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Volume 9 Issue 10
Oct.  2022

IEEE/CAA Journal of Automatica Sinica

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Article Contents
X. L. Zhu, W. Hu, Z. J. Deng, J. W. Zhang, F. Q. Hu, R. Zhou, K. Q. Li, and  F.-Y. Wang,  “Interaction-aware cut-in trajectory prediction and risk assessment in mixed traffic,” IEEE/CAA J. Autom. Sinica, vol. 9, no. 10, pp. 1752–1762, Oct. 2022. doi: 10.1109/JAS.2022.105866
Citation: X. L. Zhu, W. Hu, Z. J. Deng, J. W. Zhang, F. Q. Hu, R. Zhou, K. Q. Li, and  F.-Y. Wang,  “Interaction-aware cut-in trajectory prediction and risk assessment in mixed traffic,” IEEE/CAA J. Autom. Sinica, vol. 9, no. 10, pp. 1752–1762, Oct. 2022. doi: 10.1109/JAS.2022.105866

Interaction-Aware Cut-In Trajectory Prediction and Risk Assessment in Mixed Traffic

doi: 10.1109/JAS.2022.105866
Funds:  This work was supported in part by the Key-Area Research and Development Program of Guangdong Province (2020B0909050003) and the Program of Jiangxi (20204ABC03A13)
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  • Accurately predicting the trajectories of surrounding vehicles and assessing the collision risks are essential to avoid side and rear-end collisions caused by cut-in. To improve the safety of autonomous vehicles in the mixed traffic, this study proposes a cut-in prediction and risk assessment method with considering the interactions of multiple traffic participants. The integration of the support vector machine and Gaussian mixture model (SVM-GMM) is developed to simultaneously predict cut-in behavior and trajectory. The dimension of the input features is reduced through Chebyshev fitting to improve the training efficiency as well as the online inference performance. Based on the predicted trajectory of the cut-in vehicle and the responsive actions of the autonomous vehicles, two risk measurements are introduced to formulate the comprehensive interaction risk through the combination of Sigmoid function and Softmax function. Finally, the comparative analysis is performed to validate the proposed method using the naturalistic driving data. The results show that the proposed method can predict the trajectory with higher precision and effectively evaluate the risk level of a cut-in maneuver compared to the methods without considering interaction.

     

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    Highlights

    • An integrated trajectory prediction and risk assessment framework is proposed for the autonomous vehicle in cut-in scenarios
    • A hierarchical interaction-aware prediction method SVM-GMM is proposed and developed to model the interaction behavior of the cut-in scenarios to compromise the computation efficiency and performance of the online application
    • A new risk assessment method is proposed to assess the threat of a cut-in maneuver by combining different risk measurements to compensate the deficiency of the existing risk measures and improve the adaptability for various driving environments

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