4,080 research outputs found
Integration of a Contextual Observation System in a Multi-Process Architecture for Autonomous Vehicles
We propose a software layered architecture for autonomous vehicles whose efficiency is driven by pull-based acquisition of sensor data. This multiprocess software architecture, to be embedded into the control loop of these vehicles, includes a Belief-Desire-Intention agent that can consistently assist the achievement of intentions. Since driving on roads implies huge dynamic considerations, we tackle both reactivity and context awareness considerations on the execution loop of the vehicle. While the proposed architecture gradually offers 4 levels of reactivity, from arch-reflex to the deep modification of the previously built execution plan, the observation module concurrently exploits noise filtering and introduces frequency control to allow symbolic feature extraction while both fuzzy and first order logic management are used to enforce consistency and certainty over the context information properties. The presented use-case, the daily delivery of a network of pharmacy offices by an autonomous vehicle taking into account contextual (spatio-temporal) traffic features, shows the efficiency and the modularity of the architecture, as well as the scalability of the reaction levels
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
Towards Ubiquitous Semantic Metaverse: Challenges, Approaches, and Opportunities
In recent years, ubiquitous semantic Metaverse has been studied to
revolutionize immersive cyber-virtual experiences for augmented reality (AR)
and virtual reality (VR) users, which leverages advanced semantic understanding
and representation to enable seamless, context-aware interactions within
mixed-reality environments. This survey focuses on the intelligence and
spatio-temporal characteristics of four fundamental system components in
ubiquitous semantic Metaverse, i.e., artificial intelligence (AI),
spatio-temporal data representation (STDR), semantic Internet of Things (SIoT),
and semantic-enhanced digital twin (SDT). We thoroughly survey the
representative techniques of the four fundamental system components that enable
intelligent, personalized, and context-aware interactions with typical use
cases of the ubiquitous semantic Metaverse, such as remote education, work and
collaboration, entertainment and socialization, healthcare, and e-commerce
marketing. Furthermore, we outline the opportunities for constructing the
future ubiquitous semantic Metaverse, including scalability and
interoperability, privacy and security, performance measurement and
standardization, as well as ethical considerations and responsible AI.
Addressing those challenges is important for creating a robust, secure, and
ethically sound system environment that offers engaging immersive experiences
for the users and AR/VR applications.Comment: 18 pages, 7 figures, 3 table
A Simulation-based Approach for Constructing All-day Travel Chains from Mobile Phone Data
The purpose of this work is to investigate replacing travel diaries with sets of call detail records (CDRs) as inputs for an agent-oriented traffic simulation. We propose constructing an agent population directly from a CDR dataset and fusing it with link volume counts to reduce spatio-temporal uncertainty and correct for underrepresented traffic segments. The problem of finding a set of travel plans which realizes a set of CDR trajectories and is consistent with a set of link volume counts is rephrased in terms of calibrating a choice model. This enables us to make use of an existing calibration scheme for agent-oriented simulations. We demonstrate our approach by an illustrative scenario with synthetic data
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