Contextual Driving Scene Perception from Anonymous Vehicle Bus Data for Automotive Applications

Abstract

In recent years, driving context perception has emerged as one of the key aspects to design driving assistance algorithms and user interfaces that are effective in adapting to different traffic situations or environments. To this aim, we introduce the Anonymous Driving Scene Perception (ADSP) Model, a novel deep neural network designed to classify anony-mous Controller Area Network (CAN)-bus data into multiple driving context domains. ADSP extends the idea of driving scene classification to time series signals, as previous works relied heavily on visual features. Our model achieved a multi -domain classification accuracy of 84.9% on our custom-built naturalistic data set, as a combination of 92.7% on road type classification and 90.1 % on binary traffic detection, performing 2.0% and 1.6% better than the state-of-the-art model for multivariate time series classification. Our work demonstrates the feasibility of driving scene classification from anonymous CAN-bus data, without collecting sensitive data from users (images or GPS).