23,907 research outputs found
Design Criteria to Architect Continuous Experimentation for Self-Driving Vehicles
The software powering today's vehicles surpasses mechatronics as the
dominating engineering challenge due to its fast evolving and innovative
nature. In addition, the software and system architecture for upcoming vehicles
with automated driving functionality is already processing ~750MB/s -
corresponding to over 180 simultaneous 4K-video streams from popular
video-on-demand services. Hence, self-driving cars will run so much software to
resemble "small data centers on wheels" rather than just transportation
vehicles. Continuous Integration, Deployment, and Experimentation have been
successfully adopted for software-only products as enabling methodology for
feedback-based software development. For example, a popular search engine
conducts ~250 experiments each day to improve the software based on its users'
behavior. This work investigates design criteria for the software architecture
and the corresponding software development and deployment process for complex
cyber-physical systems, with the goal of enabling Continuous Experimentation as
a way to achieve continuous software evolution. Our research involved reviewing
related literature on the topic to extract relevant design requirements. The
study is concluded by describing the software development and deployment
process and software architecture adopted by our self-driving vehicle
laboratory, both based on the extracted criteria.Comment: Copyright 2017 IEEE. Paper submitted and accepted at the 2017 IEEE
International Conference on Software Architecture. 8 pages, 2 figures.
Published in IEEE Xplore Digital Library, URL:
http://ieeexplore.ieee.org/abstract/document/7930218
WiseMove: A Framework for Safe Deep Reinforcement Learning for Autonomous Driving
Machine learning can provide efficient solutions to the complex problems
encountered in autonomous driving, but ensuring their safety remains a
challenge. A number of authors have attempted to address this issue, but there
are few publicly-available tools to adequately explore the trade-offs between
functionality, scalability, and safety.
We thus present WiseMove, a software framework to investigate safe deep
reinforcement learning in the context of motion planning for autonomous
driving. WiseMove adopts a modular learning architecture that suits our current
research questions and can be adapted to new technologies and new questions. We
present the details of WiseMove, demonstrate its use on a common traffic
scenario, and describe how we use it in our ongoing safe learning research
Emergent behaviors in the Internet of things: The ultimate ultra-large-scale system
To reach its potential, the Internet of Things (IoT) must break down the silos that limit applications' interoperability and hinder their manageability. Doing so leads to the building of ultra-large-scale systems (ULSS) in several areas, including autonomous vehicles, smart cities, and smart grids. The scope of ULSS is both large and complex. Thus, the authors propose Hierarchical Emergent Behaviors (HEB), a paradigm that builds on the concepts of emergent behavior and hierarchical organization. Rather than explicitly programming all possible decisions in the vast space of ULSS scenarios, HEB relies on the emergent behaviors induced by local rules at each level of the hierarchy. The authors discuss the modifications to classical IoT architectures required by HEB, as well as the new challenges. They also illustrate the HEB concepts in reference to autonomous vehicles. This use case paves the way to the discussion of new lines of research.Damian Roca work was supported by a Doctoral Scholarship provided by Fundación La Caixa. This work has been supported by the Spanish Government (Severo Ochoa
grants SEV2015-0493) and by the Spanish Ministry of Science and Innovation (contracts TIN2015-65316-P).Peer ReviewedPostprint (author's final draft
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