5,123 research outputs found
Social Clustering of Vehicles Based on Semi-Markov Processes
The full text version attached to this record is the authors final peer reviewed version. The publisher's final version of record can be found by following the DOI link.Vehicle clustering is a crucial network managementtask for vehicular networks in order to address the broadcaststorm problem, and also to cope with rapidly changing networktopology. Developing algorithms that createstable clustersis avery challenging procedure because of the highly dynamic movingpatterns of vehicles and the dense topology. Previous approachesto vehicle clustering have been based on either topology-agnosticfeatures, such as vehicle IDs, on hard to set parameters, orhave exploited very limited knowledge of vehicle trajectories.This article develops a pair of algorithms, namelySociologicalPattern Clustering (SPC), andRoute Stability Clustering (RSC),the latter being a specialization of the former that exploit, forthe first time in the relevant literature, the “social behavior”of vehicles, i.e. their tendency to share the same/similar routes.Both methods exploit the historic trajectories of vehiclesgatheredby road-side units located in each subnetwork of a city, anduse the recently introduced clustering primitive ofvirtual forces.The mobility, i.e. mobile patterns of each vehicle are modeledas semi-Markov processes. In order to assess the performanceof the proposed clustering algorithms, we performed a detailedexperimentation by simulation to compare its behavior withthat of high-performance state-of-the-art algorithms, namely, theLow-Id,DDVCandMPBCprotocols. The comparison involvedthe investigation of the impact of a range of parameters onthe performance of the protocols, including vehicle speed andtransmission range as well as the existence and strength of socialpatterns, for both urban and highway-like environments. Allthe received results attested to the superiority of the proposedalgorithms for creating stable and meaningful clusters
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
Generalized gap acceptance models for unsignalized intersections
This paper contributes to the modeling and analysis of unsignalized
intersections. In classical gap acceptance models vehicles on the minor road
accept any gap greater than the CRITICAL gap, and reject gaps below this
threshold, where the gap is the time between two subsequent vehicles on the
major road. The main contribution of this paper is to develop a series of
generalizations of existing models, thus increasing the model's practical
applicability significantly. First, we incorporate {driver impatience behavior}
while allowing for a realistic merging behavior; we do so by distinguishing
between the critical gap and the merging time, thus allowing MULTIPLE vehicles
to use a sufficiently large gap. Incorporating this feature is particularly
challenging in models with driver impatience. Secondly, we allow for multiple
classes of gap acceptance behavior, enabling us to distinguish between
different driver types and/or different vehicle types. Thirdly, we use the
novel M/SM2/1 queueing model, which has batch arrivals, dependent service
times, and a different service-time distribution for vehicles arriving in an
empty queue on the minor road (where `service time' refers to the time required
to find a sufficiently large gap). This setup facilitates the analysis of the
service-time distribution of an arbitrary vehicle on the minor road and of the
queue length on the minor road. In particular, we can compute the MEAN service
time, thus enabling the evaluation of the capacity for the minor road vehicles
Markov Chain Methods For Analyzing Complex Transport Networks
We have developed a steady state theory of complex transport networks used to
model the flow of commodity, information, viruses, opinions, or traffic. Our
approach is based on the use of the Markov chains defined on the graph
representations of transport networks allowing for the effective network
design, network performance evaluation, embedding, partitioning, and network
fault tolerance analysis. Random walks embed graphs into Euclidean space in
which distances and angles acquire a clear statistical interpretation. Being
defined on the dual graph representations of transport networks random walks
describe the equilibrium configurations of not random commodity flows on
primary graphs. This theory unifies many network concepts into one framework
and can also be elegantly extended to describe networks represented by directed
graphs and multiple interacting networks.Comment: 26 pages, 4 figure
Thirty Years of Machine Learning: The Road to Pareto-Optimal Wireless Networks
Future wireless networks have a substantial potential in terms of supporting
a broad range of complex compelling applications both in military and civilian
fields, where the users are able to enjoy high-rate, low-latency, low-cost and
reliable information services. Achieving this ambitious goal requires new radio
techniques for adaptive learning and intelligent decision making because of the
complex heterogeneous nature of the network structures and wireless services.
Machine learning (ML) algorithms have great success in supporting big data
analytics, efficient parameter estimation and interactive decision making.
Hence, in this article, we review the thirty-year history of ML by elaborating
on supervised learning, unsupervised learning, reinforcement learning and deep
learning. Furthermore, we investigate their employment in the compelling
applications of wireless networks, including heterogeneous networks (HetNets),
cognitive radios (CR), Internet of things (IoT), machine to machine networks
(M2M), and so on. This article aims for assisting the readers in clarifying the
motivation and methodology of the various ML algorithms, so as to invoke them
for hitherto unexplored services as well as scenarios of future wireless
networks.Comment: 46 pages, 22 fig
Symbol Emergence in Robotics: A Survey
Humans can learn the use of language through physical interaction with their
environment and semiotic communication with other people. It is very important
to obtain a computational understanding of how humans can form a symbol system
and obtain semiotic skills through their autonomous mental development.
Recently, many studies have been conducted on the construction of robotic
systems and machine-learning methods that can learn the use of language through
embodied multimodal interaction with their environment and other systems.
Understanding human social interactions and developing a robot that can
smoothly communicate with human users in the long term, requires an
understanding of the dynamics of symbol systems and is crucially important. The
embodied cognition and social interaction of participants gradually change a
symbol system in a constructive manner. In this paper, we introduce a field of
research called symbol emergence in robotics (SER). SER is a constructive
approach towards an emergent symbol system. The emergent symbol system is
socially self-organized through both semiotic communications and physical
interactions with autonomous cognitive developmental agents, i.e., humans and
developmental robots. Specifically, we describe some state-of-art research
topics concerning SER, e.g., multimodal categorization, word discovery, and a
double articulation analysis, that enable a robot to obtain words and their
embodied meanings from raw sensory--motor information, including visual
information, haptic information, auditory information, and acoustic speech
signals, in a totally unsupervised manner. Finally, we suggest future
directions of research in SER.Comment: submitted to Advanced Robotic
- …