Recently, there has been a significant escalation in both academic and industrial commitment towards the development of autonomous driving systems (ADSs). A number of simulation testing approaches have been proposed to generate diverse driving scenarios for ADS testing. However, scenarios generated by these previous approaches are static and lack interactions between the EGO vehicle and the NPC vehicles, resulting in a large amount of time on average to find violation scenarios. Besides, a large number of the violations they found are caused by aggressive behaviors of NPC vehicles, revealing none bugs of ADS. In this work, we propose the concept of adversarial NPC vehicles and introduce AdvFuzz, a novel simulation testing approach, to generate adversarial scenarios on main lanes (e.g., urban roads and highways). AdvFuzz allows NPC vehicles to dynamically interact with the EGO vehicle and regulates the behaviors of NPC vehicles, finding more violation scenarios caused by the EGO vehicle more quickly. We compare AdvFuzz with a random approach and three state-of-the-art scenario-based testing approaches. Our experiments demonstrate that AdvFuzz can generate 198.34% more violation scenarios compared to the other four approaches in 12 hours and increase the proportion of violations caused by the EGO vehicle to 87.04%, which is more than 7 times that of other approaches. Additionally, AdvFuzz is at least 92.21% faster in finding one violation caused by the EGO vehicle than that of the other approaches.

With the rapid development of autonomous vehicles, there is an increasing demand for scenario-based testing to simulate diverse driving scenarios. However, as the base of any driving scenarios, road scenarios (e.g., road topology and geometry) have received little attention by the literature. Despite several advances, they either generate basic road components without a complete road network, or generate a complete road network but with simple road components. The resulting road scenarios lack diversity in both topology and geometry. To address this problem, we propose RoadGen to systematically generate diverse road scenarios. The key idea is to connect eight types of parameterized road components to form road scenarios with high diversity in topology and geometry. Our evaluation has demonstrated the effectiveness and usefulness of RoadGen in generating diverse road scenarios for simulation.

Supervised learning usually requires a large amount of labelled data. However, attaining ground-truth labels is costly for many tasks. Alternatively, weakly supervised methods learn with cheap weak signals that only approximately label some data. Many existing weakly supervised learning methods learn a deterministic function that estimates labels given the input data and weak signals. In this paper, we develop label learning flows (LLF), a general framework for weakly supervised learning problems. Our method is a generative model based on normalizing flows. The main idea of LLF is to optimize the conditional likelihoods of all possible labelings of the data within a constrained space defined by weak signals. We develop a training method for LLF that trains the conditional flow inversely and avoids estimating the labels. Once a model is trained, we can make predictions with a sampling algorithm. We apply LLF to three weakly supervised learning problems. Experiment results show that our method outperforms many baselines we compare against.

Traditional structured prediction models try to learn the conditional likelihood, i.e., p(y x), to capture the relationship between the structured output y and the input features x. For many models, computing the likelihood is intractable. These models are therefore hard to train, requiring the use of surrogate objectives or variational inference to approximate likelihood. In this paper, we propose conditional Glow (c-Glow), a conditional generative flow for structured output learning. C-Glow benefits from the ability of flow-based models to compute p(y x) exactly and efficiently. Learning with c-Glow does not require a surrogate objective or performing inference during training. Once trained, we can directly and efficiently generate conditional samples to do structured prediction. We evaluate this approach on different structured prediction tasks and find c-Glow's structured outputs comparable in quality with state-of-the-art deep structured prediction approaches.

Traditional learning methods for training Markov random fields require doing inference over all variables to compute the likelihood gradient. The iteration complexity for those methods therefore scales with the size of the graphical models. In this paper, we propose \emph{block belief propagation learning} (BBPL), which uses block-coordinate updates of approximate marginals to compute approximate gradients, removing the need to compute inference on the entire graphical model. Thus, the iteration complexity of BBPL does not scale with the size of the graphs. We prove that the method converges to the same solution as that obtained by using full inference per iteration, despite these approximations, and we empirically demonstrate its scalability improvements over standard training methods.

We have constructed an information extraction system called the Mars Target Encyclopedia that takes in planetary science publications and extracts scientific knowledge about target compositions. The extracted knowledge is stored in a searchable database that can greatly accelerate the ability of scientists to compare new discoveries with what is already known. To date, we have applied this system to ~6000 documents and achieved 41-56% precision in the extracted information.

NASA has acquired more than 22 million images from the planet Mars. To help users find images of interest, we developed a content-based search capability for Mars rover surface images and Mars orbital images. We started with the AlexNet convolutional neural network, which was trained on Earth images, and used transfer learning to adapt the network for use with Mars images. We report on our deployment of these classifiers within the PDS Imaging Atlas, a publicly accessible web interface, to enable the first content-based image search for NASA’s Mars images.