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PUBLICATIONS

Robust Safety for Autonomous Vehicles through Reconfigurable Networking

Published

Author(s)

Khalid Halba, Charif Mahmoudi, Edward R. Griffor

Abstract

Autonomous vehicles bring the promise of enhancing the consumers experience in terms of comfort and convenience and, in particular, the safety of the autonomous vehicle. Safety functions in autonomous vehicles such as Automatic Emergency Braking and Lane Centering Assist rely on computation, information sharing, and the timely actuation of the safety functions. One opportunity to achieve robust autonomous vehicle safety is by enhancing the robustness of in-vehcile networking architectures that support built-in resiliency mechanisms. Software Defined Networking (SDN) is an advanced networking paradigm that allows fine-grained manipulation of routing tables and routing engines and the implementation of complex features such as failover. In this paper, we leverage SDN network programmability features to enable resiliency in the autonomous vehicle realm. We prove that a Software Defined In-Vehicle Networking (SDIVN) does not add overhead compared to Legacy In-Vehicle Networks (LIVNs) under non-failure conditions and we highlight its superiority in the case of a link failure and its timely delivery of messages. We verify the proposed architectures benefits using a simulation environment that we have developed and we validate our design choices through testing and simulations.
Conference Dates
April 10-13, 2018
Conference Location
Porto
Conference Title
CPS Week/2nd International Workshop on Safe Control of Autonomous Vehicles
Pub Type
Conferences

Keywords

Autonomous Vehicle, Safety, Software Defined Networking
Cyber-physical systems, Networking, Software defined and virtual networks and Network security and robustness

Citation

Halba, K. , Mahmoudi, C. and Griffor, E. (2018), Robust Safety for Autonomous Vehicles through Reconfigurable Networking, CPS Week/2nd International Workshop on Safe Control of Autonomous Vehicles, Porto, -1 (Accessed March 15, 2025)

Issues

If you have any questions about this publication or are having problems accessing it, please contact reflib@nist.gov.

Created July 18, 2018