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01 Intelligent Vehicles Localization Based on Semantic Map Representation from 3D Point Clouds
02 Indoor Sign-based Visual Localization Method
03 A Cooperative Map Matching Algorithm Applied in Intelligent and Connected Vehicle Positioning
04 Companion Relationship Discovering Algorithm for Passengers in the Cruise Based on UWB Positioning
05 An Overview of Traffic Management in "Automatic+Manual" Driving Environment
06 An Image Generation Method for Automated Driving Based on Improved GAN
08 An Analysis of Injury Severities in School Bus Accidents Based on Random Parameter Logit Models
10 An Analysis of Highway-traffic Safety Based on Dynamic Risk Saturation
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2022, 40(5): 1-11.
doi: 10.3963/j.jssn.1674-4861.2022.05.001
Abstract:
Developing a dynamic model with a high accuracy and reliability is essential for analyzing ship maneuverability and ensuring shipping safety. Compared with the existing three popular methods, including empirical, experimental and computational fluid dynamics (CFD), numerical parameter identification methods are practical, effective, and powerful solutions for dynamic modeling. However, it faces major challenges due to the influences from the following factors, such as strong nonlinear motion characteristics of ships, varying environmental interfer-ences, and the multiple internal and external uncertainties. This paper reviews the state-of-the-art of parameter identification of ship dynamic models from the following four critical perspectives, including the optimal input design related to the informative characteristics acquisition, the mathematical model of ship motion, parameter estimation algorithms, and the verification and validation of the identified models. Several critical problems are discussed including noise interference, parameter drift, parameter variation, and selection of evaluation indexes. Based on a comprehensive survey, two challenging issues are pointed out. Currently, there is no method available that can provide high-quality data covering motion characteristics over a wide area, and due to the complexity of the models, the parameter drift fluctuates and cannot be completely avoided. Potential research questions closely related to the parameter identification of ship dynamic models are discussed. For instance, ship dynamic data acquired and processed using robust estimation techniques or information fusion techniques is worthy to be addressed to provide high-quality data; robust online parameter identification based on the multi-innovation intelligent approach can be a valuable solution to real-time identification of ship dynamic models; and complex conditions, such as ships sailing in restricted waters, should to be examined.
Developing a dynamic model with a high accuracy and reliability is essential for analyzing ship maneuverability and ensuring shipping safety. Compared with the existing three popular methods, including empirical, experimental and computational fluid dynamics (CFD), numerical parameter identification methods are practical, effective, and powerful solutions for dynamic modeling. However, it faces major challenges due to the influences from the following factors, such as strong nonlinear motion characteristics of ships, varying environmental interfer-ences, and the multiple internal and external uncertainties. This paper reviews the state-of-the-art of parameter identification of ship dynamic models from the following four critical perspectives, including the optimal input design related to the informative characteristics acquisition, the mathematical model of ship motion, parameter estimation algorithms, and the verification and validation of the identified models. Several critical problems are discussed including noise interference, parameter drift, parameter variation, and selection of evaluation indexes. Based on a comprehensive survey, two challenging issues are pointed out. Currently, there is no method available that can provide high-quality data covering motion characteristics over a wide area, and due to the complexity of the models, the parameter drift fluctuates and cannot be completely avoided. Potential research questions closely related to the parameter identification of ship dynamic models are discussed. For instance, ship dynamic data acquired and processed using robust estimation techniques or information fusion techniques is worthy to be addressed to provide high-quality data; robust online parameter identification based on the multi-innovation intelligent approach can be a valuable solution to real-time identification of ship dynamic models; and complex conditions, such as ships sailing in restricted waters, should to be examined.
2022, 40(5): 12-22.
doi: 10.3963/j.jssn.1674-4861.2022.05.002
Abstract:
Texture morphology of asphalt pavement is an important indicator of the anti-skid performance of asphalt pavement. Meanwhile, pavement texture has its fractal characteristics. Therefore, it is important to study the correlation between pavement texture and anti-skid performance by quantifying its texture with fractal properties. Single and multi-fractal properties of pavement texture and aggregate texture are studied and summarized at both the macroscopic and microscopic levels. The applications of fractal theory in the design of anti-skid gradations and selection of anti-skid aggregate for pavements are analyzed. A variety of design of anti-skid gradation models based on the fractal theory are summarized, and a new method of anti-skid aggregate selection for pavements based on the fractal theory is proposed. Finally, the utility of the fractal theory in combination with parametric statistical methods, mechanical analysis, and finite element simulation in the field of prediction of pavement anti-skid performance is compared and analyzed. Study results show that fractal feature analysis provides a new way for the description of pavement texture. However, the fractal analysis methods with a high precision are still the bottlenecks in this field. There are still limitations within the fractal models, in terms of their accuracy, standardization and systematization of their process. The application of fractal theory in combination with gradation design and aggregate selection is still in its preliminary stage. Therefore, it is recommended that design methods in engineering should be validated based on their usefulness in the practice. The performance of various types of prediction models for forecasting anti-skid performance based on texture fractal properties are compared. Study results indicate that the prediction mod-els based on finite element simulation can more accurately restore the tire-road contact state under complex conditions in practice, indicating that such prediction models have a good potential. Future research areas on anti-skid performance of asphalt pavement based on the fractal properties of texture are discussed and proposed, including the correlation between multi-scale texture fractal characteristics and anti-skid performance, criteria for specifying fractal parameters in the practice, and the development of intelligent anti-skid prediction system based on the fractal theory.
Texture morphology of asphalt pavement is an important indicator of the anti-skid performance of asphalt pavement. Meanwhile, pavement texture has its fractal characteristics. Therefore, it is important to study the correlation between pavement texture and anti-skid performance by quantifying its texture with fractal properties. Single and multi-fractal properties of pavement texture and aggregate texture are studied and summarized at both the macroscopic and microscopic levels. The applications of fractal theory in the design of anti-skid gradations and selection of anti-skid aggregate for pavements are analyzed. A variety of design of anti-skid gradation models based on the fractal theory are summarized, and a new method of anti-skid aggregate selection for pavements based on the fractal theory is proposed. Finally, the utility of the fractal theory in combination with parametric statistical methods, mechanical analysis, and finite element simulation in the field of prediction of pavement anti-skid performance is compared and analyzed. Study results show that fractal feature analysis provides a new way for the description of pavement texture. However, the fractal analysis methods with a high precision are still the bottlenecks in this field. There are still limitations within the fractal models, in terms of their accuracy, standardization and systematization of their process. The application of fractal theory in combination with gradation design and aggregate selection is still in its preliminary stage. Therefore, it is recommended that design methods in engineering should be validated based on their usefulness in the practice. The performance of various types of prediction models for forecasting anti-skid performance based on texture fractal properties are compared. Study results indicate that the prediction mod-els based on finite element simulation can more accurately restore the tire-road contact state under complex conditions in practice, indicating that such prediction models have a good potential. Future research areas on anti-skid performance of asphalt pavement based on the fractal properties of texture are discussed and proposed, including the correlation between multi-scale texture fractal characteristics and anti-skid performance, criteria for specifying fractal parameters in the practice, and the development of intelligent anti-skid prediction system based on the fractal theory.
2022, 40(5): 23-33.
doi: 10.3963/j.jssn.1674-4861.2022.05.003
Abstract:
It is nearly impossible for intelligent automobiles to achieve a fast upgradation from L1 to L5 in a short time due to the limitations of technologies, regulations, and other factors. Thus, human-machine shared driving will be the case for a long run. The human-machine shared control (HMSC) technology based on haptic guidance provides an effective way for intelligent automobiles operating at the L2 level. Through reviewing the literature regarding the current progress of HMSC technology, this study focuses on studying conflicts created over the human-machine collaboration in the process of route planning, intention commitment, and control assignment related to the maneuvers like lane keeping, lane changing, collision avoidance and backing-up, which may result in reduced vehicle stability, poor driving safety, and deteriorated operating comfort and freedom. Meanwhile, the driving styles and cognition differences of drivers are studied to identify the design methods of human-machine shared controller and the mechanism of human-machine conflict. Therefore, it is proposed that the intelligent driving system should be iteratively optimized base on massive simulated or measured driving data in the future and the accuracy of the intelligent driving system in recognizing driving environment and driver's status should be improved. In this way, the control weights of human and machine can be assigned and the problems of control conflict, vehicle stability, driving safety, deteriorated operating comfort and degree of freedom can be solved. Based on the existing issues identified within the research to date, it is pointed out that shared controllers based on the adaptive haptic-guidance, assignment of control weight, neuromuscular response, and advanced assistance systems are major research directions of the HMSC.
It is nearly impossible for intelligent automobiles to achieve a fast upgradation from L1 to L5 in a short time due to the limitations of technologies, regulations, and other factors. Thus, human-machine shared driving will be the case for a long run. The human-machine shared control (HMSC) technology based on haptic guidance provides an effective way for intelligent automobiles operating at the L2 level. Through reviewing the literature regarding the current progress of HMSC technology, this study focuses on studying conflicts created over the human-machine collaboration in the process of route planning, intention commitment, and control assignment related to the maneuvers like lane keeping, lane changing, collision avoidance and backing-up, which may result in reduced vehicle stability, poor driving safety, and deteriorated operating comfort and freedom. Meanwhile, the driving styles and cognition differences of drivers are studied to identify the design methods of human-machine shared controller and the mechanism of human-machine conflict. Therefore, it is proposed that the intelligent driving system should be iteratively optimized base on massive simulated or measured driving data in the future and the accuracy of the intelligent driving system in recognizing driving environment and driver's status should be improved. In this way, the control weights of human and machine can be assigned and the problems of control conflict, vehicle stability, driving safety, deteriorated operating comfort and degree of freedom can be solved. Based on the existing issues identified within the research to date, it is pointed out that shared controllers based on the adaptive haptic-guidance, assignment of control weight, neuromuscular response, and advanced assistance systems are major research directions of the HMSC.
2022, 40(5): 34-43.
doi: 10.3963/j.jssn.1674-4861.2022.05.004
Abstract:
Simulation of environment, risk identification, collision avoidance and decision-making support for ship maneuvers are studied in order to counteract the high risk associated with shipping at Chengshanjiao waters under traffic separation scheme(TSS). The shipping environment of the Chengshanjiao waters is analyzed and digitalized to develop a static nautical environment model, and the dynamic traffic is investigated to set up a digitalized shipping simulation system. A method for risk identification is proposed based on the positions of ships and a collision risk model considering both space and time dimension. Good Seamanship and International Regulations for Preventing Collisions at Sea(COLREGS)are used to summarize the principles of ship maneuvers in different scenarios, and a ship maneuver meeting the principle of"minimum course altering"is produced based on the collision avoidance mechanism. A decision-making model adaptive to the random motions of target ships is developed using a rolling-window method and a feedback compensation method. Shipping traffic at the Chenshanjiao waters is simulated and multiple-ship scenarios are introduced. Study results show that: ①The proposed risk identification method using dead reckoning can identify the risk 1 168 s earlier than the regular methods at the Chengshanjiao waters when the own ship locating at(41 200 m, 38 000 m)heading to 000°with a speed of 12 n mile/h encounters with the target ship at(44 600 m, 62 300 m)heading to 210° with a speed of 12 n mile/h. ②Under a simulated multiple-ship scenario, the own ship can avoid collisions with target ships by altering to the starboard 17° at 245 s, resuming to sail at 617 s, altering to the starboard 11° at 2005 s, and resuming to sail at 2 405 s, which meets the principle of Good Seamanship. In conclusion, the proposed method can identify the collision risk earlier and support decision-making on ship maneuvers better than other methods at the Chenshanjiao waters, which provides a theoretical foundation for developing intelligent navigation systems on waters under TSS.
Simulation of environment, risk identification, collision avoidance and decision-making support for ship maneuvers are studied in order to counteract the high risk associated with shipping at Chengshanjiao waters under traffic separation scheme(TSS). The shipping environment of the Chengshanjiao waters is analyzed and digitalized to develop a static nautical environment model, and the dynamic traffic is investigated to set up a digitalized shipping simulation system. A method for risk identification is proposed based on the positions of ships and a collision risk model considering both space and time dimension. Good Seamanship and International Regulations for Preventing Collisions at Sea(COLREGS)are used to summarize the principles of ship maneuvers in different scenarios, and a ship maneuver meeting the principle of"minimum course altering"is produced based on the collision avoidance mechanism. A decision-making model adaptive to the random motions of target ships is developed using a rolling-window method and a feedback compensation method. Shipping traffic at the Chenshanjiao waters is simulated and multiple-ship scenarios are introduced. Study results show that: ①The proposed risk identification method using dead reckoning can identify the risk 1 168 s earlier than the regular methods at the Chengshanjiao waters when the own ship locating at(41 200 m, 38 000 m)heading to 000°with a speed of 12 n mile/h encounters with the target ship at(44 600 m, 62 300 m)heading to 210° with a speed of 12 n mile/h. ②Under a simulated multiple-ship scenario, the own ship can avoid collisions with target ships by altering to the starboard 17° at 245 s, resuming to sail at 617 s, altering to the starboard 11° at 2005 s, and resuming to sail at 2 405 s, which meets the principle of Good Seamanship. In conclusion, the proposed method can identify the collision risk earlier and support decision-making on ship maneuvers better than other methods at the Chenshanjiao waters, which provides a theoretical foundation for developing intelligent navigation systems on waters under TSS.
2022, 40(5): 44-52.
doi: 10.3963/j.jssn.1674-4861.2022.05.005
Abstract:
The emerging of mixed traffic involving both connected autonom ous vehicles(CAVs)and human-driven vehicles(HDVs)may change the capacity of urban road networks. A bi-level programming model is proposed to analyze the impacts of mixed traffic flow on the reliability of the capacity of urban road network in an intelligent network environment. Assuming that CAVs follow the path selected based on the system optimization principle and the drivers of the HDVs select their paths according to their own experience, a lower model is developed for the assignment of traffic flow based on the differences in the path selection between the two types of vehicles. Furthermore, the modeling of the assignment of mixed traffic at the lower level is transformed into a nonlinear complementarity problem to reduce runtime. Considering the randomness of road capacity in a network, an upper model is set up for modeling the reliability of capacity by using a uniform random distribution with multiple correlations. A Monte Carlo simulation is used to analyze the reliability of road network capacity under different market penetration rate(MPR)of CAVs. A sensitivity analysis is then carried out for studying the reliability of road capacity under such a scenario. Numerical results show that, when the level of the demand d > 0.5, the reliability of road network capacity decreases. When level of the demand d > 0.7 and the market penetration rate of CAVs λ=0, the reliability is less than 0.4. However, when d > 0.7 and λ=1, the reliability is found close to 1, indicating that CAVs can enhance the reliability of road network capacity. It is also found that when the level of the demand is between 0.7 and 1, the MPRof CAVs significantly affects the reliability of road network capacity. When the road network is overloaded, the MPR has a very minor impact on the reliability of road network capacity with the increase of traffic demand. In addition, when λ increases from 0 to 1, the number of roads showing"capacity paradox"in the road network decreases from 19 to 3. When λ=1, only one road in the entire network show the issue. Study results show that the increase of MPR can not only reduce the possibility of"road capacity paradox", but also improve the stability of the road network.
The emerging of mixed traffic involving both connected autonom ous vehicles(CAVs)and human-driven vehicles(HDVs)may change the capacity of urban road networks. A bi-level programming model is proposed to analyze the impacts of mixed traffic flow on the reliability of the capacity of urban road network in an intelligent network environment. Assuming that CAVs follow the path selected based on the system optimization principle and the drivers of the HDVs select their paths according to their own experience, a lower model is developed for the assignment of traffic flow based on the differences in the path selection between the two types of vehicles. Furthermore, the modeling of the assignment of mixed traffic at the lower level is transformed into a nonlinear complementarity problem to reduce runtime. Considering the randomness of road capacity in a network, an upper model is set up for modeling the reliability of capacity by using a uniform random distribution with multiple correlations. A Monte Carlo simulation is used to analyze the reliability of road network capacity under different market penetration rate(MPR)of CAVs. A sensitivity analysis is then carried out for studying the reliability of road capacity under such a scenario. Numerical results show that, when the level of the demand d > 0.5, the reliability of road network capacity decreases. When level of the demand d > 0.7 and the market penetration rate of CAVs λ=0, the reliability is less than 0.4. However, when d > 0.7 and λ=1, the reliability is found close to 1, indicating that CAVs can enhance the reliability of road network capacity. It is also found that when the level of the demand is between 0.7 and 1, the MPRof CAVs significantly affects the reliability of road network capacity. When the road network is overloaded, the MPR has a very minor impact on the reliability of road network capacity with the increase of traffic demand. In addition, when λ increases from 0 to 1, the number of roads showing"capacity paradox"in the road network decreases from 19 to 3. When λ=1, only one road in the entire network show the issue. Study results show that the increase of MPR can not only reduce the possibility of"road capacity paradox", but also improve the stability of the road network.
2022, 40(5): 53-60.
doi: 10.3963/j.jssn.1674-4861.2022.05.006
Abstract:
Hazard perception is a critical factor of a driving behavior model. A simulator-based method and an extreme gradient boosting tree(XGBoost)algorithm are proposed, in order to study the impacts of hazard perception on driving behaviors and improve the accuracy of hazard perception. Three typical scenarios of traffic conflicts are simulated, and a large amount of driving behavior data are collected. The correlation between hazard perception and driving behavior models is discussed under the three scenarios. The correlation analysis reveals that when hazard perception(e.g., dangerous behaviors of pedestrians)is weak, and the vehicle speed(p=0.01), braking reaction position(p < 0.01), and reaction time(p < 0.01)are significantly negatively correlated with the drivers'hazard perception. Based on the correlation analysis, the XGBoost algorithm is used to identify important features determining the capability of hazard perception of drivers. Then, a discriminant model of hazard perception is proposed with following the indicators, such as braking reaction position, reaction time, vehicle speed, braking depth, and acceleration. Compared the proposed method with Light Gradient Boosting Machine(LightGBM), Support Vector Machine(SVM), and Logistic Regression(LR)algorithms, it is found that the accuracy of the XGBoost-based method is 84.8%, its F1-score is 83.4%, and the area under the receiver operating characteristic Curve(AUC)is 0.959, which is better than the LightGBM(accuracy is 78.8%, F1-score is 76.7%, and AUC is 0.924), SVM(accuracy is 57.6%, F1-score is 42.2%, and AUC is 0.859)and LR algorithm(accuracy is 69.7%, F1-score is 65.5%, and AUC is 0.836). In conclusion, the proposed method can provide a more reliable way for understanding the capability of hazard perception of drivers and its impacts on driving behavior models.
Hazard perception is a critical factor of a driving behavior model. A simulator-based method and an extreme gradient boosting tree(XGBoost)algorithm are proposed, in order to study the impacts of hazard perception on driving behaviors and improve the accuracy of hazard perception. Three typical scenarios of traffic conflicts are simulated, and a large amount of driving behavior data are collected. The correlation between hazard perception and driving behavior models is discussed under the three scenarios. The correlation analysis reveals that when hazard perception(e.g., dangerous behaviors of pedestrians)is weak, and the vehicle speed(p=0.01), braking reaction position(p < 0.01), and reaction time(p < 0.01)are significantly negatively correlated with the drivers'hazard perception. Based on the correlation analysis, the XGBoost algorithm is used to identify important features determining the capability of hazard perception of drivers. Then, a discriminant model of hazard perception is proposed with following the indicators, such as braking reaction position, reaction time, vehicle speed, braking depth, and acceleration. Compared the proposed method with Light Gradient Boosting Machine(LightGBM), Support Vector Machine(SVM), and Logistic Regression(LR)algorithms, it is found that the accuracy of the XGBoost-based method is 84.8%, its F1-score is 83.4%, and the area under the receiver operating characteristic Curve(AUC)is 0.959, which is better than the LightGBM(accuracy is 78.8%, F1-score is 76.7%, and AUC is 0.924), SVM(accuracy is 57.6%, F1-score is 42.2%, and AUC is 0.859)and LR algorithm(accuracy is 69.7%, F1-score is 65.5%, and AUC is 0.836). In conclusion, the proposed method can provide a more reliable way for understanding the capability of hazard perception of drivers and its impacts on driving behavior models.
2022, 40(5): 61-69.
doi: 10.3963/j.jssn.1674-4861.2022.05.007
Abstract:
In order to study the impacts of traffic incidents on freeway operation, a method for predicting the duration of freeway traffic incidents is studied. Time-dependent characteristics of traffic incidents on freeways are extracted from time series data based on the recurrent neural network (RNN) theory. The feature and the temporal attention layer of a long short-term memory (LSTM) network are combined to study the correlation between historical and current moment data. Based on attention (ATT) mechanism and the LSTM, a model for predicting the duration of traffic incidentson freeways is developed. Validation of the model is carried out based on traffic monitoring dataset collected in 2018 along the Xi'an Ring Freeway. The prediction accuracy of the proposed model is compared with the following models: back propagation neural network (BP), random forest (RF), support vector machine (SVM), and long short-term memory (LSTM). The impacts of different factors, including the types of events, weather conditions, types of vehicles, and traffic volume, on the duration is also analyzed. Study results indicate that under the condition of the same dataset, the mean absolute error (MAE), the mean absolute percentage error (MAPE), and the root mean square error (RMSE) of the ATT-LSTM model is 24.43, 25.24%, and 21.17, respectively, which is better than that of other models. The "type of events" has the maximum weight of 0.375 among all of factors considered within the model, followed by the "number of lanes" "vehicle type" and "weather". By using the hourly traffic volume at the entrances and exits of interchanges as the correction parameter, the prediction accuracy is improved, and the MAE, MAPE, and RMSE of the model is decreased by 21.3%, 7.5%, and 16.9%, respectively. This study improves the prediction accuracy of the duration of traffic incidents on freeways and provides technical support for their safe and efficient operation.
In order to study the impacts of traffic incidents on freeway operation, a method for predicting the duration of freeway traffic incidents is studied. Time-dependent characteristics of traffic incidents on freeways are extracted from time series data based on the recurrent neural network (RNN) theory. The feature and the temporal attention layer of a long short-term memory (LSTM) network are combined to study the correlation between historical and current moment data. Based on attention (ATT) mechanism and the LSTM, a model for predicting the duration of traffic incidentson freeways is developed. Validation of the model is carried out based on traffic monitoring dataset collected in 2018 along the Xi'an Ring Freeway. The prediction accuracy of the proposed model is compared with the following models: back propagation neural network (BP), random forest (RF), support vector machine (SVM), and long short-term memory (LSTM). The impacts of different factors, including the types of events, weather conditions, types of vehicles, and traffic volume, on the duration is also analyzed. Study results indicate that under the condition of the same dataset, the mean absolute error (MAE), the mean absolute percentage error (MAPE), and the root mean square error (RMSE) of the ATT-LSTM model is 24.43, 25.24%, and 21.17, respectively, which is better than that of other models. The "type of events" has the maximum weight of 0.375 among all of factors considered within the model, followed by the "number of lanes" "vehicle type" and "weather". By using the hourly traffic volume at the entrances and exits of interchanges as the correction parameter, the prediction accuracy is improved, and the MAE, MAPE, and RMSE of the model is decreased by 21.3%, 7.5%, and 16.9%, respectively. This study improves the prediction accuracy of the duration of traffic incidents on freeways and provides technical support for their safe and efficient operation.
Journal of Transport Information and Safety
(Founded in 1983 bimonthly )
Former Name：Computer and Communications
Supervised by：Ministry of Education of P. R. CHINA
Sponsored by：Wuhan University of Technology
Network of Computer Application Information in Transportation
In Association With：Intelligent Transportation Committee of China Association of Artificial Intelligence
Editor-in-Chief：ZHONG Ming
Edited and Published by：Editorial Office of Transport Information and Safety
Address：No. 1178，Heping Avenue, Wuchang, Wuhan, CHINA
Postcode：430063
Tel：027-86580355
E-mail：jtjsj@vip.163.com
Website：http://www.jtxa.net/
Postal Code：38-94
Domestic Issue：
CN 42-1781/U
Publication No.：ISSN 1674-4861
Indexed In
- Chinese Core Journal in “Integrated Transportation” category
- Chinese Science Citation Database (CSCD)
- Core Science and Technology Journals
- Chinese Scientific and Technological Papers and Citations (CSTPCD)
- Class A of Research Center for Chinese Science Evaluation (RCCSE)
- Chinese Academic Journal Comprehensive Evaluation Database (CAJ-CED)
- Chinese Core Journals (Selection) Database
- Chinese Scientific and Technological Periodicals Database
- China National Knowledge Infrastructure (CNKI)
- Chinese Academic Journals (CAJ-CD)
- Chinese Lifelong Education Academic Research Database
- Japan Science and Technology Agency (JST)
- World Journal Clout Index Report (2020 STM)