Open Access
Issue
Published:
30 September 2023
Vehicle sideslip trajectory prediction based on time-series analysis and multi-physical model fusion
)
Download citation
74
Views
5
Downloads
3
Crossref
N/A
WoS
2
Scopus
N/A
CSCD
On highways, vehicles that swerve out of their lane due to sideslip can pose a serious threat to the safety of autonomous vehicles. To ensure their safety, predicting the sideslip trajectories of such vehicles is crucial. However, the scarcity of data on vehicle sideslip scenarios makes it challenging to apply data-driven methods for prediction. Hence, this study uses a physical model-based approach to predict vehicle sideslip trajectories. Nevertheless, the traditional physical model-based method relies on constant input assumption, making its long-term prediction accuracy poor. To address this challenge, this study presents the time-series analysis and interacting multiple model-based (IMM) sideslip trajectory prediction (TSIMMSTP) method, which encompasses time-series analysis and multi-physical model fusion, for the prediction of vehicle sideslip trajectories. Firstly, we use the proposed adaptive quadratic exponential smoothing method with damping (AQESD) in the time-series analysis module to predict the input state sequence required by kinematic models. Then, we employ an IMM approach to fuse the prediction results of various physical models. The implementation of these two methods allows us to significantly enhance the long-term predictive accuracy and reduce the uncertainty of sideslip trajectories. The proposed method is evaluated through numerical simulations in vehicle sideslip scenarios, and the results clearly demonstrate that it improves the long-term prediction accuracy and reduces the uncertainty compared to other model-based methods.
menu
Vehicle sideslip trajectory prediction based on time-series analysis and multi-physical model fusion
)
Abstract
On highways, vehicles that swerve out of their lane due to sideslip can pose a serious threat to the safety of autonomous vehicles. To ensure their safety, predicting the sideslip trajectories of such vehicles is crucial. However, the scarcity of data on vehicle sideslip scenarios makes it challenging to apply data-driven methods for prediction. Hence, this study uses a physical model-based approach to predict vehicle sideslip trajectories. Nevertheless, the traditional physical model-based method relies on constant input assumption, making its long-term prediction accuracy poor. To address this challenge, this study presents the time-series analysis and interacting multiple model-based (IMM) sideslip trajectory prediction (TSIMMSTP) method, which encompasses time-series analysis and multi-physical model fusion, for the prediction of vehicle sideslip trajectories. Firstly, we use the proposed adaptive quadratic exponential smoothing method with damping (AQESD) in the time-series analysis module to predict the input state sequence required by kinematic models. Then, we employ an IMM approach to fuse the prediction results of various physical models. The implementation of these two methods allows us to significantly enhance the long-term predictive accuracy and reduce the uncertainty of sideslip trajectories. The proposed method is evaluated through numerical simulations in vehicle sideslip scenarios, and the results clearly demonstrate that it improves the long-term prediction accuracy and reduces the uncertainty compared to other model-based methods.
References(32)
Arulampalam, M. S., Maskell, S., Gordon, N., Clapp, T., 2002. A tutorial on particle filters for online nonlinear/non-Gaussian Bayesian tracking. IEEE Trans Signal Process, 50, 174–188.
Eskandarian, A., Wu, C., Sun, C., 2021. Research advances and challenges of autonomous and connected ground vehicles. IEEE Trans Intell Transp Syst, 22, 683–711.
Gardner, E. S., 1985. Exponential smoothing: The state of the art. J Forecast, 4, 1–28.
Ghorai, P., Eskandarian, A., Kim, Y. K., Mehr, G., 2022. State estimation and motion prediction of vehicles and vulnerable road users for cooperative autonomous driving: A survey. IEEE Trans Intell Transp Syst, 23, 16983–17002.
Gulzar, M., Muhammad, Y., Muhammad, N., 2021. A survey on motion prediction of pedestrians and vehicles for autonomous driving. IEEE Access, 9, 137957–137969.
Huang, Y., Du, J., Yang, Z., Zhou, Z., Zhang, L., Chen, H., 2022. A survey on trajectory-prediction methods for autonomous driving. IEEE Trans Intell Veh, 7, 652–674.
Izquierdo, R., Quintanar, Á., Llorca, D. F., Daza, I. G., Hernández, N., Parra, I. et al., 2023. Vehicle trajectory prediction on highways using bird eye view representations and deep learning. Appl Intell, 53, 8370–8388.
Kim, W., Kang, C. M., Son, Y. S., Lee, S. H., Chung, C. C., 2018. Vehicle path prediction using yaw acceleration for adaptive cruise control. IEEE Trans Intell Transp Syst, 19, 3818–3829.
Lefèvre, S., Vasquez, D., Laugier, C., 2014. A survey on motion prediction and risk assessment for intelligent vehicles. ROBOMECH J, 1, 1–14.
Liu, C. S., Peng, H., 1996. Road friction coefficient estimation for vehicle path prediction. Veh Syst Dyn, 25, 413–425.
Liu, J., Luo, Y., Zhong, Z., Li, K., Huang, H., Xiong, H., 2022. A probabilistic architecture of long-term vehicle trajectory prediction for autonomous driving. Engineering, 19, 228–239.
Messaoud, K., Yahiaoui, I., Verroust-Blondet, A., Nashashibi, F., 2021. Attention based vehicle trajectory prediction. IEEE Trans Intell Veh, 6, 175–185.
Mozaffari, S., Al-Jarrah, O. Y., Dianati, M., Jennings, P., Mouzakitis, A., 2022. Deep learning-based vehicle behavior prediction for autonomous driving applications: A review. IEEE Trans Intell Transp Syst, 23, 33–47.
Park, J. H., Tai, Y. W., 2015. A simulation based method for vehicle motion prediction. Comput Vis Image Underst, 136, 79–91.
Schreier, M., Willert, V., Adamy, J., 2016. An integrated approach to maneuver-based trajectory prediction and criticality assessment in arbitrary road environments. IEEE Trans Intell Transp Syst, 17, 2751–2766.
Shiller, Z., Sundar, S., 1998. Emergency lane-change maneuvers of autonomous vehicles. J Dyn Syst Meas Contr, 120, 37–44.
Xiang, Y., He, Y., Luo, Y., Bu, D., Kong, W., Chen, J., 2022. Recognition model of sideslip of surrounding vehicles based on perception information of driverless vehicle. IEEE Intell Syst, 37, 79–91.
Xie, G., Gao, H., Qian, L., Huang, B., Li, K., Wang, J., 2018. Vehicle trajectory prediction by integrating physics- and maneuver-based approaches using interactive multiple models. IEEE Trans Ind Electron, 65, 5999–6008.
Yao, H., Li, Q., Li, X., 2022. Trajectory prediction dimensionality reduction for low-cost connected automated vehicle systems. Transp Res Part D Transp Environ, 111, 103439.
Yao, H., Li, X., Yang, X., 2023. Physics-aware learning-based vehicle trajectory prediction of congested traffic in a connected vehicle environment. IEEE Trans Veh Technol, 72, 102–112.
Publication history
Copyright
Acknowledgements
This research was supported by the National Natural Science Foundation of China (Grant No. 51975310).
Rights and permissions
This is an open access article under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/).
FEEDBACK 
TOP