Neural Network-Based Prediction for Lateral Acceleration of Vehicles

Kontos, János; Kránicz, Balázs; Vathy-Fogarassy, Ágnes

doi:10.1109/citds54976.2022.9914270

Cited by 3 publications

(1 citation statement)

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“…During this activity, the aim was to design and implement a low-computational-complexity, yet high-accuracy neural network that can be built into an electric control unit with limited hardware resources. The hyperparameters of the neural network were tuned using a hybrid search method introduced in [ 27 ]. The maximum value of the

was set to 60, so that the developed network can predict future yaw rate values for a maximum of 600 ms. To find the best parameters and hyperparameters, the Adam optimizer [ 28 ] was applied with a learning rate of 0.001, and the monitored loss function was the mean squared error.…”

Section: Resultsmentioning

confidence: 99%

Prediction for Future Yaw Rate Values of Vehicles Using Long Short-Term Memory Network

Kontos

Kránicz

Vathy-Fogarassy

2023

Sensors

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Currently, electric mobility and autonomous vehicles are of top priority from safety, environmental and economic points of view. In the automotive industry, monitoring and processing accurate and plausible sensor signals is a crucial safety-critical task. The vehicle’s yaw rate is one of the most important state descriptors of vehicle dynamics, and its prediction can significantly contribute to choosing the correct intervention strategy. In this article, a Long Short-Term Memory network-based neural network model is proposed for predicting the future values of the yaw rate. The training, validating and testing of the neural network was conducted based on experimental data gathered from three different driving scenarios. The proposed model can predict the yaw rate value in 0.2 s in the future with high accuracy, using sensor signals of the vehicle from the last 0.3 s in the past. The R2 values of the proposed network range between 0.8938 and 0.9719 in the different scenarios, and in a mixed driving scenario, it is 0.9624.

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