Combining Low-Cost Inertial Measurement Unit (IMU) and Deep Learning Algorithm for Predicting Vehicle Attitude

In the paper, we propose an acceleration-based and angular-velocity-based vehicle attitude recognition method by using a popular deep learning algorithm, i.e., Convolution Neural Network (CNN). We use an Inertial Measurement Unit (IMU) to collect six-axial signal of a vehicle. In particular, we construct a CNN model to learn the characteristics of six-axial IMU signal and the model can be used to predict vehicle attitudes. We constructed training data consists of 800 package from six attitudes. In addition, we preprocess the 800 package that each package will be broken down. Finally, our training data is 59200 sample-train. The experiment results show that the CNN works well, which can reach an average accuracy of 98% by the time of 1/5 of the overall action without any feature extraction methods. Because we use CNN model that it's the number of convolution kernel is less, we can reach real-time. Each estimated time is less than 0.5 sec based on the raspberry pi3.