collaborative process of simulation

8/21/2019 Collaborative Process of Simulation

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COLLABORATIVE PROCESS OF SIMULATION MODELLING AND EXPERT SYSTEMS IN

REAL TIME

4.1 Introduction

Embedded systems are those in which a digital computing element, typically running control

software, interacts with devices, such as mechanical or electrical units, and external sources

of inputs such as users and the physical environment. The development of such systems is

challenging for several reasons. First, the embedded systems market demands rapid

innovation and assessment of designs. Second, in critical applications, evidence must be

provided to the assurance process that relevant operational scenarios have been considered

from an early stage. Third, the development of expert embedded systems is multidisciplinary

in that software engineers must design controllers that implement laws defined by specialists

in the application technologies, such as mechanical or electronic engineering. Treating the

disciplines separately runs the risk of miscommunication, and inefficiency in handling the

many cross-cutting design concerns. Fourth, a major source of complexity, and hence risk,

lies in the logic of the controller, and particularly of the supervisory controller that manages

higher-level functions such as mode switching, error detection and recovery[1] an aspect of

particular importance in safety-related systems. The move to distributed multiprocessor

control adds further urgency to the need to find ways of managing the complexity of controller

software design.

Model-based development approaches have the potential to address some of the challenges

of embedded systems development. Models produced in the early stages of product

development may be analyzed in order to identify the strongest design alternatives and

provide evidence to the assurance process. Although models can provide a basis for

collaboration between engineers, each discipline has its own established abstractions and

analysis methods. For example, software is typically modelled using discrete-event

formalisms, while mechanical and electrical systems use models based on continuous-time

descriptions of phenomena expressed as differential equations. Effective model-based design

for embedded control systems should bridge this gap.

Modeling and simulation tools are being increasingly acclaimed in the research field of

autonomous vehicles expert systems, as they provide suitable test beds for the development

and evaluation of such complex systems. This chapter describes the simulation modeling and

integration architecture of two types of simulators, namely a robotics and a traffic simulator for

the autonomous self-driving vehicle expert system. This integration should enable

autonomous vehicles to be deployed in a rather realistic traffic flow as an agent entity (on the

traffic simulator), at the same time it simulates all its sensors and actuators (on the robotics

counterpart).

Also, the statistical tools available in the traffic simulator will allow practitioners to infer what


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kind of advantages such a novel technology will bring to our every days lives

4.2 Imprecise Computation

Imprecise computation technique has been proposed as a way to handle transient overload

and to enhance fault tolerance of real-time systems. In a system based on this technique,

each time critical task is designed in such a way that it can produce a usable, approximate

result in time whenever a failure or overload prevents it from producing the desired precise

result. (Liu, Shih, Lin, Bettati, & Chung, January 1994)

In a real-time system, many tasks are time crucial, tasks such as a file transfer, a unit of data

transmission etc. These time crucial tasks have timing constraints. This timing begins at the

tasks ready time; the task can begin execution at or after this time. The interval ends at the

tasks deadline; this task must complete and produce its result by its deadline. A failure to this

results in a timing fault. These timing faults occur when a real time system becomes

overloaded and overload conditions are unavoidable. A way to prevent these timing faults

during transient overloads and to make a real time system responsive and robust is to use a

technique called Imprecise Computation technique. (Lin, Natarajan, & Liu, December 1987)

To understand the imprecise computation technique, we note that the bad effects of timing

faults are often tolerable as long as the entire important task is completed before their

deadline. Therefore rather allowing all task be treated equally, the programmer identifies

some task as mandatory, meaning that they must be completed in their feasible intervals and

others less important task as optional, meaning that these tasks can be skipped without

causing intolerable timing faults. In this way the operating system is allowed to skip the less

important task during overloads so that important task can complete in time.

Having this principle of imprecise computation in mind, we can now discuss the collaborative

process of simulation modeling and expert systems in real time. Firstly, the methods used in

integrating simulation modeling and expert systems and secondly, the performance and

behavior of the initially proposed expert system in carrying out tasks in real time.

4.3 Proposed Architecture

A software architecture for the autonomous vehicle simulation in a traffic environment is

proposed. This architecture has a distributed nature, as each simulator must use the

maximum possible resources. It consists of four major modules.


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