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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(4421) PDF(595)
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(4093) PDF(585)
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(1960) PDF(106)
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(3893) PDF(350)
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(4127) PDF(73)
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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A Review on Road Driving Safety Based on Driving Simulation Technologies
ZHANG Chi, WEI Dongdong, LAN Fu'an, BAI Hao, HUANG Jun
2022, 40(4): 1-12.   doi: 10.3963/j.jssn.1674-4861.2022.04.001
Abstract(0) HTML(1) PDF(1)
Abstract:
The current status and problems of the studies and applications of driving simulation technologies in the field of road traffic safety are analyzed. On the basis of extensive relevant literatures in China and abord, the driving simulators are classified. The development history of the typical driving simulators for scientific research is summarized, and the degrees of freedom, main features, and application areas of them are analyzed. With a main line of "human-vehicle-road-environment-accident", the current situations of the application studies, problems, and prospects are systematically analyzed from five aspects including risky driving behaviors, active safety technologies, road and traffic design, driving environment, and road traffic accidents. For the studies of risky driving behaviors, the identification of distracted and fatigue driving behaviors are analyzed with the application of driving characteristics. For the studies of active safety technologies, the vehicle cha ssis integrated control technology, safety-assisted driving control technology, and evaluation of take-over behaviors of automated driving are summarized. For the studies of road traffic design, the evaluation of geometric road design and traffic signs are analyzed. For the studies of driving environment, the effects of adverse weather, roadside views, and traffic conflicts are summarized. For the studies of road traffic accidents, the reproduction of accidents and influencing factors of traffic safety are analyzed. In addition, an application prospect of driving simulation technology is presented, mainly including driving behaviors of special groups, system testing of intelligent networked vehicles, and driving safety under the environment of mixed traffic flow. In order to better promote the development of driving simulation technology, the efficiency evaluation, discomfort, and secondary development of driving simulators will be studied in the future.
A Review on Research Status and Trends of Eco-driving on Intelligent Connected Vehicles
CHEN Zhijun, ZHANG Jingming, XIONG Shengguang, SU Zipeng, HU Junnan, WU Chaozhong
2022, 40(4): 13-25.   doi: 10.3963/j.jssn.1674-4861.2022.04.002
Abstract(0) HTML(0) PDF(0)
Abstract:
In recent years, eco-driving has become a major research focus within intelligent connected vehicles, aiming to effectively alleviate problems such as energy consumption and emission by improving driving behaviors, which attracts the great attention from governments, businesses, universities, and research centers. Meanwhile, with the rapid advancement of intelligent networked vehicles, the networked environment provides new development opportunities for eco-driving. To analyze the research progress of eco-driving on intelligent connected vehicles, the influencing factors are analyzed from four aspects compared with traditional eco-driving: vehicle characteristics, drivers' personality, road traffic conditions, and social environment. The existing studies on intelligent connected eco-driving are summarized from two aspects: eco-driving control strategies and current status of eco-driving applications. To provide useful guidance and references for future research, the significance, application, and current problems of eco-driving are also discussed from three aspects: influencing factors, control strategies, and decision optimization. The analysis results show that the influencing factors of eco-driving under intelligent connected environment or traditional environment are relatively similar; however, the networked sensors and communication conditions have greater impacts on eco-driving under the intelligent connected environment. Compared with traditional eco-driving, the control strategies and decision optimization for eco-driving under the intelligent connected environment consider more complex driving conditions, as well as global eco-driving at multi-vehicle levels. In addition, with the rapid growth of new technologies, combining advanced technologies and adapting them to the development of the industry will become an inevitable trend of eco-driving on intelligent connected vehicles in the future.
A Review on Railway Traffic Safety Under Harsh Environments
LI Decang, CHEN Xiaoqiang, MENG Jianjun, XU Ruxun, QI Wenzhe, ZHANG Zijian
2022, 40(4): 26-37.   doi: 10.3963/j.jssn.1674-4861.2022.04.003
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Abstract:
The harsh environments including strong sandstorm, earthquake, and debris flow seriously threaten the operation safety of trains. To study and prevention of railroad natural disasters, to ensure the safety operation of trains, to ensure the safe and smooth flow of transport has become a major task of railroad research. It has become a major task of railroad research to study and prevent natural disasters of railways, ensure the safety operation of trains, and guarantee smooth transportation. To prevent and avoid the accidents of train derailment or overturning caused by harsh environments, the mechanism of train derailment and dynamic characteristics, environmental measurement system, dispatching system, early-warning system, control system, experimental verification, and disaster prevention measures under harsh environments are reviewed. The classification and characteristics of harsh environments are summarized, and the impacts of different harsh environments on key aspects of safety operation of trains are analyzed according to the vehicle dynamics and safety performance indicators, under different harsh environments, road conditions, and for different types of trains. The corresponding safety control methods and measures for railway traffic safety in harsh environments (e.g. speed limits or emergency stops, de-icing devices for turnouts and pantographs, windbreak or wind barriers, monitoring and early warning systems, and traffic command systems, etc.), and the research methods adopted in the implementation of these methods and measures (e.g. theoretical analysis, numerical calculations, wind tunnel tests, and online driving simulations, etc.) are outlined. Moreover, it also looks forward to the research emphasis and development trend on the safety operation of railway trains under the harsh environment.
Optimization of the Transportation Network of Hazardous Materials Considering Bounded Rationality and Equity
ZHANG Honggang, WANG Wei, PAN Minrong, LIU Zhiyuan
2022, 40(4): 38-45.   doi: 10.3963/j.jssn.1674-4861.2022.04.004
Abstract(0) HTML(0) PDF(0)
Abstract:
For the optimization of the transportation network of hazardous materials (hazmat) with risk control, the effects of route selection for hazmat carriers considering bounded rationality on transportation risk is studied. A bi-level programming model is developed based on a robust optimization method to achieve risk equity by increasing the upper bound constraint on the maximum link risk. In which, the upper level aims to minimize the maximum total risk of the transportation network, the upper bound value of maximum link risk, and the total number of link closures by closing quite a few links. The lower lever indicates that the hazmat carriers considering bounded rationality chose the route with minimum total cost considering perceptual errors. For the traditional heuristic algorithms easily fall into the local optimal solutions, a cutting plane algorithm is proposed to solve the model by redefining the problems of upper and lower levels, and finally a numerical example is given. The results show that, the total cost of hazmat carriers considering bounded rationality increases by 3.5%, but the maximum total risk of the transportation network of hazmat decreases by 8.4%. By changing the focus of government departments on each objective, boundedly rational route choice behaviors of hazmat carriers can be influenced. The variance coefficient and the Gini coefficient decrease by 36.1% and 26.2%, respectively, which results in achieving the goal of risk equity between different links. In a case of vehicle restriction strategy, a sensitivity analysis is carried out on the perceptual errors of hazmat carriers considering bounded rationality. It shows that the minimum value of the maximum total risk of the transportation network would not change, but has impacts on the total number of link closures. In the case that hazmat carriers are bounded rational decision makers, a more realistic transportation network for hazmat can be designed for government departments, thus effectively reducing transportation risks.
An Analysis of Occupant Death Risk of 5-Seater Cars in Two-vehicle Collisions
ZHAN Junjun, YUN Meiping, ZHANG Wei, DONG Yijia
2022, 40(4): 46-53.   doi: 10.3963/j.jssn.1674-4861.2022.04.005
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Abstract:
To study occupant death risk of 5-seater cars in two-vehicle collisions, the impacts of six variables on fatality rates are compared. Moreover, binary logistic regression is used to analyze the influences of different features and their combinations on occupant death risk. A model to forecast occupant death risk is developed based on a parameter adjustment method of GridSearchCV, three algorithms with larger F1 values are selected from nine classification algorithms, which are voting classifier, gradient boosting, and decision tree. The results show that: ① travel direction, type of road sections, type of crushed vehicles, and different seats have significant effects on occupant death risk. The occupant death risk increases by 72% when compared accidents including vehicles driving in opposite directions with that including vehicles driving in the same direction. The risk decreases by 69% when compared accidents occur at non-freeway intersections with that occur at freeway sections. The occupant death risks for commercial trucks and commercial buses are 5 times and 3 times higher than passenger cars, respectively. The risk rises to around 8 times and 15 times for non-freeway non-intersection sections and freeway sections, respectively. The death risk for passenger seats increased by 70% compared with that for driver seat, and the death risk for passenger seat is nearly 4 times higher than that for driver seat at freeway sections. ② Different vehicles and road-section types are the most important features affecting occupant death. ③ The proposed model indicates that if a commercial truck collides at the front or rear of a 5-seater car at freeway sections or non-freeway non-intersection sections, the occupants have higher risk of death than chance of survival.
Capacity of Mountainous Roads with Ice and Snow Pavement During Beijing Winter Olympics Based on a Safe Speed Model
GUO Yaming, LI Meng, LI Yunxuan, YAN Huimin, WANG Xiaoyan
2022, 40(4): 54-63.   doi: 10.3963/j.jssn.1674-4861.2022.04.006
Abstract(0) HTML(0) PDF(0)
Abstract:
A novel challenge for traffic management is setting speed limits as well as guaranteeing road capacity under complex mountainous roads under the condition of ice and snow pavement. A safe speed model is proposed to solve this problem in Yanqing competition zone of Beijing Winter Olympics. The model studies relationships of safe speed, road alignment design, and adhesion coefficient, taking the safe speed as a basis to obtain the critical road capacity of mountainous roads under different conditions. A three-dimensional spatial model of mountainous road is developed by combining road horizontal curve, vertical curve, and cross section data. Based on the model, the forces acting on the vehicle in a mountainous road section of horizontal and vertical alignments is analyzed. The relationships between the safe speed and its influencing factors including radius of curves, road superelevation, downward slope, and adhesion coefficients of road is studied. The road capacity is analyzed based on the safe speed model. Two pavement conditions and two vehicle types are selected as case studies to obtain safe speeds on ice and snow pavement of mountain roads under different conditions. A total of 20 simulation scenarios are designed by VISSIM to verify the safe model. Combined with the actual traffic data, the simulation results show that compared with the traditional full speed limit model, the travel time of the developed model can reduce by 38% (car) and 32% (bus) with ice pavement; and reduce by 26% (car) and 24% (bus) with snow pavement. In addition, there is a phase transition from free flow to saturated flow in the traffic flow of mountainous road. The maximum road capacity for cars of the downward slope with ice pavement is 241 vehicles/h (one-way driving) and 231 vehicles/h (two-way driving); for buses is 227 vehicles/h (one-way driving) and 222 vehicles/h (two-way driving). The maximum road capacity for cars of the downward slope with snow pavement is 319 vehicles/h (one-way driving) and 249 vehicles/ h (two-way driving); for buses is 301 vehicles/h (one-way driving) and 236 vehicles/h (two-way driving).
A Collision Risk Model for Small UAVs Based on Velocity Random Distribution in Low-altitude Airspace
WANG Lili, YANG Jie
2022, 40(4): 64-70.   doi: 10.3963/j.jssn.1674-4861.2022.04.007
Abstract(0) HTML(0) PDF(0)
Abstract:
Collision risk is a key indicator to evaluate the safety of aircraft and the main factor to determine the aircraft's operating conditions in the airspace. To handle the potential conflict due to the increasing number of small Unmanned Aerial Vehicles (UAVs) in low-altitude airspace, a novel collision risk model based on velocity random distribution is developed to determine the safe operating conditions of UAVs in low-altitude airspaces. New collision templates for UAVs are proposed, incorporating the maneuverability and flexibility of small UAVs. For a free-flying UAV, a double-layer sphere collision template is developed, including a collision layer and an avoidance layer. For a UAV following a fixed path, a cuboid collision template is proposed, incorporating the fuselage size of the UAV. Considering the rapid change of course and speed of the UAV, a stochastic velocity model is adopted instead of a linear model, and the relative velocity between UAVs is calculated, which is used to determine the space swept by the collision template. Considering positioning errors and speed errors of UAVs, the collision risk model based on velocity random distribution is proposed for UAVs in low-altitude airspace. Two types of UAVs, DJI M300 and M600, are selected as verification models. The Matlab software is used to simulate specific airspace scenarios. Then the relationships between collision risk and the density of small UAVs are analyzed. The simulations show that the collision risk in the airspace is positively correlated with the density of UAVs. According to the safety standards from the International Civil Aviation Organization, the maximum densities for the safe operation of the two types of verification models are 4.2 aircraft/km3 and 5.0 aircraft/km3, respectively. Under the premise of satisfying the safe conditions, the proposed model can increase the upper limit of the density of the two types of UAVs in the airspace by 106.9% and 88.7%, respectively. The results reveal that the proposed model is more consistent with the operating characteristics of UAVs. It can be used to improve the utilization of airspace, increase the capacity of UAVs in the airspace, and improve their operational efficiency in the future.
Real-time Forecast Models for Traffic Accidents on Expressways Using Stability Coefficients of Traffic Flow
LIU Xingliang, SHAN Jue, LIU Tangzhi, RAO Chang, LIU Tong
2022, 40(4): 71-81.   doi: 10.3963/j.jssn.1674-4861.2022.04.008
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Abstract:
Real-time forecast models for traffic accidents requires a large number of variables, which causes difficulties in data collection and decreases reliability of the model due to overfitting. Two interpretable variables, vertical and horizontal stability coefficients of traffic flow, are proposed to simplify the set of variables, which can facilitate the implementation of forecast models for traffic accidents and reduce the effects of overfitting. Three algorithms including support vector machine, random forest, and logistic regression are selected to develop real-time forecast models for traffic accidents on expressways, respectively. The experiments are conducted based on data of traffic accidents and historical traffic flow collected from the expressway G3001 in the city of Xi'an. In addition, the improved GI index is used to evaluate the significance of the proposed two stability coefficients of traffic flow. The effects of the two proposed coefficients on reducing overfitting is verified through comparing accuracies and AUC values of the set of variables in the test and training data.Besides, the computational efficiency is evaluated by the training time to verify the reliability of the developed models with the two coefficients. The results show that the improved GI indices of the models with horizontal and vertical stability coefficients of traffic flow are 0.952 and 0.922, respectively, which indicates that the proposed two coefficients are more significant for forecasting accidents on expressways than other variables. In the three models, the simplified set of variables based on the two coefficientshas prediction accuracy of 91.1% and 90.5%, respectively, in training and test data, which is similar to the original set of variables. The differences of average prediction accuracy between the simplified set of variables and the original set of variables are 0.69% and 4.87%, respectively. The difference of average AUC values between the two sets of variables are 1.61% and 5.87%, respectively. The average time cost of model training with the simplified set of variables decreases by 15.2%. Thus, the two proposed stability coefficients of traffic flow can improve both the reliability and the computational efficiency of the models.
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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](4421) [PDF 4082KB](118)
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](4093) [PDF 1759KB](62)
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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