15,961 research outputs found
End-to-end Driving via Conditional Imitation Learning
Deep networks trained on demonstrations of human driving have learned to
follow roads and avoid obstacles. However, driving policies trained via
imitation learning cannot be controlled at test time. A vehicle trained
end-to-end to imitate an expert cannot be guided to take a specific turn at an
upcoming intersection. This limits the utility of such systems. We propose to
condition imitation learning on high-level command input. At test time, the
learned driving policy functions as a chauffeur that handles sensorimotor
coordination but continues to respond to navigational commands. We evaluate
different architectures for conditional imitation learning in vision-based
driving. We conduct experiments in realistic three-dimensional simulations of
urban driving and on a 1/5 scale robotic truck that is trained to drive in a
residential area. Both systems drive based on visual input yet remain
responsive to high-level navigational commands. The supplementary video can be
viewed at https://youtu.be/cFtnflNe5fMComment: Published at the International Conference on Robotics and Automation
(ICRA), 201
Ontology based Scene Creation for the Development of Automated Vehicles
The introduction of automated vehicles without permanent human supervision
demands a functional system description, including functional system boundaries
and a comprehensive safety analysis. These inputs to the technical development
can be identified and analyzed by a scenario-based approach. Furthermore, to
establish an economical test and release process, a large number of scenarios
must be identified to obtain meaningful test results. Experts are doing well to
identify scenarios that are difficult to handle or unlikely to happen. However,
experts are unlikely to identify all scenarios possible based on the knowledge
they have on hand. Expert knowledge modeled for computer aided processing may
help for the purpose of providing a wide range of scenarios. This contribution
reviews ontologies as knowledge-based systems in the field of automated
vehicles, and proposes a generation of traffic scenes in natural language as a
basis for a scenario creation.Comment: Accepted at the 2018 IEEE Intelligent Vehicles Symposium, 8 pages, 10
figure
Towards Social Autonomous Vehicles: Efficient Collision Avoidance Scheme Using Richardson's Arms Race Model
Background Road collisions and casualties pose a serious threat to commuters
around the globe. Autonomous Vehicles (AVs) aim to make the use of technology
to reduce the road accidents. However, the most of research work in the context
of collision avoidance has been performed to address, separately, the rear end,
front end and lateral collisions in less congested and with high
inter-vehicular distances. Purpose The goal of this paper is to introduce the
concept of a social agent, which interact with other AVs in social manners like
humans are social having the capability of predicting intentions, i.e.
mentalizing and copying the actions of each other, i.e. mirroring. The proposed
social agent is based on a human-brain inspired mentalizing and mirroring
capabilities and has been modelled for collision detection and avoidance under
congested urban road traffic.
Method We designed our social agent having the capabilities of mentalizing
and mirroring and for this purpose we utilized Exploratory Agent Based Modeling
(EABM) level of Cognitive Agent Based Computing (CABC) framework proposed by
Niazi and Hussain.
Results Our simulation and practical experiments reveal that by embedding
Richardson's arms race model within AVs, collisions can be avoided while
travelling on congested urban roads in a flock like topologies. The performance
of the proposed social agent has been compared at two different levels.Comment: 48 pages, 21 figure
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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