Estimation of Vehicle State Based on IMM-AUKF
Establishing a symmetrical model of surrounding vehicles and accurately obtaining the driving state of the surrounding vehicles in the driving environment can improve the safety of driving, which is an important issue that needs to be considered in the automatic driving system or auxiliary driving s...
| Main Authors: | , , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2022-01-01
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| Series: | Symmetry |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2073-8994/14/2/222 |
| _version_ | 1797476473890668544 |
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| author | Ying Xu Wenjie Zhang Wentao Tang Chengxiang Liu Rong Yang Li He Yun Wang |
| author_facet | Ying Xu Wenjie Zhang Wentao Tang Chengxiang Liu Rong Yang Li He Yun Wang |
| author_sort | Ying Xu |
| collection | DOAJ |
| description | Establishing a symmetrical model of surrounding vehicles and accurately obtaining the driving state of the surrounding vehicles in the driving environment can improve the safety of driving, which is an important issue that needs to be considered in the automatic driving system or auxiliary driving system. Therefore, we propose an adaptive unscented Kalman filter algorithm based on Interacting Multiple Model (IMM) theory to estimate the state of target vehicle in the high-speed driving environment. To be specific, we use the Constant Turn Rate and Acceleration (CTRA) theory to establish the target vehicle kinematics model, simultaneously, in order to overcome the problem of estimator failure when the yaw rate is close to zero, a simplified version of the CTRA model is also introduced into the estimation process. In addition, the parameter adaptation strategy is added, so the proposed estimator can overcome the uncertainty of the noise model and improve its accuracy. Finally, the effectiveness of proposed state estimation algorithm is verified on the Carsim and Simulink co-simulation platform. The results of simulations and experiments show that the accuracy and stability of IMM-based algorithm is better than the single-model algorithm in different scenarios, and the parameter adaptation strategy brings performance improvement. |
| first_indexed | 2024-03-09T20:58:20Z |
| format | Article |
| id | doaj.art-5f2840f7c0a2423bb8723fb32355cf70 |
| institution | Directory Open Access Journal |
| issn | 2073-8994 |
| language | English |
| last_indexed | 2024-03-09T20:58:20Z |
| publishDate | 2022-01-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Symmetry |
| spelling | doaj.art-5f2840f7c0a2423bb8723fb32355cf702023-11-23T22:15:15ZengMDPI AGSymmetry2073-89942022-01-0114222210.3390/sym14020222Estimation of Vehicle State Based on IMM-AUKFYing Xu0Wenjie Zhang1Wentao Tang2Chengxiang Liu3Rong Yang4Li He5Yun Wang6College of Mechatronics and Control Engineering, Shenzhen University, Shenzhen 518060, ChinaCollege of Mechatronics and Control Engineering, Shenzhen University, Shenzhen 518060, ChinaCollege of Mechatronics and Control Engineering, Shenzhen University, Shenzhen 518060, ChinaCollege of Mechatronics and Control Engineering, Shenzhen University, Shenzhen 518060, ChinaCollege of Mechatronics and Control Engineering, Shenzhen University, Shenzhen 518060, ChinaCollege of Mechatronics and Control Engineering, Shenzhen University, Shenzhen 518060, ChinaCollege of Mechatronics and Control Engineering, Shenzhen University, Shenzhen 518060, ChinaEstablishing a symmetrical model of surrounding vehicles and accurately obtaining the driving state of the surrounding vehicles in the driving environment can improve the safety of driving, which is an important issue that needs to be considered in the automatic driving system or auxiliary driving system. Therefore, we propose an adaptive unscented Kalman filter algorithm based on Interacting Multiple Model (IMM) theory to estimate the state of target vehicle in the high-speed driving environment. To be specific, we use the Constant Turn Rate and Acceleration (CTRA) theory to establish the target vehicle kinematics model, simultaneously, in order to overcome the problem of estimator failure when the yaw rate is close to zero, a simplified version of the CTRA model is also introduced into the estimation process. In addition, the parameter adaptation strategy is added, so the proposed estimator can overcome the uncertainty of the noise model and improve its accuracy. Finally, the effectiveness of proposed state estimation algorithm is verified on the Carsim and Simulink co-simulation platform. The results of simulations and experiments show that the accuracy and stability of IMM-based algorithm is better than the single-model algorithm in different scenarios, and the parameter adaptation strategy brings performance improvement.https://www.mdpi.com/2073-8994/14/2/222state estimationInteracting Multiple Modelparameter adaptationsymmetrical model of surrounding vehicles |
| spellingShingle | Ying Xu Wenjie Zhang Wentao Tang Chengxiang Liu Rong Yang Li He Yun Wang Estimation of Vehicle State Based on IMM-AUKF Symmetry state estimation Interacting Multiple Model parameter adaptation symmetrical model of surrounding vehicles |
| title | Estimation of Vehicle State Based on IMM-AUKF |
| title_full | Estimation of Vehicle State Based on IMM-AUKF |
| title_fullStr | Estimation of Vehicle State Based on IMM-AUKF |
| title_full_unstemmed | Estimation of Vehicle State Based on IMM-AUKF |
| title_short | Estimation of Vehicle State Based on IMM-AUKF |
| title_sort | estimation of vehicle state based on imm aukf |
| topic | state estimation Interacting Multiple Model parameter adaptation symmetrical model of surrounding vehicles |
| url | https://www.mdpi.com/2073-8994/14/2/222 |
| work_keys_str_mv | AT yingxu estimationofvehiclestatebasedonimmaukf AT wenjiezhang estimationofvehiclestatebasedonimmaukf AT wentaotang estimationofvehiclestatebasedonimmaukf AT chengxiangliu estimationofvehiclestatebasedonimmaukf AT rongyang estimationofvehiclestatebasedonimmaukf AT lihe estimationofvehiclestatebasedonimmaukf AT yunwang estimationofvehiclestatebasedonimmaukf |