6,118 research outputs found
FlightGoggles: A Modular Framework for Photorealistic Camera, Exteroceptive Sensor, and Dynamics Simulation
FlightGoggles is a photorealistic sensor simulator for perception-driven
robotic vehicles. The key contributions of FlightGoggles are twofold. First,
FlightGoggles provides photorealistic exteroceptive sensor simulation using
graphics assets generated with photogrammetry. Second, it provides the ability
to combine (i) synthetic exteroceptive measurements generated in silico in real
time and (ii) vehicle dynamics and proprioceptive measurements generated in
motio by vehicle(s) in a motion-capture facility. FlightGoggles is capable of
simulating a virtual-reality environment around autonomous vehicle(s). While a
vehicle is in flight in the FlightGoggles virtual reality environment,
exteroceptive sensors are rendered synthetically in real time while all complex
extrinsic dynamics are generated organically through the natural interactions
of the vehicle. The FlightGoggles framework allows for researchers to
accelerate development by circumventing the need to estimate complex and
hard-to-model interactions such as aerodynamics, motor mechanics, battery
electrochemistry, and behavior of other agents. The ability to perform
vehicle-in-the-loop experiments with photorealistic exteroceptive sensor
simulation facilitates novel research directions involving, e.g., fast and
agile autonomous flight in obstacle-rich environments, safe human interaction,
and flexible sensor selection. FlightGoggles has been utilized as the main test
for selecting nine teams that will advance in the AlphaPilot autonomous drone
racing challenge. We survey approaches and results from the top AlphaPilot
teams, which may be of independent interest.Comment: Initial version appeared at IROS 2019. Supplementary material can be
found at https://flightgoggles.mit.edu. Revision includes description of new
FlightGoggles features, such as a photogrammetric model of the MIT Stata
Center, new rendering settings, and a Python AP
Advances and Applications of Computer Vision Techniques in Vehicle Trajectory Generation and Surrogate Traffic Safety Indicators
The application of Computer Vision (CV) techniques massively stimulates
microscopic traffic safety analysis from the perspective of traffic conflicts
and near misses, which is usually measured using Surrogate Safety Measures
(SSM). However, as video processing and traffic safety modeling are two
separate research domains and few research have focused on systematically
bridging the gap between them, it is necessary to provide transportation
researchers and practitioners with corresponding guidance. With this aim in
mind, this paper focuses on reviewing the applications of CV techniques in
traffic safety modeling using SSM and suggesting the best way forward. The CV
algorithm that are used for vehicle detection and tracking from early
approaches to the state-of-the-art models are summarized at a high level. Then,
the video pre-processing and post-processing techniques for vehicle trajectory
extraction are introduced. A detailed review of SSMs for vehicle trajectory
data along with their application on traffic safety analysis is presented.
Finally, practical issues in traffic video processing and SSM-based safety
analysis are discussed, and the available or potential solutions are provided.
This review is expected to assist transportation researchers and engineers with
the selection of suitable CV techniques for video processing, and the usage of
SSMs for various traffic safety research objectives
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