Abstract
With the automotive industry moving towards automated driving, sensing is increasingly important in enabling technology. The virtual sensors allow data fusion from various vehicle sensors and provide a prediction for measurement that is hard or too expensive to measure in another way or in the case of demand on continuous detection. In this paper, virtual sensing is discussed for the case of vehicle suspension control, where information about the relative velocity of the unsprung mass for each vehicle corner is required. The corresponding goal can be identified as a regression task with multi-input sequence input. The hypothesis is that the state-of-art method of Bidirectional Long–Short Term Memory (BiLSTM) can solve it. In this paper, a virtual sensor has been proposed and developed by training a neural network model. The simulations have been performed using an experimentally validated full vehicle model in IPG Carmaker. Simulations provided the reference data which were used for Neural Network (NN) training. The extensive dataset covering 26 scenarios has been used to obtain training, validation and testing data. The Bayesian Search was used to select the best neural network structure using root mean square error as a metric. The best network is made of 167 BiLSTM, 256 fully connected hidden units and 4 output units. Error histograms and spectral analysis of the predicted signal compared to the reference signal are presented. The results demonstrate the good applicability of neural network-based virtual sensors to estimate vehicle unsprung mass relative velocity.
Highlights
Nowadays, the automotive industry focuses intensely on Automated Driving (AD)as a promising solution to improve safety and comfort
The results show that Root Mean Squared Error (RMSE) reduces when window sizes from 3–19 are used
Measurements are made at 100 samples per second; we provide graphs from 0–15 Hz based on primary and secondary ride quality assessment
Summary
The automotive industry focuses intensely on Automated Driving (AD)as a promising solution to improve safety and comfort. Sensors are crucial components needed for AD, as they provide the data required to perceive the environment and vehicle state estimation [1]. The first group includes laser imaging detection and ranging (LIDAR), radio detection and ranging (Radar) [2], ultrasonic distance sensor and a camera. These sensors are placed outside the vehicle, and measurement accuracy may be affected if covered by dirt, snow or ice. Cameras can be installed inside the cabin, which may prevent the disadvantages mentioned above. Cameras can be installed inside the cabin, which may prevent the disadvantages mentioned above They can be applied to detect and track traffic objects, vision-based localisation and navigation, and capture textures and colours. Usage of stereo and infrared cameras can expand their uses for geometric parameter estimation, capturing objects in dark lighting and more [3,4,5]
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