Estimation of Vehicle Motion State Based on Hybrid Neural Network

被引：0
|
作者：
Gao Z. [1 ]
Wen W. [1 ]
Tang M. [1 ]
Zhang J. [2 ]
Chen G. [1 ]
机构：
[1] Jilin University, State Key Laboratory of Automobile Simulation and Control, Changchun
[2] Intelligent Connected Vehicle Development Institute, China FAW Group Co., Ltd., Changchun
来源：
Qiche Gongcheng/Automotive Engineering | 2022年 / 44卷 / 10期
关键词：
deep learning; gated recurrent unit; hybrid neural network; multilayer perceptron; vehicle state estimation;
D O I：
10.19562/j.chinasae.qcgc.2022.10.007
中图分类号：
学科分类号：
摘要：
For the problem that the existing vehicle motion state estimation algorithm relies heavily on the accuracy of the dynamic model and the accuracy is difficult to guarantee under large slip angle, the paper proposes a vehicle motion state estimation algorithm based on the hybrid neural network （HNN）. By analyzing the basic dynamic characteristics of the vehicle itself, an hybrid neural network architecture suitable for vehicle motion state estimation is designed, and the deep learning estimation of vehicle motion state is realized. Based on the dataset composed of multi standard operating conditions and typical real vehicle test conditions, network training and test verification are carried out. The results show that compared with the traditional algorithm, the proposed HNN algorithm realizes estimation of vehicle motion state without dynamic vehicle model, improves estimation accuracy, and is robust to road adhesion coefficient change. © 2022 SAE-China. All rights reserved.
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页码：1527 / 1536
页数：9
相关论文
共 21 条
  • [1] GUO H Y, CAO D P, CHEN H, Et al., Vehicle dynamic state estimation: state of the art schemes and perspectives ［J］, IEEE-CAA J Automatica Sin, 5, 2, pp. 418-431, (2018)
  • [2] PENDLETON S D, ANDERSEN H, DU X X, Et al., Perception, planning, control, and coordination for autonomous vehicles［J］, Machines, 5, 1, (2017)
  • [3] SINGH K B, ARAT M A, TAHERI S., Literature review and fundamental approaches for vehicle and tire state estimation［J］, Vehicle System Dynamics, 57, 11, pp. 1643-1665, (2019)
  • [4] GUO H Y, CHEN H, XU F, Et al., Implementation of EKF for vehicle velocities estimation on FPGA［J］, IEEE Transactions on Industrial Electronics, 60, 9, pp. 3823-3835, (2013)
  • [5] KATRINIOK A, ABEL D., Adaptive EKF-based vehicle state estimation with online assessment of local observability［J］, IEEE Transactions on Control Systems Technology, 24, 4, pp. 1368-1381, (2016)
  • [6] LIU J, WANG Z, ZHANG L, Et al., Sideslip angle estimation of ground vehicles: a comparative study［J］, IET Control Theory & Applications, 14, 20, pp. 3490-3505, (2020)
  • [7] KIM D, KIM G, CHOI S, Et al., An integrated deep ensemble-unscented Kalman filter for sideslip angle estimation with sensor filtering network［J］, IEEE Access, 9, pp. 149681-149689, (2021)
  • [8] CHINDAMO D, LENZO B, GADOLA M., On the vehicle sideslip angle estimation: a literature review of methods, models, and innovations［J］, Applied Sciences, 8, 3, (2018)
  • [9] ZHOU W Q, QI X, CHEN L, Et al., Vehicle state estimation based on the combination of unscented Kalman filtering and genetic algorithm ［J］, Automotive Engineering, 41, 2, pp. 198-205, (2019)
  • [10] SCHMIDHUBER J., Deep learning in neural networks: an overview［J］, Neural Netw, 61, pp. 85-117, (2015)
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