9,274 research outputs found
Deep Learning in the Automotive Industry: Applications and Tools
Deep Learning refers to a set of machine learning techniques that utilize
neural networks with many hidden layers for tasks, such as image
classification, speech recognition, language understanding. Deep learning has
been proven to be very effective in these domains and is pervasively used by
many Internet services. In this paper, we describe different automotive uses
cases for deep learning in particular in the domain of computer vision. We
surveys the current state-of-the-art in libraries, tools and infrastructures
(e.\,g.\ GPUs and clouds) for implementing, training and deploying deep neural
networks. We particularly focus on convolutional neural networks and computer
vision use cases, such as the visual inspection process in manufacturing plants
and the analysis of social media data. To train neural networks, curated and
labeled datasets are essential. In particular, both the availability and scope
of such datasets is typically very limited. A main contribution of this paper
is the creation of an automotive dataset, that allows us to learn and
automatically recognize different vehicle properties. We describe an end-to-end
deep learning application utilizing a mobile app for data collection and
process support, and an Amazon-based cloud backend for storage and training.
For training we evaluate the use of cloud and on-premises infrastructures
(including multiple GPUs) in conjunction with different neural network
architectures and frameworks. We assess both the training times as well as the
accuracy of the classifier. Finally, we demonstrate the effectiveness of the
trained classifier in a real world setting during manufacturing process.Comment: 10 page
A Real-Time Remote IDS Testbed for Connected Vehicles
Connected vehicles are becoming commonplace. A constant connection between
vehicles and a central server enables new features and services. This added
connectivity raises the likelihood of exposure to attackers and risks
unauthorized access. A possible countermeasure to this issue are intrusion
detection systems (IDS), which aim at detecting these intrusions during or
after their occurrence. The problem with IDS is the large variety of possible
approaches with no sensible option for comparing them. Our contribution to this
problem comprises the conceptualization and implementation of a testbed for an
automotive real-world scenario. That amounts to a server-side IDS detecting
intrusions into vehicles remotely. To verify the validity of our approach, we
evaluate the testbed from multiple perspectives, including its fitness for
purpose and the quality of the data it generates. Our evaluation shows that the
testbed makes the effective assessment of various IDS possible. It solves
multiple problems of existing approaches, including class imbalance.
Additionally, it enables reproducibility and generating data of varying
detection difficulties. This allows for comprehensive evaluation of real-time,
remote IDS.Comment: Peer-reviewed version accepted for publication in the proceedings of
the 34th ACM/SIGAPP Symposium On Applied Computing (SAC'19
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