An approach towards data change rule based classification of driving
maneuver with LSTM network
Abstract
The proposed work develops a Long Short Term Memory (LSTM) model for
multi class classification of driving maneuver from sensor fusion time
series dataset. The work also analyzes the significance of sensor fusion
data change rule and utilized the idea with deep learning time series
multi class classification of driving maneuver. We also proposed some
hypotheses which are proven by the experimental results. The proposed
model provides Train Accuracy: 99.98, Test Accuracy: 97.2021, Precision:
0.974848, Recall: 0.960154 and F1 score: 0.967028. The Mean Per Class
Error (MPCE) is 0.01386. The significant rules can accelerate the
feature extraction process of driving data. Moreover, it helps in
automatic labeling of unlabeled dataset. Our future approach is to
develop a tool for generating categorical label for unlabeled dataset.
Besides, we have plan to optimize the proposed classifier using grid
search.