Four-Wheeled Vehicle Sideslip Angle Estimation: A Machine Learning-Based Technique for Real-Time Virtual Sensor Development
Source abstract
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.
Source: crossref · Quality: baseline
Research context
OpenAlex topics: Vehicle Dynamics and Control Systems · Vehicle emissions and performance · Autonomous Vehicle Technology and Safety
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
Affiliations
Department of Industrial Engineering, University of Naples Federico II, 80125 Naples, Italy
Department of Civil Engineering and Architecture, University of Catania, 95123 Catania, Italy
References (57)
Show source-provided references
1. An insight into crash avoidance and overtaking advice systems for Autonomous Vehicles: A review, challenges and solutions10.1016/j.engappai.2021.104406 ↗
2. Impact of connected and autonomous vehicles on traffic efficiency and safety of an on-ramp10.1016/j.simpat.2021.102374 ↗
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.10.23919/EUSIPCO.2017.8081338 ↗
5. Rajamani, R. (2012). Vehicle Dynamics and Control, Springer. [2nd ed.].10.1007/978-1-4614-1433-9 ↗
6. Santini, S., Albarella, N., Arricale, V.M., Brancati, R., and Sakhnevych, A. (2021). On-board road friction estimation technique for autonomous driving vehicle-following maneuvers. Appl. Sci., 11.10.3390/app11052197 ↗
7. Lefevre, S., Laugier, C., and Ibanez-Guzman, J. (2012, January 3–7). Risk assessment at road intersections: Comparing intention and expectation. Proceedings of the 2012 IEEE Intelligent Vehicles Symposium, Alcalá de Henares, Madrid, Spain.10.1109/IVS.2012.6232198 ↗
8. Short, M., and Pont, M. (2005, January 16). Hardware in the loop simulation of embedded automotive control system. Proceedings of the 2005 IEEE Intelligent Transportation Systems, Vienna, Austria.10.1109/ITSC.2005.1520052 ↗
9. State of the art: Hardware in the loop modeling and simulation with its applications in design, development and implementation of system and control software10.1007/s40435-014-0108-3 ↗
10. Zhao, P., Chen, J., Mei, T., and Liang, H. (2011, January 5–9). Dynamic motion planning for autonomous vehicle in unknown environments. Proceedings of the 2011 IEEE Intelligent Vehicles Symposium (IV), Baden-Baden, Germany.10.1109/IVS.2011.5940506 ↗
11. Vehicle dynamics integrated control for four-wheel-distributed steering and four-wheel-distributed traction/braking systems10.1080/00423110500385790 ↗
12. Halbach, S., Sharer, P., Pagerit, S., Rousseau, A.P., and Folkerts, C. (2010, January 13). Model Architecture, Methods, and Interfaces for Efficient Math-Based Design and Simulation of Automotive Control Systems. Proceedings of the SAE 2010 World Congress & Exhibition, Detroit, MI, USA.10.4271/2010-01-0241 ↗
13. Sakhnevych, A., Arricale, V.M., Bruschetta, M., Censi, A., Mion, E., Picotti, E., and Frazzoli, E. (2021). Investigation on the model-based control performance in vehicle safety critical scenarios with varying tyre limits. Sensors, 21.10.3390/s21165372 ↗
14. End-to-End Velocity Estimation For Autonomous Racing10.1109/LRA.2020.3016929 ↗
15. An integrated longitudinal and lateral vehicle following control system with radar and vehicle-to-vehicle communication10.1109/TVT.2018.2890418 ↗
16. Guiggiani, M. (2023). The Science of Vehicle Dynamics, Springer.10.1007/978-3-031-06461-6 ↗
17. Development of a portable instrument for non-destructive characterization of the polymers viscoelastic properties10.1016/j.ymssp.2020.107259 ↗
18. Genovese, A., Farroni, F., and Sakhnevych, A. (2022). Fractional calculus approach to reproduce material viscoelastic behavior, including the time–temperature superposition phenomenon. Polymers, 14.10.3390/polym14204412 ↗
19. A novel methodology for non-destructive characterization of polymers’ viscoelastic properties10.1142/S175882512250017X ↗
20. Recurrent Neural Network Model for On-Board Estimation of the Side-Slip Angle in a Four-Wheel Drive and Steering Vehicle10.4271/15-17-01-0003 ↗
21. Numerical simulation analysis of an oversteer in-wheel small electric vehicle integrated with four-wheel drive and independent steering
22. Electric vehicle side-slip control via electronic differential10.1504/IJVAS.2015.070724 ↗
23. Hac, A., and Bedner, E. (2007, January 18–21). Robustness of side slip estimation and control algorithms for vehicle chassis control. Proceedings of the Proceedings of ESV Conference, Lyon, France.
24. Adaptive vehicle dynamics state estimator for onboard automotive applications and performance analysis10.1080/00423114.2022.2158567 ↗
25. Bian, M., Chen, L., Luo, Y., and Li, K. (2014). A Dynamic Model for Tire/Road Friction Estimation under Combined Longitudinal/Lateral Slip Situation, SAE. Technical Report, SAE Technical Paper.10.4271/2014-01-0123 ↗
26. Shao, L., Jin, C., Lex, C., and Eichberger, A. (2016, January 12–14). Nonlinear adaptive observer for side slip angle and road friction estimation. Proceedings of the 2016 IEEE 55th Conference on Decision and Control (CDC), Las Vegas, NV, USA.10.1109/CDC.2016.7799232 ↗
27. Adaptive estimations of tyre–road friction coefficient and body’s sideslip angle based on strong tracking and interactive multiple model theories10.1177/0954407020941410 ↗
28. Tire wear modeling10.20858/tp.2016.11.3.11 ↗
29. Ziaukas, Z., Busch, A., and Wielitzka, M. (2021, January 9–11). Estimation of Vehicle Side-Slip Angle at Varying Road Friction Coefficients Using a Recurrent Artificial Neural Network. Proceedings of the 2021 IEEE Conference on Control Technology and Applications (CCTA), San Diego, CA, USA.10.1109/CCTA48906.2021.9658710 ↗
30. Chindamo, D., Lenzo, B., and Gadola, M. (2018). On the vehicle sideslip angle estimation: A literature review of methods, models, and innovations. Appl. Sci., 8.10.3390/app8030355 ↗
31. Zhang, C., Feng, Y., Wang, J., Gao, P., and Qin, P. (2023). Vehicle Sideslip Angle Estimation Based on Radial Basis Neural Network and Unscented Kalman Filter Algorithm. Actuators, 12.10.3390/act12100371 ↗
32. An extended adaptive Kalman filter for real-time state estimation of vehicle handling dynamics10.1076/0042-3114(200008)34:1;1-K;FT057 ↗
33. Sideslip angle estimation using extended Kalman filter10.1080/00423110801958550 ↗
34. Autonomous vehicle kinematics and dynamics synthesis for sideslip angle estimation based on consensus kalman filter10.1109/TCST.2022.3174511 ↗
35. On the vehicle sideslip angle estimation through neural networks: Numerical and experimental results10.1016/j.ymssp.2010.10.015 ↗
36. Measurement of machine performance degradation using a neural network model10.1016/0166-3615(96)00013-9 ↗
37. Neural network model-based automotive engine air/fuel ratio control and robustness evaluation10.1016/j.engappai.2008.08.001 ↗
38. Meijer, G.C. (2008). Smart Sensor Systems, John Wiley & Sons, Ltd.10.1002/9780470866931 ↗
39. Song, R., and Fang, Y. (2022, January 1–3). Estimation of Vehicle Sideslip Angle based on Modified Sliding Mode Observer and Recurrent Neural Network. Proceedings of the 2022 7th Asia-Pacific Conference on Intelligent Robot Systems (ACIRS), Tianjin, China.10.1109/ACIRS55390.2022.9845624 ↗
40. Demuth, H.B., Beale, M.H., De Jess, O., and Hagan, M.T. (2014). Neural Network Design, Martin Hagan.
41. Wielitzka, M., Busch, A., Dagen, M., Ortmaier, T., and Serra, G. (2018). Kalman Filters-Theory for Advanced Applications, InTech.
42. Napolitano Dell’Annunziata, G., Arricale, V.M., Farroni, F., Genovese, A., Pasquino, N., and Tranquillo, G. (2022). Estimation of Vehicle Longitudinal Velocity with Artificial Neural Network. Sensors, 22.10.3390/s22239516 ↗
43. The Development of Data Acquisition System of Formula SAE Race Car Based on CAN Bus Communication Interface and Closed-Loop Design of Racing Car10.1155/2021/4211010 ↗
44. Johansson, K.H., Törngren, M., and Nielsen, L. (2005). Handbook of Networked and Embedded Control Systems, Springer.
45. HPL, S.C. (2002). Application Report SLOA101, Texas Instruments.
46. Meyers, R.A. (2003). Encyclopedia of Physical Science and Technology, Academic Press. [3rd ed.].
47. Mandic, D., and Chambers, J. (2001). Recurrent Neural Networks for Prediction: Learning Algorithms, Architectures and Stability, John Wiley & Sons Inc.10.1002/047084535X ↗
48. Korstanje, J. (2021). Advanced Forecasting with Python, Apress.10.1007/978-1-4842-7150-6 ↗
49. Hecht-Nielsen, R. (1992). Neural Networks for Perception, Elsevier.
50. McClarren, R.G. (2021). Machine Learning for Engineers, Springer.10.1007/978-3-030-70388-2 ↗
51. A review of recurrent neural networks: LSTM cells and network architectures10.1162/neco_a_01199 ↗
52. Sideslip Angle Estimation of Ground Vehicles: A Comparative Study10.1049/iet-cta.2020.0516 ↗
53. The use of NARX neural networks to predict chaotic time series
54. Heaton, J. (2005). Introduction to Neural Networks with Java, Heaton Research.
55. Yaro, A., Maly, F., and Pražák, P. (2023). Outlier Detection in Time-Series Receive Signal Strength Observation Using Z-Score Method with Sn Scale Estimator for Indoor Localization. Appl. Sci., 13.10.3390/app13063900 ↗
56. Importance of input data normalization for the application of neural networks to complex industrial problems10.1109/23.589532 ↗
57. James, G., Witten, D., Hastie, T., and Tibshirani, R. (2013). An Introduction to Statistical Learning, Springer. [1st ed.]. Springer Texts in Statistics.10.1007/978-1-4614-7138-7 ↗