Turn Signal Prediction: A Federated Learning Case Study
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Driving etiquette takes a different flavor for each locality as drivers not only comply with rules/laws but also abide by local unspoken convention. When to have the turn signal (indicator) on/off is one such etiquette which does not have a definitive right or wrong answer. Learning this behavior from the abundance of data generated from various sensor modalities integrated in the vehicle is a suitable candidate for deep learning. But what makes it a prime candidate for Federated Learning are privacy concerns and bandwidth limitations for any data aggregation. This paper presents a long short-term memory (LSTM) based Turn Signal Prediction (on or off) model using vehicle control area network (CAN) signal data. The model is trained using two approaches, one by centrally aggregating the data and the other in a federated manner. Centrally trained models and federated models are compared under similar hyperparameter settings. This research demonstrates the efficacy of federated learning, paving the way for in-vehicle learning of driving etiquette.
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