A future dynamically reconfigurable automotive software system

Embedded software systems in vehicles are of rapidly increasing commercial importance for the automotive industry. Current systems employ a static run-time environment; due to the difficulty and cost involved in the development of dynamic systems in a high-integrity embedded control context. A dynamic system, referring to the system configuration, would greatly increase the flexibility of the offered functionality and enable customised software configuration for individual vehicles, adding customer value through plug-and-play capability, and increased quality due to its inherent ability to adjust to changes in hardware and software. We envisage an automotive system containing a variety of components, from a multitude of organizations, not necessarily known at development time. The system dynamically adapts its configuration to suit the run-time system constraints. This paper presents our vision for future automotive control systems that will be regarded in an EU research project, referred to as DySCAS (Dynamically Self-Configuring Automotive Systems). We propose a self-configuring vehicular control system architecture, with capabilities that include automatic discovery and inclusion of new devices, self-optimisation to best-use the processing, storage and communication resources available, self-diagnostics and ultimately self-healing. Such an architecture has benefits extending to reduced development and maintenance costs, improved passenger safety and comfort, and flexible owner customisation. Specifically, this paper addresses the following issues: The state of the art of embedded software systems in vehicles, emphasising the current limitations arising from fixed run-time configurations; and the benefits and challenges of dynamic configuration, giving rise to opportunities for self-healing, self-optimisation, and the automatic inclusion of users’ Consumer Electronic (CE) devices. Our proposal for a dynamically reconfigurable automotive software system platform is outlined and a typical use-case is presented as an example to exemplify the benefits of the envisioned dynamic capabilities.

Policy-driven self-management for an automotive middleware

This paper describes work towards the deployment of self-managing capabilities into an advanced middleware for automotive systems. The middleware will support a range of futuristic use-cases requiring context-awareness and dynamic system configuration. Several use-cases are described and their specific context-awareness requirements identified. The discussion is accompanied by a justification for the selection of policy-based computing as the autonomics technique to drive the self-management. The specific policy technology to be deployed is described briefly, with a focus on its specific features that are of direct relevance to the middleware project. A selected use-case is explored in depth to illustrate the extent of dynamic behaviour achievable in the proposed middleware architecture, which is composed of several policy-configured services. An early demonstration application which facilitates concept evaluation is presented and a sequence of typical device-discovery events is worked through

Embedding dynamic behaviour into a self-configuring software system

This paper describes a methodology for embedding dynamic behaviour into software components. The implications and system architecture requirements to support this adaptivity are discussed. This work is part of a European Commission funded and industry supported project to produce a reconfigurable middleware for use in automotive systems. Such systems must be trustable against illegal internal behaviour and activity with external origins, additional devices for example. Policy-based computing is used here as an example of embedded logic. A key contribution of this work is the way in which static and dynamic aspects of the system are interfaced, such that the behaviour can be changed very flexibly (even during run-time), without modification, recompilation or redeployment of the embedded application code. An implementation of these concepts is presented, focussing on achieving trust in the use of dynamic behaviour.

Fire field modelling using the SMARTFIRE automated dynamic solution control environment

SMARTFIRE is a fire field model based on an open architecture integrated CFD code and knowledge-based system. It makes use of the expert system to assist the user in setting up the problem specification and new computational techniques such as Group Solvers to reduce the computational effort involved in solving the equations. This paper concentrates on recent research into the use of artificial intelligence techniques to assist in dynamic solution control of fire scenarios being simulated using fire field modelling techniques. This is designed to improve the convergence capabilities of the software while further decreasing the computational overheads. The technique automatically controls solver relaxations using an integrated production rule engine with a blackboard to monitor and implement the required control changes during solution processing. Initial results for a two-dimensional fire simulation are presented that demonstrate the potential for considerable savings in simulation run-times when compared with control sets from various sources. Furthermore, the results demonstrate enhanced solution reliability due to obtaining acceptable convergence within each time step unlike some of the comparison simulations.

SMARTFIRE: an integrated computational fluid dynamics code and expert system for fire field modelling

SMARTFIRE, an open architecture integrated CFD code and knowledge based system attempts to make fire field modeling accessible to non-experts in Computational Fluid Dynamics (CFD) such as fire fighters, architects and fire safety engineers. This is achieved by embedding expert knowledge into CFD software. This enables the 'black-art' associated with the CFD analysis such as selection of solvers, relaxation parameters, convergence criteria, time steps, grid and boundary condition specification to be guided by expert advice from the software. The user is however given the option of overriding these decisions, thus retaining ultimate control. SMARTFIRE also makes use of recent developments in CFD technology such as unstructured meshes and group solvers in order to make the CFD analysis more efficient. This paper describes the incorporation within SMARTFIRE of the expert fire modeling knowledge required for automatic problem setup and mesh generation as well as the concept and use of group solvers for automatic and manual dynamic control of the CFD code.

A dynamically reconfigurable automotive control system architecture

This paper proposes a vehicular control system architecture that supports self-configuration. The architecture is based on dynamic mapping of processes and services to resources to meet the challenges of future demanding use-scenarios in which systems must be flexible to exhibit context-aware behaviour and to permit customization. The architecture comprises a number of low-level services that provide the required system functionalities, which include automatic discovery and incorporation of new devices, self-optimisation to best-use the processing, storage and communication resources available, and self-diagnostics. The benefits and challenges of dynamic configuration and the automatic inclusion of users' Consumer Electronic (CE) devices are briefly discussed. The dynamic configuration and control-theoretic technologies used are described in outline and the way in which the demands of highly flexible dynamic configuration and highly robust operation are simultaneously met without compromise, is explained. A number of generic use-cases have been identified, each with several specific use-case scenarios. One generic use-case is described to provide an insight into the extent of the flexible reconfiguration facilitated by the architecture.