Abstract: An optical encryption camera includes a sensor array and a filter positioned over the sensor array to receive light prior to the sensor array. The filter includes a multiplexing mask and a scaling mask in sequence. The multiplexing mask and the scaling mask combine to provide an encryption key to encrypt image data prior to capture.

Abstract: Methods and systems for training a trajectory prediction model and performing a vehicle maneuver include encoding a set of training data to generate encoded training vectors, where the training data includes trajectory information for agents over time. Trajectory scenarios are simulated based on the encoded training vectors, with each simulated trajectory scenario representing one or more agents with respective agent trajectories, to generate simulated training data. A predictive neural network model is trained using the simulated training data to generate predicted trajectory scenarios based on a detected scene.

Abstract: Methods and systems for training a neural network include generate an image of a mask. A copy of an image is generated from an original set of training data. The copy is altered to add the image of a mask to a face detected within the copy. An augmented set of training data is generated that includes the original set of training data and the altered copy. A neural network model is trained to recognize masked faces using the augmented set of training data.

Abstract: A method for driving path prediction is provided. The method concatenates past trajectory features and lane centerline features in a channel dimension at an agent's respective location in a top view map to obtain concatenated features thereat. The method obtains convolutional features derived from the top view map, the concatenated features, and a single representation of the training scene the vehicle and agent interactions. The method extracts hypercolumn descriptor vectors which include the convolutional features from the agent's respective location in the top view map. The method obtains primary and auxiliary trajectory predictions from the hypercolumn descriptor vectors. The method generates a respective score for each of the primary and auxiliary trajectory predictions.

Abstract: A method for employing a general label space voting-based differentially private federated learning (DPFL) framework is presented. The method includes labeling a first subset of unlabeled data from a first global server, to generate first pseudo-labeled data, by employing a first voting-based DPFL computation where each agent trains a local agent model by using private local data associated with the agent, labeling a second subset of unlabeled data from a second global server, to generate second pseudo-labeled data, by employing a second voting-based DPFL computation where each agent maintains a data-independent feature extractor, and training a global model by using the first and second pseudo-labeled data to provide provable differential privacy (DP) guarantees for both instance-level and agent-level privacy regimes.

Abstract: A method for acquiring privacy-enhancing encodings in an optical domain before image capture is presented. The method includes feeding a differentiable sensing model with a plurality of images to obtain encoded images, the differentiable sensing model including parameters for sensor optics, integrating the differentiable sensing model into an adversarial learning framework where parameters of attack networks, parameters of utility networks, and the parameters of the sensor optics are concurrently updated, and, once adversarial training is complete, validating efficacy of a learned sensor design by fixing the parameters of the sensor optics and training the attack networks and the utility networks to learn to estimate private and public attributes, respectively, from a set of the encoded images.

Abstract: Methods and systems for training a neural network include generate an image of a mask. A copy of an image is generated from an original set of training data. The copy is altered to add the image of a mask to a face detected within the copy. An augmented set of training data is generated that includes the original set of training data and the altered copy. A neural network model is trained to recognize masked faces using the augmented set of training data.

Abstract: Methods and systems for training a trajectory prediction model and performing a vehicle maneuver include encoding a set of training data to generate encoded training vectors, where the training data includes trajectory information for agents over time. Trajectory scenarios are simulated based on the encoded training vectors, with each simulated trajectory scenario representing one or more agents with respective agent trajectories, to generate simulated training data. A predictive neural network model is trained using the simulated training data to generate predicted trajectory scenarios based on a detected scene.

Abstract: Embodiments are directed to an optical privatizing device (100), system and methods of use. The device includes a removable frame (101) removably attachable to a sensor housing (1A). A device includes a blurring lens (130) coupled to the removable frame (101) and configured to optically modify light passing to a depth sensor wherein the optical modified light has a privatizing blur level to neutralize a profile of an object sensed by the depth sensor within a working volume of the depth sensor to an unidentifiable state while maintaining a depth parameter sensed by the depth sensor.

Abstract: A de-identification assembly comprising an object tracking sensor (435) to track features of an object; and a mask generator (432) to produce rays of light in response to the tracked features of the object, the rays of light representing a de-identification mask of the object. The assembly includes a beamsplitter (422) having a first side configured to receive rays of light representing the object and a second side configured to receive the rays of light of the mask from the mask generator (432). The beamsplitter (422) produces a composite image of the object superimposed with the de-identification mask to anonymize an image of the object. A system including the de-identification assembly and a method are also provided.

Abstract: Embodiments are directed to an optical privatizing device (100), system and methods of use. The device includes a removable frame (101) removably attachable to a sensor housing (1A). A device includes a blurring lens (130) coupled to the removable frame (101) and configured to optically modify light passing to a depth sensor wherein the optical modified light has a privatizing blur level to neutralize a profile of an object sensed by the depth sensor within a working volume of the depth sensor to an unidentifiable state while maintaining a depth parameter sensed by the depth sensor.