Abstract: Methods and systems for using images that represent time-series data to forecast corresponding images depicting future values of the time-series data are provided. The method includes: receiving a set of time-series data; converting the set of time-series data into a partial first image that includes a blank region to which future data to be included in the first set of time-series data corresponds; and performing an inpainting operation with respect to the partial first image by generating pixels for filling in the blank region in order to produce an augmented version of the first image. A machine learning algorithm that is trained by using historical time-series data may be used to perform the inpainting operation.

Abstract: An autonomous vehicle uses machine learning based models such as neural networks to predict hidden context attributes associated with traffic entities. The hidden context represents behavior of the traffic entities in the traffic. The machine learning based model is configured to receive a video frame as input and output likelihoods of receiving user responses having particular ordinal values. The system uses a loss function based on cumulative histogram of user responses corresponding to various ordinal values. The system identifies user responses that are unlikely to be valid user responses to generate training data for training the machine learning mode. The system identifies invalid user responses based on response time of the user responses.

Abstract: An autonomous vehicle uses machine learning based models such as neural networks to predict hidden context attributes associated with traffic entities. The hidden context represents behavior of the traffic entities in the traffic. The machine learning based model is configured to receive a video frame as input and output likelihoods of receiving user responses having particular ordinal values. The system uses a loss function based on cumulative histogram of user responses corresponding to various ordinal values. The system identifies user responses that are unlikely to be valid user responses to generate training data for training the machine learning mode. The system identifies invalid user responses based on response time of the user responses.

Abstract: An autonomous vehicle collects sensor data of an environment surrounding the autonomous vehicle including traffic entities such as pedestrians, bicyclists, or other vehicles. The sensor data is provided to a machine learning based model along with an expected turn direction of the autonomous vehicle to determine a hidden context attribute of a traffic entity given the expected turn direction of the autonomous vehicle. The hidden context attribute of the traffic entity represents factors that affect the behavior of the traffic entity, and the hidden context attribute is used to predict future behavior of the traffic entity. Instructions to control the autonomous vehicle are generated based on the hidden context attribute.

Abstract: An autonomous vehicle uses probabilistic neural networks to predict hidden context attributes associated with traffic entities. The hidden context represents behavior of the traffic entities in the traffic. The probabilistic neural network is configured to receive an image of traffic as input and generate output representing hidden context for a traffic entity displayed in the image. The system executes the probabilistic neural network to generate output representing hidden context for traffic entities encountered while navigating through traffic. The system determines a measure of uncertainty for the output values. The autonomous vehicle uses the measure of uncertainty generated by the probabilistic neural network during navigation.

Abstract: An autonomous vehicle collects sensor data of an environment surrounding the autonomous vehicle including traffic entities such as pedestrians, bicyclists, or other vehicles. The sensor data is provided to a machine learning based model along with an expected turn direction of the autonomous vehicle to determine a hidden context attribute of a traffic entity given the expected turn direction of the autonomous vehicle. The hidden context attribute of the traffic entity represents factors that affect the behavior of the traffic entity, and the hidden context attribute is used to predict future behavior of the traffic entity. Instructions to control the autonomous vehicle are generated based on the hidden context attribute.

Abstract: An autonomous vehicle uses machine learning based models such as neural networks to predict hidden context attributes associated with traffic entities. The hidden context represents behavior of the traffic entities in the traffic. The machine learning based model is configured to receive a video frame as input and output likelihoods of receiving user responses having particular ordinal values. The system uses a loss function based on cumulative histogram of user responses corresponding to various ordinal values. The system identifies user responses that are unlikely to be valid user responses to generate training data for training the machine learning mode. The system identifies invalid user responses based on response time of the user responses.

Abstract: An autonomous vehicle uses machine learning based models such as neural networks to predict hidden context attributes associated with traffic entities. The hidden context represents behavior of the traffic entities in the traffic. The machine learning based model is configured to receive a video frame as input and output likelihoods of receiving user responses having particular ordinal values. The system uses a loss function based on cumulative histogram of user responses corresponding to various ordinal values. The system identifies user responses that are unlikely to be valid user responses to generate training data for training the machine learning mode. The system identifies invalid user responses based on response time of the user responses.

Abstract: An autonomous vehicle uses probabilistic neural networks to predict hidden context attributes associated with traffic entities. The hidden context represents behavior of the traffic entities in the traffic. The probabilistic neural network is configured to receive an image of traffic as input and generate output representing hidden context for a traffic entity displayed in the image. The system executes the probabilistic neural network to generate output representing hidden context for traffic entities encountered while navigating through traffic. The system determines a measure of uncertainty for the output values. The autonomous vehicle uses the measure of uncertainty generated by the probabilistic neural network during navigation.