Data Science For Car Anomalies Detection And Root Cause Analysis_By STELLANTIS

BACKGROUND

Artificial Intelligence is revolutionizing automotive engineering in several areas, such as: predictive maintenance to maximize performance and quality of various vehicle components, vehicle automation to understand complex traffic scenarios, energy management to optimize batteries and thermal systems, and personalized services for comfort and infotainment. Innovative data science algorithms with advanced learning capabilities represent the new frontier for the preventive and real time identification of vehicle components’ anomalies that can occur during the whole vehicle’s lifecycle and the root causes that originated them: a very important step forward for the future of the automotive world.

CHALLENGE

The challenge is contextualized in offering cars the ability to perform real time and preventive diagnosis of HW/SW components for immediate interventions and quality analysis.
The goalis to design SW algorithms, based on Data Science and Machine Learning techniques, to determine the root causes of HW/SW component anomalies that can occur throughout the vehicle’s lifecycle. The challenge will work on automotive use cases with the related HW/SW components, to explore: the root causes of HW/SW component anomalies and their impacts on vehicle operation, the most promising Data Science and Machine Learning techniques to determine the root causes, and the design of innovative SW algorithmic solutions.

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