34,624 research outputs found
Characterizing Driving Context from Driver Behavior
Because of the increasing availability of spatiotemporal data, a variety of
data-analytic applications have become possible. Characterizing driving
context, where context may be thought of as a combination of location and time,
is a new challenging application. An example of such a characterization is
finding the correlation between driving behavior and traffic conditions. This
contextual information enables analysts to validate observation-based
hypotheses about the driving of an individual. In this paper, we present
DriveContext, a novel framework to find the characteristics of a context, by
extracting significant driving patterns (e.g., a slow-down), and then
identifying the set of potential causes behind patterns (e.g., traffic
congestion). Our experimental results confirm the feasibility of the framework
in identifying meaningful driving patterns, with improvements in comparison
with the state-of-the-art. We also demonstrate how the framework derives
interesting characteristics for different contexts, through real-world
examples.Comment: Accepted to be published at The 25th ACM SIGSPATIAL International
Conference on Advances in Geographic Information Systems (ACM SIGSPATIAL
2017
Heterogeneity in the Driver Behavior: An Exploratory Study Using Real-Time Driving Data
Driver behavior heterogeneity is a significant aspect to understand the individual behavioral variations and develop driver assistance systems. This study characterizes the heterogeneity in driving behavior using real-time driving performance features. In this context, the study investigates the extent of variations in the individual's driving styles during routine driving. The driving styles are conceptualized using the vehicle kinematic data, that is, speed and accelerations performed during longitudinal control. The data is collected for 42 professional drivers using instrumented vehicle over a defined study stretch. An algorithm is developed for data extraction and total 7548 acceleration and 6156 braking maneuvers and corresponding driving performance features are extracted. The driving maneuver data are analyzed using the unsupervised techniques (PCA and K-means clustering) and three patterns of acceleration and braking are identified, which are further associated with two patterns of speed behavior. The results showed that each driver is found to exhibit different driving patterns in different driving regimes and no driver shows constantly safe or aggressive behavior. The aggression scores are found to be different among drivers, indicating the behavioral heterogeneity. This study results demonstrate that, driver's level of aggression in different driving regimes is not constant and characterizing the driver by means of abstract driving features is not indicative of the diversified driving behavior. The proposed method identifies the individualized driving behaviors, reflecting the driver's choice of driving maneuvers. Thus, the insights from the study are highly useful to design driver-specific safety models for driver assistance and driver identification. © 2022 Jahnavi Yarlagadda and Digvijay S. Pawar
Characterizing driving behavior using automatic visual analysis
In this work, we present the problem of rash driving detection algorithm
using a single wide angle camera sensor, particularly useful in the Indian
context. To our knowledge this rash driving problem has not been addressed
using Image processing techniques (existing works use other sensors such as
accelerometer). Car Image processing literature, though rich and mature, does
not address the rash driving problem. In this work-in-progress paper, we
present the need to address this problem, our approach and our future plans to
build a rash driving detector.Comment: 4 pages,7 figures, IBM-ICARE201
An Action-Based Approach to Presence: Foundations and Methods
This chapter presents an action-based approach to presence. It starts by briefly describing the theoretical and empirical foundations of this approach, formalized into three key notions of place/space, action and mediation. In the light of these notions, some common assumptions about presence are then questioned: assuming a neat distinction between virtual and real environments, taking for granted the contours of the mediated environment and considering presence as a purely personal state. Some possible research topics opened up by adopting action as a unit of analysis are illustrated. Finally, a case study on driving as a form of mediated presence is discussed, to provocatively illustrate the flexibility of this approach as a unified framework for presence in digital and physical environment
Deterministically Driven Avalanche Models of Solar Flares
We develop and discuss the properties of a new class of lattice-based
avalanche models of solar flares. These models are readily amenable to a
relatively unambiguous physical interpretation in terms of slow twisting of a
coronal loop. They share similarities with other avalanche models, such as the
classical stick--slip self-organized critical model of earthquakes, in that
they are driven globally by a fully deterministic energy loading process. The
model design leads to a systematic deficit of small scale avalanches. In some
portions of model space, mid-size and large avalanching behavior is scale-free,
being characterized by event size distributions that have the form of
power-laws with index values, which, in some parameter regimes, compare
favorably to those inferred from solar EUV and X-ray flare data. For models
using conservative or near-conservative redistribution rules, a population of
large, quasiperiodic avalanches can also appear. Although without direct
counterparts in the observational global statistics of flare energy release,
this latter behavior may be relevant to recurrent flaring in individual coronal
loops. This class of models could provide a basis for the prediction of large
solar flares.Comment: 24 pages, 11 figures, 2 tables, accepted for publication in Solar
Physic
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