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12 pages, 6431 KiB

Open AccessArticle

Aircraft Target Detection from Remote Sensing Images under Complex Meteorological Conditions

by Dan Zhong, Tiehu Li, Zhang Pan and Jinxiang Guo

Sustainability 2023, 15(14), 11463; https://doi.org/10.3390/su151411463 - 24 Jul 2023

Viewed by 937

Abstract

Taking all-day, all-weather airport security protection as the application demand, and aiming at the lack of complex meteorological conditions processing capability of current remote sensing image aircraft target detection algorithms, this paper takes the YOLOX algorithm as the basis, reduces model parameters by [...] Read more.

Taking all-day, all-weather airport security protection as the application demand, and aiming at the lack of complex meteorological conditions processing capability of current remote sensing image aircraft target detection algorithms, this paper takes the YOLOX algorithm as the basis, reduces model parameters by using depth separable convolution, improves feature extraction speed and detection efficiency, and at the same time, introduces different cavity convolution in its backbone network to increase the perceptual field and improve the model’s detection accuracy. Compared with the mainstream target detection algorithms, the proposed YOLOX-DD algorithm has the highest detection accuracy under complex meteorological conditions such as nighttime and dust, and can efficiently and reliably detect the aircraft in other complex meteorological conditions including fog, rain, and snow, with good anti-interference performance. Full article

(This article belongs to the Special Issue Smart Transportation and Intelligent and Connected Driving)

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20 pages, 2823 KiB

Open AccessArticle

Comprehensive Evaluation of Freeway Driving Risks Based on Fuzzy Logic

by Lian Xie, Jiaxin Zhang and Rui Cheng

Sustainability 2023, 15(1), 810; https://doi.org/10.3390/su15010810 - 2 Jan 2023

Cited by 2 | Viewed by 1455

Abstract

The quantitative evaluation of driving risk is a crucial prerequisite for intelligent vehicle accident warning, and it is necessary to predict it comprehensively and accurately. Therefore, a simulated driving experiment was conducted with 16 experimental scenarios designed through an orthogonal design, and 44 [...] Read more.

The quantitative evaluation of driving risk is a crucial prerequisite for intelligent vehicle accident warning, and it is necessary to predict it comprehensively and accurately. Therefore, a simulated driving experiment was conducted with 16 experimental scenarios designed through an orthogonal design, and 44 subjects were recruited to explore the driving risks in different situations. A two-layer fuzzy integrated evaluation model was constructed, which considered the workload as an important element for balancing driving risk and driving behavior. Workload and road environment indicators were taken as the underlying input variables. The results show that the comprehensive evaluation model is well-suited to identify the risks of each scenario. The effectiveness of the proposed method is further confirmed by comparing the results with those of the technique for order preference by similarity to an ideal solution (TOPSIS) model. The proposed method could be used for real-time vehicle safety warning and provide a reference for accident prevention. Full article

(This article belongs to the Special Issue Smart Transportation and Intelligent and Connected Driving)

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14 pages, 958 KiB

Open AccessArticle

A Method of Reducing Invalid Steering for AUVs Based on a Wave Peak Frequency Tracker

by Jianping Yuan, Jin Li, Zhihui Dong, Qinglong Chen and Hanbing Sun

Sustainability 2022, 14(22), 15357; https://doi.org/10.3390/su142215357 - 18 Nov 2022

Cited by 2 | Viewed by 882

Abstract

The motion control of autonomous underwater vehicles (AUVs) is affected by waves near the ocean surface or in shallow-water areas. Therefore, to counteract the influence of waves, we need to remove them by designing a filter. The wave peak frequency is important in [...] Read more.

The motion control of autonomous underwater vehicles (AUVs) is affected by waves near the ocean surface or in shallow-water areas. Therefore, to counteract the influence of waves, we need to remove them by designing a filter. The wave peak frequency is important in wave filter design. This paper focuses on the identification of the wave peak frequency using the least-squares parameter estimation algorithm. The input–output expression of the wave disturbance model is derived by eliminating the intermediate variable. Based on the obtained identification model, an auxiliary model-based recursive extended least-squares identification algorithm is developed to estimate the model parameters. The effectiveness of the proposed method is verified with simulated tests of the heading control system of an AUV. The simulation results demonstrate that the proposed method is effective for the identification of the wave peak frequency, and an observer with a wave peak frequency tracker can significantly reduce invalid steering. Full article

(This article belongs to the Special Issue Smart Transportation and Intelligent and Connected Driving)

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17 pages, 5201 KiB

Open AccessArticle

Optimal Aggregate Size of Traffic Sequence Data Based on Fuzzy Entropy and Mutual Information

by Junzhuo Li, Wenyong Li and Guan Lian

Sustainability 2022, 14(22), 14767; https://doi.org/10.3390/su142214767 - 9 Nov 2022

Cited by 1 | Viewed by 1033

Abstract

Before traffic forecasting, it is usually necessary to aggregate the information by a certain length of time. An aggregation size that is too short will make the data unstable and cause the forecast result to be too biased. On the other hand, if [...] Read more.

Before traffic forecasting, it is usually necessary to aggregate the information by a certain length of time. An aggregation size that is too short will make the data unstable and cause the forecast result to be too biased. On the other hand, if the aggregation size is too large, the data information will be lost, resulting in the forecast results tending towards an average or slow response. With the development of intelligent transportation systems, especially the development of urban traffic control systems, high requirements are placed on the real-time accuracy of traffic forecasting. Therefore, it is an essential topic of traffic forecasting research to determine aggregation sizes. In this paper, the mutual information between the forecast input information and the forecast result and the sequence complexity of the forecast result measured by approximate entropy, sample entropy, and fuzzy entropy are considered; then, the optimal data aggregation size is given. To verify the proposed method, the validated data obtained from the simulation is aggregated and calculated with different aggregation sizes, then used for forecasting. By comparing the prediction performance of different aggregate sizes, the optimal aggregate size was found to reduce MSE by 14–30%. The results show that the method proposed in this paper is helpful for selecting the optimal data aggregation size in forecasting and can improve the performance of prediction. Full article

(This article belongs to the Special Issue Smart Transportation and Intelligent and Connected Driving)

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27 pages, 2020 KiB

Open AccessArticle

Research on Emotion Activation Efficiency of Different Drivers

by Xiaoyuan Wang, Yaqi Liu, Longfei Chen, Huili Shi, Junyan Han, Shijie Liu and Fusheng Zhong

Sustainability 2022, 14(21), 13938; https://doi.org/10.3390/su142113938 - 26 Oct 2022

Cited by 3 | Viewed by 1673

Abstract

Emotion is an implicit psychological characteristic that changes over time. When it accumulates to a certain extent, it will be accompanied by certain external manifestations. Drivers with different traits have different emotional performance, which leads to different effects from different driver traits on [...] Read more.

Emotion is an implicit psychological characteristic that changes over time. When it accumulates to a certain extent, it will be accompanied by certain external manifestations. Drivers with different traits have different emotional performance, which leads to different effects from different driver traits on the driver’s emotional activation efficacy. In this study, we thoroughly explore the effects of different genders, age, driving competence, driving anger tendency, driving safety attitude and stress state on driver’s emotional activation efficacy. This paper selects 74 young and middle-aged drivers with an age distribution between 20 and 41 years old. The eight most typical driving emotions (anger, surprise, fear, anxiety, helplessness, contempt, ease and pleasure) were screened through questionnaires. An experimental framework for the emotional stimulation and measurement of eight driving emotions was designed based on multiple emotional stimulation methods and PAD emotional model. The effect of emotional activation on drivers of different genders, age, driving competence, driving anger tendency, driving safety attitude and stress state was explored in depth. The results show that gender, age, driving safety attitude, driving anger tendency, stress state, etc., all have different degrees of influence upon the activation efficacy of emotion. The research results reveal the rules for the generation of different driving emotions to a certain extent and provide a theoretical basis for further exploring the cognitive and behavioral characteristics of drivers with different emotions. Full article

(This article belongs to the Special Issue Smart Transportation and Intelligent and Connected Driving)

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12 pages, 1596 KiB

Open AccessArticle

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

by Chengju Song and Hongfei Jia

Sustainability 2022, 14(20), 13562; https://doi.org/10.3390/su142013562 - 20 Oct 2022

Cited by 2 | Viewed by 1285

Abstract

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 [...] Read more.

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. Full article

(This article belongs to the Special Issue Smart Transportation and Intelligent and Connected Driving)

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20 pages, 5355 KiB

Open AccessArticle

Analysis of Lane-Changing Decision-Making Behavior and Molecular Interaction Potential Modeling for Connected and Automated Vehicles

by Kekun Zhang, Dayi Qu, Hui Song, Tao Wang and Shouchen Dai

Sustainability 2022, 14(17), 11049; https://doi.org/10.3390/su141711049 - 5 Sep 2022

Cited by 3 | Viewed by 1749

Abstract

With the technical support of an intelligent networking environment, autonomous driving technology is facing a new stage of development, and the decision-making behavior of autonomous vehicles is changing fundamentally, so it is urgent to explore the lane-changing decision-making behavior mechanism of autonomous driving. [...] Read more.

With the technical support of an intelligent networking environment, autonomous driving technology is facing a new stage of development, and the decision-making behavior of autonomous vehicles is changing fundamentally, so it is urgent to explore the lane-changing decision-making behavior mechanism of autonomous driving. Firstly, through the analysis of system similarity, the similarity between autonomous vehicles and moving molecules is sought, and the attraction and repulsion between molecules are applied to the lane-changing process of vehicles to effectively recognize the traffic scene of lane-changing vehicles. Secondly, the molecular interaction potential is introduced to unify the attraction and repulsion, and explore the dynamic influencing factors of lane-changing behavior for vehicles. Moreover, we systematically analyze the interaction relationship in the lane-changing process of Connected and Automated Vehicles, and establish the molecular interaction potential lane-changing model to explore the lane-changing decision-making behavior mechanism. Furthermore, we study the impact of micro lane-changing behavior on macro traffic flow. Finally, the SL2015 lane-changing model and the molecular interaction potential lane-changing model are compared and analyzed by using the SUMO platform. The results show that the speed fluctuation of Connected and Automated Vehicles based on the molecular interaction potential lane-changing model is reduced by 15.5%, and the number of passed vehicles is increased by 3.26% on average, which has better safety, stability, and efficiency. The molecular interaction potential modeling of lane-changing decision-making behavior for Connected and Automated Vehicles comprehensively considers the interaction relationship of dynamic factors in the traffic environment, and scientifically shows the lane-changing decision-making mechanism of Connected and Automated Vehicles. Full article

(This article belongs to the Special Issue Smart Transportation and Intelligent and Connected Driving)

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17 pages, 4319 KiB

Open AccessArticle

Crosswalk Safety Warning System for Pedestrians to Cross the Street Intelligently

by Dayi Qu, Haiyang Li, Haomin Liu, Shaojie Wang and Kekun Zhang

Sustainability 2022, 14(16), 10223; https://doi.org/10.3390/su141610223 - 17 Aug 2022

Cited by 3 | Viewed by 2757

Abstract

During signal transitions at road sections and intersections, pedestrians and vehicles often clash and cause traffic accidents due to unclear right-of-way. To solve this problem, a vehicle safety braking distance model considering human–vehicle characteristics is established and applied to the designed crosswalk safety [...] Read more.

During signal transitions at road sections and intersections, pedestrians and vehicles often clash and cause traffic accidents due to unclear right-of-way. To solve this problem, a vehicle safety braking distance model considering human–vehicle characteristics is established and applied to the designed crosswalk safety warning system to enable pedestrians to cross the street intelligently. The model developed to consider human–vehicle characteristics improves the parking sight distance and pedestrian crossing safety psychological distance models by adding consideration of the effect of vehicle size and type on pedestrian psychology. The established model considering human–vehicle characteristics was improved for the stopping sight distance and pedestrian crossing safety psychological distance models. The effects of vehicle size and type on pedestrian psychology were taken into account. The designed warning system can be divided into a detection module, control module, warning module, and wireless communication module. The system detects the position and speed of pedestrians and vehicles and discriminates the conflict situation, executing the corresponding warning plan for three different types of situations. The system provides warning to pedestrians and vehicles through the different color displays of the intelligent crosswalk. The results show that the proposed model, which synergistically couples vehicle speed, driver reaction time, road characteristic correlation coefficients, and the psychological impact of vehicle size and type on pedestrians, is safe and effective. The designed system solves the problem of pedestrian crossing safety from both theoretical and technical aspects. Full article

(This article belongs to the Special Issue Smart Transportation and Intelligent and Connected Driving)

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20 pages, 2964 KiB

Open AccessArticle

Research on the Deployment of Joint Dedicated Lanes for CAVs and Buses

by Qingyu Luo, Rui Du, Hongfei Jia and Lili Yang

Sustainability 2022, 14(14), 8686; https://doi.org/10.3390/su14148686 - 15 Jul 2022

Cited by 2 | Viewed by 1591

Abstract

CAVs (Connected Autonomous Vehicles) can be effective in improving the efficiency of transportation, but heterogeneous multi-modal traffic flows may hinder this efficiency. This paper addresses the issue of heterogeneous traffic flows affecting the efficiency of transportation when CAVs enter the market and proposes [...] Read more.

CAVs (Connected Autonomous Vehicles) can be effective in improving the efficiency of transportation, but heterogeneous multi-modal traffic flows may hinder this efficiency. This paper addresses the issue of heterogeneous traffic flows affecting the efficiency of transportation when CAVs enter the market and proposes a joint dedicated lane for CAVs and buses. In the bi-level program model for the joint dedicated lane, the lower-level is aimed at the multi-modal traffic assignment problem, while the upper-level is aimed at system optimality. For the lower-level, the paper examines the characteristics of various traffic flows in a mixed traffic flow, investigates the impact of CAV mixing on the road link’s capacity, calculates the travel time of various traffic modes accordingly, and generates a generalized travel cost function for each mode, which is solved using the diagonalized weighted successive averaging method (MSWA) algorithm. The upper-level issue considers the continuity of dedicated and non-dedicated road segments, and the goal is to reduce the overall cost for all travelers by utilizing the dedicated road deployment scheme as the decision variable, which is addressed using a genetic algorithm. Finally, numerical examples and sensitivity analyses are designed accordingly. The numerical example demonstrates that the joint dedicated lane not only lowers the overall cost of the system, but also enhances the efficiency of CAV and bus travel, optimizing the road network and promoting bus and CAV travel modes. The sensitivity analysis shows that in order to set up a joint dedicated lane, the frequency of bus departures and the penetration of CAVs are conditions that must be considered, and that the benefits of a joint dedicated lane can only be fully realized if the frequency of bus departures and the penetration of CAVs are appropriate. Full article

(This article belongs to the Special Issue Smart Transportation and Intelligent and Connected Driving)

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19 pages, 2825 KiB

Open AccessArticle

Research on the Division Method of Signal Control Sub-Region Based on Macroscopic Fundamental Diagram

by Xianglun Mo, Xiaohong Jin, Jinpeng Tian, Zhushuai Shao and Gangqing Han

Sustainability 2022, 14(13), 8173; https://doi.org/10.3390/su14138173 - 4 Jul 2022

Cited by 1 | Viewed by 1419

Abstract

The macroscopic fundamental diagram (MFD) provides a method to evaluate macro traffic operation through micro traffic parameters, which can be applied to traffic control to prevent traffic congestion transfer and improve road network efficiency. However, due to the large scale of the urban [...] Read more.

The macroscopic fundamental diagram (MFD) provides a method to evaluate macro traffic operation through micro traffic parameters, which can be applied to traffic control to prevent traffic congestion transfer and improve road network efficiency. However, due to the large scale of the urban road network as well as the complex temporal and spatial distribution of road congestion, the application of the MFD for signal control first requires the partition of the urban road network. Based on the analysis of MFD partition purposes, a set of MFD partition methods based on graph theory was designed. Firstly, graph theory was used to transform the urban road network; secondly, the minimum spanning tree method was used to divide the urban traffic network map. Moreover, the attribution of the link between connected regions is determined. Our method can solve the problem of ambiguous intersection ownership, and the road sections belonging to the same road in opposite directions are separated. This method has the ability to control the size of the area by limiting the number of intersections; Finally, the evaluation index of regional clustering results was drawn. To achieve the research objective, we collected and processed vehicle information data from the Xuzhou car-hailing platform to obtain traffic density information. Then, we selected an area with sufficient data and a large enough road network. The empirical value range of the regional control value was obtained by comparing the values of multiple groups of measurement data k and evaluation indexes. In this process, it was found that during the period of flat peak and peak transition, while the regional average traffic density changes, the uniformity of traffic density first decreases and then increases. The traffic density uniformity of the signal control area can be improved by controlling the size of the signal control area. We obtained the empirical value range of the regional control value k by comparing the values of multiple groups of measurement data k and evaluation indexes. Then, we compared them with the two kinds of traditional partition algorithms and improved multiple dichotomy algorithms. Our method improves road network balance by 5% over existing methods. Full article

(This article belongs to the Special Issue Smart Transportation and Intelligent and Connected Driving)

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16 pages, 3492 KiB

Open AccessArticle

A Car-Following Model Based on Trajectory Data for Connected and Automated Vehicles to Predict Trajectory of Human-Driven Vehicles

by Dayi Qu, Shaojie Wang, Haomin Liu and Yiming Meng

Sustainability 2022, 14(12), 7045; https://doi.org/10.3390/su14127045 - 9 Jun 2022

Cited by 14 | Viewed by 2321

Abstract

Connected and Automated Vehicles (CAV) have been rapidly developed, which, inevitably, renders that human-driven and autonomous vehicles share the road. Thus, trajectory prediction is an important research topic, which helps each CAV to efficiently follow a Human-Driven Vehicle (HV). In a wider scope, [...] Read more.

Connected and Automated Vehicles (CAV) have been rapidly developed, which, inevitably, renders that human-driven and autonomous vehicles share the road. Thus, trajectory prediction is an important research topic, which helps each CAV to efficiently follow a Human-Driven Vehicle (HV). In a wider scope, trajectory prediction, also, helps to improve the throughput of traffic flow and enhance its stability. To realize the trajectory prediction of Connected and Automated Vehicles to Human-Driven Vehicles, a car-following model, which is based on trajectory data, was established. Adding deep neural networks and an Attention mechanism, this paper established a data-driven car-following model, based on CNN-BiLSTM-Attention for CAV, to predict trajectory, by referring to the modeling idea of the traditional car-following model. The trajectory data in the next-generation-simulation (NGSIM) datasets that match the car-following characteristics were selected. In addition, noise-reduction pre-processing of the trajectory data was performed, to make it match the actual car-following situation. Experiments, for selecting the optimal structure of the model and the method of trajectory prediction, were carried out. The data-driven car-following models, such as LSTM, GRU, and CNN-BiLSTM, were selected for comparative analysis of trajectory prediction. The results show that the CNN-BiLSTM-Attention model has the smallest MAE and MSE as well as the largest R². The CNN-BiLSTM-Attention model has the highest accuracy in vehicle-trajectory prediction. The model can, effectively, realize vehicle-trajectory prediction and provide a theoretical basis for vehicle-trajectory-based velocity guidance of Human-Driven Vehicles. In the future, the model can, also, provide the theoretical basis for Connected and Automated Vehicles, to make car-following decisions in mixed traffic flow. Full article

(This article belongs to the Special Issue Smart Transportation and Intelligent and Connected Driving)

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19 pages, 2395 KiB

Open AccessArticle

An Evaluation Model for the Comfort of Vehicle Intelligent Cockpits Based on Passenger Experience

by Jianjun Yang, Shanshan Xing, Yimeng Chen, Ruizhi Qiu, Chunrong Hua and Dawei Dong

Sustainability 2022, 14(11), 6827; https://doi.org/10.3390/su14116827 - 2 Jun 2022

Cited by 6 | Viewed by 2999

Abstract

With the development of intelligence and network connectivity, the development of the automotive industry is also moving toward intelligent systems. For passengers, the utility of intelligence is to achieve more convenience and comfort. The intelligent cockpit is the place where passengers directly interact [...] Read more.

With the development of intelligence and network connectivity, the development of the automotive industry is also moving toward intelligent systems. For passengers, the utility of intelligence is to achieve more convenience and comfort. The intelligent cockpit is the place where passengers directly interact with the car, which directly affects the experience of passengers in the car. For the intelligent cockpits that have emerged in recent years, a reasonable and accurate comfort evaluation model is urgently needed. Therefore, in this article, from the passenger’s perspective, a subjective evaluation experiment was set up to collect data on four important indicators affecting the comfort of the intelligent cockpit: sound, light, heat, and human–computer interaction. The subjective evaluation weights were derived from a questionnaire, and the entropy weighting method was used to obtain the objective weights. Finally, the two weights were combined using the idea of game theory combination assignment to get the final accurate weights. Using the idea of penalty type substitution, the four index models were then synthesized to get the final evaluation model. The feasibility of the model was verified when measuring the car cockpit. The feasibility of the method means it can evaluate the comfort level of an intelligent cockpit more reasonably, facilitate the enhancement and improvement of the model, and promote the development of the model to achieve maximum passenger comfort. Full article

(This article belongs to the Special Issue Smart Transportation and Intelligent and Connected Driving)

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17 pages, 11703 KiB

Open AccessArticle

Prediction of Clearance Vibration for Intelligent Vehicles Motion Control

by Yunhe Zhang, Faping Zhang, Wuhong Wang, Fanjun Meng, Dashun Zhang and Haixun Wang

Sustainability 2022, 14(11), 6698; https://doi.org/10.3390/su14116698 - 30 May 2022

Cited by 2 | Viewed by 1405

Abstract

Motion control analysis should consider the system’s uncertainty to ensure the intelligent vehicle’s autonomy. The clearance structure of the transmission shaft is modeled as a cantilever beam with double clearance to predict the clearance vibration for mitigating the nonlinearity. Based on the Kelvin–Voigt [...] Read more.

Motion control analysis should consider the system’s uncertainty to ensure the intelligent vehicle’s autonomy. The clearance structure of the transmission shaft is modeled as a cantilever beam with double clearance to predict the clearance vibration for mitigating the nonlinearity. Based on the Kelvin–Voigt collision model, a clearance model was developed using time-varying parameters identified by the wavelet transform. Comparing the frequency response functions (FRF) of the initial model with constant parameters and the updated model with time-varying parameters, the experimental results from the updated model indicate that the modal assurance criterion (MAC) is increased by 42.92%, 31.08%, 38.97%, and 50.74% in the first-four order. Cross-signature assurance criteria (CSAC) and cross-signature scale factor (CSF) have been increased by 6.55% and 12.37%. The control method based on the clearance model has been verified. In the case of 120 km/h, compared with model-predictive control (MPC) and sliding mode control (SMC), the peak of the lateral position error was reduced by 35.7% and 14.3%, and the peak of the heading error was reduced by 50% and 15.6%. Full article

(This article belongs to the Special Issue Smart Transportation and Intelligent and Connected Driving)

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Review

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27 pages, 725 KiB

Open AccessReview

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

by Junyan Han, Xiaoyuan Wang and Gang Wang

Sustainability 2022, 14(13), 8179; https://doi.org/10.3390/su14138179 - 5 Jul 2022

Cited by 18 | Viewed by 3292

Abstract

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 [...] Read more.

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. Full article

(This article belongs to the Special Issue Smart Transportation and Intelligent and Connected Driving)

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