The basic theory of YOLO series object detection algorithms is discussed, the dangerous driving behavior dataset is collected and produced, and then the YOLOv7 network is introduced in detail, the deep separable convo...The basic theory of YOLO series object detection algorithms is discussed, the dangerous driving behavior dataset is collected and produced, and then the YOLOv7 network is introduced in detail, the deep separable convolution and CA attention mechanism are introduced, the YOLOv7 bounding box loss function and clustering algorithm are optimized, and the DB-YOLOv7 network structure is constructed. In the first stage of the experiment, the PASCAL VOC public dataset was utilized for pre-training. A comparative analysis was conducted to assess the recognition accuracy and inference time before and after the proposed improvements. The experimental results demonstrated an increase of 1.4% in the average recognition accuracy, alongside a reduction in the inference time by 4 ms. Subsequently, a model for the recognition of dangerous driving behaviors was trained using a specialized dangerous driving behavior dataset. A series of experiments were performed to evaluate the efficacy of the DB-YOLOv7 algorithm in this context. The findings indicate a significant enhancement in detection performance, with a 4% improvement in accuracy compared to the baseline network. Furthermore, the model’s inference time was reduced by 20%, from 25 ms to 20 ms. These results substantiate the effectiveness of the DB-YOLOv7 recognition algorithm for detecting dangerous driving behaviors, providing comprehensive validation of its practical applicability.展开更多
In order to give a new way for modeling driving behavior, identifying road traffic accident causation and solving a variety of road traffic safety problems such as driving errors prevention and driving behavior analys...In order to give a new way for modeling driving behavior, identifying road traffic accident causation and solving a variety of road traffic safety problems such as driving errors prevention and driving behavior analysis, a new driving behavior shaping model is proposed, which could be used to assess the degree of effect of driving error upon road traffic safety. Driver behavior shaping model based on driving reliability and safety analysis could be used to identify the road traffic accident causation, to supply data for driver's behavior training, to evaluate driving procedures, to human factor design of road traffic system.展开更多
Based on the driver surveillance video data and controller area network(CAN)data,the methods of studying commercial vehicles’driving behavior is relatively advanced.However,these methods have difficulty in covering p...Based on the driver surveillance video data and controller area network(CAN)data,the methods of studying commercial vehicles’driving behavior is relatively advanced.However,these methods have difficulty in covering private vehicles.Naturalistic driving studies have disadvantages of small sample size and high cost,one new driving behavior evaluation method using massive vehicle trajectory data is put forward.An automatic encoding machine is used to reduce the noise of raw data,and then driving dynamics and self-organizing mapping(SOM)classification are used to give thresholds or the judgement method of overspeed,rapid speed change,rapid turning and rapid lane changing.The proportion of different driving behaviors and typical dangerous driving behaviors is calculated,then the temporal and spatial distribution of drivers’driving behavior and the driving behavior characteristics on typical roads are analyzed.Driving behaviors on accident-prone road sections and normal road sections are compared.Results show that in Shenzhen,frequent lane changing and overspeed are the most common unsafe driving behaviors;16.1%drivers have relatively aggressive driving behavior;the proportion of dangerous driving behavior is higher outside the original economic special zone;dangerous driving behavior is highly correlated with traffic accident frequency.展开更多
To prevent and reduce road traffic accidents and improve driver safety awareness and bad driving be-haviors,we propose a safety evaluation method for commercial vehicle driving behavior.Three driving style clas-sifica...To prevent and reduce road traffic accidents and improve driver safety awareness and bad driving be-haviors,we propose a safety evaluation method for commercial vehicle driving behavior.Three driving style clas-sification indexes were extracted using driving data from commercial vehicles and four primary and ten secondary safety evaluation indicators.Based on the stability of commercial vehicles transporting goods,the acceleration index is divided into three levels according to the statistical third quartile,and the evaluation expression of the safety index evaluation is established.Drivers were divided into conservative,moderate,and radical using K-means++.The weights corresponding to each index were calculated using a combination of the analytic hierarchy process(AHP)and criteria importance through intercriteria correlation(CRITIC),and the driving behavior scores of various drivers were calculated according to the safety index score standard.The established AHP-CRITIC safety evaluation model was verified using the actual driving behavior data of commercial vehicle drivers.The calculation results show that the proposed evaluation model can clearly distinguish between the types of drivers with different driving styles,verifying its rationality and validity.The evaluation results can provide a reference for transportation management departments and enterprises.展开更多
The complexity of signal controlled traffic largely stems from the various driving behaviors developed in response to the traffic signal. However, the existing models take a few driving behaviors into account and cons...The complexity of signal controlled traffic largely stems from the various driving behaviors developed in response to the traffic signal. However, the existing models take a few driving behaviors into account and consequently the traffic dynamics has not been completely explored. Therefore, a new cellular automaton model, which incorporates the driving behaviors typically manifesting during the different stages when the vehicles are moving toward a traffic light, is proposed in this paper. Numerical simulations have demonstrated that the proposed model can produce the spontaneous traffic breakdown and the dissolution of the over-saturated traffic phenomena. Furthermore, the simulation results indicate that the slow-to-start behavior and the inch-forward behavior can foster the traffic breakdown. Particularly, it has been discovered that the over-saturated traffic can be revised to be an under-saturated state when the slow-down behavior is activated after the spontaneous breakdown. Finally, the contributions of the driving behaviors on the traffic breakdown have been examined.展开更多
In the light of the visual angle model(VAM),an improved car-following model considering driver's visual angle,anticipated time and stabilizing driving behavior is proposed so as to investigate how the driver's...In the light of the visual angle model(VAM),an improved car-following model considering driver's visual angle,anticipated time and stabilizing driving behavior is proposed so as to investigate how the driver's behavior factors affect the stability of the traffic flow.Based on the model,linear stability analysis is performed together with bifurcation analysis,whose corresponding stability condition is highly fit to the results of the linear analysis.Furthermore,the time-dependent Ginzburg–Landau(TDGL)equation and the modified Korteweg–de Vries(m Kd V)equation are derived by nonlinear analysis,and we obtain the relationship of the two equations through the comparison.Finally,parameter calibration and numerical simulation are conducted to verify the validity of the theoretical analysis,whose results are highly consistent with the theoretical analysis.展开更多
Abnormal driving behavior identification( ADBI) has become a research hotspot because of its significance in driver assistance systems. However,current methods still have some limitations in terms of accuracy and reli...Abnormal driving behavior identification( ADBI) has become a research hotspot because of its significance in driver assistance systems. However,current methods still have some limitations in terms of accuracy and reliability under severe traffic scenes. This paper proposes a new ADBI method based on direction and position offsets,where a two-factor identification strategy is proposed to improve the accuracy and reliability of ADBI. Self-adaptive edge detection based on Sobel operator is used to extract edge information of lanes. In order to enhance the efficiency and reliability of lane detection,an improved lane detection algorithm is proposed,where a Hough transform based on local search scope is employed to quickly detect the lane,and a validation scheme based on priori information is proposed to further verify the detected lane. Experimental results under various complex road conditions demonstrate the validity of the proposed ADBI.展开更多
Bus safety is a matter of great importance in many developing countries, with driving behaviors among bus drivers identified as a primary factor contributing to accidents. This concern is particularly amplified in mix...Bus safety is a matter of great importance in many developing countries, with driving behaviors among bus drivers identified as a primary factor contributing to accidents. This concern is particularly amplified in mixed traffic flow (MTF) environments with time pressure (TP). However, there is a lack of sufficient research exploring the relationships among these factors. This study consists of two papers that aim to investigate the impact of MTF environments with TP on the driving behaviors of bus drivers. While the first paper focuses on violated driving behaviors, this particular paper delves into mistake-prone driving behaviors (MDB). To collect data on MDB, as well as perceptions of MTF and TP, a questionnaire survey was implemented among bus drivers. Factor analyses were employed to create new measurements for validating MDB in MTF environments. The study utilized partial correlation and linear regression analyses with the Bayesian Model Averaging (BMA) method to explore the relationships between MDB and MTF/TP. The results revealed a modified scale for MDB. Two MTF factors and two TP factors were found to be significantly associated with MDB. A high presence of motorcycles and dangerous interactions among vehicles were not found to be associated with MDB among bus drivers. However, bus drivers who perceived motorcyclists as aggressive, considered road users’ traffic habits as unsafe, and perceived bus routes’ punctuality and organization as very strict were more likely to exhibit MDB. Moreover, the results from the three MDB predictive models demonstrated a positive impact of bus route organization on MDB among bus drivers. The study also examined various relationships between the socio-demographic characteristics of bus drivers and MDB. These findings are of practical significance in developing interventions aimed at reducing MDB among bus drivers operating in MTF environments with TP.展开更多
Electric vehicles are widely embraced as a promising solution to reduce energy consumption and emission to achieve the Carbon Peak and Carbon Neutrality vision,especially in developing countries.Specifically,it’s vit...Electric vehicles are widely embraced as a promising solution to reduce energy consumption and emission to achieve the Carbon Peak and Carbon Neutrality vision,especially in developing countries.Specifically,it’s vital important to understand the ecological performance of electric vehicles and its association with driving behaviors under varying road and environmental conditions.However,current researches on ecological driving behavior mostly use structured data to reflect the characteristics of ecological driving behavior,and it is difficult to accurately reveal the recessive relationship between driving behavior and energy consumption.One promising and prevalent method for comprehensively and in-depth characterizing driving behaviors is“graph spectrums”,which allows for an effective and illustrative representation of complex driving behavior characteristics.This study presented an assessment method of ecological driving for electric vehicles based on the graph.Firstly,a multi-source refined data set was constructed through naturalistic driving experiments(NDE).Four typical traffic state(CCCF:congested close car-following;CSSF:constrained slow free-flow;CSCF:constrained slow carfollowing;UFFF:unconstrained fast free-flow)were classified through longitudinal acceleration data,and driving behavior graph was constructed to realize the visual representation of driving behavior.Then,the energy consumption graph was constructed using the energy loss of 100 km(EL)index.After the six drivers with the highest and lowest ecological assessment of driving behavior using the behavior graph and energy consumption graph,proposing the quantitative analysis of fifteen drivers'ecology driving behavior.The results show that:1)The graphical method can describe the individual features of a driver’s ecological driving behavior;2)Rapid acceleration of driving behavior leads to high energy consumption;3)In the comparison among the six ecodrivers and energy-intensive drivers,founding that the energy-intensive drivers accelerate and decelerate significantly more in CCCF traffic state;4)The driving behavior was more complex and unecological in CCCF traffic state;5)Fifteen drivers had lower ecological scores in start-up driving.This study proposes a method for visualizing ecology driving behavior that not only help understand the individual characteristics of ecological driving behaviors,but also offers substantial application value for the subsequent construction of Ecological driving behavior regulation models.展开更多
The aim of this paper is to understand the factors that influence unsafe driving practices by examining published studies that utilized the theory of planned behavior(TPB)to predict driving behavior.To this end,42 stu...The aim of this paper is to understand the factors that influence unsafe driving practices by examining published studies that utilized the theory of planned behavior(TPB)to predict driving behavior.To this end,42 studies published up to the end of 2021 are reviewed to evaluate the predictive utility of TPB by employing a meta-analysis and structural equation model.The results indicate that these studies sought to predict 20 distinct driving behaviors(e.g.,drink-driving,use of cellphone while driving,aggressive driving)using the original TPB constructs and 43 additional variables.The TPB model with the three original constructs is found to account for 32%intentional variance and 34%behavioral variance.Among the 43 variables researchers have examined in TPB studies related to driving behavior,this study identified the six that are commonly used to enhance the TPB model’s predictive power.These variables are past behavior,self-identity,descriptive norm,anticipated regret,risk perception,and moral norm.When past behavior is added to the original TPB model,it increases the explained variance in intention to 52%.When all six factors are added to the original TPB model,the best model has only four variables(perceived risk,self-identity,descriptive norm,and moral norm);and increases the explained variance to 48%.The influence of the TPB constructs on intention is modified by behavior category and traffic category.The findings of this paper validate the application of TPB to predicting driving behavior.It is the first study to do this through the use of meta-analysis and structural equation modeling.展开更多
Expressway tunnels are semi-enclosed structures characterized by monotonous alignment transitions and unique lighting environments,which can easily lead to drivers developing constrained and irritable psychology.This ...Expressway tunnels are semi-enclosed structures characterized by monotonous alignment transitions and unique lighting environments,which can easily lead to drivers developing constrained and irritable psychology.This may result in risky behaviors,e.g.,speeding and fatigued driving.Previous research on tunnel driving behaviors mainly focuses on visual factors,neglecting the impacts of nonstationary time-series combined parameters on risky driving.Firstly,30 drivers were recruited to carry out the real test.Then,based on the evolution of time series,drawing inspiration from the concept of lineage in biology,and considering multiple driving performance indicators,driving behavior chains and the feature spectrum were constructed.The characteristics of the behavior spectrum were divided into six groups:electroencephalogram,heart rate,eye movement,speed,steering,and carfollowing behaviors.Subsequently,the spectral analysis using the spectral radius property of matrix theory revealed the distinctive characteristics of risky driving behaviors.The study deeply explored the inducing mechanism,hidden patterns,and rules of risky driving behaviors under the coupling effect of tunnel environment and drivers’attributes.Finally,the significant features that influence driving behaviors were used as the input variables for constructing identification models using the adaptive boosting(AdaBoost)and random forest(RF)algorithms.The synthetic minority over-sampling technique(SMOTE)and adaptive synthetic sampling(ADASYN)were employed for oversampling.The results indicate that the ADASYN-RF algorithm outperformed others,achieving a precise recall rate area under the curve(AUPRC)of 0.978 when using the spectral radius of the speed and steering groups as input variables.These findings offer theoretical guidance for developing tunnel traffic safety strategies.展开更多
Purpose:This study aimed to investigate the possible association between psychological disorders and riskydriving behavior(RDB)in Iran.Methods:This case-control study conducted in Shiraz,Iran in 2021.The case group in...Purpose:This study aimed to investigate the possible association between psychological disorders and riskydriving behavior(RDB)in Iran.Methods:This case-control study conducted in Shiraz,Iran in 2021.The case group included drivers with psychological disorders and the control group included those without any disorders.The inclusion criteria for selecting patients were:active driving at the time of the study,being 18-65 years old,having a driving license,having a psychological disorder including depression,bipolar disorder,anxiety spectrum disorder,or psychotic disorder spectrum confirmed by a psychiatrist,and completing an informed consent form.The exclusion criterion was the existence of conditions that interfered with answering and understanding the questions.The inclusion criteria for selecting the healthy cases were:active driving at the time of the study,being 18-65 years old,having a driving license,lack of any past or present history of psychiatric problems,and completing an informed consent form.The data were gathered using a researcher-made checklist and Manchester driving behavior questionnaire.First,partition around medoids method was used to extract clusters of RDB.Then,backward logistic regression was applied to investigate the association between the independent variables and the clusters of RDB.Results:The sample comprised of 344(153 with psychological disorder and 191 without confirmed psychological disorder)drivers.Backward elimination logistic regression on total data revealed that share of medical expenditure≤10%of total household expenditure(OR=3.27,95%Cl:1.48-7.24),psychological disorder(OR=3.08,95%Cl:1.67-5.70),and substance abuse class(OR=6.38,95%CI:3.55-11.48)wereassociatedwithhighlevelof RDB.Conclusion:Substance abuse,psychological illnesses,and share of medical costs from total household expenditure were found to be main predictors of RDB.Further investigations are necessary to explain the impact of different psychological illnesses on driving behavior.展开更多
Road traffic safety should be evaluated throughout the entire life-cycle of road design,operation,maintenance,and expansion construction.However,traditional methods for evaluating road traffic safety based on traffic ...Road traffic safety should be evaluated throughout the entire life-cycle of road design,operation,maintenance,and expansion construction.However,traditional methods for evaluating road traffic safety based on traffic accidents and conflict technology are limited by their inability to account for the complex environmental factors involved.To address this issue,a new road safety evaluation method has emerged that is based on driving behavior.Because drivers’behaviors may vary depending on the driving environment and their personal characteristics,evaluating road safety from the perspective of driver behavior has become a popular research topic.This paper analyzes current research trends and mainstream journals in the field of road safety evaluation of driving behavior.Additionally,it reviews the three most commonly used driving behavior data collection methods,and compares the advantages and disadvantages of each.The paper proposes the main application scenarios of road safety evaluation methods based on driving behavior,such as road design,evaluation of the effects of road appurtenances,and intelligent highways.Furthermore,the paper summarizes a driving behavior index system based on vehicle data,driver’s physiological and psychological data,and driver’s subjective questionnaire data.A comprehensive evaluation method based on the fusion of each index system is presented in detail.Finally,the paper points out current research problems and the future development direction of the road safety evaluation method based on driving behavior.展开更多
To better understand the research focus and development direction in the field of driving behavior active intervention,thereby laying a scientific foundation for further research,we used the combination of topic words...To better understand the research focus and development direction in the field of driving behavior active intervention,thereby laying a scientific foundation for further research,we used the combination of topic words and keywords to retrieve relevant articles from the Core Collection Database of Web of Science(WOS).A total of 578 articles published from1992 to 2022 were finally obtained.Firstly,the time distribution characteristics,country distribution,institution distribution and main source journal distribution of published articles were explored.Then,by using the Cite Space and VOSviewer software,cited reference co-citation analysis,keyword co-occurrence analysis and burst detection analysis were carried out respectively to visually explore the knowledge base,research topic,research frontier and development trend of this field.The results indicate that the USA,Australia and China are the three most active countries in the studies of driving behavior active intervention.Accidental Analysis&Prevention,Transportation Research Part F:Traffic Psychology and Behavior,and Journal of Safety Research are widely selected journals for publications related to this field.The research frontiers in the field of driving behavior active intervention focus on:“traffic safety and crashes analysis,as well as enforcement intervention”,“driving risk and education for young drivers”,“information provision and driving behavior”,“workload and situation awareness for automated driving”.It is worth noting that in recent years,“warning system”,“time”,“work load”have become research hotspots in this field.To sum up,by a bibliometric overview of research on driving behavior active intervention over the past thirty years,this paper clarifies the development skeleton of this research field,determines its hot topics and research progress,and provides a reference for the follow-up exploratory scientific research in this field.展开更多
With the high older-related accident ratio and increasing population aging problem, understanding older drivers＇ driving behaviors has become more and more important for building and improving transportation system. ...With the high older-related accident ratio and increasing population aging problem, understanding older drivers＇ driving behaviors has become more and more important for building and improving transportation system. This paper examines older driver＇s driving behavior which includes road selection, left/right turn and driving speed. A two-month experiment of 108 participants was carried out in Aichi Prefecture, Japan. Since apparently contradictory statements were often drawn in survey-based or simulators-based studies, this study collected not only drivers＇ basic information but also GPS data. Analysis of road selection demonstrates that older drivers are reluctant to drive on expressway not only in short trips but also in long trips. The present study did not find significant difference be- tween older drivers and others while turning at the intersections. To investigate the impact factors on driving speed, a random-effects regression model is constructed with explan- atory variables including age, gender, road types and the interaction terms between age and road types. Compared with other variables, it fails to find that age （60 years old or over） has significant impact on driving speed. Moreover, the results reflect that older drivers drive even faster than others at particular road types： national road and ordinary municipal road. The results in this study are expected to help improve transportation planning and develop driving assistance systems for older drivers.展开更多
Time headway is an important index used in characterizing dangerous driving behaviors. This research focuses on the decreasing tendency of time headway and investigates its association with crash occurrence. An autore...Time headway is an important index used in characterizing dangerous driving behaviors. This research focuses on the decreasing tendency of time headway and investigates its association with crash occurrence. An autoregressive（AR） time-series model is improved and adopted to describe the dynamic variations of average daily time headway. Based on the model, a simple approach for dangerous driving behavior recognition is proposed with the aim of significantly decreasing headway. The effectivity of the proposed approach is validated by means of empirical data collected from a medium-sized city in northern China. Finally, a practical early-warning strategy focused on both the remaining life and low headway is proposed to remind drivers to pay attention to their driving behaviors and the possible occurrence of crash-related risks.展开更多
This study was aimed at determining the driving distractions which are perceived most hazardous and determining the effects of these distractions and age on speed and headway. A questionnaire survey was done to find o...This study was aimed at determining the driving distractions which are perceived most hazardous and determining the effects of these distractions and age on speed and headway. A questionnaire survey was done to find out the opinion of drivers related to the most hazardous distraction. 639 responses were collected in the survey which were used to determine the top-rated distractions for drivers in Bahrain. Roadside observations were taken to observe the speed, headway, age and type of distraction for the driver. Speed was observed for 48 drivers while headway was observed for 36 drivers along with other parameters. The most hazardous distractions, according to the participants of the questionnaire, are using mobile phones, handling children, and accidents or incidents on the road. Further, the results of the two-way analysis of variance(ANOVA) test and regression analysis demonstrated that using mobile phones and age have a significant effect on both speed and headway. Speed tends to decrease with distraction for all age groups while decreasing the headway for young and middle-aged drivers. The effect of distraction is higher than the effect of age on speed, as well as headway. Texting has the most significant effect among distractions on headway. It is hereby recommended that policymakers should focus on increasing awareness and stringent law enforcement related to handling mobile phones and children, especially for young and middle-aged drivers.展开更多
Driving behavior is heterogeneous for various drivers due to the different influencing factors as reaction time,gender,driving years and so on.Some existing works tried to reproduce some of the complex characteristics...Driving behavior is heterogeneous for various drivers due to the different influencing factors as reaction time,gender,driving years and so on.Some existing works tried to reproduce some of the complex characteristics of real traffic flow by taking into account the heterogeneous driving behavior,and the drivers are generally divided into two classes(including aggressive drivers and careful drivers)or three classes(including aggressive drivers,normal drivers and careful drivers).Nevertheless,the classification approaches have not been verified,and the rationality of the classifications has not been confirmed as well.In this study,the trajectory data of drivers is extracted from the NGSIM datasets.By combining the K-Means method and Silhouette measure index,the drivers are classified into four clusters(named as clusters A,B,C and D,respectively)in accordance with the acceleration and time headway.The two-dimensional approach is applied to analyze the characteristics of different clusters.Here,one dimension consists of“Cautious”and“Aggressive”behaviors in terms of velocity and acceleration,and the other dimension consists of“Sensitive”and“Insensitive”behaviors in terms of reaction time.Finally,the fuel consumption and emissions for different clusters are calculated by using the VT-Micro model.A surprising result indicates that overly“cautious”and“sensitive”behaviors may result in more fuel consumption and emissions.Therefore,it is necessary to find the balance between the driving characteristics.展开更多
Driving behavior normalization is important for a fair evaluation of the driving style.The longitudinal control of a vehicle is investigated in this study.The normalization task can be considered as mapping of the dri...Driving behavior normalization is important for a fair evaluation of the driving style.The longitudinal control of a vehicle is investigated in this study.The normalization task can be considered as mapping of the driving behavior in a different environment to the uniform condition.Unlike the model-based approach as in previous work,where a necessary driver model is employed to conduct the driving cycle test,the approach we propose directly normalizes the driving behavior using an autoencoder(AE)when following a standard speed profile.To ensure a positive correlation between the vehicle speed and driving behavior,a gate constraint is imposed in between the encoder and decoder to form a gated AE(gAE).This approach is model-free and efficient.The proposed approach is tested for consistency with the model-based approach and for its applications to quantitative evaluation of the driving behavior and fuel consumption analysis.Simulations are conducted to verify the effectiveness of the proposed scheme.展开更多
In order to make full use of the driver’s long-term driving experience in the process of perception, interaction and vehicle control of road traffic information, a driving behavior rule extraction algorithm based on ...In order to make full use of the driver’s long-term driving experience in the process of perception, interaction and vehicle control of road traffic information, a driving behavior rule extraction algorithm based on artificial neural network interface(ANNI) and its integration is proposed. Firstly, based on the cognitive learning theory, the cognitive driving behavior model is established, and then the cognitive driving behavior is described and analyzed. Next, based on ANNI, the model and the rule extraction algorithm(ANNI-REA) are designed to explain not only the driving behavior but also the non-sequence. Rules have high fidelity and safety during driving without discretizing continuous input variables. The experimental results on the UCI standard data set and on the self-built driving behavior data set, show that the method is about 0.4% more accurate and about 10% less complex than the common C4.5-REA, Neuro-Rule and REFNE. Further, simulation experiments verify the correctness of the extracted driving rules and the effectiveness of the extraction based on cognitive driving behavior rules. In general, the several driving rules extracted fully reflect the execution mechanism of sequential activity of driving comprehensive cognition, which is of great significance for the traffic of mixed traffic flow under the network of vehicles and future research on unmanned driving.展开更多
文摘The basic theory of YOLO series object detection algorithms is discussed, the dangerous driving behavior dataset is collected and produced, and then the YOLOv7 network is introduced in detail, the deep separable convolution and CA attention mechanism are introduced, the YOLOv7 bounding box loss function and clustering algorithm are optimized, and the DB-YOLOv7 network structure is constructed. In the first stage of the experiment, the PASCAL VOC public dataset was utilized for pre-training. A comparative analysis was conducted to assess the recognition accuracy and inference time before and after the proposed improvements. The experimental results demonstrated an increase of 1.4% in the average recognition accuracy, alongside a reduction in the inference time by 4 ms. Subsequently, a model for the recognition of dangerous driving behaviors was trained using a specialized dangerous driving behavior dataset. A series of experiments were performed to evaluate the efficacy of the DB-YOLOv7 algorithm in this context. The findings indicate a significant enhancement in detection performance, with a 4% improvement in accuracy compared to the baseline network. Furthermore, the model’s inference time was reduced by 20%, from 25 ms to 20 ms. These results substantiate the effectiveness of the DB-YOLOv7 recognition algorithm for detecting dangerous driving behaviors, providing comprehensive validation of its practical applicability.
文摘In order to give a new way for modeling driving behavior, identifying road traffic accident causation and solving a variety of road traffic safety problems such as driving errors prevention and driving behavior analysis, a new driving behavior shaping model is proposed, which could be used to assess the degree of effect of driving error upon road traffic safety. Driver behavior shaping model based on driving reliability and safety analysis could be used to identify the road traffic accident causation, to supply data for driver's behavior training, to evaluate driving procedures, to human factor design of road traffic system.
基金The National Natural Science Foundation of China(No.71641005)the National Key Research and Development Program of China(No.2018YFB1601105)
文摘Based on the driver surveillance video data and controller area network(CAN)data,the methods of studying commercial vehicles’driving behavior is relatively advanced.However,these methods have difficulty in covering private vehicles.Naturalistic driving studies have disadvantages of small sample size and high cost,one new driving behavior evaluation method using massive vehicle trajectory data is put forward.An automatic encoding machine is used to reduce the noise of raw data,and then driving dynamics and self-organizing mapping(SOM)classification are used to give thresholds or the judgement method of overspeed,rapid speed change,rapid turning and rapid lane changing.The proportion of different driving behaviors and typical dangerous driving behaviors is calculated,then the temporal and spatial distribution of drivers’driving behavior and the driving behavior characteristics on typical roads are analyzed.Driving behaviors on accident-prone road sections and normal road sections are compared.Results show that in Shenzhen,frequent lane changing and overspeed are the most common unsafe driving behaviors;16.1%drivers have relatively aggressive driving behavior;the proportion of dangerous driving behavior is higher outside the original economic special zone;dangerous driving behavior is highly correlated with traffic accident frequency.
基金the Natural Science Foundation of Guangxi(No.2020GXNSFDA238011)the Open Fund Project of Guangxi Key Laboratory of Automation Detection Technology and Instrument(No.YQ21203)the Independent Research Project of Guangxi Key Laboratory of Auto Parts and Vehicle Technology(No.2020GKLACVTZZ02)。
文摘To prevent and reduce road traffic accidents and improve driver safety awareness and bad driving be-haviors,we propose a safety evaluation method for commercial vehicle driving behavior.Three driving style clas-sification indexes were extracted using driving data from commercial vehicles and four primary and ten secondary safety evaluation indicators.Based on the stability of commercial vehicles transporting goods,the acceleration index is divided into three levels according to the statistical third quartile,and the evaluation expression of the safety index evaluation is established.Drivers were divided into conservative,moderate,and radical using K-means++.The weights corresponding to each index were calculated using a combination of the analytic hierarchy process(AHP)and criteria importance through intercriteria correlation(CRITIC),and the driving behavior scores of various drivers were calculated according to the safety index score standard.The established AHP-CRITIC safety evaluation model was verified using the actual driving behavior data of commercial vehicle drivers.The calculation results show that the proposed evaluation model can clearly distinguish between the types of drivers with different driving styles,verifying its rationality and validity.The evaluation results can provide a reference for transportation management departments and enterprises.
基金supported by the National Basic Research Program of China(Grand No.2012CB723303)the Beijing Committee of Science and Technology,China(Grand No.Z1211000003120100)
文摘The complexity of signal controlled traffic largely stems from the various driving behaviors developed in response to the traffic signal. However, the existing models take a few driving behaviors into account and consequently the traffic dynamics has not been completely explored. Therefore, a new cellular automaton model, which incorporates the driving behaviors typically manifesting during the different stages when the vehicles are moving toward a traffic light, is proposed in this paper. Numerical simulations have demonstrated that the proposed model can produce the spontaneous traffic breakdown and the dissolution of the over-saturated traffic phenomena. Furthermore, the simulation results indicate that the slow-to-start behavior and the inch-forward behavior can foster the traffic breakdown. Particularly, it has been discovered that the over-saturated traffic can be revised to be an under-saturated state when the slow-down behavior is activated after the spontaneous breakdown. Finally, the contributions of the driving behaviors on the traffic breakdown have been examined.
基金the Natural Science Foundation of Zhejiang Province,China(Grant Nos.LY22G010001,LY20G010004)the Program of Humanities and Social Science of Education Ministry of China(Grant No.20YJA630008)+1 种基金the National Key Research and Development Program of China-Traffic Modeling,Surveillance and Control with Connected&Automated Vehicles(Grant No.2017YFE9134700)the K.C.Wong Magna Fund in Ningbo University,China。
文摘In the light of the visual angle model(VAM),an improved car-following model considering driver's visual angle,anticipated time and stabilizing driving behavior is proposed so as to investigate how the driver's behavior factors affect the stability of the traffic flow.Based on the model,linear stability analysis is performed together with bifurcation analysis,whose corresponding stability condition is highly fit to the results of the linear analysis.Furthermore,the time-dependent Ginzburg–Landau(TDGL)equation and the modified Korteweg–de Vries(m Kd V)equation are derived by nonlinear analysis,and we obtain the relationship of the two equations through the comparison.Finally,parameter calibration and numerical simulation are conducted to verify the validity of the theoretical analysis,whose results are highly consistent with the theoretical analysis.
基金Supported by the National Natural Science Foundation of China(No.61304205,61502240)Natural Science Foundation of Jiangsu Province(BK20141002)+1 种基金Innovation and Entrepreneurship Training Project of College Students(No.201710300051,201710300050)Foundation for Excellent Undergraduate Dissertation(Design) of Naning University of Information Science & Technology
文摘Abnormal driving behavior identification( ADBI) has become a research hotspot because of its significance in driver assistance systems. However,current methods still have some limitations in terms of accuracy and reliability under severe traffic scenes. This paper proposes a new ADBI method based on direction and position offsets,where a two-factor identification strategy is proposed to improve the accuracy and reliability of ADBI. Self-adaptive edge detection based on Sobel operator is used to extract edge information of lanes. In order to enhance the efficiency and reliability of lane detection,an improved lane detection algorithm is proposed,where a Hough transform based on local search scope is employed to quickly detect the lane,and a validation scheme based on priori information is proposed to further verify the detected lane. Experimental results under various complex road conditions demonstrate the validity of the proposed ADBI.
文摘Bus safety is a matter of great importance in many developing countries, with driving behaviors among bus drivers identified as a primary factor contributing to accidents. This concern is particularly amplified in mixed traffic flow (MTF) environments with time pressure (TP). However, there is a lack of sufficient research exploring the relationships among these factors. This study consists of two papers that aim to investigate the impact of MTF environments with TP on the driving behaviors of bus drivers. While the first paper focuses on violated driving behaviors, this particular paper delves into mistake-prone driving behaviors (MDB). To collect data on MDB, as well as perceptions of MTF and TP, a questionnaire survey was implemented among bus drivers. Factor analyses were employed to create new measurements for validating MDB in MTF environments. The study utilized partial correlation and linear regression analyses with the Bayesian Model Averaging (BMA) method to explore the relationships between MDB and MTF/TP. The results revealed a modified scale for MDB. Two MTF factors and two TP factors were found to be significantly associated with MDB. A high presence of motorcycles and dangerous interactions among vehicles were not found to be associated with MDB among bus drivers. However, bus drivers who perceived motorcyclists as aggressive, considered road users’ traffic habits as unsafe, and perceived bus routes’ punctuality and organization as very strict were more likely to exhibit MDB. Moreover, the results from the three MDB predictive models demonstrated a positive impact of bus route organization on MDB among bus drivers. The study also examined various relationships between the socio-demographic characteristics of bus drivers and MDB. These findings are of practical significance in developing interventions aimed at reducing MDB among bus drivers operating in MTF environments with TP.
基金supported by the National Key R&D Program of China(2023YFC3081700)the National Natural Science Foundation of China(52372341).
文摘Electric vehicles are widely embraced as a promising solution to reduce energy consumption and emission to achieve the Carbon Peak and Carbon Neutrality vision,especially in developing countries.Specifically,it’s vital important to understand the ecological performance of electric vehicles and its association with driving behaviors under varying road and environmental conditions.However,current researches on ecological driving behavior mostly use structured data to reflect the characteristics of ecological driving behavior,and it is difficult to accurately reveal the recessive relationship between driving behavior and energy consumption.One promising and prevalent method for comprehensively and in-depth characterizing driving behaviors is“graph spectrums”,which allows for an effective and illustrative representation of complex driving behavior characteristics.This study presented an assessment method of ecological driving for electric vehicles based on the graph.Firstly,a multi-source refined data set was constructed through naturalistic driving experiments(NDE).Four typical traffic state(CCCF:congested close car-following;CSSF:constrained slow free-flow;CSCF:constrained slow carfollowing;UFFF:unconstrained fast free-flow)were classified through longitudinal acceleration data,and driving behavior graph was constructed to realize the visual representation of driving behavior.Then,the energy consumption graph was constructed using the energy loss of 100 km(EL)index.After the six drivers with the highest and lowest ecological assessment of driving behavior using the behavior graph and energy consumption graph,proposing the quantitative analysis of fifteen drivers'ecology driving behavior.The results show that:1)The graphical method can describe the individual features of a driver’s ecological driving behavior;2)Rapid acceleration of driving behavior leads to high energy consumption;3)In the comparison among the six ecodrivers and energy-intensive drivers,founding that the energy-intensive drivers accelerate and decelerate significantly more in CCCF traffic state;4)The driving behavior was more complex and unecological in CCCF traffic state;5)Fifteen drivers had lower ecological scores in start-up driving.This study proposes a method for visualizing ecology driving behavior that not only help understand the individual characteristics of ecological driving behaviors,but also offers substantial application value for the subsequent construction of Ecological driving behavior regulation models.
文摘The aim of this paper is to understand the factors that influence unsafe driving practices by examining published studies that utilized the theory of planned behavior(TPB)to predict driving behavior.To this end,42 studies published up to the end of 2021 are reviewed to evaluate the predictive utility of TPB by employing a meta-analysis and structural equation model.The results indicate that these studies sought to predict 20 distinct driving behaviors(e.g.,drink-driving,use of cellphone while driving,aggressive driving)using the original TPB constructs and 43 additional variables.The TPB model with the three original constructs is found to account for 32%intentional variance and 34%behavioral variance.Among the 43 variables researchers have examined in TPB studies related to driving behavior,this study identified the six that are commonly used to enhance the TPB model’s predictive power.These variables are past behavior,self-identity,descriptive norm,anticipated regret,risk perception,and moral norm.When past behavior is added to the original TPB model,it increases the explained variance in intention to 52%.When all six factors are added to the original TPB model,the best model has only four variables(perceived risk,self-identity,descriptive norm,and moral norm);and increases the explained variance to 48%.The influence of the TPB constructs on intention is modified by behavior category and traffic category.The findings of this paper validate the application of TPB to predicting driving behavior.It is the first study to do this through the use of meta-analysis and structural equation modeling.
基金supported by the National Natural Science Foundation of China(No.51978069)Science and Technology Project of Shandong Transportation Department(No.2022-KJ-044)+1 种基金Key Research and Development Plan of Shaanxi Province(No.2021KWZ-09)the Fundamental Research Funds for the Centrl Universities,CHD(No.300102342202).
文摘Expressway tunnels are semi-enclosed structures characterized by monotonous alignment transitions and unique lighting environments,which can easily lead to drivers developing constrained and irritable psychology.This may result in risky behaviors,e.g.,speeding and fatigued driving.Previous research on tunnel driving behaviors mainly focuses on visual factors,neglecting the impacts of nonstationary time-series combined parameters on risky driving.Firstly,30 drivers were recruited to carry out the real test.Then,based on the evolution of time series,drawing inspiration from the concept of lineage in biology,and considering multiple driving performance indicators,driving behavior chains and the feature spectrum were constructed.The characteristics of the behavior spectrum were divided into six groups:electroencephalogram,heart rate,eye movement,speed,steering,and carfollowing behaviors.Subsequently,the spectral analysis using the spectral radius property of matrix theory revealed the distinctive characteristics of risky driving behaviors.The study deeply explored the inducing mechanism,hidden patterns,and rules of risky driving behaviors under the coupling effect of tunnel environment and drivers’attributes.Finally,the significant features that influence driving behaviors were used as the input variables for constructing identification models using the adaptive boosting(AdaBoost)and random forest(RF)algorithms.The synthetic minority over-sampling technique(SMOTE)and adaptive synthetic sampling(ADASYN)were employed for oversampling.The results indicate that the ADASYN-RF algorithm outperformed others,achieving a precise recall rate area under the curve(AUPRC)of 0.978 when using the spectral radius of the speed and steering groups as input variables.These findings offer theoretical guidance for developing tunnel traffic safety strategies.
基金supported by the National Institute for Medical Research Development(NIMAD)under Grant No.4000217。
文摘Purpose:This study aimed to investigate the possible association between psychological disorders and riskydriving behavior(RDB)in Iran.Methods:This case-control study conducted in Shiraz,Iran in 2021.The case group included drivers with psychological disorders and the control group included those without any disorders.The inclusion criteria for selecting patients were:active driving at the time of the study,being 18-65 years old,having a driving license,having a psychological disorder including depression,bipolar disorder,anxiety spectrum disorder,or psychotic disorder spectrum confirmed by a psychiatrist,and completing an informed consent form.The exclusion criterion was the existence of conditions that interfered with answering and understanding the questions.The inclusion criteria for selecting the healthy cases were:active driving at the time of the study,being 18-65 years old,having a driving license,lack of any past or present history of psychiatric problems,and completing an informed consent form.The data were gathered using a researcher-made checklist and Manchester driving behavior questionnaire.First,partition around medoids method was used to extract clusters of RDB.Then,backward logistic regression was applied to investigate the association between the independent variables and the clusters of RDB.Results:The sample comprised of 344(153 with psychological disorder and 191 without confirmed psychological disorder)drivers.Backward elimination logistic regression on total data revealed that share of medical expenditure≤10%of total household expenditure(OR=3.27,95%Cl:1.48-7.24),psychological disorder(OR=3.08,95%Cl:1.67-5.70),and substance abuse class(OR=6.38,95%CI:3.55-11.48)wereassociatedwithhighlevelof RDB.Conclusion:Substance abuse,psychological illnesses,and share of medical costs from total household expenditure were found to be main predictors of RDB.Further investigations are necessary to explain the impact of different psychological illnesses on driving behavior.
基金supported by the National Key Research and Development Program of China(2020YFB1600302)the National Nature Science Foundation of China(Grant No.52072290)the Key Research and Development Program of Hubei Province(2022BAD142)。
文摘Road traffic safety should be evaluated throughout the entire life-cycle of road design,operation,maintenance,and expansion construction.However,traditional methods for evaluating road traffic safety based on traffic accidents and conflict technology are limited by their inability to account for the complex environmental factors involved.To address this issue,a new road safety evaluation method has emerged that is based on driving behavior.Because drivers’behaviors may vary depending on the driving environment and their personal characteristics,evaluating road safety from the perspective of driver behavior has become a popular research topic.This paper analyzes current research trends and mainstream journals in the field of road safety evaluation of driving behavior.Additionally,it reviews the three most commonly used driving behavior data collection methods,and compares the advantages and disadvantages of each.The paper proposes the main application scenarios of road safety evaluation methods based on driving behavior,such as road design,evaluation of the effects of road appurtenances,and intelligent highways.Furthermore,the paper summarizes a driving behavior index system based on vehicle data,driver’s physiological and psychological data,and driver’s subjective questionnaire data.A comprehensive evaluation method based on the fusion of each index system is presented in detail.Finally,the paper points out current research problems and the future development direction of the road safety evaluation method based on driving behavior.
基金supported by the National Natural Science Foundation of China(Grant No.52002063)the Humanities and Social Sciences Foundation of the Ministry of Education(Grant No.21YJCZH129)。
文摘To better understand the research focus and development direction in the field of driving behavior active intervention,thereby laying a scientific foundation for further research,we used the combination of topic words and keywords to retrieve relevant articles from the Core Collection Database of Web of Science(WOS).A total of 578 articles published from1992 to 2022 were finally obtained.Firstly,the time distribution characteristics,country distribution,institution distribution and main source journal distribution of published articles were explored.Then,by using the Cite Space and VOSviewer software,cited reference co-citation analysis,keyword co-occurrence analysis and burst detection analysis were carried out respectively to visually explore the knowledge base,research topic,research frontier and development trend of this field.The results indicate that the USA,Australia and China are the three most active countries in the studies of driving behavior active intervention.Accidental Analysis&Prevention,Transportation Research Part F:Traffic Psychology and Behavior,and Journal of Safety Research are widely selected journals for publications related to this field.The research frontiers in the field of driving behavior active intervention focus on:“traffic safety and crashes analysis,as well as enforcement intervention”,“driving risk and education for young drivers”,“information provision and driving behavior”,“workload and situation awareness for automated driving”.It is worth noting that in recent years,“warning system”,“time”,“work load”have become research hotspots in this field.To sum up,by a bibliometric overview of research on driving behavior active intervention over the past thirty years,this paper clarifies the development skeleton of this research field,determines its hot topics and research progress,and provides a reference for the follow-up exploratory scientific research in this field.
文摘With the high older-related accident ratio and increasing population aging problem, understanding older drivers＇ driving behaviors has become more and more important for building and improving transportation system. This paper examines older driver＇s driving behavior which includes road selection, left/right turn and driving speed. A two-month experiment of 108 participants was carried out in Aichi Prefecture, Japan. Since apparently contradictory statements were often drawn in survey-based or simulators-based studies, this study collected not only drivers＇ basic information but also GPS data. Analysis of road selection demonstrates that older drivers are reluctant to drive on expressway not only in short trips but also in long trips. The present study did not find significant difference be- tween older drivers and others while turning at the intersections. To investigate the impact factors on driving speed, a random-effects regression model is constructed with explan- atory variables including age, gender, road types and the interaction terms between age and road types. Compared with other variables, it fails to find that age （60 years old or over） has significant impact on driving speed. Moreover, the results reflect that older drivers drive even faster than others at particular road types： national road and ordinary municipal road. The results in this study are expected to help improve transportation planning and develop driving assistance systems for older drivers.
文摘Time headway is an important index used in characterizing dangerous driving behaviors. This research focuses on the decreasing tendency of time headway and investigates its association with crash occurrence. An autoregressive（AR） time-series model is improved and adopted to describe the dynamic variations of average daily time headway. Based on the model, a simple approach for dangerous driving behavior recognition is proposed with the aim of significantly decreasing headway. The effectivity of the proposed approach is validated by means of empirical data collected from a medium-sized city in northern China. Finally, a practical early-warning strategy focused on both the remaining life and low headway is proposed to remind drivers to pay attention to their driving behaviors and the possible occurrence of crash-related risks.
基金support provided by the University of Bahrain and King Fahd University of Petroleum and Minerals。
文摘This study was aimed at determining the driving distractions which are perceived most hazardous and determining the effects of these distractions and age on speed and headway. A questionnaire survey was done to find out the opinion of drivers related to the most hazardous distraction. 639 responses were collected in the survey which were used to determine the top-rated distractions for drivers in Bahrain. Roadside observations were taken to observe the speed, headway, age and type of distraction for the driver. Speed was observed for 48 drivers while headway was observed for 36 drivers along with other parameters. The most hazardous distractions, according to the participants of the questionnaire, are using mobile phones, handling children, and accidents or incidents on the road. Further, the results of the two-way analysis of variance(ANOVA) test and regression analysis demonstrated that using mobile phones and age have a significant effect on both speed and headway. Speed tends to decrease with distraction for all age groups while decreasing the headway for young and middle-aged drivers. The effect of distraction is higher than the effect of age on speed, as well as headway. Texting has the most significant effect among distractions on headway. It is hereby recommended that policymakers should focus on increasing awareness and stringent law enforcement related to handling mobile phones and children, especially for young and middle-aged drivers.
基金partially supported by the National Natural Science Foundation of China(Grant Nos.71621001,71671014 and 71631007)the National Key R&D Program of China(Grant No.2018YFB1601200)+1 种基金the Central Public-interest Scientific Institution Basal Research Fund(Grant No.20196104)the Strategic planning policy of the Ministry of transport(Grant No.2019-17-4).
文摘Driving behavior is heterogeneous for various drivers due to the different influencing factors as reaction time,gender,driving years and so on.Some existing works tried to reproduce some of the complex characteristics of real traffic flow by taking into account the heterogeneous driving behavior,and the drivers are generally divided into two classes(including aggressive drivers and careful drivers)or three classes(including aggressive drivers,normal drivers and careful drivers).Nevertheless,the classification approaches have not been verified,and the rationality of the classifications has not been confirmed as well.In this study,the trajectory data of drivers is extracted from the NGSIM datasets.By combining the K-Means method and Silhouette measure index,the drivers are classified into four clusters(named as clusters A,B,C and D,respectively)in accordance with the acceleration and time headway.The two-dimensional approach is applied to analyze the characteristics of different clusters.Here,one dimension consists of“Cautious”and“Aggressive”behaviors in terms of velocity and acceleration,and the other dimension consists of“Sensitive”and“Insensitive”behaviors in terms of reaction time.Finally,the fuel consumption and emissions for different clusters are calculated by using the VT-Micro model.A surprising result indicates that overly“cautious”and“sensitive”behaviors may result in more fuel consumption and emissions.Therefore,it is necessary to find the balance between the driving characteristics.
基金supported by the Ford Motor Company(No.URP 2018-J077.4)。
文摘Driving behavior normalization is important for a fair evaluation of the driving style.The longitudinal control of a vehicle is investigated in this study.The normalization task can be considered as mapping of the driving behavior in a different environment to the uniform condition.Unlike the model-based approach as in previous work,where a necessary driver model is employed to conduct the driving cycle test,the approach we propose directly normalizes the driving behavior using an autoencoder(AE)when following a standard speed profile.To ensure a positive correlation between the vehicle speed and driving behavior,a gate constraint is imposed in between the encoder and decoder to form a gated AE(gAE).This approach is model-free and efficient.The proposed approach is tested for consistency with the model-based approach and for its applications to quantitative evaluation of the driving behavior and fuel consumption analysis.Simulations are conducted to verify the effectiveness of the proposed scheme.
基金Project(2017YFB0102503)supported by the National Key Research and Development Program of ChinaProjects(U1664258,51875255,61601203)supported by the National Natural Science Foundation of China+1 种基金Projects(DZXX-048,2018-TD-GDZB-022)supported by the Jiangsu Province’s Six Talent Peak,ChinaProject(18KJA580002)supported by Major Natural Science Research Project of Higher Learning in Jiangsu Province,China
文摘In order to make full use of the driver’s long-term driving experience in the process of perception, interaction and vehicle control of road traffic information, a driving behavior rule extraction algorithm based on artificial neural network interface(ANNI) and its integration is proposed. Firstly, based on the cognitive learning theory, the cognitive driving behavior model is established, and then the cognitive driving behavior is described and analyzed. Next, based on ANNI, the model and the rule extraction algorithm(ANNI-REA) are designed to explain not only the driving behavior but also the non-sequence. Rules have high fidelity and safety during driving without discretizing continuous input variables. The experimental results on the UCI standard data set and on the self-built driving behavior data set, show that the method is about 0.4% more accurate and about 10% less complex than the common C4.5-REA, Neuro-Rule and REFNE. Further, simulation experiments verify the correctness of the extracted driving rules and the effectiveness of the extraction based on cognitive driving behavior rules. In general, the several driving rules extracted fully reflect the execution mechanism of sequential activity of driving comprehensive cognition, which is of great significance for the traffic of mixed traffic flow under the network of vehicles and future research on unmanned driving.
