Probabilistic Long-term Vehicle Trajectory Prediction via Driver Awareness Model

Liu, Jinxin; Xiong, Hui; Huang, Heye; Luo, Yugong; Zhong, Zhihua; Li, Keqiang

doi:10.1109/iv47402.2020.9304753

Cited by 2 publications

(1 citation statement)

References 21 publications

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“…Probabilistic prediction methods usually predict possible future trajectories based on the current state of the vehicle, and express the magnitude of the possibility through probability. 27 Kim and Yi 28 estimated the current vehicle information and road geometry information through sensors, and then obtained the desired angular velocity and future state of the vehicle. Li et al 29 proposed a conditional generative neural system for long-term trajectory prediction, which took into account both static context information and dynamic evolution of traffic situations.…”

Section: Introductionmentioning

confidence: 99%

A comprehensive lateral motion prediction method of surrounding vehicles integrating driver intention prediction and vehicle behavior recognition

Luan

Huang

Zhao

et al. 2022

Proceedings of the Institution of Mechanical Engineers, Part D:

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In order to solve the accuracy problem of the future motion prediction of the surrounding vehicles with different types of drivers, this paper proposes a comprehensive lateral motion prediction method that combines driver intention prediction and vehicle behavior recognition. For different drivers, different driver optimization models are established: personal optimal model and system optimal model. Then, the driver’s intention prediction probability is obtained through the game theory. Different from the traditional game theory, we apply different model for different driver on the premise of getting the type of driver instead of using the Nash equilibrium for all players. According to the results of driver intention prediction and the vehicle behavior recognition, the optimized polynomial trajectory is used to obtain the driver’s intention prediction trajectory and the vehicle behavior recognition trajectory. Next, the Nash-optimization function of the intention prediction trajectory and the behavior recognition trajectory are established respectively according to the trajectory error, and the balance between the two is our comprehensive trajectory. In order to verify the effectiveness of the proposed method, we conducted simulations under mandatory lane changing and discretionary lane changing conditions. The simulation results show that our algorithm can accurately predict the future motion of the vehicle under different drivers, different speeds and different gaps between vehicles. Its prediction performance is much better than other algorithms.

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Section: Introductionmentioning

confidence: 99%