Architecture Challenges for Intelligent Autonomous Machines
Machines are displaying a trend of increasing autonomy. This has a far reaching impact on the architectures of the embedded systems within the machine. The impact needs to be clearly understood and the main obstacles to autonomy need to be identified. The obstacles, especially from an industrial perspective, are not just technological but also relate to system aspects like certification, development processes and product safety. In this paper, we identify and discuss some of the main obstacles to autonomy from the viewpoint of technical specialists working on advanced industrial product development. The identified obstacles cover topics like world modeling, user interaction, complexity and system safety.
KeywordsAutonomy Architecture Embedded systems
This research was conducted within the VINNOVA (Swedish Governmental Agency for Innovation Systems) funded FUSE project. FUSE conducts research on functional safety and evolvable architectures for autonomy. The autonomy workshop from which several of this paper’s insights were drawn was conducted under the aegis of the Innovative Center for Embedded Systems (ICES) at KTH, Stockholm.
- 1.Takayama, L., Ju, W., Nass, C.: Beyond dirty, dangerous and dull: what everyday people think robots should do. In: 3rd ACM/IEEE International Conference on Human-Robot Interaction. (2008) 25–32Google Scholar
- 2.Albus, J.: Outline for a theory of intelligence. Systems, Man and Cybernetics, IEEE Transactions 21(3) (1991) 473–509Google Scholar
- 3.: ICES Workshop on Architectures for Autonomous Automotive Systems. http://www.ices.kth.se/events.aspx?pid=3&evtKeyId=ab27fb03b44a4dd79536cd4b048d6a7b (2014) [Online; accessed 14-February-2014].
- 4.Broy, M.: Model-driven architecture-centric engineering of (embedded) software intensive systems: modeling theories and architectural milestones. Innovations in Systems and Software Engineering 3(1) (November 2006) 75–102Google Scholar
- 5.Broy, M.: Two Sides of Structuring Multi-Functional Software Systems: Function Hierarchy and Component Architecture. 5th ACIS International Conference on Software Engineering Research, Management & Applications (SERA 2007) (August 2007) 3–12Google Scholar
- 6.Bruyninckx, H., Klotzbücher, M., Hochgeschwender, N., Kraetzschmar, G., Gherardi, L., Brugali, D.: The BRICS component model. In: Proceedings of the 28th Annual ACM Symposium on Applied Computing - SAC ’13, New York, New York, USA, ACM Press (2013) 1758Google Scholar
- 7.Di Natale, M., Sangiovanni-Vincentelli, A.: Moving From Federated to Integrated Architectures in Automotive: The Role of Standards, Methods and Tools. Proceedings of the IEEE 98(4) (April 2010) 603–620Google Scholar
- 8.Watkins, C.B., Walter, R.: Transitioning from federated avionics architectures to Integrated Modular Avionics. In: 2007 IEEE/AIAA 26th Digital Avionics Systems Conference, IEEE (October 2007) 2.A.1–1–2.A.1–10Google Scholar
- 9.Pritchett, A.R.: Aviation automation: General perspectives and specific guidance for the design of modes and alerts. Reviews of Human Factors and Ergonomics 5(1) (2009) 82–113Google Scholar
- 10.Fitts, P.M., Viteles, M.S., Barr, N.L., Brimhall, D.R., Finch, G., Gardner, E., Grether, W.F., Kellum, W.E., Stevens, S.S.: Human engineering for an effective air navigation and traffic control system. Technical Report ADB815893, Ohio State University Research Foundation (1951)Google Scholar
- 11.Bredereke, J., Lankenau, A.: A rigorous view of mode confusion. Computer Safety, Reliability and Security 2434 (2002) 19–31Google Scholar
- 12.Rushby, J.: Modeling the Human in Human Factors Extended Abstract. In: Computer Safety, Reliability and Security. Volume 2187. (2001) 86–91Google Scholar
- 13.The PARC/CAST Flight Deck Automation WG: Operational use of flight path management systems. Technical report (2013)Google Scholar
- 14.Kopetz, H.: The Complexity Challenge in Embedded System Design. In: 2008 11th IEEE International Symposium on Object and Component-Oriented Real-Time Distributed Computing (ISORC), IEEE (May 2008) 3–12Google Scholar
- 15.Cameron, E., Griffeth, N., Lin, Y.J., Nilson, M., Schnure, W., Velthuijsen, H.: A feature-interaction benchmark for IN and beyond. IEEE Communications Magazine 31(3) (March 1993) 64–69Google Scholar
- 16.Metzger, A.: Feature interactions in embedded control systems. Computer Networks 45(5) (August 2004) 625–644Google Scholar
- 17.Juarez Dominguez, A.L.: Feature Interaction Detection in the Automotive Domain. In: 2008 23rd IEEE/ACM International Conference on Automated Software Engineering, IEEE (September 2008) 521–524Google Scholar
- 18.Hay, J., Atlee, J.: Composing features and resolving interactions. ACM SIGSOFT Software Engineering Notes 25(6) (November 2000) 110–119Google Scholar
- 19.Tsang, S., Magill, E.: Learning to detect and avoid run-time feature interactions in intelligent networks. IEEE Transactions on Software Engineering 24(10) (1998) 818–830Google Scholar
- 20.Velthuijsen, H.: Distributed artificial intelligence for runtime feature-interaction resolution. Computer (1993)Google Scholar
- 21.: IEC 61508:2010 Functional safety of electrical/electronic/programmable electronic safety-related systems (2010)Google Scholar
- 22.Bieber, P., Boniol, F., Boyer, M., Noulard, E., Pagetti, C.: New Challenges for Future Avionic Architectures. Aerospace Lab (4) (2012) 1–10Google Scholar
- 23.: ISO 26262:2011 Road vehicles – Functional safety (2011)Google Scholar
- 24.: SS-EN ISO 13849–1:2008 Safety of machinery – Safety-related parts of control systems – Part 1: General principles for design (ISO 13849–1:2006) (2008)Google Scholar
- 25.: Designing a digital future: Federally funded research and development in networking and information technology. Report to the president and Congress. http://www.whitehouse.gov/sites/default/files/microsites/ostp/pcast-nitrd-report-2010.pdf (2010) [Online report; accessed 14-February-2014].
- 26.Ploeg, J., Shladover, S., Nijmeijer, H., van de Wouw, N.: Introduction to the Special Issue on the 2011 Grand Cooperative Driving Challenge. IEEE Transactions on Intelligent Transportation Systems 13(3) (September 2012) 989–993Google Scholar