Modeling Drivers' Dynamic Decision-Making Behavior During the Phase Transition Period: An Analytical Approach Based on Hidden Markov Model Theory

Tang, Keshuang; Zhu, Shengfa; Xu, Yanqing; Wang, Fen

doi:10.1109/tits.2015.2462738

Cited by 40 publications

(23 citation statements)

References 30 publications

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“…As a result, smooth traffic flow in many cities is being increasingly disrupted, while the operational efficiency and safety of intersections are deteriorating. For example, data from Shanghai's Songjiang District indicate that 70% of accidents at intersections occur during signal phase transitions, mostly relating to collisions between motor vehicles and e-bikes [1]. This is similar to patterns observed in other Chinese cities, where many such accidents involve undisciplined driver behavior such as red-light violations related to either intentional violations or incorrect stop/pass decisions.…”

Section: Introductionmentioning

confidence: 53%

“…Only the last-tostop e-bikes after the onset of flashing green were selected for the analysis to avoid the influence of existing leading vehicles. The image processing software George 2.1, developed by Nagoya University [1,26] with a resolution of 1/30 s, was used for data reduction. This allowed every e-bike's position to be tracked after it entered the camera's scope along with signal states for each time.…”

Section: Data Reductionmentioning

confidence: 99%

“…The Markov process has been proved to be capable of modeling highly stochastic systems in the field of transportation, such as path choice and traffic control strategy [30,31]. The use of Markov models in microcosmic driving behavior research has gradually increased in recent years, but the prediction of decision-making behavior based on Markov models is still in its infancy [1,32,33].…”

Section: Hidden Markov Modelmentioning

confidence: 99%

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Determining E-Bike Drivers’ Decision-Making Mechanisms during Signal Change Interval Using the Hidden Markov Driving Model

Dong

Zhou

Zhang

2019

Journal of Advanced Transportation

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Rapidly increasing e-bike use in China has resulted in new traffic problems including rising accident rates at intersections related to e-bike drivers’ decision-making during multiple signal phases. Traditional one-step decision models (such as GHM) lack randomness and cannot adequately model e-bike drivers’ complex behavior. Therefore, this study used a Hidden Markov Driving Model (HMDM) to analyze e-bike drivers’ decision-making process based on high-resolution trajectory data. Video data were collected at three intersections in Shanghai and processed for use in the HMDM model. Five decision types (pass, stop, stop-pass, pass-stop, and multiple) composed of speed and acceleration/deceleration information were defined and used to analyze the impact of flashing green signals on e-bike drivers’ behavior and decision-making processes. Approximately 40% of drivers made multiple decisions during the flashing green and yellow signal phases, in contrast to the traditional GHM model assumption that drivers only make one decision. Distance from stop-line had the most obvious influence on the number of decisions. The use of flashing green signals nearly eliminated the dilemma zone for e-bike drivers but enlarged the option zone, inducing more stop/pass decisions. HMDM can be applied to improve the accuracy of traffic simulation, the fine design of traffic signals, the stability analysis of traffic control schemes, and so on.

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Section: Introductionmentioning

confidence: 53%

Section: Data Reductionmentioning

confidence: 99%

Section: Hidden Markov Modelmentioning

confidence: 99%

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Determining E-Bike Drivers’ Decision-Making Mechanisms during Signal Change Interval Using the Hidden Markov Driving Model

Dong

Zhou

Zhang

2019

Journal of Advanced Transportation

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“…HMM has been widely used to model driver dynamic behaviors due to its powerful ability to describe the dynamic process and infer unobserved (hidden) states [22]. In the HMM, we apply the component of GMM to representing the hidden modes ( Fig.…”

Section: Proposed Methodsmentioning

confidence: 99%

“…Based on (22), we estimate the transfer probability between mode i and mode j by α i,j = F i,j n i , i, j = 1, 2, · · · , K…”

Section: Appendix Amentioning

confidence: 99%

A Learning-Based Approach for Lane Departure Warning Systems With a Personalized Driver Model

Wang

Zhao

Han

et al. 2018

IEEE Trans. Veh. Technol.

108

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Misunderstanding of driver correction behaviors (DCB) is the primary reason for false warnings of lane-departureprediction systems. We propose a learning-based approach to predicting unintended lane-departure behaviors (LDB) and the chance for drivers to bring the vehicle back to the lane. First, in this approach, a personalized driver model for lanedeparture and lane-keeping behavior is established by combining the Gaussian mixture model and the hidden Markov model. Second, based on this model, we develop an online model-based prediction algorithm to predict the forthcoming vehicle trajectory and judge whether the driver will demonstrate an LDB or a DCB. We also develop a warning strategy based on the model-based prediction algorithm that allows the lane-departure warning system to be acceptable for drivers according to the predicted trajectory. In addition, the naturalistic driving data of 10 drivers is collected through the University of Michigan Safety Pilot Model Deployment program to train the personalized driver model and validate this approach. We compare the proposed method with a basic time-to-lane-crossing (TLC) method and a TLC-directional sequence of piecewise lateral slopes (TLC-DSPLS) method. The results show that the proposed approach can reduce the falsewarning rate to 3.07%.Index Terms-Learning-based approach, lane departure warning system, Gaussian mixture model, hidden Markov model, personalized driver model

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An adaptive cascade predictive control strategy for connected and automated vehicles

Landolfi

Natale

2023

Adaptive Control & Signal

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SummaryConnectivity is a key element enabling intelligent vehicles to communicate with each other and the Smart Road. In general, the connectivity is allowed by an On‐Board Unit enabling the Vehicle to Everything communication. This paper proposes an innovative unit acting not only as a device allowing connectivity but also as an intelligent electronic control unit to perform advanced and adaptive control strategies able to give reference trajectories and actuator set‐points to the low‐level control systems of power‐train and vehicle dynamics. In particular, based on a cascade model predictive control, an adaptive control strategy is proposed, considering time‐varying parameters and information related to vehicle kinematics and dynamics. Such a strategy allows the vehicle to follow a certain origin‐destination path in the inertial frame, based on an upper controller that considers a vehicle kinematic model so as to give speed references and target values for the steering angle at the wheels to the inner predictive control loop acting on both longitudinal and lateral vehicle dynamics. To check the effectiveness of the proposed approach, a comparison is made with existing cascade control strategies. The results show better safety and lane‐keeping performance. Then, using Hardware‐In‐the‐Loop testing for the proposed Intelligent On‐Board Unit, the considered approach demonstrates robustness against parametric variations, signal delay and noise both in the Global Positioning System and in the yaw rate sensor. The achieved results show good robustness properties, safety performances and confirm the real‐time execution of the cascade control strategy, paving the way to future developments necessary before tests in a real road scenario.

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Modeling Drivers' Dynamic Decision-Making Behavior During the Phase Transition Period: An Analytical Approach Based on Hidden Markov Model Theory

Cited by 40 publications

References 30 publications

Determining E-Bike Drivers’ Decision-Making Mechanisms during Signal Change Interval Using the Hidden Markov Driving Model

Determining E-Bike Drivers’ Decision-Making Mechanisms during Signal Change Interval Using the Hidden Markov Driving Model

A Learning-Based Approach for Lane Departure Warning Systems With a Personalized Driver Model

An adaptive cascade predictive control strategy for connected and automated vehicles

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