A Cloud IoT Edge Framework for Efficient Data-Driven Automotive Diagnostics

Preventative automotive maintenance is important to detect faults in the vehicle early enough before they become failures. With vehicles becoming connected, we are able to obtain streams of big data from vehicles which can be processed in the cloud, then deep learning models can be used for fault detection in real time and executed in the vehicle as an IoT edge. The challenge is to determine which model is appropriate to use in the vehicle, accounting for tradeoff in accuracy, prediction time and memory. In this paper, we address this challenge by extending our previous work [1] in data-driven automotive diagnostics and use a cloud IoT edge framework for development and deployment on a Raspberry Pi 3B+. Using pre-ignition data, our results demonstrate that there is a tradeoff between F1-score and computational cost. A model based on our previous work DADN (Deep Automotive Diagnostic Network) achieves the highest F1-score but does not execute the fastest. Simple models such as 1 CNN (Convolutional Neural Network) and 0 LSTM (Long Short-Term Memory) may be adequate enough for pre-ignition detection.