Identity Recognition in Intelligent Cars with Behavioral Data and LSTM-ResNet Classifier
This addresses the problem of personalizing intelligent cars and enhancing safety for drivers, but it is incremental as it builds on existing deep learning methods for time series classification.
The paper tackled identity recognition in car cabins using gas and brake pedal pressure data, achieving a final accuracy of 79.49% on a 10-drivers subset of NUDrive and 96.90% on a 5-drivers subset of UTDrive with an LSTM-ResNet classifier.
Identity recognition in a car cabin is a critical task nowadays and offers a great field of applications ranging from personalizing intelligent cars to suit drivers physical and behavioral needs to increasing safety and security. However, the performance and applicability of published approaches are still not suitable for use in series cars and need to be improved. In this paper, we investigate Human Identity Recognition in a car cabin with Time Series Classification (TSC) and deep neural networks. We use gas and brake pedal pressure as input to our models. This data is easily collectable during driving in everyday situations. Since our classifiers have very little memory requirements and do not require any input data preproccesing, we were able to train on one Intel i5-3210M processor only. Our classification approach is based on a combination of LSTM and ResNet. The network trained on a subset of NUDrive outperforms the ResNet and LSTM models trained solely by 35.9 % and 53.85 % accuracy respectively. We reach a final accuracy of 79.49 % on a 10-drivers subset of NUDrive and 96.90 % on a 5-drivers subset of UTDrive.