Self-driving vehicles (SDVs) hold great potential for improving traffic safety and are poised to positively affect the quality of life of millions of people. To unlock this potential one of the critical aspects of the autonomous technology is understanding and predicting future movement of vehicles surrounding the SDV. This work presents a deep-learning- based method for kinematically feasible motion prediction of such traffic actors. Previous work did not explicitly encode vehicle kinematics and instead relied on the models to learn the constraints directly from the data, potentially resulting in kinematically infeasible, suboptimal trajectory predictions. To address this issue we propose a method that seamlessly combines ideas from the AI with physically grounded vehicle motion models. In this way we employ best of the both worlds, coupling powerful learning models with strong feasibility guarantees for their outputs. The proposed approach is general, being applicable to any type of learning method. Extensive experiments using deep convnets on real-world data strongly indicate its benefits, outperforming the existing state-of-the-art.

Following detection and tracking of traffic actors, prediction of their future motion is the next critical component of a self-driving vehicle (SDV) technology, allowing the SDV to operate safely and efficiently in its environment. This is particularly important when it comes to vulnerable road users (VRUs), such as pedestrians and bicyclists. These actors need to be handled with special care due to an increased risk of injury, as well as the fact that their behavior is less predictable than that of motorized actors. To address this issue, in the current study we present a deep learning-based method for predicting VRU movement, where we rasterize high-definition maps and actor's surroundings into a bird's-eye view image used as an input to deep convolutional networks. In addition, we propose a fast architecture suitable for real-time inference, and perform an ablation study of various rasterization approaches to find the optimal choice for accurate prediction. The results strongly indicate benefits of using the proposed approach for motion prediction of VRUs, both in terms of accuracy and latency.

We address one of the crucial aspects necessary for safe and efficient operations of autonomous vehicles, namely predicting future state of traffic actors in the autonomous vehicle's surroundings. We introduce a deep learning-based approach that takes into account a current world state and produces raster images of each actor's vicinity. The rasters are then used as inputs to deep convolutional models to infer future movement of actors while also accounting for and capturing inherent uncertainty of the prediction task. Extensive experiments on real-world data strongly suggest benefits of the proposed approach. Moreover, following successful tests the system was deployed to a fleet of autonomous vehicles.

Autonomous driving presents one of the largest problems that the robotics and artificial intelligence communities are facing at the moment, both in terms of difficulty and potential societal impact. Self-driving vehicles (SDVs) are expected to prevent road accidents and save millions of lives while improving the livelihood and life quality of many more. However, despite large interest and a number of industry players working in the autonomous domain, there still remains more to be done in order to develop a system capable of operating at a level comparable to best human drivers. One reason for this is high uncertainty of traffic behavior and large number of situations that an SDV may encounter on the roads, making it very difficult to create a fully generalizable system. To ensure safe and efficient operations, an autonomous vehicle is required to account for this uncertainty and to anticipate a multitude of possible behaviors of traffic actors in its surrounding. We address this critical problem and present a method to predict multiple possible trajectories of actors while also estimating their probabilities. The method encodes each actor's surrounding context into a raster image, used as input by deep convolutional networks to automatically derive relevant features for the task. Following extensive offline evaluation and comparison to state-of-the-art baselines, the method was successfully tested on SDVs in closed-course tests.

Healthcare systems around the world are facing the challenge of information overload in caring for patients in an affordable, safe and high-quality manner in a system with limited healthcare resources and increasing costs. To alleviate this problem, we develop an integrative healthcare analytics system called GEMINI which allows point of care analytics for doctors where real-time usable and relevant information of their patients are required through the questions they asked about the patients they are caring for. GEMINI extracts data of each patient from various data sources and stores them as information in a patient profile graph. The data sources are complex and varied consisting of both structured data (such as, patients' demographic data, laboratory results and medications) and unstructured data (such as, doctors' notes). Hence, the patient profile graph provides a holistic and comprehensive information of patients' healthcare profile, from which GEMINI can infer implicit information useful for administrative and clinical purposes, and extract relevant information for performing predictive analytics. At the core, GEMINI keeps interacting with the healthcare professionals as part of a feedback loop to gather, infer, ascertain and enhance the self-learning knowledge base. We present a case study on using GEMINI to predict the risk of unplanned patient readmissions.

In this paper, we study the problem of efficient spatiotemporal indexing of moving objects. In order to reduce the frequency of object location updates, a linear motion model is used to model the near future location of moving objects. A number of existing spatiotemporal indexes have already been proposed for indexing these models. However, these indexes are either designed to offer high query performance or high update performance. Therefore, they are all ill suited to handle situations where both queries and updates arrive at a high rate. In this paper, we propose the SeTPR*-tree which extends the TPR*-tree to more efficiently use a limited-sized RAM buffer for processing queries in batches and rapidly arriving updates. We provide both theoretical and empirical evidence of the effectiveness of the SeTPR*-tree in improving query and update performance. We have conducted extensive experiments using a recognized spatiotemporal benchmark on a solid state drive. The SeTPR*-tree simultaneously outperforms the best tested index optimized for queries by up to a factor of 5.6 for query I/O and outperforms the best tested index for updates by up to a factor of 11.5 for update I/O.

There have been intense research interests in moving object indexing in the past decade. However, existing work did not exploit the important property of skewed velocity distributions. In many real world scenarios, objects travel predominantly along only a few directions. Examples include vehicles on road networks, flights, people walking on the streets, etc. The search space for a query is heavily dependent on the velocity distribution of the objects grouped in the nodes of an index tree. Motivated by this observation, we propose the velocity partitioning (VP) technique, which exploits the skew in velocity distribution to speed up query processing using moving object indexes. The VP technique first identifies the "dominant velocity axes (DVAs)" using a combination of principal components analysis (PCA) and k-means clustering. Then, a moving object index (e.g., a TPR-tree) is created based on each DVA, using the DVA as an axis of the underlying coordinate system. An object is maintained in the index whose DVA is closest to the object's current moving direction. Thus, all the objects in an index are moving in a near 1-dimensional space instead of a 2-dimensional space. As a result, the expansion of the search space with time is greatly reduced, from a quadratic function of the maximum speed (of the objects in the search range) to a near linear function of the maximum speed. The VP technique can be applied to a wide range of moving object index structures. We have implemented the VP technique on two representative ones, the TPR*-tree and the B x -tree. Extensive experiments validate that the VP technique consistently improves the performance of those index structures.

The increasing popularity of flash memory means more database systems will run on flash memory in the future. One of the most important database operations is the external sort. Hence, this paper is focused on studying the problem of efficient external sorting on flash memory. In contrast to most previous work, we target the situation where previously sorted data have become progressively unsorted due to data updates. Accordingly, we call this ‘partially’ sorted data. We focus on re-sorting partially sorted data by taking advantage of the partial sorted nature of the data to speed up the run generation phase of the traditional external merge sort. We do this by finding ‘naturally occurring’ page runs in the partially sorted data. Our algorithm can perform up to a factor of 1024 less write IO compared with a traditional external merge sort during the run generation phase. We map the problem of finding naturally occurring runs into the shortest distance problem in a directed acyclic graph (DAG). Accordingly, we propose an optimal solution to the problem using the well-known DAG-Shortest-Paths algorithm. However, we found that the optimal solution was too slow for even moderate-sized data sets and accordingly propose a fast heuristic solution that—we experimentally show—finds a high percentage of page runs using a minimum of computational overhead. Experiments using both real and synthetic data sets show that our heuristic algorithm can halve the external sorting time when compared with three likely competing external sorting algorithms.

A virtual hierarchical design optimization method has been developed to combine strength of the flat and the hierarchical optimization methods for efficient and quality optimization of multi-million gate designs in a distributed computing environment. A novel design representation "virtual hierarchy" is proposed for sub-design optimization in a distributed computing environment. The principle and the implementation details of the method are described. The method has been successfully applied to a number of designs.