Analysis of Machine Learning Approach for the mode model in SWC Mapping in Automotive Systems

Owes Khan,Geri Shahini,Wolfram Hardt

doi:10.14464/ess.v7i2.473

Abstract

Automotive technologies are ever-increasinglybecoming digital. Highly autonomous driving together withdigital E/E control mechanisms include thousands of softwareapplications which are called as software components.Together with the industry requirements, and rigoroussoftware development processes, mapping of components as asoftware pool becomes very difficult. This article analyses anddiscusses the integration possibilities of machine learningapproaches to our previously introduced concept of mappingof software components through a common software pool

Highlights

The last two decades, have shown an exponential growth in digitalization of the automotive industry
This article focuses on the analysis of machine learning approaches for the mode model in SWC mapping in automotive systems
The permutation of all the variables will create an enormous set of test cases that are needed to verify and improve an already given set of modes. Another problem that needs to be taken into consideration is that the prediction accuracy can never be 100 percent, which in safety critical system such as automotive system may lead to hazardous situation even loss of human life

Summary

INTRODUCTION

The last two decades, have shown an exponential growth in digitalization of the automotive industry. Recent trends are evolving for high performance computing in cars, further on leading to autonomous driving.[11] A complete system methodology that includes all the requirements and generates a SW-HW resource allocation map is still missing for ease of development. The above mentioned requirements are some of the most widely used They only address problems related to development and deployment. RESEARCH FOCUS A methodology for achieving a complete system wide mapping scheme has been proposed in [11]. As already mentioned in requirements section, there are several aspects that need to be considered when developing software for automotive ECUs. The proposed method works on the basis four main models: safety, resource, mode and scheduling. This article focuses on the analysis of machine learning approaches for the mode model in SWC mapping in automotive systems

CONCEPTUALISATION

REALISATION

Findings

CONCLUSION AND OUTLOOK