21,833 research outputs found
Detection of Feature Interactions in Automotive Active Safety Features
With the introduction of software into cars, many
functions are now realized with reduced cost,
weight and energy. The development of these software
systems is done in a distributed manner independently
by suppliers, following the traditional approach of
the automotive industry, while the car maker takes
care of the integration. However, the integration can
lead to unexpected and unintended interactions among
software systems, a phenomena regarded as feature
interaction. This dissertation addresses the problem
of the automatic detection of feature interactions
for automotive active safety features.
Active safety features control the vehicle's motion
control systems independently from the driver's request,
with the intention of increasing passengers' safety
(e.g., by applying hard braking in the case of an
identified imminent collision), but their unintended
interactions could instead endanger the passengers
(e.g., simultaneous throttle increase and sharp narrow
steering, causing the vehicle to roll over).
My method decomposes the problem into three parts:
(I) creation of a definition of feature interactions
based on the set of actuators and domain expert knowledge;
(II) translation of automotive active safety features
designed using a subset of Matlab's Stateflow into the
input language of the model checker SMV;
(III) analysis using model checking at design time to
detect a representation of all feature interactions
based on partitioning the counterexamples into
equivalence classes.
The key novel characteristic of my work is exploiting
domain-specific information about the feature interaction
problem and the structure of the model to produce a
method that finds a representation of all different
feature interactions for automotive active safety
features at design time.
My method is validated by a case study with the set
of non-proprietary automotive feature design models
I created. The method generates a set of counterexamples
that represent the whole set of feature interactions in
the case study.By showing only a set of representative
feature interaction cases, the information is concise
and useful for feature designers. Moreover, by generating
these results from feature models designed in Matlab's
Stateflow translated into SMV models, the feature
designers can trace the counterexamples generated by SMV
and understand the results in terms of the Stateflow
model. I believe that my results and techniques will
have relevance to the solution of the feature
interaction problem in other cyber-physical systems,
and have a direct impact in assessing the safety of
automotive systems
Detecting Real-World Influence Through Twitter
In this paper, we investigate the issue of detecting the real-life influence
of people based on their Twitter account. We propose an overview of common
Twitter features used to characterize such accounts and their activity, and
show that these are inefficient in this context. In particular, retweets and
followers numbers, and Klout score are not relevant to our analysis. We thus
propose several Machine Learning approaches based on Natural Language
Processing and Social Network Analysis to label Twitter users as Influencers or
not. We also rank them according to a predicted influence level. Our proposals
are evaluated over the CLEF RepLab 2014 dataset, and outmatch state-of-the-art
ranking methods.Comment: 2nd European Network Intelligence Conference (ENIC), Sep 2015,
Karlskrona, Swede
Sustainability, transport and design: reviewing the prospects for safely encouraging eco-driving
Private vehicle use contributes a disproportionately large amount to the degradation of the environment we inhabit. Technological advancement is of course critical to the mitigation of climate change, however alone it will not suffice; we must also see behavioural change. This paper will argue for the application of Ergonomics to the design of private vehicles, particularly low-carbon vehicles (e.g. hybrid and electric), to encourage this behavioural change. A brief review of literature is offered concerning the effect of the design of a technological object on behaviour, the inter-related nature of goals and feedback in guiding performance, the effect on fuel economy of different driving styles, and the various challenges brought by hybrid and electric vehicles, including range anxiety, workload and distraction, complexity, and novelty. This is followed by a discussion on the potential applicability of a particular design framework, namely Ecological Interface Design, to the design of in-vehicle interfaces that encourage energy-conserving driving behaviours whilst minimising distraction and workload, thus ensuring safety
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
Ethernet - a survey on its fields of application
During the last decades, Ethernet progressively became the most widely used local area networking (LAN) technology. Apart from LAN installations, Ethernet became also attractive for many other fields of application, ranging from industry to avionics, telecommunication, and multimedia. The expanded application of this technology is mainly due to its significant assets like reduced cost, backward-compatibility, flexibility, and expandability. However, this new trend raises some problems concerning the services of the protocol and the requirements for each application. Therefore, specific adaptations prove essential to integrate this communication technology in each field of application. Our primary objective is to show how Ethernet has been enhanced to comply with the specific requirements of several application fields, particularly in transport, embedded and multimedia contexts. The paper first describes the common Ethernet LAN technology and highlights its main features. It reviews the most important specific Ethernet versions with respect to each application field’s requirements. Finally, we compare these different fields of application and we particularly focus on the fundamental concepts and the quality of service capabilities of each proposal
Procedural Modeling and Physically Based Rendering for Synthetic Data Generation in Automotive Applications
We present an overview and evaluation of a new, systematic approach for
generation of highly realistic, annotated synthetic data for training of deep
neural networks in computer vision tasks. The main contribution is a procedural
world modeling approach enabling high variability coupled with physically
accurate image synthesis, and is a departure from the hand-modeled virtual
worlds and approximate image synthesis methods used in real-time applications.
The benefits of our approach include flexible, physically accurate and scalable
image synthesis, implicit wide coverage of classes and features, and complete
data introspection for annotations, which all contribute to quality and cost
efficiency. To evaluate our approach and the efficacy of the resulting data, we
use semantic segmentation for autonomous vehicles and robotic navigation as the
main application, and we train multiple deep learning architectures using
synthetic data with and without fine tuning on organic (i.e. real-world) data.
The evaluation shows that our approach improves the neural network's
performance and that even modest implementation efforts produce
state-of-the-art results.Comment: The project web page at
http://vcl.itn.liu.se/publications/2017/TKWU17/ contains a version of the
paper with high-resolution images as well as additional materia
Simple yet efficient real-time pose-based action recognition
Recognizing human actions is a core challenge for autonomous systems as they
directly share the same space with humans. Systems must be able to recognize
and assess human actions in real-time. In order to train corresponding
data-driven algorithms, a significant amount of annotated training data is
required. We demonstrated a pipeline to detect humans, estimate their pose,
track them over time and recognize their actions in real-time with standard
monocular camera sensors. For action recognition, we encode the human pose into
a new data format called Encoded Human Pose Image (EHPI) that can then be
classified using standard methods from the computer vision community. With this
simple procedure we achieve competitive state-of-the-art performance in
pose-based action detection and can ensure real-time performance. In addition,
we show a use case in the context of autonomous driving to demonstrate how such
a system can be trained to recognize human actions using simulation data.Comment: Submitted to IEEE Intelligent Transportation Systems Conference
(ITSC) 2019. Code will be available soon at
https://github.com/noboevbo/ehpi_action_recognitio
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