117 research outputs found
Provably good multiprocessor scheduling with resource sharing
We present a 12(1 + 3R/(4m)) competitive algorithm for scheduling
implicit-deadline sporadic tasks on a platform comprising m processors, where a task
may request one of R shared resources
Demo Abstract: Real-Time Out-of-Distribution Detection on a Mobile Robot
In a cyber-physical system such as an autonomous vehicle (AV), machine
learning (ML) models can be used to navigate and identify objects that may
interfere with the vehicle's operation. However, ML models are unlikely to make
accurate decisions when presented with data outside their training
distribution. Out-of-distribution (OOD) detection can act as a safety monitor
for ML models by identifying such samples at run time. However, in safety
critical systems like AVs, OOD detection needs to satisfy real-time constraints
in addition to functional requirements. In this demonstration, we use a mobile
robot as a surrogate for an AV and use an OOD detector to identify potentially
hazardous samples. The robot navigates a miniature town using image data and a
YOLO object detection network. We show that our OOD detector is capable of
identifying OOD images in real-time on an embedded platform concurrently
performing object detection and lane following. We also show that it can be
used to successfully stop the vehicle in the presence of unknown, novel
samples.Comment: 3 pages, 5 figures, RTSS 202
A Low-Cost Lane-Following Algorithm for Cyber-Physical Robots
Duckiebots are low-cost mobile robots that are widely used in the fields of
research and education. Although there are existing self-driving algorithms for
the Duckietown platform, they are either too complex or perform too poorly to
navigate a multi-lane track. Moreover, it is essential to give memory and
computational resources to a Duckiebot so it can perform additional tasks such
as out-of-distribution input detection. In order to satisfy these constraints,
we built a low-cost autonomous driving algorithm capable of driving on a
two-lane track. The algorithm uses traditional computer vision techniques to
identify the central lane on the track and obtain the relevant steering angle.
The steering is then controlled by a PID controller that smoothens the movement
of the Duckiebot. The performance of the algorithm was compared to that of the
NeurIPS 2018 AI Driving Olympics (AIDO) finalists, and it outperformed all but
one finalists. The two main contributions of our algorithm are its low
computational requirements and very quick set-up, with ongoing efforts to make
it more reliable
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