Abstract
Traditional lane-changing models are mostly based on mathematical and physical models that do not adequately take into account the uncertainty and randomness of the driver behavior in a complex road environment, and the data for training models are often generated from driving simulators rather than from vehicles traveling under real road conditions. In this paper, a neural network model is proposed to identify a driver’s lane changing behavior based on real-time driving data collected in the connected vehicle environment. An in-depth analysis of the lane-changing process is conducted, dividing the process into different stages and identifying the factors that characterize the lane-changing behavior. Research data is extracted from the Safety Pilot Model Development (SPMD) project. Feedforward Neural Network (FNN) is selected to construct the model given its good ability for nonlinear fitting. The model is trained using SPMD data to determine the optimal network structure. Applying the trained model to identify the lane-changing behavior, the corresponding recognition rate under the optimal time window reaches 90.7%. The curve of Receiver Operating Characteristic (ROC) shows that the FNN model can satisfy the requirements for the identification of lane-changing vehicles. The findings of this paper may contribute to the safety of vehicle lane changing in the connected vehicle environment and reduce traffic crashes caused by lane changing through advance warnings.
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References
Li, X., Sun, J.Q.: Studies of vehicle lane-changing dynamics and its effect on traffic efficiency, safety and environmental impact. Phys. A 467, 41–58 (2017)
Park, H., Oh, C., Moon, J., Kim, S.: Development of a lane change risk index using vehicle trajectory data. Accid. Anal. Prev. 110, 1–8 (2018)
Li, M., Li, Z., Xu, C., Liu, T.: Short-term prediction of safety and operation impacts of lane changes in oscillations with empirical vehicle trajectories. Accid. Anal. Prev. 135, 1–8 (2020)
Zheng, Z.: Recent developments and research needs in modeling lane changing. Transp. Res. Res. B Methodological 60(1), 16–32 (2014)
Xie, D.F., Fang, Z.Z., Jia, B., He, Z.: A data-driven lane-changing model based on deep learning. Transp. Res. Part C Emerg. Technol. 106, 41–60 (2019)
Zheng, J., Suzuki, K., Fujita, M.: Predicting driver’s lane-changing decisions using a neural network model. Simul. Model. Pract. Theory 42, 73–83 (2014)
Kuge, N., Yamamura, T., Shimoyama, O.: A driver behavior recognition method based on a driver model framework. In: Proceedings of the Society of Automotive Engineers World Congress (2000)
Mandalia, H.M., Salvucci, D.D.: Using support vector machines for lane-change detection. Proc. Hum. Factors Ergon. Soc. Annu. Meet. 49(22), 1965–1969 (2005)
Salvucci, D.D., Mandalia, H.M., Kuge, N.: Lane-change detection using a computational driver model. Hum. Factors 49(3), 532–542 (2007)
Dogan, U., Edelbrunner, H., Iossifidis, I.: Towards a driver model: preliminary study of lane change behavior. In: International IEEE Conference on Intelligent Transportation Systems (2008)
Gao, J., Zhu, H., Murphey, Y.L.: A personalized model for driver lane-changing behavior prediction using deep neural network. In: 2nd International Conference on Artificial Intelligence and Big Data (2019)
Xu, T., Jiang, R., Wen, C., Liu, M., Zhou, J.: A hybrid model for lane change prediction with V2X-based driver assistance. Phys. A 534, 122033 (2019)
Xu, G., Liu, L., Song, Z.: Generating lane-change trajectories using the dynamic model of driving behavior. In: Proceeding of the IEEE International Conference on Information and Automation pp. 464–469 (2011)
Zhou, B., Wang, Y., Yu, G., Wu, X.: A lane-change trajectory model from drivers’ vision view. Transp. Res. Part C: Emerg. Technol. 85, 609–627 (2017)
Acknowledgment
This research was financially supported by The Special Fund of Chinese Central Government for Basic Scientific Research Operations in Commonweal Research Institutes (No. 20194808). The research data of this study is from the SPMD project in Ann Arbor, Michigan, US.
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Wu, Z., Zhang, D., Wang, Y., Zhou, H., Qi, H. (2022). Lane-Changing Behavior Recognition in the Connected Vehicle Environment. In: Zhang, Z. (eds) 2021 6th International Conference on Intelligent Transportation Engineering (ICITE 2021). ICITE 2021. Lecture Notes in Electrical Engineering, vol 901. Springer, Singapore. https://doi.org/10.1007/978-981-19-2259-6_5
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DOI: https://doi.org/10.1007/978-981-19-2259-6_5
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