4,063 research outputs found
Sharing Human-Generated Observations by Integrating HMI and the Semantic Sensor Web
Current âInternet of Thingsâ concepts point to a future where connected objects gather meaningful information about their environment and share it with other objects and people. In particular, objects embedding Human Machine Interaction (HMI), such as mobile devices and, increasingly, connected vehicles, home appliances, urban interactive infrastructures, etc., may not only be conceived as sources of sensor information, but, through interaction with their users, they can also produce highly valuable context-aware human-generated observations. We believe that the great promise offered by combining and sharing all of the different sources of information available can be realized through the integration of HMI and Semantic Sensor Web technologies. This paper presents a technological framework that harmonizes two of the most influential HMI and Sensor Web initiatives: the W3Câs Multimodal Architecture and Interfaces (MMI) and the Open Geospatial Consortium (OGC) Sensor Web Enablement (SWE) with its semantic extension, respectively. Although the proposed framework is general enough to be applied in a variety of connected objects integrating HMI, a particular development is presented for a connected car scenario where driversâ observations about the traffic or their environment are shared across the Semantic Sensor Web. For implementation and evaluation purposes an on-board OSGi (Open Services Gateway Initiative) architecture was built, integrating several available HMI, Sensor Web and Semantic Web technologies. A technical performance test and a conceptual validation of the scenario with potential users are reported, with results suggesting the approach is soun
Human Motion Trajectory Prediction: A Survey
With growing numbers of intelligent autonomous systems in human environments,
the ability of such systems to perceive, understand and anticipate human
behavior becomes increasingly important. Specifically, predicting future
positions of dynamic agents and planning considering such predictions are key
tasks for self-driving vehicles, service robots and advanced surveillance
systems. This paper provides a survey of human motion trajectory prediction. We
review, analyze and structure a large selection of work from different
communities and propose a taxonomy that categorizes existing methods based on
the motion modeling approach and level of contextual information used. We
provide an overview of the existing datasets and performance metrics. We
discuss limitations of the state of the art and outline directions for further
research.Comment: Submitted to the International Journal of Robotics Research (IJRR),
37 page
Sim2real and Digital Twins in Autonomous Driving: A Survey
Safety and cost are two important concerns for the development of autonomous
driving technologies. From the academic research to commercial applications of
autonomous driving vehicles, sufficient simulation and real world testing are
required. In general, a large scale of testing in simulation environment is
conducted and then the learned driving knowledge is transferred to the real
world, so how to adapt driving knowledge learned in simulation to reality
becomes a critical issue. However, the virtual simulation world differs from
the real world in many aspects such as lighting, textures, vehicle dynamics,
and agents' behaviors, etc., which makes it difficult to bridge the gap between
the virtual and real worlds. This gap is commonly referred to as the reality
gap (RG). In recent years, researchers have explored various approaches to
address the reality gap issue, which can be broadly classified into two
categories: transferring knowledge from simulation to reality (sim2real) and
learning in digital twins (DTs). In this paper, we consider the solutions
through the sim2real and DTs technologies, and review important applications
and innovations in the field of autonomous driving. Meanwhile, we show the
state-of-the-arts from the views of algorithms, models, and simulators, and
elaborate the development process from sim2real to DTs. The presentation also
illustrates the far-reaching effects of the development of sim2real and DTs in
autonomous driving
Autonomous Vehicles: Open-Source Technologies, Considerations, and Development
Autonomous vehicles are the culmination of advances in many areas such as
sensor technologies, artificial intelligence (AI), networking, and more. This
paper will introduce the reader to the technologies that build autonomous
vehicles. It will focus on open-source tools and libraries for autonomous
vehicle development, making it cheaper and easier for developers and
researchers to participate in the field. The topics covered are as follows.
First, we will discuss the sensors used in autonomous vehicles and summarize
their performance in different environments, costs, and unique features. Then
we will cover Simultaneous Localization and Mapping (SLAM) and algorithms for
each modality. Third, we will review popular open-source driving simulators, a
cost-effective way to train machine learning models and test vehicle software
performance. We will then highlight embedded operating systems and the security
and development considerations when choosing one. After that, we will discuss
Vehicle-to-Vehicle (V2V) and Internet-of-Vehicle (IoV) communication, which are
areas that fuse networking technologies with autonomous vehicles to extend
their functionality. We will then review the five levels of vehicle automation,
commercial and open-source Advanced Driving Assistance Systems, and their
features. Finally, we will touch on the major manufacturing and software
companies involved in the field, their investments, and their partnerships.
These topics will give the reader an understanding of the industry, its
technologies, active research, and the tools available for developers to build
autonomous vehicles.Comment: 13 pages, 7 figure
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