DeepCAN: Hybrid Method for Road Type Classification Using Vehicle Sensor Data for Smart Autonomous Mobility

Usually, image-and radar-based data are used to perform environmental characteristics related tasks in autonomous cars, while the use of valuable sensor data from the Controller Area Network (CAN) bus has been limited. The vehicle's CAN bus data consist of multivariate time series data, such as velocity, RPM, and acceleration, which contain meaningful information about the vehicle dynamics and environmental characteristics. The ability to use these data to sense the environment, along with the sight sense (from image-based data), can prevent a single point of failure when image-or radar-based data are missing or incomplete, and contribute to increased understanding of the vehicle's environment. Moreover, a solution that does not rely on image-or radar-based data also addresses concerns about privacy and the use of location-based data. We present DeepCAN, a novel hybrid method for road type classification that utilizes solely vehicle dynamics data and combines two main approaches for time series classification. In the end-to-end approach, a long short-term memory autoencoder (LSTM AE) is trained, and the learned embedding serves as the input to a fully convolutional network autoencoder (FCN AE), while the feature-based approach utilizes an XGBoost classifier with aggregated time series feature representation. In our comprehensive evaluation on two real-world datasets, we assessed the performance of each model component as an independent solution, as well as a model integrating all of the components in a hybrid solution. The results demonstrate DeepCAN's efficiency and accuracy and provide a solid basis for its future use by the automobile industry.