8,166 research outputs found
Unmanned Aerial Systems for Wildland and Forest Fires
Wildfires represent an important natural risk causing economic losses, human
death and important environmental damage. In recent years, we witness an
increase in fire intensity and frequency. Research has been conducted towards
the development of dedicated solutions for wildland and forest fire assistance
and fighting. Systems were proposed for the remote detection and tracking of
fires. These systems have shown improvements in the area of efficient data
collection and fire characterization within small scale environments. However,
wildfires cover large areas making some of the proposed ground-based systems
unsuitable for optimal coverage. To tackle this limitation, Unmanned Aerial
Systems (UAS) were proposed. UAS have proven to be useful due to their
maneuverability, allowing for the implementation of remote sensing, allocation
strategies and task planning. They can provide a low-cost alternative for the
prevention, detection and real-time support of firefighting. In this paper we
review previous work related to the use of UAS in wildfires. Onboard sensor
instruments, fire perception algorithms and coordination strategies are
considered. In addition, we present some of the recent frameworks proposing the
use of both aerial vehicles and Unmanned Ground Vehicles (UV) for a more
efficient wildland firefighting strategy at a larger scale.Comment: A recent published version of this paper is available at:
https://doi.org/10.3390/drones501001
Identifying Modes of Intent from Driver Behaviors in Dynamic Environments
In light of growing attention of intelligent vehicle systems, we propose
developing a driver model that uses a hybrid system formulation to capture the
intent of the driver. This model hopes to capture human driving behavior in a
way that can be utilized by semi- and fully autonomous systems in heterogeneous
environments. We consider a discrete set of high level goals or intent modes,
that is designed to encompass the decision making process of the human. A
driver model is derived using a dataset of lane changes collected in a
realistic driving simulator, in which the driver actively labels data to give
us insight into her intent. By building the labeled dataset, we are able to
utilize classification tools to build the driver model using features of based
on her perception of the environment, and achieve high accuracy in identifying
driver intent. Multiple algorithms are presented and compared on the dataset,
and a comparison of the varying behaviors between drivers is drawn. Using this
modeling methodology, we present a model that can be used to assess driver
behaviors and to develop human-inspired safety metrics that can be utilized in
intelligent vehicular systems.Comment: Submitted to ITSC 201
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