We are presenting a novel approach for the task of driving behavior classification based on stacked LSTM Recurrent Neural Networks. Given a nine different sensor data captured using a smart phone internal sensors during a naturalistic driving sessions, we formulated the driving behavior classification problem as time-series classification task. Whereas, given a window sequence of fused feature vectors of sensor data at any time step of a driving trip, we can accurately classify the driving behavior during that window sequence from a three distinctive driving behavior classes, namely normal, aggressive or drowsy driving. We evaluated our proposed Stacked-LSTM model on one of the recent naturalistic driving behavior analysis and classification dataset, UAH-DriveSet. Our proposed Stacked-LSTM model has achieved state-of-the-art results on the UAH-DriveSet with much higher true positive rate as well as lower false positive rate in comparison to the baseline approach. We have also compared the performance of our proposed Stacked-LSTM model against a number of the common classification algorithms used in the driving behavior classification and analysis studies and we achieved F1-measure score of 91% with an improvement of more than 10% over the closest compared approaches.
Driving behavior classification based on sensor data fusion using LSTM recurrent neural networks
2017-10-01
471583 byte
Aufsatz (Konferenz)
Elektronische Ressource
Englisch