25,564 research outputs found
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
Measuring Sociality in Driving Interaction
Interacting with other human road users is one of the most challenging tasks
for autonomous vehicles. For congruent driving behaviors, it is essential to
recognize and comprehend sociality, encompassing both implicit social norms and
individualized social preferences of human drivers. To understand and quantify
the complex sociality in driving interactions, we propose a Virtual-Game-based
Interaction Model (VGIM) that is parameterized by a social preference
measurement, Interaction Preference Value (IPV). The IPV is designed to capture
the driver's relative inclination towards individual rewards over group
rewards. A method for identifying IPV from observed driving trajectory is also
developed, with which we assessed human drivers' IPV using driving data
recorded in a typical interactive driving scenario, the unprotected left turn.
Our findings reveal that (1) human drivers exhibit particular social preference
patterns while undertaking specific tasks, such as turning left or proceeding
straight; (2) competitive actions could be strategically conducted by human
drivers in order to coordinate with others. Finally, we discuss the potential
of learning sociality-aware navigation from human demonstrations by
incorporating a rule-based humanlike IPV expressing strategy into VGIM and
optimization-based motion planners. Simulation experiments demonstrate that (1)
IPV identification improves the motion prediction performance in interactive
driving scenarios and (2) the dynamic IPV expressing strategy extracted from
human driving data makes it possible to reproduce humanlike coordination
patterns in the driving interaction
Proceedings of the 2009 Joint Workshop of Fraunhofer IOSB and Institute for Anthropomatics, Vision and Fusion Laboratory
The joint workshop of the Fraunhofer Institute of Optronics, System Technologies and Image Exploitation IOSB, Karlsruhe, and the Vision and Fusion Laboratory (Institute for Anthropomatics, Karlsruhe Institute of Technology (KIT)), is organized annually since 2005 with the aim to report on the latest research and development findings of the doctoral students of both institutions. This book provides a collection of 16 technical reports on the research results presented on the 2009 workshop
Markov Decision Processes with Applications in Wireless Sensor Networks: A Survey
Wireless sensor networks (WSNs) consist of autonomous and resource-limited
devices. The devices cooperate to monitor one or more physical phenomena within
an area of interest. WSNs operate as stochastic systems because of randomness
in the monitored environments. For long service time and low maintenance cost,
WSNs require adaptive and robust methods to address data exchange, topology
formulation, resource and power optimization, sensing coverage and object
detection, and security challenges. In these problems, sensor nodes are to make
optimized decisions from a set of accessible strategies to achieve design
goals. This survey reviews numerous applications of the Markov decision process
(MDP) framework, a powerful decision-making tool to develop adaptive algorithms
and protocols for WSNs. Furthermore, various solution methods are discussed and
compared to serve as a guide for using MDPs in WSNs
Determinants of Successful Cooperation in a Face-to-Face Social Dilemma
What makes you a successful cooperator? Using data from the British television game show "Golden Balls" we analyze a prisoner's dilemma game and its pre-play. We find that players strategically select their partner for the PD, e.g., they bear in mind whether contestants lied. Players' expectations about the stake size strongly influence the outcome of the PD: The lower the stakes, the more likely players successfully cooperate. Most interestingly, unilateral cooperation is encouraged by mutually promising not to defect and shaking hands on it, but a mere handshake serves as manipulating device and increases successful defection.prisoner's dilemma, cooperative behavior, communication, promises, voting
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