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Informative Article | Engineering Science | India | Volume 9 Issue 12, December 2020 | Rating: 5.7 / 10
Benchmarking Machine Learning Tools and Development Process for Automotive Embedded Controls
Roopak Ingole, Ruchi Gupta Neema
Abstract: This study systematically benchmarks a variety of machine learning (ML) tools for their application in automotive embedded controls, with a particular focus on engine control units (ECUs). The advent of ML in the automotive industry has catalyzed significant advancements in vehicle automation, enhancing safety, efficiency, and performance. However, the deployment of ML technologies in embedded automotive systems presents unique challenges due to the stringent requirements for real-time processing and limited computational resources. In this research, we evaluate several ML tools and frameworks, including both commercial software and custom-developed algorithms, to determine their suitability for real-time automotive applications. Using criteria such as computational efficiency, memory usage, and ease of integration with existing automotive systems, we provide a comprehensive comparison and analysis. The tools examined range from high-level programming environments like Python and MATLAB to specific commercial services tailored for embedded systems. Our findings reveal significant variations in the performance and applicability of these tools in an automotive context. We also discuss the implications of these findings for the design and optimization of ML-driven automotive systems. The outcomes of this study offer valuable insights for automotive engineers and system designers, aiding in the selection of optimal ML tools that meet the dual demands of performance and practical implementation in embedded systems. This research underscores the potential of ML to revolutionize automotive systems and lays the groundwork for future innovations in this rapidly evolving field.
Keywords: Machine Learning Tool, Automotive Embedded Controls
Edition: Volume 9 Issue 12, December 2020
Pages: 1843 - 1849