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Intelligent Vehicles Localization Based on Semantic Map Representation from 3D Point Clouds
ZHU Yuntao, LI Fei, HU Zhaozheng, WU Huawei
Abstract(4638) PDF(5706)
Abstract:
In order to improve the accuracy of node localization for intelligent vehicles,an intelligent vehicles localization method based on three-dimensional point clouds semantic map representation is proposed. The method is divided into three parts. Semantic segmentation based on 3D laser point clouds includes ground segmentation,traffic signs segmentation and pole-shaped target segmentation. Semantic map representation for intelligent vehicles uses segmented targets to project. Finally directional projections with weight,semantic roads and semantic codeing are generated. The codeing and global location from high-precision GPS make up representation model. Localization based on semantic representation model includes coarse localization from GPS matching and node localization from semantic coding matching. The experiments are carried out in three road scenes with different length and complexity,and the localization accuracy is 98.5%,97.6% and 97.8%,respectively. The results show that proposed method has high accuracy and strong robustness, which is suitable for different road scenes.
Companion Relationship Discovering Algorithm for Passengers in the Cruise Based on UWB Positioning
YAN Sixun, WU Bing, SHANG Lei, LYU Jieyin, WANG Yang
Abstract(4269) PDF(2350)
Abstract:
To accurately discover the companion relationship among passengers in the interior space of a cruise, UWB positioning is employed in the cruise to carry out on-board personnel location experiment. An improved Haussdorff-DBSCAN based scheme combined with indoor positional semantics is proposed to realize the trajectory clustering of the passenger trajectories, considering the characteristics of the UWB location data. Afterwards, the LSTM neural network is applied to predict the changing similarity of the suspected companions. Traditional Hausdorff algorithm does not consider the consistency of trajectory timing while calculating the trajectory similarity, and the introduction of positional semantic sequence can solve this problem well. In the first phase, the passenger trajectory data set is input to the improved Hausdorff-DBSCAN algorithm, and the clustering radius is determined according to the overall similarity threshold of trajectories. The outputs are the emerging clusters of passenger trajectories in the same companion group. In the second phase, the LSTM neural network takes the point similarity sequence with a fixed time window as the input to predict the point similarity value at the next time. The sequential change of passengers companion relationship is analyzed by the similarity threshold and prediction results. The validity of the presented algorithm is demonstrated by the trajectory data obtained from the passengers simulation on one deck of the cruise under study, which is modeled in Anylogic. The results indicate that the precision of the proposed algorithm reaches 0.92, the recall value reaches 0.95 and the F1 value is 0.934, which are at least 5.7 percent, 8.0 percent and 6.7 percent higher than the comparing algorithm. The LSTM neural network also shows a promising effect in predicting the changing trend of the similarity, for the loss is at a stable level of 3 to 4 percent.
Data Association Method Based on Descriptor Assisted Optical flow Tracking Matching
XIA Huajia, ZHANG Hongping, CHEN Dezhong, LI Tuan
Abstract(2098) PDF(744)
Abstract:
in the view of the problem that the positioning accuracy of visual inertial odometer using multi-state constrained Kalman filter(MSCKF) is easily affected by the abnormal value of feature point matching, a data association method based on descriptor assisted optical flow tracking matching is proposed. This method uses pyramid LK optical flow to track and match the feature points in the sequence image, then calculates the rbrief descriptor of each pair of matching points, judges the similarity of the descriptor according to the Hamming distance,and eliminates the abnormal matching points. In the experiment, the effectiveness of the proposed method is evaluated from two aspects:the subjective effect of feature point matching and positioning accuracy. The results show that the proposed method can effectively filter the abnormal values of image feature matching in dynamic scene. The image processed by this method is used for msckf motion solution,and the drift rate of position result is less than 0.38%, compared with the result of msckf algorithm without eliminating abnormal matching values,The improvement is 54.7%, and the single frame image processing time is about 39 ms.
Indoor Sign-based Visual Localization Method
HUANG Gang, CAI Hao, DENG Chao, HE Zhi, XU Ningbo
Abstract(4539) PDF(975)
Abstract:
To solve the problem of localization calculation of intelligent vehicles and the mobile robot in the indoor traffic environment, by exploiting kinds of signs which existed in the indoor environment, a visual localization method is proposed through using BEBLID (Boosted Efficient Binary Local Image Descriptor) algorithm. The proposed method enforces the ability to characterize the whole image by improving the classic BEBLID. In this paper, the localization method consists of an offline stage and an online stage. In the offline stage, a scene sign map is created. In the online stage, the calculation progress is divided into 3 parts, which include holistic and local BEBLID method from current image and image in the scene sign map, closet sign site and closet image calculation by using KNN method, metric calculation by using coordinate information which is stored in the scene sign map. The experiment is conducted in three kinds of indoor scenes, including a teaching building, an office building, and an indoor parking lot. The experiment shows the scene sign recognition rate reached 90%, and the average localization error is less than 1 meter. Compared with the traditional method, the proposed method improves about 10% relative recognition rate with the same test set, which verified the effectiveness of the proposed method.
A Cooperative Map Matching Algorithm Applied in Intelligent and Connected Vehicle Positioning
CHEN Wei, DU Luyao, KONG Haiyang, FU Shuaizhi, ZHENG Hongjiang
Abstract(4364) PDF(673)
Abstract:
In order to achieve low-cost and high-precision vehicle positioning in the intelligent and connected environment,a cooperative map matching algorithm based on adaptive genetic Rao-Blackwellized particle filter is studied in this paper,improving the accuracy of vehicle positioning by using the real-time location data and road constraints of other connected vehicles. The adaptive genetic algorithm is introduced into the re-sampling process of the particle filter to increase the diversity of particles,so as to solve the problems of "particle degradation" and "particle exhaustion" that are prone to appear in traditional particle filter algorithms. Model of the algorithm is established and simulated. The positioning results under the traditional particle filter and Kalman smooth particle filter are compared,and the influence of the number of different connected vehicles on the positioning accuracy is analyzed. The experiment is completed in real-world and the performance of the algorithm is verified. The experimental results show that taking a typical intersection with four connected vehicles as an example,the range of position error of cooperative map matching is 1.67 m. It is only 41.03% and 56.80% of the traditional GNSS and the single map matching positioning results. At the same time,the circular error probable(CEP) of this algorithm is 1.06 m, which is 2.52 m higher than raw GNSS positioning result.
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2022, 40(5): .  
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Abstract:
A Review of Parameter Identification Methods for Ship Dynamic Models
ZHU Man, WEN Yuanqiao, SUN Wuqiang, ZHANG Jiahui, HAHN Axel
2022, 40(5): 1-11.   doi: 10.3963/j.jssn.1674-4861.2022.05.001
Abstract(100) HTML(41) PDF(13)
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.
A Review on Anti-skid Performance Based on Fractal Characteristics of the Texture of Asphalt Pavement
GAO Qiannan, GUO Runhua, GENG Jingjie
2022, 40(5): 12-22.   doi: 10.3963/j.jssn.1674-4861.2022.05.002
Abstract(59) HTML(30) PDF(5)
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.
A Review of Progresses and Prospects of Human-machine Shared Control Technology for L2 Intelligent Driving Based on Haptic Guidance
DENG Xiujin, WANG Yanyang, HUANG Qiushi, WANG Ke, LIAO Kaikai
2022, 40(5): 23-33.   doi: 10.3963/j.jssn.1674-4861.2022.05.003
Abstract(88) HTML(48) PDF(6)
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.
A Method of Risk Identification and Decision-making Support for Ship Maneuvers at Chengshanjiao Waters Under Traffic Separation Scheme
HE Yixiong, YU Deqing, LIU Xiao, WANG Feng, MOU Junmin
2022, 40(5): 34-43.   doi: 10.3963/j.jssn.1674-4861.2022.05.004
Abstract(60) HTML(31) PDF(7)
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.
A Reliability Analysis of the Capacity of Urban Road Network Under a Mixed Human-driven and Connected Traffic Environment
HAO Wei, XIAO Lei, ZHANG Zhaolei, ZHENG Nan
2022, 40(5): 44-52.   doi: 10.3963/j.jssn.1674-4861.2022.05.005
Abstract(84) HTML(37) PDF(7)
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.
An Analysis of Driving Behavior Model and Safety Assessment Under Risky Scenarios Based on an XGBoost Algorithm
WEI Tianzheng, WEI Wen, LI Haimei, LIU Haoxue, ZHU Tong, LIU Fei
2022, 40(5): 53-60.   doi: 10.3963/j.jssn.1674-4861.2022.05.006
Abstract(103) HTML(59) PDF(13)
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.
Prediction of the Duration of Freeway Traffic Incidents Based on an ATT-LSTM Model
JIA Xingli, LI Shuangqing, YANG Hongzhi, CHEN Xingpeng
2022, 40(5): 61-69.   doi: 10.3963/j.jssn.1674-4861.2022.05.007
Abstract(70) HTML(42) PDF(9)
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.
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Intelligent Vehicles Localization Based on Semantic Map Representation from 3D Point Clouds
ZHU Yuntao, LI Fei, HU Zhaozheng, WU Huawei
[Abstract](4638) [PDF 4082KB](159)
Abstract:
In order to improve the accuracy of node localization for intelligent vehicles,an intelligent vehicles localization method based on three-dimensional point clouds semantic map representation is proposed. The method is divided into three parts. Semantic segmentation based on 3D laser point clouds includes ground segmentation,traffic signs segmentation and pole-shaped target segmentation. Semantic map representation for intelligent vehicles uses segmented targets to project. Finally directional projections with weight,semantic roads and semantic codeing are generated. The codeing and global location from high-precision GPS make up representation model. Localization based on semantic representation model includes coarse localization from GPS matching and node localization from semantic coding matching. The experiments are carried out in three road scenes with different length and complexity,and the localization accuracy is 98.5%,97.6% and 97.8%,respectively. The results show that proposed method has high accuracy and strong robustness, which is suitable for different road scenes.
Companion Relationship Discovering Algorithm for Passengers in the Cruise Based on UWB Positioning
YAN Sixun, WU Bing, SHANG Lei, LYU Jieyin, WANG Yang
[Abstract](4269) [PDF 1759KB](117)
Abstract:
To accurately discover the companion relationship among passengers in the interior space of a cruise, UWB positioning is employed in the cruise to carry out on-board personnel location experiment. An improved Haussdorff-DBSCAN based scheme combined with indoor positional semantics is proposed to realize the trajectory clustering of the passenger trajectories, considering the characteristics of the UWB location data. Afterwards, the LSTM neural network is applied to predict the changing similarity of the suspected companions. Traditional Hausdorff algorithm does not consider the consistency of trajectory timing while calculating the trajectory similarity, and the introduction of positional semantic sequence can solve this problem well. In the first phase, the passenger trajectory data set is input to the improved Hausdorff-DBSCAN algorithm, and the clustering radius is determined according to the overall similarity threshold of trajectories. The outputs are the emerging clusters of passenger trajectories in the same companion group. In the second phase, the LSTM neural network takes the point similarity sequence with a fixed time window as the input to predict the point similarity value at the next time. The sequential change of passengers companion relationship is analyzed by the similarity threshold and prediction results. The validity of the presented algorithm is demonstrated by the trajectory data obtained from the passengers simulation on one deck of the cruise under study, which is modeled in Anylogic. The results indicate that the precision of the proposed algorithm reaches 0.92, the recall value reaches 0.95 and the F1 value is 0.934, which are at least 5.7 percent, 8.0 percent and 6.7 percent higher than the comparing algorithm. The LSTM neural network also shows a promising effect in predicting the changing trend of the similarity, for the loss is at a stable level of 3 to 4 percent.

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

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Postal Code:38-94

Domestic Issue:
CN 42-1781/U

Publication No.:ISSN 1674-4861

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