17,927 research outputs found
DeepPicar: A Low-cost Deep Neural Network-based Autonomous Car
We present DeepPicar, a low-cost deep neural network based autonomous car
platform. DeepPicar is a small scale replication of a real self-driving car
called DAVE-2 by NVIDIA. DAVE-2 uses a deep convolutional neural network (CNN),
which takes images from a front-facing camera as input and produces car
steering angles as output. DeepPicar uses the same network architecture---9
layers, 27 million connections and 250K parameters---and can drive itself in
real-time using a web camera and a Raspberry Pi 3 quad-core platform. Using
DeepPicar, we analyze the Pi 3's computing capabilities to support end-to-end
deep learning based real-time control of autonomous vehicles. We also
systematically compare other contemporary embedded computing platforms using
the DeepPicar's CNN-based real-time control workload. We find that all tested
platforms, including the Pi 3, are capable of supporting the CNN-based
real-time control, from 20 Hz up to 100 Hz, depending on hardware platform.
However, we find that shared resource contention remains an important issue
that must be considered in applying CNN models on shared memory based embedded
computing platforms; we observe up to 11.6X execution time increase in the CNN
based control loop due to shared resource contention. To protect the CNN
workload, we also evaluate state-of-the-art cache partitioning and memory
bandwidth throttling techniques on the Pi 3. We find that cache partitioning is
ineffective, while memory bandwidth throttling is an effective solution.Comment: To be published as a conference paper at RTCSA 201
Hazard Contribution Modes of Machine Learning Components
Amongst the essential steps to be taken towards developing and deploying safe systems with embedded learning-enabled components (LECs) i.e., software components that use ma- chine learning (ML)are to analyze and understand the con- tribution of the constituent LECs to safety, and to assure that those contributions have been appropriately managed. This paper addresses both steps by, first, introducing the notion of hazard contribution modes (HCMs) a categorization of the ways in which the ML elements of LECs can contribute to hazardous system states; and, second, describing how argumentation patterns can capture the reasoning that can be used to assure HCM mitigation. Our framework is generic in the sense that the categories of HCMs developed i) can admit different learning schemes, i.e., supervised, unsupervised, and reinforcement learning, and ii) are not dependent on the type of system in which the LECs are embedded, i.e., both cyber and cyber-physical systems. One of the goals of this work is to serve a starting point for systematizing L analysis towards eventually automating it in a tool
Governing autonomous vehicles: emerging responses for safety, liability, privacy, cybersecurity, and industry risks
The benefits of autonomous vehicles (AVs) are widely acknowledged, but there
are concerns about the extent of these benefits and AV risks and unintended
consequences. In this article, we first examine AVs and different categories of
the technological risks associated with them. We then explore strategies that
can be adopted to address these risks, and explore emerging responses by
governments for addressing AV risks. Our analyses reveal that, thus far,
governments have in most instances avoided stringent measures in order to
promote AV developments and the majority of responses are non-binding and focus
on creating councils or working groups to better explore AV implications. The
US has been active in introducing legislations to address issues related to
privacy and cybersecurity. The UK and Germany, in particular, have enacted laws
to address liability issues, other countries mostly acknowledge these issues,
but have yet to implement specific strategies. To address privacy and
cybersecurity risks strategies ranging from introduction or amendment of non-AV
specific legislation to creating working groups have been adopted. Much less
attention has been paid to issues such as environmental and employment risks,
although a few governments have begun programmes to retrain workers who might
be negatively affected.Comment: Transport Reviews, 201
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