An Improved Intelligent Driver Model Considering the Information of Multiple Front and Rear Vehicles

Zong, Fang; Wang, Meng; Tang, Ming; Li, Xiying; Zeng, Mi

doi:10.1109/access.2021.3072058

Cited by 30 publications

(16 citation statements)

References 40 publications

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“…The establishment of the driving safety distance monitoring and collision avoidance control model is affected by many factors, and the establishment methods are also diverse. There are traditional models based on mathematics and practice and models based on intelligent algorithms [33], such as the stereo vision algorithm [34]. The establishment and integration of a general virtual simulation system can only verify a model related to the simulation system.…”

Section: Intelligent Driver Model-avoidance Structurementioning

confidence: 99%

“…3. Vehicles are divided into two different types, which are special and ordinary vehicles, and it is assumed that each vehicle can receive the avoidance signal sent by the special vehicle through WiFi [33]. The method of the vehicle's driving flowchart is as follows.…”

Section: Intelligent Driver Model-avoidance Structurementioning

confidence: 99%

“…1) Communication distance, 5G vehicle networking V2V communication maximum distance approximately 800 m, which can solve IEEE 802.11p short-lived, discontinuous connection problems in vehicle ad hoc network communications. Especially it is a non-line-of-sight (NLOS) ring that encounters large objects occlusion during communication, under the border [31][32][33][34][35][36][37][38][39][40][41][42][43][44][45][46].…”

Section: E Better Communication Qualitymentioning

confidence: 99%

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Intelligent Driver Model-Based Vehicular Ad Hoc Network Communication in Real-Time Using 5G New Radio Wireless Networks

et al. 2023

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Vehicular ad hoc networks (VANETs) are self-organizing, open-structure inter-vehicle communication networks, along with wireless communication technology and transportation. Vehicle communication aims to improve the driver's response-ability and ensure traffic safety when encountering traffic accidents. The 5G NR on V2V communication, compared to fourth-generation (4G) long-term evolution (LTE), is more sophisticated and has ultra-high reliability. This research proposes intelligent driver model-lane changes (IDM-LC) and driver model-avoidance (IDM-A) models, which enhance the performance accuracy of the V2V communication through the 5G NR system. In this case, the authenticity of the vehicle movement models is tested on different road scenarios, i.e., square, hexagon, heptagon and triangle. In these scenarios, the vehicles' movements over the 5G networks receive widespread coverage whenever the vehicles receive signals from the roadside unit (RSU). Hence, the flexibility of VANET is used to improve the device-to-device (D2D) or vehicle-to-vehicle (V2V) communication efficiency in the fifth-generation (5G) new radio (NR) system. In addition, the VANET simulation platform, i.e., simulation of urban mobility (SUMO) and network simulator-3 (NS-3), simulate and evaluate the comparison of V2V through the 5G networks, which receives widespread coverage in 15 to 20 m/s. The simulation results and analysis show that the V2V communication through the 5G system performs better on the IDM models.INDEX TERMS Vehicular ad hoc network, fifth generation, vehicle-to-vehicle, internet of vehicles, intelligent driver model, SUMO, NS-3

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Section: Intelligent Driver Model-avoidance Structurementioning

confidence: 99%

Section: Intelligent Driver Model-avoidance Structurementioning

confidence: 99%

Section: E Better Communication Qualitymentioning

confidence: 99%

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Intelligent Driver Model-Based Vehicular Ad Hoc Network Communication in Real-Time Using 5G New Radio Wireless Networks

et al. 2023

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“…The re-defined optimal velocity is the output of optimal velocity function about the headway between the vehicle and the other two vehicles. Recently, Ma et al extended the BLFVD model by introducing the relative velocity of two preceding vehicles [62], and Zong et al proposed an extended ID model with consideration of the velocity and acceleration of multiple vehicles in the front and back of the object vehicle, as well as the headway and relative velocity between these vehicles and the object vehicle [63]. The incorporated information and the expressions of the above models considering the motion state of following vehicles in the current lane are as shown in Table 2.…”

Section: Information Of Multiple Following Vehiclesmentioning

confidence: 99%

The Car-Following Model and Its Applications in the V2X Environment: A Historical Review

et al. 2021

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The application of vehicle-to-everything (V2X) technology has resulted in the traffic environment being different from how it was in the past. In the V2X environment, the information perception ability of the driver–vehicle unit is greatly enhanced. With V2X technology, the driver–vehicle unit can obtain a massive amount of traffic information and is able to form a connection and interaction relationship between multiple vehicles and themselves. In the traditional car-following models, only the dual-vehicle interaction relationship between the object vehicle and its preceding vehicle was considered, making these models unable to be employed to describe the car-following behavior in the V2X environment. As one of the core components of traffic flow theory, research on car-following behavior needs to be further developed. First, the development process of the traditional car-following models is briefly reviewed. Second, previous research on the impacts of V2X technology, car-following models in the V2X environment, and the applications of these models, such as the calibration of the model parameters, the analysis of traffic flow characteristics, and the methods that are used to estimate a vehicle’s energy consumption and emissions, are comprehensively reviewed. Finally, the achievements and shortcomings of these studies along with trends that require further exploration are discussed. The results that were determined here can provide a reference for the further development of traffic flow theory, personalized advanced driving assistance systems, and anthropopathic autonomous-driving vehicles.

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“…However, front vehicles at different positions had different effects on the host vehicle [13]. Zong et al [14] took into account the data from multitype vehicle and proposed an improved IDM. They discovered that as the distance between the vehicles increased, the influence of the front vehicles on the movement state of the host vehicle steadily decreased.…”

Section: Introductionmentioning

confidence: 99%

Modeling car-following behavior in heterogeneous traffic mixing human-driven, automated and connected vehicles: considering multitype vehicle interactions

Song

Ding

2023

Nonlinear Dyn

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In this study, a car-following model is put forth to explain the car-following behavior of HDVs (human-driven vehicles), AVs (autonomous vehicles) and CAVs (connected and automated vehicles) in mixed traffic. Based on the IDM (intelligent-driver model) and molecular dynamic theory, the model includes the velocity of surrounding vehicles along with the difference of velocity and the headway between each pair of vehicles. The drivers' sensitivity to the deceleration of the nearest front vehicle was considered in HDV carfollowing modeling and the influences of various kinds of nearest front and rear vehicles were distinguished in the CAV model. Based on the information obtained from the real road test mixed with HDVs, AVs and CAVs, the optimum value of the model parameters was obtained and the accuracy of model was verified with simulation. The results show that the simulation for HDVs', AVs' and CAVs' car-following behavior under the proposed model is more accurate than that for the IDM, ACC (adaptive cruise control) model and CACC (cooperative adaptive cruise control) model. The model can be applied to car-following simulations of HDVs, AVs and CAVs in mixed traffic, in the model the tools and concepts of nonlinear dynamics especially molecular dynamic theory is applied which can benefit road traffic vehicle interaction analysis. In addition, this study provides valuable suggestions and guidance for effectively guiding AVs and CAVs to follow vehicles and improving the stability of CF behavior.

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An Improved Intelligent Driver Model Considering the Information of Multiple Front and Rear Vehicles

Cited by 30 publications

References 40 publications

Intelligent Driver Model-Based Vehicular Ad Hoc Network Communication in Real-Time Using 5G New Radio Wireless Networks

Intelligent Driver Model-Based Vehicular Ad Hoc Network Communication in Real-Time Using 5G New Radio Wireless Networks

The Car-Following Model and Its Applications in the V2X Environment: A Historical Review

Modeling car-following behavior in heterogeneous traffic mixing human-driven, automated and connected vehicles: considering multitype vehicle interactions

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