While recent online HD mapping methods relieve burdened offline pipelines and solve map freshness, they remain limited by perceptual inaccuracies, occlusion in dense traffic, and an inability to fuse multi-agent observations. We propose RTMap to enhance these single-traversal methods by persistently crowdsourcing a multi-traversal HD map as a self-evolutional memory. On onboard agents, RTMap simultaneously addresses three core challenges in an end-to-end fashion: (1) Uncertainty-aware positional modeling for HD map elements, (2) probabilistic-aware localization w.r.t. the crowdsourced prior-map, and (3) real-time detection for possible road structural changes. Experiments on several public autonomous driving datasets demonstrate our solid performance on both the prior-aided map quality and the localization accuracy, demonstrating our effectiveness of robustly serving downstream prediction and planning modules while gradually improving the accuracy and freshness of the crowdsourced prior-map asynchronously. Our source-code will be made publicly available at https://github.com/CN-ADLab/RTMap.

In autonomous driving, 3D occupancy prediction outputs voxel-wise status and semantic labels for more comprehensive understandings of 3D scenes compared with traditional perception tasks, such as 3D object detection and bird’s-eye view (BEV) semantic segmentation. Recent researchers have extensively explored various aspects of this task, including view transformation techniques, ground-truth label generation, and elaborate network design, aiming to achieve superior performance. However, the inference speed, crucial for running on an autonomous vehicle, is neglected. To this end, a new method, dubbed FastOcc, is proposed. By carefully analyzing the network effect and latency from four parts, including the input image resolution, image backbone, view transformation, and occupancy prediction head, it is found that the occupancy prediction head holds considerable potential for accelerating the model while keeping its accuracy. Targeted at improving this component, the time-consuming 3D convolution network is replaced with a novel residual-like architecture, where features are mainly digested by a lightweight 2D BEV convolution network and compensated by integrating the 3D voxel features interpolated from the original image features. Experiments on the Occ3D-nuScenes benchmark demonstrate that our FastOcc achieves state-of-the-art results with a fast inference speed.

Queries used to draw data from high-volume, high-velocity social media data streams, such as Twitter, typically require a set of keywords to filter the data. When topics and conversations change rapidly, initial keywords may become outdated and irrelevant, which may result in incomplete data. We propose a novel technique that improves data collection from social media streams in two ways. First, we develop a query expansion method that identifies and adds emergent keywords to the initial query, which makes the data collection a dynamic process that adapts to changes in social conversations. Second, we develop a "predictive query expansion" method that combines keywords from the streams with external data sources, which enables the construction of new queries that effectively capture emergent events that a user may not have anticipated when initiating the data collection stream. We demonstrate the effectiveness of our approach with an analysis of more than 20.5 million Twitter messages related to the 2015 Baltimore protests. We use newspaper archives as an external data source from which we collect keywords to expand the queries built from the primary stream.Reproducibility: https://github.com/FarahAlshanik/QE

Yuheng Du, Shereen Oraby, Vittorio Perera, Minmin Shen, Anjali Narayan-Chen, Tagyoung Chung, Anushree Venkatesh, Dilek Hakkani-Tur. Proceedings of the 13th International Conference on Natural Language Generation. 2020.

A complex and vast amount of data will be collected from on-board sensors of operational connected vehicles (CVs), infrastructure data sources such as roadway sensors and traffic signals, mobile data sources such as cell phones, social media sources such as Twitter, and news and weather data services. Unfortunately, these data will create a bottleneck at data centers for processing and retrievals of collected data, and will require the deployment of additional message transfer infrastructure between data producers and consumers to support diverse CV applications. In this paper, we present a strategy for creating an efficient and low-latency distributed message delivery system for CV applications using a distributed message delivery platform. This strategy enables large-scale ingestion, curation, and transformation of unstructured data (roadway traffic-related and roadway non-traffic-related data) into labeled and customized topics for a large number of subscribers or consumers, such as CVs, mobile devices, and data centers. We evaluate the performance of this strategy by developing a prototype infrastructure using Apache Kafka, an open source message delivery system, and compared its performance with the latency requirements of CV applications. We present experimental results of the message delivery infrastructure on two different distributed computing testbeds at Clemson University: the Holocron cluster and the Palmetto cluster. Experiments were performed to measure the latency of the message delivery system for a variety of testing scenarios. These experiments reveal that measured latencies are less than the U.S. Department of Transportation recommended latency requirements for CV applications, which prove the efficacy of the system for CV related data distribution and management tasks.

Methods for detecting and summarizing emergent keywords have been extensively studied since social media and microblogging activities have started to play an important role in data analysis and decision making. We present a fast system for monitoring emergent keywords and summarizing a document stream based on the dynamic semantic graphs of streaming documents. We introduce the notion of dynamic eigenvector centrality for ranking emergent keywords, and present an algorithm for summarizing emergent events that is based on the minimum weight set cover. Our system is demonstrated on the streaming Twitter data related to public security.

Common Crawl is a massive multi-petabyte dataset hosted by Amazon. It contains archived HTML web page data from 2008 to date. Common Crawl has been widely used for text mining purposes. Using data extracted from Common Crawl has several advantages over a direct crawl of web data, among which is removing the likelihood of a user's home IP address becoming blacklisted for accessing a given web site too frequently. However, Common Crawl is a data sample, and so questions arise about the quality of Common Crawl as a representative sample of the original data. We perform systematic tests on the similarity of topics estimated from Common Crawl compared to topics estimated from the full data of online forums. Our target is online discussions from a user forum for automotive enthusiasts, but our research strategy can be applied to other domains and samples to evaluate the representativeness of topic models. We show that topic proportions estimated from Common Crawl are not significantly different than those estimated on the full data. We also show that topics are similar in terms of their word compositions, and not worse than topic similarity estimated under true random sampling, which we simulate through a series of experiments. Our research will be of interest to analysts who wish to use Common Crawl to study topics of interest in user forum data, and analysts applying topic models to other data samples.

Substantial progresses have been made in building versatile anthropomorphic prosthetic hands in the past two decades using emerging technologies. However the trade-offs between functionality, reliability, affordability, appearance, etc. have not been fully settled. Many existing designs, particularly the commercial prosthetic hands, are underactuated and they can realize various grasps through compliant structures or differential mechanisms. This paper presents the design of an underactuated prosthetic hand with one actuator using a continuum differential mechanism. Structure of the continuum differential mechanism is simple enough to allow all the components, including a battery pack, to be packed into the palm. The design concept, component descriptions, and hand constructions are elaborated. Experimental verifications are presented to demonstrate the efficacy of the proposed design.

Sensor networks must be monitored to identify and correct problems as they occur.We present a comparison of two approaches to monitoring deployed sensors.The first relies on configuration parameters to define expected reporting behavior.The second automatically identifies normal reporting patterns based on a combination of configuration parameters and an analysis of reporting times.Using these patterns, the system notifies personnel of possible malfunctions.We present empirical evaluations in the context of the Intelligent River R system [11].

It is known that human controls muscles for hand poses in a coordinated manner and the coordination is referred to as a postural synergy. Using postural synergies, dexterous grasping tasks could be accomplished on a prosthetic hand via only a few (usually two) control inputs. Instead of implementing postural synergies digitally, this paper presents the design of a simple continuum mechanism for implementing the postural synergies mechanically. The design, fabrication and assembly of a prosthetic hand are firstly presented, followed by the synthesis of postural synergies from various grasping poses. Referring to the extracted postural synergies, structural parameters of the continuum mechanism are calculated based on a kinematics model. Experimental verifications are also presented to demonstrate the efficacy of the proposed idea.