11,126 research outputs found
Real-to-Virtual Domain Unification for End-to-End Autonomous Driving
In the spectrum of vision-based autonomous driving, vanilla end-to-end models
are not interpretable and suboptimal in performance, while mediated perception
models require additional intermediate representations such as segmentation
masks or detection bounding boxes, whose annotation can be prohibitively
expensive as we move to a larger scale. More critically, all prior works fail
to deal with the notorious domain shift if we were to merge data collected from
different sources, which greatly hinders the model generalization ability. In
this work, we address the above limitations by taking advantage of virtual data
collected from driving simulators, and present DU-drive, an unsupervised
real-to-virtual domain unification framework for end-to-end autonomous driving.
It first transforms real driving data to its less complex counterpart in the
virtual domain and then predicts vehicle control commands from the generated
virtual image. Our framework has three unique advantages: 1) it maps driving
data collected from a variety of source distributions into a unified domain,
effectively eliminating domain shift; 2) the learned virtual representation is
simpler than the input real image and closer in form to the "minimum sufficient
statistic" for the prediction task, which relieves the burden of the
compression phase while optimizing the information bottleneck tradeoff and
leads to superior prediction performance; 3) it takes advantage of annotated
virtual data which is unlimited and free to obtain. Extensive experiments on
two public driving datasets and two driving simulators demonstrate the
performance superiority and interpretive capability of DU-drive
Exploring the Limitations of Behavior Cloning for Autonomous Driving
Driving requires reacting to a wide variety of complex environment conditions
and agent behaviors. Explicitly modeling each possible scenario is unrealistic.
In contrast, imitation learning can, in theory, leverage data from large fleets
of human-driven cars. Behavior cloning in particular has been successfully used
to learn simple visuomotor policies end-to-end, but scaling to the full
spectrum of driving behaviors remains an unsolved problem. In this paper, we
propose a new benchmark to experimentally investigate the scalability and
limitations of behavior cloning. We show that behavior cloning leads to
state-of-the-art results, including in unseen environments, executing complex
lateral and longitudinal maneuvers without these reactions being explicitly
programmed. However, we confirm well-known limitations (due to dataset bias and
overfitting), new generalization issues (due to dynamic objects and the lack of
a causal model), and training instability requiring further research before
behavior cloning can graduate to real-world driving. The code of the studied
behavior cloning approaches can be found at
https://github.com/felipecode/coiltraine
Transdisciplinarity seen through Information, Communication, Computation, (Inter-)Action and Cognition
Similar to oil that acted as a basic raw material and key driving force of
industrial society, information acts as a raw material and principal mover of
knowledge society in the knowledge production, propagation and application. New
developments in information processing and information communication
technologies allow increasingly complex and accurate descriptions,
representations and models, which are often multi-parameter, multi-perspective,
multi-level and multidimensional. This leads to the necessity of collaborative
work between different domains with corresponding specialist competences,
sciences and research traditions. We present several major transdisciplinary
unification projects for information and knowledge, which proceed on the
descriptive, logical and the level of generative mechanisms. Parallel process
of boundary crossing and transdisciplinary activity is going on in the applied
domains. Technological artifacts are becoming increasingly complex and their
design is strongly user-centered, which brings in not only the function and
various technological qualities but also other aspects including esthetic, user
experience, ethics and sustainability with social and environmental dimensions.
When integrating knowledge from a variety of fields, with contributions from
different groups of stakeholders, numerous challenges are met in establishing
common view and common course of action. In this context, information is our
environment, and informational ecology determines both epistemology and spaces
for action. We present some insights into the current state of the art of
transdisciplinary theory and practice of information studies and informatics.
We depict different facets of transdisciplinarity as we see it from our
different research fields that include information studies, computability,
human-computer interaction, multi-operating-systems environments and
philosophy.Comment: Chapter in a forthcoming book: Information Studies and the Quest for
Transdisciplinarity - Forthcoming book in World Scientific. Mark Burgin and
Wolfgang Hofkirchner, Editor
Secure, reliable and dynamic access to distributed clinical data
An abundance of statistical and scientific data exists in the area of clinical and epidemiological studies. Much of this data is distributed across regional, national and international boundaries with different policies on access and usage, and a multitude of different schemata for the data often complicated by the variety of supporting clinical coding schemes. This prevents the wide scale collation and analysis of such data as is often needed to infer clinical outcomes and to determine the often moderate effect of drugs. Through grid technologies it is possible to overcome the barriers introduced by distribution of heterogeneous data and services. However reliability, dynamicity and fine-grained security are essential in this domain, and are not typically offered by current grids. The MRC funded VOTES project (Virtual Organisations for Trials and Epidemiological Studies) has implemented a prototype infrastructure specifically designed to meet these challenges. This paper describes this on-going implementation effort and the lessons learned in building grid frameworks for and within a clinical environment
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