Perceiving the location of dangerous moving vehicles and broadcasting this information to vehicles nearby are essential to achieve active safety in the Internet of Vehicles (IOV). To address this issue, we implement a real-time high-precision lane-level danger region service for moving vehicles. A traditional service depends on static geofencing and fails to deal with dynamic vehicles. To overcome this defect, we devised a new type of IOV service that manages to track dangerous moving vehicles in real time and recognize their danger regions quickly and accurately. Next, we designed algorithms to distinguish the vehicles in danger regions and broadcast the information to these vehicles. Our system can simultaneously manipulate a mass of danger regions for various dangerous vehicles and broadcast this information to surrounding vehicles at a large scale. This new system was tested in Shanghai, Guangzhou, Wuhan, and other cities; the data analysis is presented in this paper as well.
For the Internet of Vehicles (IOV), vehicle active safety services have become one of the most vital research directions. Active safety focuses on the following two stages, the accident avoidance stage during vehicle crashes [
Up till now, it has only been possible to send static danger information (the location of static dangerous vehicles) successfully [
Traffic accidents’ statistics from the 2010 yearbook of Chinese public security department [
Thus, our motivation is to effectively control danger by developing in our service the capacity to quickly detect threats stemming from dynamic danger targets’ moving statuses, share this information with surrounding vehicles or pedestrians, and guide them safely around the danger targets. The proposed system only monitors dynamic danger targets’ moving statuses. Given the large number of vehicles, in order to prevent the IOV from detecting and sharing locational information for all vehicles, we only focus on a limited number of dynamic danger targets. In this way, all users can receive warnings about latent dangers around them while lessening the pressure on the IOV.
Our proposed system efficiently deals with the following two traffic safety situations.
Sharing safety information about dynamic danger targets with surrounding vehicles requires the precise location of those dynamic danger targets. Thanks to the rapid development of GNSS, it is possible to get precise location at present. We realize lane-level positioning based on BeiDou-Xihe system [
The contributions of this work are summarized as follows: We realize CRPP by means of a lane-level terminal device, and with inexpensive MEMs sensors, we can monitor the locations of multiple dynamic danger targets. We propose an IOV active safety model for dynamic danger targets; the implementation structure and active cooperation algorithms for the modeling process are detailed in this paper. Our proposed system was tested in Shanghai, Guangzhou, Wuhan, and other cities. Test results show that the IOV model for 71,178 dynamic danger targets was verified for the Guangzhou province IOV system.
Danger control based on the data from GPS has become one of the most vital applications of GPS. Reference [
At present, there has been much research on lane-level positioning and perception of vehicles’ moving states. Reference [
Dynamic danger targets query of related objects can build on the Continuous k-Nearest Neighbor (CkNN) approach. Reference [
Nowadays, GNSS is widely used for positioning. However, due to errors caused by disturbances in the satellite orbits, satellite clocks, and the atmosphere, GNSS positioning precision is typically about 10~20 m, which fails to realize accurate positioning. To improve the precision, our system applies Wide-Area Real-Time Precise Positioning GNSS (WRPP-GNSS). This approach takes full advantage of satellite observation data from GNSS services and satellite reference stations to model the main positioning errors, sends correction messages for each error source to terminal users, and thus helps positioning terminals enhance location precision.
Our system is capable of providing lane-level positioning of danger targets and ascertaining their danger regions by our algorithms. Then this system broadcasts warning messages to surrounding vehicles of danger targets, which belong to danger regions.
Figure
Framework of vehicle positioning system.
Based on lane-level positioning, our system achieves a lane-level map service (Figure
The lane-level map service.
To test how well this positioning service might work, we conducted an experiment in several cities and chose three representative situations to show our test results. Results seen in Figure
Vehicle movement test. We drove a test car back and forth from Chengshan Road to Jinxiu Road in Pudong new area, Shanghai, on June 23th, 2014.
Turning
Lane-changing
Moving straight
We must achieve lane-level positioning of danger targets to provide precise warnings to vehicles nearby and reduce the processing demand on the IOV. As the statistics of introduction section suggests, it is much more likely for danger targets to have accidents than any other vehicles. Using the lane positioning of a danger target, for instance, a school bus, we can send warnings to vehicles nearby the bus, which in turn cuts down the risk of accidents. Considering the high cost of each precise positioning vehicle terminal as well as the massive number of vehicles in the IOV, it would be prohibitively expensive to equip every vehicle with terminals. Moreover, processing the data from all these vehicles would overwhelm the Internet. Hence, we just equip the limited number of high-priority and high-danger targets and then share warnings with surrounding vehicles. In this way, not only do we reduce the cost, but also we lessen the pressure on the IOV.
A danger region for vehicles is the region where danger targets are prone to cause accidents. Based on danger regions, we can sort the objects located in this region and push warnings to them. A simple way to define a danger region is to draw a circle around the danger target; however, without considering the road site, this way may push information to vehicles located inside but on a nonadjacent road. Therefore, it is of immense value to define danger region in a more rational way.
It is defined as an undirected weighted graph
Each nonintersecting road in the road network can be defined as
Related moving object (object)
It is a collection of various security events,
Dynamic danger target (target)
Suppose that, at time
The safety distance between danger targets and related objects.
Target
Target
We start with each
In a real system, in order to simplify the computation and management of the IOV when computing danger regions, we grid the road networks to transform the calculation of the distance between two points into a calculation of the distance between two cells.
When a certain cell
After defining the edge cell, we compute the danger regions for each danger target. However, to explain this computation process clearly, we first consider the situation without gridded road networks. For example, Figure
Edge cell and danger region.
Now, we consider the computation of danger region with the gridded road network. Instead of finding the adjacent edges, we need to find the adjacent edge cells; instead of computing the distance of two points, we need to compute the distance of two edge cells. As seen in Figure
In the model implementation, the danger regions
Input: Output: (1) FOR (2) IF (3) // To get the mapping edge cell set (4) (5) FOR (6) // Computing the distance (7) (8) (9) (10) (11) IF ( (12) Put (13) END IF (14) END FOR (15) END IF (16) END FOR (17) RETURN
In Algorithm
For each danger target
In practical applications, danger targets are always moving on a road network. As a consequence, danger regions are changing all the time. We calculate more than one danger region on a road network.
For every edge cell the danger target goes through within a certain time
In order to manage dynamic danger regions, we need to query more than one dynamic danger target in real time and push the corresponding information to the related moving objects in road networks. The whole management process can be divided into two parts, the query phase and pushing phase, as in Figure
Input: Output: No
MapReduce computing framework.
We focused on how to ensure the moving objects are set
The main aim of query phase is to express the dynamic danger regions during the time period
Input: Output:
The filter algorithm has two major functions. The first one is to find out the moving objects that locate in those edge cells returned from Algorithm
In this paper, active cooperation and information pushing methods for dynamic danger targets were used as plug-in software on the IOV real-time monitoring platform. An experimental deployment was conducted on the traffic management platform in Zhongshan, Guangdong. This platform supplies the latest data for road networks and was the data source for the index. This platform was accessed through the monitoring terminal or the moving objects, so we can collect the locations of vehicles at any time. Based on this, our algorithm is able to update the location of danger targets and the mapping relationship between edge cells and moving objects. After the query phase, our algorithm sends the set of pushing objects to the platform so that the platform can communicate with corresponding vehicles. The system architecture and the running interface are shown in Figure
System architecture and the running interface.
The traffic management platform in Zhongshan, Guangdong, provides us with a counterpart node. Based on this counterpart, we carried out a series of experiments to estimate the accuracy and efficiency of the whole active safety service. The targets of the experiments are 71178 vehicles that have accessed to this system; the types of vehicles are shown in Figure
Experimental data.
Active safety warning depends on the accuracy of the selection of related objects highly associated with the layout of road networks and the relationship among dynamic targets. We define the accuracy of active safety warning as follows:
Figure
The system accuracy varies with different length of edge cell.
Warning time is 30 s
Warning time is 150 s
Warning time is 210 s
Extending the range of edge cell helps to enhance the query efficiency of related vehicle objects surrounding dynamic danger targets so as to meet the demand of response time of active safety location-based services for vehicles’ normal driving phases. We made a simulation experiment in Zhongshan during the peak period, 8:00-9:00, and test the danger targets 1/6 times per second.
Figure
The analysis efficiency under 300 dynamic danger regions with different warning time.
Edge cell length is 50 m
Edge cell length is 125 m
Edge cell length is 250 m
Gridding is an important implement for our experiment; therefore it is necessary to analyze the effect of gridding. Figure
The analysis efficiency with different warning time without gridding.
In order to further extend our system to a larger IOV platform so that the active safety warning services can be provided for more vehicles, we carried out a numerical simulation on the extended capabilities of the system. The simulation experiment was based on the single server and single node computation. The experimental environment included an Intel Xeon E3-1230 v2 CPU, 16 GB of memory, Linux Debian 3.13-1-amd64 system operation, and an ext4 file system as well as Java programming language. The NoSQL database used for storage was MongoDB. In this experiment, we constructed a road network consisting of 5521 nodes and 8980 edges and created a moving objects generator to produce dynamic targets and related objects. The velocity of both the dynamic targets and related objects was set at 40–140 km/h. As for the security level of dynamic targets, 10% of them were set to high, 20% were set to medium, and the rest were set to low.
The selection of parameters is essential under various road and traffic situations. Consider the following situation. A vehicle drives at 100 km/h on the highway; according to the “Specification for Layout of Highway Traffic Signs and Markings” [
In the experiment, the length of edge cell was limited to 100 m and the warning time was set at 120 s. Figure
The simulation results of different number of dynamic targets and related objects.
This paper proposes an active safety model for the IOV, focusing on dynamic danger targets consisting of high-danger and high-priority-of-way vehicles. We elaborate a relevant modeling process, implementation structure, and active cooperation algorithm. We use these proposed solutions to monitor all kinds of dynamic danger targets in traffic and facilitate cooperation among surrounding related objects.
According to the results of verification and simulation experiments, our method can track and query dynamic danger targets on a large scale, providing active safety services for vehicles in dynamic danger regions. Relevant achievements were brought into the national BeiDou-Xihe research project.
Chi Guo is the first author.
The authors declare that there is no conflict of interests regarding the publication of this paper.
The authors thank Professor Lou Yidong and postgraduate ChenXi for their help. This paper is supported by