Abstract: Methods and systems are provided for detecting objects by utilizing uncertainties. In some aspects, a process can include steps for receiving, by an autonomous vehicle system, a frame of a scene with a detected object; estimating, by the autonomous vehicle system, an overall probability of the detected object in the frame; estimating, by the autonomous vehicle system, covariances for each state of at least one bounding box; and balancing, by the autonomous vehicle system, confidence values of the at least one bounding box based on the overall probability of the detected object and the covariances of each state of the at least one bounding box.

Abstract: Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for performing rare example mining in driving log data. In one aspect, a method includes maintaining a plurality of density estimation models that each correspond to a different rareness type with respect to historical sensor inputs in a driving log generated by sensors on-board a vehicle; receiving a query that references a sensor input; generating, from the sensor input, a corresponding density estimation model input for each of the plurality of density estimation models; processing, using each of the plurality of density estimation models, the corresponding density estimation model input to generate a corresponding density score; generating, for the sensor input, and from the density scores, a rareness score associated with each different rareness type; and providing the rareness scores in response to receiving the query.

Abstract: Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for performing rare example mining in driving log data. In one aspect, a method includes obtaining a sensor input; processing the sensor input using an encoder neural network to generate one or more feature vectors for the sensor input; processing each of the one or more feature vectors using a density estimation model to generate a density score for the feature vector; and generating a rareness score for each of the one or more feature vectors from the density score. For example, the rareness score can represent a degree to which a classification of an object depicted in the sensor input is rare relative to other objects. As another example, the rareness score can represent a degree to which a predicted behavior of an agent depicted in the sensor input is rare relative to other objects.

Abstract: Methods, systems, and apparatus, including computer programs encoded on computer storage media, for generating trajectory predictions for one or more target agents, e.g., a vehicle, a cyclist, or a pedestrian, in an environment. In one aspect, one of the methods include: obtaining scene context data characterizing a scene at a current time point in an environment that includes multiple target agents; generating, from the scene context data, an encoded representation of the scene in the environment; and generating, by a diffusion model based on the encoded representation, a respective trajectory prediction output that predicts a respective future trajectory for each of the multiple target agents after the current time point.

Abstract: Methods and systems are provided for detecting objects by utilizing uncertainties. In some aspects, a process can include steps for receiving, by an autonomous vehicle system, a frame of a scene with a detected object; estimating, by the autonomous vehicle system, an overall probability of the detected object in the frame; estimating, by the autonomous vehicle system, covariances for each state of at least one bounding box; and balancing, by the autonomous vehicle system, confidence values of the at least one bounding box based on the overall probability of the detected object and the covariances of each state of the at least one bounding box.