AI-augmented Automation for Real Driving Prediction: an Industrial Use Case

• URL: http://arxiv.org/abs/2404.02841v1
• Date: Wed, 3 Apr 2024 16:19:47 GMT
• Title: AI-augmented Automation for Real Driving Prediction: an Industrial Use Case
• Authors: Romina Eramo, Hamzeh Eyal Salman, Matteo Spezialetti, Darko Stern, Pierre Quinton, Antonio Cicchetti,
• Abstract summary: This paper reports on a practical experience of developing an AI-augmented solution based on Machine Learning and Model-based Engineering. In particular, historical data collected in real driving conditions is leveraged to synthesize a high-fidelity driving simulator. Based on this practical experience, this paper also proposes a conceptual framework to support predictions based on real driving behavior.
• Score: 1.9131868049527914
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

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