Machine Learning Frameworks for Predictive Vehicle Design and Maintenance

Contemporary machine learning (ML) research, especially in predictive vehicle design optimization and fleet maintenance, receives a technical overview regarding core foundations and emerging frameworks. The introduction condenses a larger work considering predictive vehicle design and maintenance from a data-centric, ML-driven standpoint. These areas are only partially covered and are limited to aspects most relevant to ML activity. The discussion is targeted primarily at vehicle manufacturers, operators, and others interested in ML applications toward tomorrow's vehicles and their design. The background concerning predictive maintenance and design optimization is informative for all stakeholders, especially ML-aware vehicle manufacturers.

Machine Learning Frameworks for Predictive Vehicle Design and Vehicle Maintenance may also be used by academic and industrial researchers developing ML methods and architectures in vehicle design and maintenance. Predictive vehicle design refers to the investigation of design parameterizations and optimizations enabling predictive ML throughout the life cycle. Predictive maintenance deals with fleets of vehicles equipped with telemetry for cost-effective monitoring and maintenance of integrity, availability, and safety. These activities constitute the final two stages of a vehicle's life cycle.