Research on Traffic Vehicle Behavior Prediction Method Based on Game Theory and HMM

Zhang, Sumin; Zhi, Yongshuai; He, Rui; Li, Jianping

doi:10.1109/access.2020.2971705

Cited by 26 publications

(13 citation statements)

References 26 publications

(22 reference statements)

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“…Furthermore, as the opponent vehicles may not always act as the formulated Stackelberg game expects, an online estimation algorithm is proposed using historical data to improve the gamebased interactions. [22,23] Additionally, there exists a problem in finding the Nash equilibrium. There may be more than one Nash equilibrium, conflicting with one another.…”

Section: Decision-making Behind the Scenesmentioning

confidence: 99%

Human‐Like Interactive Behavior Generation for Autonomous Vehicles: A Bayesian Game‐Theoretic Approach with Turing Test

Zhang

Hang

Huang

et al. 2022

Advanced Intelligent Systems

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Interacting with surrounding road users is a key feature of autonomous vehicles and is critical for their intelligence testing. Existing interaction modalities in autonomous vehicle simulation and testing are not sufficiently smart, and they barely reflect human‐like behaviors in real‐world driving scenarios. Therefore, a novel hierarchical game‐theoretical framework is presented to represent naturalistic multimodal interactions among road users during simulation and testing, which is subsequently validated by the Turing test. Given that human drivers have no access to the complete information of the surrounding road users, the Bayesian game theory is used to model the decision‐making process. Next, a probing behavior is generated by the proposed game‐theoretic model, and is further applied to control the vehicle via the Markov chain. The proposed method is tested through a series of experiments and compared with existing approaches. In addition, Turing tests are conducted to quantify the human‐likeness of the proposed algorithm. The experimental results show that the proposed Bayesian game‐theoretic framework can effectively generate representative scenes of human‐like decision‐making during autonomous vehicle interactions, demonstrating its feasibility and effectiveness. An interactive preprint version of the article can be found at: https://www.authorea.com/doi/full/10.22541/au.163482630.00283278.

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Section: Decision-making Behind the Scenesmentioning

confidence: 99%

Human‐Like Interactive Behavior Generation for Autonomous Vehicles: A Bayesian Game‐Theoretic Approach with Turing Test

Zhang

Hang

Huang

et al. 2022

Advanced Intelligent Systems

View full text Add to dashboard Cite

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“…Liu 16 proposed a semi-Markov model based on nonlinear polynomial regression and recursive hidden model (R-HSMM), which can identify driver intentions earlier than common methods and better adapt to long-term continuous state. Considering the influence of interaction between vehicles on behavior prediction, Zhang 10 proposed an interactive prediction and recognition based on game theory and GMM + HMM model to predict the intention of other vehicles and identify their behaviors. Wang 17 proposed an intention reasoning algorithm based on interactive games to solve the interactive “double-blind” intention reasoning problem between two agents.…”

Section: Related Workmentioning

confidence: 99%

“…Human intentions are internal processes and can generally be inferred by observing the actions they produce 7 , 8 . At present, the main research methods on intention estimation and behavior prediction include: traditional machine learning based on classical probability (model driven) and deep learning (data driven) 9 , 10 .…”

Section: Introductionmentioning

confidence: 99%

Research on quantum cognition in autonomous driving

Song

Wang

et al. 2022

Sci Rep

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Autonomous vehicles for the intention of human behavior of the estimated traffic participants and their interaction is the main problem in automatic driving system. Classical cognitive theory assumes that the behavior of human traffic participants is completely reasonable when studying estimation of intention and interaction. However, according to the quantum cognition and decision theory as well as practical traffic cases, human behavior including traffic behavior is often unreasonable, which violates classical cognition and decision theory. Based on the quantum cognitive theory, this paper studies the cognitive problem of pedestrian crossing. Through the case analysis, it is proved that the Quantum-like Bayesian (QLB) model can consider the reasonability of pedestrians when crossing the street compared with the classical probability model, being more consistent with the actual situation. The experiment of trajectory prediction proves that the QLB model can cover the edge events in interactive scenes compared with the data-driven Social-LSTM model, being more consistent with the real trajectory. This paper provides a new reference for the research on the cognitive problem of intention on bounded rational behavior of human traffic participants in autonomous driving.

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“…Hand-crafted cost functions were used to model inter-vehicle interactions. 1,22,29 Cost function based approaches do not depend on training data and can generalize to new traffic configurations. However, they can be limited by how well the hand-crafted cost function is designed.…”

Section: Related Workmentioning

confidence: 99%

BiGRU based online multi-modal driving maneuvers and trajectory prediction

Zhi

Bao

Zhang

et al. 2021

Proceedings of the Institution of Mechanical Engineers, Part D:

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Accurately predicting maneuvers and trajectory of vehicles are essential prerequisites for intelligent systems such as autonomous vehicles to achieve safe and high-quality decision making and motion planning. Motions of each vehicle in a scene is governed by the traffic context, that is, the motion and relative spatial positions of neighboring vehicles, and is also affected by its motion inertia, that is, the trajectory history. In this paper, we propose a novel scheme based on Bidirectional Gated Recurrent Unit (BiGRU) to conduct online multi-modal driving maneuvers and trajectory prediction. The motivation for this BiGRU based method relies on its enhanced prediction accuracy and computational efficiency in outputting the predicted results within the limited prediction horizon. We utilize a BiGRU to extract the complete history and future information of every point in the trajectory history sequence, apply dilated convolutional social (DCS) for learning interdependencies in vehicle motion, and subsequently use a GRU decoder model to make predictions. Additionally, our model simultaneously outputs a multi-modal predictive distribution over future trajectory and vehicle’s behavior prediction results. We evaluate our model using the publicly available NGSIM US-101and I-80 datasets. Our results show improvements over the state-of-the-art in terms of Root Mean Square Error (RMSE) values and Negative Log-Likelihoods (NLL). We also present a qualitative analysis of the model’s predicted maneuvers and multi-model trajectories for various traffic scenarios.

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Research on Traffic Vehicle Behavior Prediction Method Based on Game Theory and HMM

Cited by 26 publications

References 26 publications

Human‐Like Interactive Behavior Generation for Autonomous Vehicles: A Bayesian Game‐Theoretic Approach with Turing Test

Human‐Like Interactive Behavior Generation for Autonomous Vehicles: A Bayesian Game‐Theoretic Approach with Turing Test

Research on quantum cognition in autonomous driving

BiGRU based online multi-modal driving maneuvers and trajectory prediction

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