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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(5813) PDF(5770)
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(5342) PDF(2383)
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(2686) PDF(759)
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(6747) PDF(1007)
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(6798) PDF(721)
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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2023, 41(4): .  
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A Review of Safety Studies on Lane Change Decision-makings for Connected Automated Vehicles
CUI Bingyan, LI He, CUI Zhe, JI Haojie, GUAN Yuxin
2023, 41(4): 1-13.   doi: 10.3963/j.jssn.1674-4861.2023.04.001
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Abstract:
Safety of lane change decision-makings for connected automated vehicles (CAVs) is a key task to improve traffic safety and enhance road mobility. In this paper, the safety issues related to lane changing of CAVs are investigated. From the perspective of driving safety, the adverse impacts of extreme lane-changing behavior and emergency lane-changing behavior on traffic safety are analyzed, emphasizing the importance of risk assessment. The various risk assessment methods of lane changing are reviewed, including the use of environmental sensors, traffic conflict indicators, and vehicle-level micro-trajectory data. Identifying risks through risk assessment and taking corresponding measures can significantly reduce traffic accidents caused by dangerous lane-changing behavior. Furthermore, the methods for CAVs to make lane-changing decisions by obtaining environmental information in both traditional and vehicle to everything (V2X) environments are elaborated. Particularly focusing on the CAVs in V2X environment, the decision-making through the environment perception and recognition, targets detection, and data processing is analyzed. Reasonable recommendations are proposed for achieving safe decision-making by CAVs in V2X environment in the future. Then the existing models for decision-making of lane-changing are analyzed and categorized into four types: rule-based models, discrete choice models, artificial intelligence models, and game theory models. The status of research and application, existing problems, and prospects of decision-making models in the field of road traffic safety are systematically summarized, both domestically and internationally. In summary, despite significant research achievements in lane-changing technologies for CAVs, there are still many challenges ahead. To tackle the existing problems in research, such as ensuring safe and reliable decisions in low-level automated driving environments, making more efficient and intelligent driving decisions for CAVs in low-penetration scenarios, achieving safe decision-making in situations with incomplete information, and improving the optimization of the algorithms for lane-changing decision-making, feasible solutions are proposed accordingly.
A Method for Evaluating the Safety over the Takeover Process of the Level 3 Automated Vehicles Based on IAHP-EWM-LDM
LI Zhenlong, PAN Mengniu, QU Yansong, ZHAO Xiaohua, GONG Jianguo, WANG Qiuhong
2023, 41(4): 14-23.   doi: 10.3963/j.jssn.1674-4861.2023.04.002
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Abstract:
In the Level 3 autonomous driving stage, the driver needs to respond and take over the vehicle when the system sends a takeover request. Therefore, to accurately assess the safety of the takeover process of Level 3 autonomous vehicles, the safety evaluation index system of the takeover process of autonomous driving is constructed. In this paper, a 4×2×2 takeover scenario factor is used to design a driving simulation test, and a driving simulator is used to collect various types of driving data. Based on the coefficient of variation method and Spearman correlation discriminant method, 13 security evaluation indicators are obtained from the analysis of 3 aspects, such as risk perception, risk avoidance manipulation and takeover performance. The subjective weights of the indicators are obtained using an improved hierarchical analysis that characterizes the experience of the experts, and the subjective weights of the indicators are obtained using entropy weights that reflect the characteristics of the data. To combine the advantages of the two methods, a composite weight incorporating both subjective and objective weights is obtained using the grade maximization method. The combined weights of risk perception, risk avoidance manipulation, and takeover performance are calculated to be 0.259, 0.475, and 0.271, which are used to construct the security evaluation index system of the takeover process. In this paper, the system is applied to comprehensively evaluate 655 takeover processes obtained from driving simulation tests, and they are classified into 3 categories of A, B and C takeover processes according to the evaluation results. Comparing the scores of the 3 types of takeover processes in 3 aspects: risk perception, risk avoidance manipulation and takeover performance, it is found that the A-type takeover process performs well in three aspects, the C-type takeover process performs poorly in risk avoidance manipulation and takeover performance, and the B-type takeover process performs intermediary between the A-type and C-type. Different types of takeover process have a better degree of differentiation in each indicator. The indicator system is constructed that effectively combines expert experience and indicator characteristics. The evaluation index system constructed in this paper effectively combines expert experience and index characteristics. It can provide theoretical support for a more comprehensive, reasonable and scientific evaluation of the safety in the process of automatic driving takeover.
A Study on Longitudinal Collision Risk of Airplanes during Paired Approach Under the Influence of Positioning Error
LU Fei, ZHAO Erli, LIANG Xianyun
2023, 41(4): 24-32.   doi: 10.3963/j.jssn.1674-4861.2023.04.003
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Studying longitudinal collision risk of paired approach on closely spaced parallel runways (CSPRs) is crucial for assessing its safety, where positioning errors directly influence the longitudinal collision risk during the process. Given the lack of consideration on actual data fitting for positioning error distribution in previous studies, this study aims investigate the longitudinal collision risk during paired approach under the influence of actual data-fitted positioning error. According to the implementation process of paired approach, a kinematic model for the longitudinal spacing between aircrafts before and after pairing is established. In terms of positioning error during flight, statistical data of actual aircraft positioning errors are utilized to fit the distribution. Next, utilizing Automatic Dependent Surveillance-Broadcast (ADS-B) data, the longitudinal positioning error during the final approach phase is analyzed and fitted to identify the best-fitting distribution, that is, normal distribution. The collision risk between the aircraft fuselages in paired approach and the collision risk between the wake turbulence of lead aircraft and the fuselage of trailing aircraft are studied separately, and integral intervals for each collision risk model are determined. Based on the normal distribution and the movements of the paired aircrafts during paired approach, an assessment model for the longitudinal collision risk is established. Finally, data about the B737-800 aircraft at Shanghai Hongqiao Airport in December 2020 are collected for a case study. Simulations are conducted to analyze the changes in collision risk of fuselage Px1 and collision risk of wake turbulence Px2 over time under the initial longitudinal separations of 926 m and 2 778 m. Further, the relationship between different initial longitudinal separations and Px1 / Px2 or the maximum value of overall longitudinal collision risk. The results indicate that: ①when the initial longitudinal separation is 926 m, Px1 gradually decreases while Px2 increases over time, and Px1 is significantly greater than Px2. ②When the initial longitudinal separation is 2 778 m, the results are the opposite. ③ Px1 decreases while Px2 increases as the initial longitudinal separation increases. ④The overall longitudinal collision risk between the lead and trailing aircrafts decreases first and then increases with increasing initial longitudinal separation; ⑤when the initial longitudinal separation is smaller than 2 136 m, the longitudinal collision risk is primarily determined by the collision risk between the fuselages of lead and trailing aircrafts; when the initial longitudinal separation is larger than 2 136 m, it is determined by the collision risk between the wake turbulence of lead aircraft and the fuselage of trailing aircraft.
An Analysis of Driving Behavior on Short Distance Section between Tunnel and the Exit of Main Roadway
TANG Hao, TANG Zhongze, ZHANG Chi, WEI Bin, ZHANG Kunlun, YANG Kun
2023, 41(4): 33-43.   doi: 10.3963/j.jssn.1674-4861.2023.04.004
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A short distance section is a section between the tunnel and the mainline exit of a mountainous highway whose length is lower than the normative value because of the limitation of geographical and investment factors. In order to analyze the driving characteristics of this area thus to enhance the theoretical base for mountain road design and traffic control, high-definition driving videos are collected by drones in 7 mountain roads (e.g., Qinling service area) with short distance sections in China. The high-precision speed and trajectory data of vehicles across the entire region have been extracted. The SIFT algorithm is used for video stabilization. The YOLOv5 and DeepSORT algorithms are adopted for vehicle detection and tracking. The Savitzky-Golay filter is utilized to filter the data. Finally, high precision driving data can be obtained based on the above methods. It is verified that the accuracy of speed can reach more than 95% and the error of trajectory is less than 20 cm. Next, the driving characteristics are analyzed from a variety of perspectives, such as clearance distance, vehicle type, lane distribution, and others. The results show that: ①the driving characteristics on short distance section are very different from those on usual road section that the speed distribution does not follow a normal distribution; ②generally the outgoing vehicles would be steady 10 to 20 m ahead of the commencement of the fading phase; ③the speed of trucks is smoother as truck drivers can identify the exit road conditions more quickly than the car drivers because of the larger angle of view; ④approximately 20 meters after the starting point of the transition section, the cars in the inner lane enter the deceleration lane with a lateral speed of 1.1 to 1.4 m/s, and when the mainline has a leftward curve it is most favorable to drive out; ⑤the clearance distance has the highest influence on driving behaviors, the traffic volume affects the most among traffic flow factors while the direction of curve deflection and deflection angle affect the most among the road geometry factors.
Classifying Road Accidents and Forecasting Level of Risk Based on a Combined PCA-LPP and DBSCAN Method
XIN Yi, LI Gang, DENG Youwei, ZHANG Shengpeng, ZHOU Pan, LIU Yiyang
2023, 41(4): 44-54.   doi: 10.3963/j.jssn.1674-4861.2023.04.005
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Abstract:
Road traffic accidents are one of the major problems causing large numbers of casualties and property losses worldwide. By classifying road traffic accidents and predicting risk levels, it becomes possible to identify high-risk vehicles and reduce the probability of accidents and casualties. Traffic accidents are often influenced by multiple factors such as environment, weather, road conditions, and infrastructure, but existing accident impact analysis methods lack comprehensive research on traffic accident data. Therefore, this paper proposes a traffic accident classification model that incorporates an improved dimensionality reduction algorithm called PCA-LPP, which measures the similarity between data of different levels to achieve secondary dimensionality reduction. The model utilizes a large-scale traffic accident dataset and applies the DBSCAN algorithm to partition the accident data into risk areas. By training the spatial representations of different risk levels iteratively, the model could assess the risk levels in simulated vehicle environments. Experimental results demonstrate the effectiveness of the proposed approach. Comparative experiments on large-scale traffic data reduced to different dimensions show that the PCA-LPP algorithm achieves higher correlation between the reduced features and sample categories compared to traditional PCA. Moreover, when handling complex and sporadic traffic accident data, the density-based DBSCAN clustering algorithm achieves a purity of 0.942 9, a Rand index of 0.946 2, and a mutual information index of 0.678 4. Comparing these results with traditional algorithms like K-means and spectral clustering, DBSCAN consistently outperforms them in various evaluation metrics. Additionally, visual analysis of the classification results indicates that the proposed model reduces the influence of noisy data. Finally, an ablation experiment confirms that the PCA-LPP algorithm with secondary dimensionality reduction achieves the highest evaluation metrics. The confusion matrix of the prediction results shows that the model achieves precision rates of 85.77%, 70.78%, and 80.65% for different risk levels, further validating its effectiveness and practicality.
A Method for Predicting the Type and Severity of Freeway Accidents Based on XGBoost
GAO Xuelin, TANG Houjun, SHEN Jiaping, XU Chengcheng, ZHANG Yujie
2023, 41(4): 55-63.   doi: 10.3963/j.jssn.1674-4861.2023.04.006
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Freeway accidents are frequent, and previous studies have failed to adequately reveal the effect of dynamic traffic flow on accident type and severity. This study focuses on a prediction method for types and severity of freeway accidents based on real-time traffic flow data. Traffic flow characteristics, including volume, density, and speed, are extracted from freeway gantry data. Simultaneously, temporal features and spatiotemporal non-uniformity features are considered. These data are then matched with accident data to constitute the full dataset for modeling. The model based on the extreme gradient boosting tree (XGBoost) algorithm is developed to predict the occurrence of accidents and accident types, and also to assess accident severity. Two types of accidents (i.e., rear-end collisions and other types of accidents) are considered and two levels of accident severity (i.e., injury or fatal accidents and proper-ty-damage-only accidents) are distinguished. The results indicate that: ①a higher risk of traffic accidents is associated with significant speed difference between upstream and downstream traffic, low speeds, high traffic volumes with frequent merging and diverging conditions; ②rear-end accidents are more likely to occur in situations with lower speeds, high traffic volumes with merging and diverging flows, and significant speed difference between upstream and downstream traffic; ③accidents involving rear-end collisions may result in higher severity when they occur on road segments with lower traffic volumes or occur during weekends or nighttime. The Area Under Curve (AUC) of the XGBoost-based models for accident types prediction and accident severity prediction reached 0.76 and 0.88 respectively. Compared with other commonly used algorithms such as Sequential Logistic, Gaussian Naive Bayes, Linear Support Vector Machine (SVM), Random Forest, and Neural Network, the XGBoost-based model demonstrates an average improvement of 0.08 and 0.24 in AUC values for predictions of accident types and accident severity. These results indicate that the XGBoost-based model exhibits better prediction performance. The research findings provide a reliable way for state warning of real-time traffic flow on freeway segments, which could be useful for improving driving safety.
An Analysis of Linguistic Characteristics and the Effectiveness of Safety Slogans for Preventing Drunk Driving
YUAN Yang, LI Donghe, LI Kexin, LI Peiling, NING Peishan, HU Guoqing
2023, 41(4): 64-71.   doi: 10.3963/j.jssn.1674-4861.2023.04.007
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To address the uncertainty regarding the effectiveness of traffic safety slogans in enhancing public awareness of traffic safety, a quantitative analysis of Chinese traffic safety slogans for preventing drunk driving and their effectiveness is conducted, aiming to analyze the linguistic characteristics and their effectiveness. Using Python 3.7 software, a total of 1 828 traffic safety slogans about drunk driving prevention, collected from the Baidu search engine between September 2019 to September 2020, are analyzed. The linguistic characteristics of these slogans were categorized and analyzed from six aspects, including person expression, emotion, rhetoric, rhyme, context, and length. The primary linguistic characteristics found in these slogans include the use of the other person expression (87.7%), negative emotion (45.4%), lack of rhetoric (49.9%), presupposed states (52.4%), and medium length (65.2%). Through an online questionnaire survey, the effectiveness of slogans with different linguistic characteristics is evaluated in terms of public attention, understanding, and acceptance. By employing the Chi-square test, the differences in linguistic characteristics of the slogans are analyzed and a generalized linear regression model is developed to identify significant linguistic factors affecting public attention, understanding, and acceptance. The results of generalized linear regression show as follow. ①Public attention to slogans is primarily influenced by the person expression and length of the slogans. Specifically, the slogans using the first- and other-person expression attract greater public attention compared to slogans using the second person expression (b =0.24, 0.49). Longer slogans (18 words or more) aroused more public attention compared to shorter slogans (less than 12 words) (b =0.26). ② Public acceptance of slogans is mainly influenced by the use of person expression. Slogans using the first-person expression receive higher public acceptance than those using the second person expression (b =0.31). ③There is no statistically significant relationship between public understanding of slogans and six linguistic characteristics of slogans. The results indicate that Chinese traffic safety slogans designed to prevent drunk driving exhibit diverse linguistic characteristics. The different linguistic characteristics affects the public attention and acceptance of the slogans. In the future, when designing traffic safety slogans, it is recommended to use the first person or other person expression and to consider increasing the length of the slogans to enhance the effectiveness of slogans in enhancing public awareness of traffic safety.
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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](5813) [PDF 4082KB](224)
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](5342) [PDF 1759KB](165)
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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