Bibliographic Details
| Title: |
AI-augmented Automation for Real Driving Prediction: an Industrial Use Case |
| Authors: |
Eramo, Romina, Salman, Hamzeh Eyal, Spezialetti, Matteo, Stern, Darko, Quinton, Pierre, Cicchetti, Antonio |
| Publication Year: |
2024 |
| Collection: |
Computer Science |
| Subject Terms: |
Computer Science - Software Engineering |
| More Details: |
The risen complexity of automotive systems requires new development strategies and methods to master the upcoming challenges. Traditional methods need thus to be changed by an increased level of automation, and a faster continuous improvement cycle. In this context, current vehicle performance tests represent a very time-consuming and expensive task due to the need to perform the tests in real driving conditions. As a consequence, agile/iterative processes like DevOps are largely hindered by the necessity of triggering frequent tests. This paper reports on a practical experience of developing an AI-augmented solution based on Machine Learning and Model-based Engineering to support continuous vehicle development and testing. In particular, historical data collected in real driving conditions is leveraged to synthesize a high-fidelity driving simulator and hence enable performance tests in virtual environments. Based on this practical experience, this paper also proposes a conceptual framework to support predictions based on real driving behavior. |
| Document Type: |
Working Paper |
| Access URL: |
http://arxiv.org/abs/2404.02841 |
| Accession Number: |
edsarx.2404.02841 |
| Database: |
arXiv |
