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Volume 8 Issue 10
Oct.  2021

IEEE/CAA Journal of Automatica Sinica

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Article Contents
Ting Zhang, Wenjie Song, Mengyin Fu, Yi Yang and Meiling Wang, "Vehicle Motion Prediction at Intersections Based on the Turning Intention and Prior Trajectories Model," IEEE/CAA J. Autom. Sinica, vol. 8, no. 10, pp. 1657-1666, Oct. 2021. doi: 10.1109/JAS.2021.1003952
Citation: Ting Zhang, Wenjie Song, Mengyin Fu, Yi Yang and Meiling Wang, "Vehicle Motion Prediction at Intersections Based on the Turning Intention and Prior Trajectories Model," IEEE/CAA J. Autom. Sinica, vol. 8, no. 10, pp. 1657-1666, Oct. 2021. doi: 10.1109/JAS.2021.1003952

Vehicle Motion Prediction at Intersections Based on the Turning Intention and Prior Trajectories Model

doi: 10.1109/JAS.2021.1003952
Funds:  This work was partly supported by the National Natural Science Foundation of China (61903034, U1913203, 61973034, 91120003), the Program for Changjiang Scholars and Innovative Research Team in University (IRT-16R06, T2014224), China Postdoctoral Science Foundation funded project (2019TQ0035), Beijing Institute of Technology Research Fund Program for Young Scholars
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  • Intersections are quite important and complex traffic scenarios, where the future motion of surrounding vehicles is an indispensable reference factor for the decision-making or path planning of autonomous vehicles. Considering that the motion trajectory of a vehicle at an intersection partly obeys the statistical law of historical data once its driving intention is determined, this paper proposes a long short-term memory based (LSTM-based) framework that combines intention prediction and trajectory prediction together. First, we build an intersection prior trajectories model (IPTM) by clustering and statistically analyzing a large number of prior traffic flow trajectories. The prior trajectories model with fitted probabilistic density is used to approximate the distribution of the predicted trajectory, and also serves as a reference for credibility evaluation. Second, we conduct the intention prediction through another LSTM model and regard it as a crucial cue for a trajectory forecast at the early stage. Furthermore, the predicted intention is also a key that is associated with the prior trajectories model. The proposed framework is validated on two publically released datasets, next generation simulation (NGSIM) and INTERACTION. Compared with other prediction methods, our framework is able to sample a trajectory from the estimated distribution, with its accuracy improved by about 20%. Finally, the credibility evaluation, which is based on the prior trajectories model, makes the framework more practical in the real-world applications.

     

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    Highlights

    • Framework: We combine intention prediction and trajectory prediction for the specified intersection scenarios. The predicted intention is not only a one-dimensional feature for trajectory prediction, but also a part of the key directly related to a prior trajectory boundary.
    • Distribution Parameter Constraints: In order to generate the trajectory boundary in the intersection, we propose an intersection prior trajectories model (IPTM) to create statistics of the historical trajectories, which approximates the distribution of the ground truth.
    • Evaluation Metrics: We analyse the credibility of the estimated trajectory by applying the modified Hausdorff distance criteria to the predicted trajectory and the prior trajectory distribution, which does not require the ground truth. The prediction that conforms to the prior trajectory distribution seems more reasonable.

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