Abstract
In the automatic driving system, the motion control system is an important part, which directly determines whether the vehicle can accurately follow the trajectory. Under conventional working conditions, the existing motion control algorithms can complete the task well, but when the vehicle is driving in complex working conditions, the control accuracy will decrease. This is because the vehicle and road parameters vary greatly in these scenarios, and treating them as constants will cause the calculated values to deviate too much from the demand values. Only by introducing real-time dynamic characteristics of the vehicle can precise control of the vehicle be carried out. In this paper, we propose a vehicle motion control service (VMCS) system based on dynamic states estimation, which takes the MPC algorithm as the core, and uses the real-time estimated vehicle and road state and parameters as the input for the lateral and longitudinal motion control of the vehicle, and carries out simulation experiments and real vehicle experiments. Experimental results show that the proposed VMCS system based on dynamic state estimation can achieve good control accuracy under extreme conditions.
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Abbreviations
- VMCS:
-
Vehicle Motion Control Service
- KF:
-
Kalman Filter
- UKF:
-
Unscented Kalman Filter
- DRL:
-
Deep Reinforcement Learning
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Tang, M., Hu, H., Bao, M., Zhang, J., Gao, Z. (2023). Design of Vehicle Motion Control Service System Based on Dynamic State Estimation. In: Proceedings of China SAE Congress 2022: Selected Papers. SAE-China 2022. Lecture Notes in Electrical Engineering, vol 1025. Springer, Singapore. https://doi.org/10.1007/978-981-99-1365-7_65
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DOI: https://doi.org/10.1007/978-981-99-1365-7_65
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