A New Car-Following Model considering Driving Characteristics and Preceding Vehicle’s Acceleration

Zhang, Yong; Ni, Ping; Li, Minwei; Líu, Hao; Yin, Baocai

doi:10.1155/2017/2437539

Cited by 15 publications

(13 citation statements)

References 33 publications

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“…ey incorporated individual preference on optimal speed and safe distance into the traditional full velocity difference (FVD) model as additional driver attributions. Another study based on FVD was implemented by Zhang et al [30], which considered the acceleration of the preceding vehicle and the ego driver's driving style. Both studies have divided driving styles into three categories (aggressive, neutral, and conservative) and extracted features of each category to establish the respective driver models.…”

Section: Literature Reviewmentioning

confidence: 99%

“…ese explanatory models' significant advantage is their computation efficiency, which enables them to be easily incorporated in traffic flow simulation, especially with a large scale of vehicles. Consequently, these models have been widely adopted in microscopic and macroscopic traffic simulation studies [29,30]. However, it should be noted that although the driving style parameters in some studies were calibrated from naturalistic driving data, these models' performance in mimicking human driving styles still lacks evaluation.…”

Section: Literature Reviewmentioning

confidence: 99%

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A Fuzzy Logic-Based Approach for Humanized Driver Modelling

Feng

Iravani

Brace

2021

Journal of Advanced Transportation

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All human drivers can be characterised by their habitual choice of driving behaviours, which results in a wide range of observed driving patterns and manoeuvres. Developing control strategies for autonomous vehicles that address this feature would increase the public acceptance of such vehicles. Therefore, this paper proposes a novel approach to developing rule-based fuzzy logic driver models that simulate different driving styles in the car-following regimes. These driver models were trained with the collected on-road driving data to capture corresponding human drivers’ characteristics. The proposed approach consists of three main components: collecting on-road driving data, developing a vehicle model, and establishing the car-following driver models. Firstly, an instrumented vehicle was used to collect driving data over the same route for three consecutive months. Car-following scenarios during these journeys were extracted, and related data were processed accordingly. Afterwards, a representative model of the instrumented vehicle was created and evaluated. Finally, a fuzzy logic driver model that uses humanized inputs was developed and calibrated with the recorded data. The developed driver model’s performance was assessed using the collected driving data and a baseline PID driver model. With the performance validated, models representing more aggressive and more defensive driving styles were derived following the same procedure. A cross-driver analysis was then implemented in a normalized car-following scenario with the established vehicle model to investigate the impacts of different driving styles further. The developed driver model can introduce driving styles into drive cycle experiments and replicate on-road real driving emission tests in the laboratory. Moreover, as the proposed method has high robustness to incomplete datasets, it can be a more cost-effective option to facilitate the development of humanized and customized vehicle control strategies for autonomous driving.

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Section: Literature Reviewmentioning

confidence: 99%

Section: Literature Reviewmentioning

confidence: 99%

A Fuzzy Logic-Based Approach for Humanized Driver Modelling

Feng

Iravani

Brace

2021

Journal of Advanced Transportation

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“…The car-following process is important to simulate the traffic flow, analyze the formation mechanism of traffic congestion, and manage the alternative proposals [13]. The car-following process takes place when "a driver follows a lead vehicle and tries to maintain distance and relative speed within an acceptable range".…”

Section: Car-following Logicmentioning

confidence: 99%

Development of Freeway Weaving Areas Microsimulation Model (FWASIM)

Alkubaisi¹

2020

cea

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“…The individual characteristics of drivers were not presented well. To avoid this problem, Tang et al [30] improved the full velocity difference model (FVDM) considering driver's behavior by grouping drivers into three categories and discussed driver's individual property; Zhang et al [31] improved FVDM by considering the acceleration of preceding vehicle and the division of driving types derived from real data. Results showed that the aggressive and regular behavior play a positive role in stabilization of traffic flow while the conservative driver plays a negative role.…”

Section: Introductionmentioning

confidence: 99%

“…But the radical degree for individual drivers was not taken into account and thus the driver's individual radical feature cannot be well performed. But except for [31], the parameters of these studies were not calibrated by empirical data.…”

Section: Introductionmentioning

confidence: 99%

An Improved Single-Lane Cellular Automaton Model considering Driver’s Radical Feature

Yang

et al. 2018

Journal of Advanced Transportation

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Traffic flow models are of vital significance to study the traffic system and reproduce typical traffic phenomena. In the process of establishing traffic flow models, human factors need to be considered particularly to enhance the performance of the models. Accordingly, a series of car-following models and cellular automaton models were proposed based on comprehensive consideration of various driving behaviors. Based on the comfortable driving (CD) model, this paper innovatively proposed an improved cellular automaton model incorporating impaired driver’s radical feature (RF). The impaired driver’s radical feature was added to the model with respect to three aspects, that is, desired speed, car-following behavior, and braking behavior. Empirical data obtained from a highway segment was used to initialize impaired driver’s radical feature distribution and calibrate the proposed model. Then, numerical simulations validated that the proposed improved model can well reproduce the traffic phenomena, as shown by the fundamental diagram and space-time diagram. Also, in low-density state, it can be found that the RF model is superior to the CD model in simulating the speed difference characteristics, where the average speed difference of adjacent vehicles for RF model is more consistent with reality. The result also discussed the potential impact of impaired drivers on rear-end collisions. It should be noted that this study is an early stage work to evaluate the existence of impaired driving behavior.

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A New Car-Following Model considering Driving Characteristics and Preceding Vehicle’s Acceleration

Cited by 15 publications

References 33 publications

A Fuzzy Logic-Based Approach for Humanized Driver Modelling

A Fuzzy Logic-Based Approach for Humanized Driver Modelling

Development of Freeway Weaving Areas Microsimulation Model (FWASIM)

An Improved Single-Lane Cellular Automaton Model considering Driver’s Radical Feature

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