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Vehicle Trajectory Prediction Considering Driver Uncertainty and Vehicle Dynamics Based on Dynamic Bayesian Network | IEEE Journals & Magazine | IEEE Xplore

Vehicle Trajectory Prediction Considering Driver Uncertainty and Vehicle Dynamics Based on Dynamic Bayesian Network


Abstract:

Vehicle trajectory prediction is a crucial but intricate problem for lateral driving assistance systems because of driver uncertainty. This article presents a probabilist...Show More

Abstract:

Vehicle trajectory prediction is a crucial but intricate problem for lateral driving assistance systems because of driver uncertainty. This article presents a probabilistic vehicle-trajectory prediction method based on a dynamic Bayesian network (DBN) model integrating the driver’s intention, maneuvering behavior, and vehicle dynamics. By selecting a most-relevant-feature vector using joint mutual information, we design a Gaussian mixture model- hidden Markov model and employ the model as a node in the DBN to identify the driver’s intention. Then, a reference path is generated using the road information. The uncertainties of drivers are captured in steering- and longitudinal-control using a stochastic driver model and a Markov chain, respectively. A vehicle dynamic model ensures that the predicted vehicle trajectory adheres to the vehicle dynamics, which improves the prediction accuracy. A particle filter is used to recursively estimate the vehicle trajectory, including position coordinates and the lateral distance from the vehicle center of gravity to the road edge. We evaluate the proposed DBN trajectory prediction method in both lane-keeping and lane-changing scenarios based on a dataset collected from a real-time dynamic driving simulator. Results show that the proposed method can achieve accurate long-term trajectory prediction.
Published in: IEEE Transactions on Systems, Man, and Cybernetics: Systems ( Volume: 53, Issue: 2, February 2023)
Page(s): 689 - 703
Date of Publication: 15 July 2022

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I. Introduction

Advanced driver-assistance systems have gained wide application in mass-production vehicles to improve driving safety and comfort. Among these intelligent systems, lateral driving-assistance systems (LDASs) provide a punctual warning (in lane departure warning system, LDW) or intervention (in lane keeping assist system, LKA) to prevent unintended lane departures and have great potential to avoid traffic accidents [1], [2]. A hierarchical structure is usually employed in the design of LDASs, which mainly consists of an upper level controller and a lower level controller, as presented in [2] and [3]. The upper layer anticipates vehicle trajectory and determines whether a warning or intervention is needed. The lower layer is used to regulate the vehicle states or alert human drivers with light, sound, or vibration. The research of this article lays an emphasis on the solution of vehicle trajectory prediction, an important basis in LDASs. However, because of the complex interaction between human drivers and vehicle dynamics, it is difficult to achieve an accurate long-term trajectory prediction. To avoid human–machine conflict and reduce the rate of false alarms and interventions, LDAS must achieve a comprehensive understanding of the driver’s characteristics and accurately predict vehicle states [4].

References

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