interest for a given scenario in order to more quickly identify relevant hazards.

Abstract: A method for autonomous vehicle localization. The method may include receiving, by an autonomous vehicle, millimeter-wave signals from at least two 5G transmission points. Bearing measurements may be calculated relative to each of the 5G transmission points based on the signals. A vehicle velocity may be determined by observing characteristics of the signals. Sensory data, including the bearing measurements and the vehicle velocity, may then be fused to localize the autonomous vehicle. A corresponding system and computer program product are also disclosed and claimed herein.