Affiliation:
1. Department of Industrial Engineering, University of Naples Federico II, 80125 Naples, Italy
2. Department of Civil Engineering and Architecture, University of Catania, 95123 Catania, Italy
Abstract
In the last few decades, the role of vehicle dynamics control systems has become crucial. In this complex scenario, the correct real-time estimation of the vehicle’s sideslip angle is decisive. Indeed, this quantity is deeply linked to several aspects, such as traction and stability optimization, and its correct understanding leads to the possibility of reaching greater road safety, increased efficiency, and a better driving experience for both autonomous and human-controlled vehicles. This paper aims to estimate accurately the sideslip angle of the vehicle using different neural network configurations. Then, the proposed approach involves using two separate neural networks in a dual-network architecture. The first network is dedicated to estimating the longitudinal velocity, while the second network predicts the sideslip angle and takes the longitudinal velocity estimate from the first network as input. This enables the creation of a virtual sensor to replace the real one. To obtain a reliable training dataset, several test sessions were conducted on different tracks with various layouts and characteristics, using the same reference instrumented vehicle. Starting from the acquired channels, such as lateral and longitudinal acceleration, steering angle, yaw rate, and angular wheel speeds, it has been possible to estimate the sideslip angle through different neural network architectures and training strategies. The goodness of the approach was assessed by comparing the estimations with the measurements obtained from an optical sensor able to provide accurate values of the target variable. The obtained results show a robust alignment with the reference values in a great number of tested conditions. This confirms that the adoption of artificial neural networks represents a reliable strategy to develop real-time virtual sensors for onboard solutions, expanding the information available for controls.
Reference57 articles.
1. An insight into crash avoidance and overtaking advice systems for Autonomous Vehicles: A review, challenges and solutions;Perumal;Eng. Appl. Artif. Intell.,2021
2. Impact of connected and autonomous vehicles on traffic efficiency and safety of an on-ramp;Yang;Simul. Model. Pract. Theory,2021
3. Ibañez-Guzmán, J., Laugier, C., Yoder, J.D., and Thrun, S. (2012). Handbook of Intelligent Vehicles, Springer London.
4. Jin, Y., Ahn, H., Kim, K., Choi, S., Mueck, M., Frascolla, V., and Haustein, T. (September, January 28). Adaptive automotive communications solutions of 10 years lifetime enabled by ETSI RRS software reconfiguration technology. Proceedings of the 2017 25th European Signal Processing Conference (EUSIPCO), Kos, Greece.
5. Rajamani, R. (2012). Vehicle Dynamics and Control, Springer. [2nd ed.].
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