Modeling Driver Behavior in Car-Following Interactions With Automated and Human-Driven Vehicles and Energy Efficiency Evaluation

Ozkan, Mehmet Fatih; Ma, Yao

doi:10.1109/access.2021.3075194

Cited by 23 publications

(3 citation statements)

References 24 publications

(31 reference statements)

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“…The results also suggest that the traffic flow volume is positively related to the above three parameters before the critical point and negatively related to them after this point, which is consistent with [85]. Utilizing the inverse reinforcement learning method, Ozkan et al [96] proposed a data-driven car-following model based on the approach of dividing vehicles with different sorts into various car-following combinations. Yao et al [97] employed the ID, ACC, and CACC models (the latter two were also proposed in [91,92]) to respectively describe the car-following behavior of manual, automatic, and connected and automatic vehicles and discussed the energy consumption and exhaust emission characteristics of the platoon with different proportions of the three sorts of vehicles or in the situation that the CACC-equipped CAV degenerates into an automatic vehicle.…”

Section: Sortssupporting

confidence: 57%

Modeling the Car-Following Behavior with Consideration of Driver, Vehicle, and Environment Factors: A Historical Review

Han

Wang

2022

Sustainability

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Car-following behavior is the result of the interaction of various elements in the specific driver-vehicle-environment aggregation. Under the intelligent and connected condition, the information perception ability of vehicles has been significantly enhanced, and abundant information about the driver-vehicle-environment factors can be obtained and utilized to study car-following behavior. Therefore, it is necessary to comprehensively take into account the driver-vehicle-environment factors when modeling car-following behavior under intelligent and connected conditions. While there are a considerable number of achievements in research on car-following behavior, a car-following model with comprehensive consideration of driver-vehicle-environment factors is still absent. To address this gap, the literature with a focus on car-following behavior research with consideration of the driver, vehicle, or environment were reviewed, the contributions and limitations of the previous studies were analyzed, and the future exploration needs and prospects were discussed in this paper. The results can help understand car-following behavior and the traffic flow characteristics affected by various factors and provide a reference for the development of traffic flow theory towards smart transportation systems and intelligent and connected driving.

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Section: Sortssupporting

confidence: 57%

Modeling the Car-Following Behavior with Consideration of Driver, Vehicle, and Environment Factors: A Historical Review

Han

Wang

2022

Sustainability

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“…They verified the correctness of the proposed car-following model using numerical simulation ( 39 ). Ozkan and Ma studied car-following behavior and energy efficiency in AV–HDV mixed traffic using inverse reinforcement learning ( 40 ). The model had the capacity to learn and replicate the observed car-following behavior.…”

Section: Literature Reviewmentioning

confidence: 99%

Prediction of Car-Following Behavior of Autonomous Vehicle and Human-Driven Vehicle Based on Drivers’ Memory and Cooperation With Lead Vehicle

Adewale,

Lee

2023

Transportation Research Record: Journal of the Transportation R

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Autonomous vehicles (AVs) have moved from hype to reality as the penetration and acceptance rate continues to increase. As they are slowly integrated into traffic with human-driven vehicles (HDVs), it is necessary to predict the car-following behaviors of AVs and HDVs for better control of AV–HDV mixed traffic. This study extends a data-driven car-following model to incorporate drivers’ memory, and cooperation with the lead vehicle. The model predicts the following vehicle’s speed in AV–HDV mixed traffic. The effect of drivers’ cooperation on car-following behavior was modeled using prospect theory (PT), whereas the driver’s memory was incorporated using the memory cell of a long short-term memory (LSTM) neural network. This extended car-following model is called the “PT-LSTM model.” Real-world vehicle trajectories of HDVs and AVs in the Waymo AV Open Dataset were used to calibrate and validate the PT-LSTM model. The PT-LSTM model demonstrated higher accuracy compared with the LSTM model that did not consider drivers’ cooperation, the multiple layer perceptron model, Gipps’ model, and the intelligent driver model that incorporated PT. The importance of variables in different time steps in the PT-LSTM model was also evaluated using SHapley Additive exPlanations (SHAP). The SHAP results showed that AV followers were more likely to cooperate with the lead HDV, whereas HDV followers were more likely to cooperate with the lead AV than the lead HDV. Thus, this study underscores the importance of considering drivers’ memory and cooperation with the lead vehicle for the prediction of car-following behaviors in AV–HDV mixed traffic.

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“…Current car-following models that consider human factors can be divided into models that consider perception thresholds [6,7], a driver's visual angles [8,9], risk perception [10,11], and distraction and errors [12,13]. For example, Ozkan et al [14] used inverse reinforcement learning to model the unique car-following behaviors of different human drivers when interacting with CAVs and other human-driven vehicles. They also considered the energy efficiency and the heterogeneous characteristics of drivers' behaviors.…”

Section: Theoretical Studiesmentioning

confidence: 99%

Multi-State Car-Following Behavior Simulation in a Mixed Traffic Flow for ICVs and MDVs

Song

Jia

2022

Sustainability

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With the development of intelligent connected vehicles (ICVs) and communication technology, collaborative operation among vehicles will become the trend of the future. Thus, traffic flow will be mixed with manual driving vehicles and ICVs. A mixed traffic flow is a traffic flow state lying between autonomous and manual traffic flows. In order to describe the car-following characteristics in a mixed traffic flow, the cooperative adaptive cruise control (CACC) car-following model and the intelligent driver model (IDM) were adopted. The car-following characteristics of different platoons from these two car-following models were analyzed. The CACC mixing ratio was used to describe the mixed traffic flow. The fixed states and disturbance states of the car-following platoons were simulated. The fixed states can be divided into three categories: the steady state, acceleration state, and deceleration state. The effects of different car-following cases and different mixing ratios on mixed traffic flow in different states were discussed. The results show that (1) in the steady state with a smaller mixing ratio, the operating speed and traffic volume of the mixed traffic flow were positively correlated. The overall traffic volume decreased with the increase in the mixing ratio, and the gap gradually narrowed. At a larger mixing ratio, the operating speed and traffic volume were negatively correlated. The overall traffic volume increased with the increase in the mixing ratio. (2) In the acceleration state, the maximum traffic volume in the platoon and the optimal mixing ratio were linearly related to the acceleration. (3) In the deceleration state with a fixed mixing ratio, the traffic volume decreased with the increase in the deceleration, with slight differences in the changing trend of the volume of the mixed flow. Under disturbances, the mixed traffic volume was positively correlated with the mixing ratio, i.e., at a larger mixing ratio, the anti-interference ability of the mixed traffic flow was higher.

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Modeling Driver Behavior in Car-Following Interactions With Automated and Human-Driven Vehicles and Energy Efficiency Evaluation

Cited by 23 publications

References 24 publications

Modeling the Car-Following Behavior with Consideration of Driver, Vehicle, and Environment Factors: A Historical Review

Modeling the Car-Following Behavior with Consideration of Driver, Vehicle, and Environment Factors: A Historical Review

Prediction of Car-Following Behavior of Autonomous Vehicle and Human-Driven Vehicle Based on Drivers’ Memory and Cooperation With Lead Vehicle

Multi-State Car-Following Behavior Simulation in a Mixed Traffic Flow for ICVs and MDVs

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