Research on Car-Following Model considering Driving Style

Wang, Keyin; Yang, Yisi; Wang, Sishan; Shi, Zhen Jun

doi:10.1155/2022/7215697

Cited by 9 publications

(5 citation statements)

References 21 publications

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“…To meet the individualized needs of different users of autonomous vehicles, Keyin Wang proposed a car tracking model that considers different driving styles. This research work uses an unsupervised machine learning algorithm to categorize the driving data into two styles: conservative and aggressive, and then compares the differences between the two driving styles in terms of speed, acceleration, and desired time-distance, etc., and then designs the parameters of the tracker based on MPC [22]. The results show that the tracking model exhibits different driving styles in terms of safety, comfort and effectiveness.…”

Section: B Machine Learning-based Driver Modelmentioning

confidence: 99%

Trajectory Tracking Control of Autonomous Vehicles Combining ACT-R Cognitive Framework and Preview Tracking Theory

Tang,

Yan,

Wang

2023

IEEE Access

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By improving the ability of Trajectory Tracking Control (TTC) algorithms to mimic the manipulation behaviors of real drivers, which is of great significance in improving the personalized driving experience of autonomous vehicles. In this paper, we propose a TTC method that combines the Adaptive Control of Thought-Rational (ACT-R) cognitive theory framework with the Preview Tracking (PT) theory. Firstly, by analyzing and describing the ACT-R cognitive framework and the PT theory, a TTC framework that combines the two theories is proposed. Secondly, a virtual driving simulator was built to collect driving data from 30 drivers and construct a driving memory database. Thirdly, a central production system for driver trajectory tracking is designed, which consists of: three control modes, production rules, a timing generator, and filtering methods for driving memory segments. Fourthly, the TTC method based on PT theory is designed to adapt to different control modes. Finally, statistical and comparative analyses of the human-like trajectory tracking results of the proposed method were carried out through co-simulation experiments, and it was verified that the human-like performance of the proposed new method had a high degree of similarity with the manipulation behaviors and the vehicle motion state of the real driver. The real-vehicle experiments are carried out, which verifies the consistency of the proposed method with the results of the simulation experiments.INDEX TERMS Trajectory tracking control; human-like driving; autonomous vehicles; ACT-R cognitive framework; preview tracking theory.

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Section: B Machine Learning-based Driver Modelmentioning

confidence: 99%

Trajectory Tracking Control of Autonomous Vehicles Combining ACT-R Cognitive Framework and Preview Tracking Theory

Tang,

Yan,

Wang

2023

IEEE Access

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“…When the vehicle accelerates or push brakes suddenly, drivers in the vehicle will have a strong sense of discomfort due to the large jerk and the degree to which diferent drivers can withstand is diferent. Terefore, to fully describe the driving behavior of drivers, jerk is also considered one of the key characteristics [31].…”

Section: Data Preprocessingmentioning

confidence: 99%

A Modified Latent Dirichlet Allocation Topic Approach for Driving Style Exploration Using Large-Scale Ride-Hailing GPS Data

Li,

Chen,

Bao

et al. 2023

Journal of Advanced Transportation

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Driving style identification is of vital importance for intelligent driving system design and urban traffic management. This study aims to identify and analyze driving styles using large-scale ride-hailing GPS data taking different time periods, traffic, and weather conditions into account. The large-scale GPS data are collected and preprocessed, and then, the k-means clustering is implemented to acquire driving behavior. The modified latent Dirichlet allocation topic approach is applied to extract the driving states as the latent variables behind driving behaviors and finally recognize driving styles. The results show that driving styles are composed of five driving states with different probability combinations. Different driving styles in different situations are further analyzed and compared. When considering the impact of peak periods on the driving style, it indicates that styles tend to be conservative in the morning peak, free and dispersed in the evening peak, and diverse in the off-peak hours. While comparing styles regarding the influence of workdays, drivers act more cautiously and conservatively on weekdays but freer on weekends without the pressure of peak hours. The weather factor is also explored and rainy days are verified to be the resistance of driving so that most drivers become cautious and conservative. Finally, two aberrant driving styles are discovered and countermeasures are suggested to improve traffic safety.

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“…Additionally, Pariota et al [131] proposed a linear dynamic CF model that utilizes the driver's anticipated equilibrium spacing and the velocity of the leading vehicle as input parameters to the model's state space, generating the vehicle's speed and acceleration. However, these methods are based on pre-defined mathematical formulas, which lack flexibility and make it difficult to adapt to different driving data, such as varying time headways, acceleration rates, and comfort levels caused by various driving styles [132]. It is challenging to address these issues using mathematical models alone, thus requiring alternative methods to tackle the abovementioned problems.…”

Section: Assistance Typementioning

confidence: 99%

A Review of Cockpit-driving Integration for Human-centric Autonomous Driving

Zhu,

Zhao,

Wang

et al. 2023

Preprint

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<p>This review focuses on the integration of intelligent driving and intelligent cockpit systems. With the advancement of intelligent levels, autonomous vehicles are capable of driving in complex driving context. However, the lack of systems that consider both driver and driving scenarios information reduces the success probability of decision-making of autonomous vehicles in driving context where driver and driving scenarios information are tightly coupled during the human-vehicle cooperation stage. To solve this problem, we present the Cockpit-Driving Integration system (CDI) and review perception and decision-making algorithms for CDI systems. Additionally, to achieve human-centric autonomous vehicles, we propose that the CDI system should consider the personalized characteristics of drivers. Finally, we present a framework for CDI systems </p>

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Research on Car-Following Model considering Driving Style

Cited by 9 publications

References 21 publications

Trajectory Tracking Control of Autonomous Vehicles Combining ACT-R Cognitive Framework and Preview Tracking Theory

Trajectory Tracking Control of Autonomous Vehicles Combining ACT-R Cognitive Framework and Preview Tracking Theory

A Modified Latent Dirichlet Allocation Topic Approach for Driving Style Exploration Using Large-Scale Ride-Hailing GPS Data

A Review of Cockpit-driving Integration for Human-centric Autonomous Driving

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