Abstract: An annotation system provides various tools for facilitating training data annotation. The annotation tools include a bidirectional annotation model that generates annotations for an image sequence based on both forward information and backward information in an image sequence. The annotation system also facilitates annotation processes by automatically suggesting annotations to the human operator based on a set of annotation predictions and locations of interactions of the human operator on the image. This way, the annotation system provides an accelerated way to generate high-quality annotations that take into account input from a human operator by using the predictions as a guide when it appears that an estimated annotation is consistent with the judgement of the human operator. The annotation system also updates annotations for an overlapping set of objects based on input from human operators.

Abstract: An autonomous control system generates synthetic data that reflect simulated environments. Specifically, the synthetic data is a representation of sensor data of the simulated environment from the perspective of one or more sensors. The system generates synthetic data by introducing one or more simulated modifications to sensor data captured by the sensors or by simulating the sensor data for a virtual environment. The autonomous control system uses the synthetic data to train computer models for various detection and control algorithms. In general, this allows autonomous control systems to augment training data to improve performance of computer models, simulate scenarios that are not included in existing training data, and/or train computer models that remove unwanted effects or occlusions from sensor data of the environment.

Abstract: An autonomous control system combines sensor data from multiple sensors to simulate sensor data from high-capacity sensors. The sensor data contains information related to physical environments surrounding vehicles for autonomous guidance. For example, the sensor data may be in the form of images that visually capture scenes of the surrounding environment, geo-location of the vehicles, and the like. The autonomous control system simulates high-capacity sensor data of the physical environment from replacement sensors that may each have lower capacity than high-capacity sensors. The high-capacity sensor data may be simulated via one or more neural network models. The autonomous control system performs various detection and control algorithms on the simulated sensor data to guide the vehicle autonomously.