WO2022000958A1 - 基于v2x的无信号灯路口碰撞预警激活概率评估方法 - Google Patents

基于v2x的无信号灯路口碰撞预警激活概率评估方法 Download PDF

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WO2022000958A1
WO2022000958A1 PCT/CN2020/131015 CN2020131015W WO2022000958A1 WO 2022000958 A1 WO2022000958 A1 WO 2022000958A1 CN 2020131015 W CN2020131015 W CN 2020131015W WO 2022000958 A1 WO2022000958 A1 WO 2022000958A1
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vehicle
probability
intersection
vehicles
node
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叶佳勇
谭国平
周思源
张芝
任勇
王耀东
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中睿智能交通技术有限公司
江苏智能交通及智能驾驶研究院
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    • G—PHYSICS
    • G08—SIGNALLING
    • G08G—TRAFFIC CONTROL SYSTEMS
    • G08G1/00—Traffic control systems for road vehicles
    • G08G1/16—Anti-collision systems
    • G08G1/161—Decentralised systems, e.g. inter-vehicle communication
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F30/00—Computer-aided design [CAD]
    • G06F30/20—Design optimisation, verification or simulation

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  • the invention relates to the technical field of wireless communication in the Internet of Vehicles, in particular to a method for evaluating the activation probability of collision warning at intersections without signal lights based on V2X, which simulates and models the collision warning problem at intersections in the Internet of Vehicles scenario based on random geometry theory.
  • the Internet of Vehicles refers to the extraction and effective use of the attribute information and static and dynamic information of all vehicles on the information network platform through the identification technologies such as electronic tags and radio frequency loaded on the vehicle, and according to different functional requirements for all vehicles.
  • the Internet of Vehicles has been endowed with more and broader functions. It refers to providing vehicle information through sensors, vehicle terminals and electronic tags on the vehicle, and using various communication technologies to achieve vehicle-to-vehicle, vehicle-to-person, and vehicle-to-vehicle information.
  • C-V2X Cellular-Vehicle to Everything
  • LTE-D2D point-to-point
  • PC5 Called Sidelink
  • the random geometry theory Compared with the fixed lattice model, the random geometry theory retains the random characteristics of the spatial node distribution in the process of simulation modeling, so the random geometry tool can more accurately model the spatial network nodes of C-V2X.
  • the initial application scenarios of random geometry theory are mobile self-organizing networks and wireless sensor networks. Their spatial node distribution has random characteristics, and random geometry theory can evenly spread points in space to randomly generate any possible network node distribution. .
  • the present invention proposes a V2X-based method for evaluating the activation probability of collision warning at intersections without signal lights, which can remind drivers to pay attention to safety, and broadcast the information of the vehicle entering the intersection to other nearby vehicles, prompting nearby vehicles to slow down to avoid Crossroads will have car collisions.
  • the present invention provides a method for evaluating the activation probability of collision warning at intersections without signal lights based on V2X, comprising the following steps:
  • the interruption probability of vehicle-to-vehicle communication is defined according to the probability that the signal-to-interference-noise ratio of the studied vehicle node is less than its threshold ⁇ 0 , and the curve trend of the interruption probability is simulated and analyzed, so as to find that the communication success probability at the intersection is greater than 95.
  • % SNR threshold ⁇ 0 the probability that the signal-to-interference-noise ratio of the studied vehicle node is less than its threshold ⁇ 0 , and the curve trend of the interruption probability is simulated and analyzed, so as to find that the communication success probability at the intersection is greater than 95. % SNR threshold ⁇ 0 ;
  • the collision warning message of the vehicle at the intersection will not be generated, and if every vehicle within a certain range can successfully communicate with the vehicle entering the intersection with priority, And there is no collision with vehicles entering the intersection with priority, the vehicle terminal will not give the driver a collision warning at the intersection, no matter which of the above points does not meet the requirements, the vehicle terminal will send the driver the intersection collision warning information , remind the driver to pay attention to safety, and broadcast the information of the vehicle entering the intersection to other nearby vehicles, prompting the nearby vehicles to slow down and avoid the occurrence of collision accidents at the intersection.
  • the present invention first models an urban road, and uses the Poisson line process to generate an urban road with a density of ⁇ L , so that a snapshot of the urban road is randomly generated, and then a snapshot of the urban road that has been generated is generated.
  • the vehicles and roadside units are scattered, and each urban road is regarded as two opposite lanes, and then the vehicles and roadside units are scattered on each lane respectively.
  • the two-dimensional Poisson point process is used to randomly generate the node positions of base stations and distribute them on the generated urban road map, where the density of base stations is ⁇ 1 , the density of vehicles in the left lane and roadside units is ⁇ l2 , while the density of vehicles and roadside units in the right lane is ⁇ r2 , and the sum of the densities of vehicles and roadside units in both lanes is ⁇ 2 .
  • Vehicle C estimates the distance between itself and the vehicles in A and B according to the received signal strength. This method is derived from the RSS algorithm in positioning.
  • the distance between the target vehicle node and the receiving vehicle node can be estimated by the energy of the signal received by the vehicle from other vehicles.
  • the strength of the received signal is inversely proportional to the propagation distance. Simply put, if the value of the signal-to-interference-to-noise ratio (SINR) is larger, the distance between the vehicle node and the vehicle node is larger, on the contrary, if the SINR value is smaller, the distance between the vehicle node and the vehicle node is smaller.
  • SINR signal-to-interference-to-noise ratio
  • Inter-vehicle communication interruption probability whether the communication between vehicle node i and vehicle node C is successful depends on the threshold value of signal to interference and noise ratio ⁇ 0 , when the signal to interference and noise ratio between vehicle node i and vehicle node C is higher than the threshold value ⁇ 0 , the vehicle The communication between node i and node C succeeds, and when the direct signal-to-interference-noise ratio between vehicle node i and node C is lower than the threshold ⁇ 0 , the communication connection between the two vehicles fails, so the communication interruption probability between vehicle node i and node C is defined. It is represented by the following formula:
  • I represents the quantified value of interference received by vehicle C
  • P 2 represents the signal transmission power of the vehicle
  • G represents the antenna gain
  • hi ,c represents the power fading coefficient of communication between vehicle i and vehicle C
  • hi ,c obeys the mean value of Exponential distribution of ⁇ i,c , d i,c represents the distance between vehicle i and vehicle c
  • -4 represents the path loss parameter
  • N 0 represents the thermal noise power, assuming that the signal transmission power of each vehicle is the same
  • P m represents The signal transmission power of the vehicle
  • P m P 2
  • Vehicle C warns the probability of collision at intersections as if all vehicles on the intersecting road communicate successfully, and warns whether each vehicle will collide with vehicle C. If vehicle C communicates successfully and does not collide, their product is the probability of no collision, and their complementary probability is the probability of possible collision, that is, the probability of collision warning of vehicles at the intersection.
  • the probability of collision warning can be expressed by the following formula:
  • PCA and PCB represent the probability that vehicle C successfully communicates with vehicles in two different directions of the intersection and does not collide, and It represents the probability that vehicle C does not collide with vehicle A i and vehicle B i in two different directions of the intersection.
  • the present invention has the following advantages: the present invention is based on the V2X-based method for evaluating the activation probability of collision warning at intersections without signal lights, based on the knowledge of stochastic geometry theory, and uses the above formula to test the collision warning at intersections, which is very close to Monte Carlo simulation.
  • the numerical solution provides theoretical support for the design of intelligent transportation systems.
  • Fig. 1 is a schematic diagram of meeting vehicles at intersections under the Internet of Vehicles of the present invention
  • Fig. 2 is the probability curve diagram of communication interruption between vehicles according to the present invention.
  • Fig. 3 is a vehicle node speed-intersection collision warning probability curve diagram of the present invention.
  • Fig. 4 is the curve diagram of urban road line density - early warning probability of collision of passing vehicles at intersections of the present invention
  • FIG. 5 is a graph showing the density of vehicle nodes and the probability of collision warning of passing vehicles at intersections according to the present invention.
  • the invention uses the Monte Carlo method to simulate the model on the MATLAB simulation platform.
  • the linear density ⁇ L of the urban road is set to 15km/km 2
  • the density ⁇ 1 of the base station node distribution is 0.5nodes/km 2
  • the sum ⁇ 2 of the densities of transmitting vehicle nodes and roadside units is 40nodes/km
  • the base station transmits
  • the power P 1 is 50 dBm
  • the transmission power P 2 of the vehicle and the roadside unit is taken from 13 dBm to 43 dBm
  • the intermediate interval is 10 dBm
  • the thermal noise power N 0 is -33 dBm.
  • the main lobe gain G of the directional antenna is 15, the side lobe gain g is 1, and the number of simulation cycles is 10,000.
  • the simulation of the interruption probability of inter-vehicle communication is carried out, as shown in Figure 2.
  • the threshold value ⁇ 0 of the signal-to-interference noise ratio is taken between -25dB and 25dB.
  • the interruption probability of vehicle-to-vehicle communication is not the same, and it shows an upward trend in the interval of -25dB to 25dB.
  • the larger the threshold ⁇ 0 of the signal-to-interference-noise ratio the lower the probability of successful communication, so the probability of communication interruption is on the rise as expected.
  • the threshold of the signal-to-interference-noise ratio ⁇ 0 The value is -20dB.
  • the probability of collision warning of vehicles meeting at the intersection also increases. The higher the speed, the more likely it is to collide with the vehicle C on other roads intersecting with the road where the vehicle C is located, so the collision probability of the traffic warning at the intersection also increases accordingly.
  • the collision probability of the intersection warning and warning is slightly increased. This is because the increase of the urban road line density will only slightly increase the number of vehicle nodes in other lanes of the intersection where the vehicle C enters, and the number of vehicle nodes in other lanes will be increased. The impact on the collision probability of passing vehicles at the intersection is relatively large, so the increase in the probability of collision warning of passing vehicles at the intersection is small.

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Abstract

一种基于V2X的无信号灯路口碰撞预警激活概率评估方法，基于泊松线过程的双重随机过程构建C-V2X网络分析模型，通过对C-V2X场景下的路口会车碰撞问题建立模型，推导出车辆间(V2V)通信的中断概率，由车辆间通信的信号强度对车辆间距离进行估计，推导路口会车的碰撞预警概率的理论解析式，依据此理论解析式，对基于V2X的无信号灯路口碰撞预警功能激活概率进行评估。仿真结果验证了碰撞预警概率的表达式，并且表明碰撞预警概率随着车辆行驶速度的增大而增大，随着车辆节点密度的增大而增大，随着城市道路线密度的增大而小幅增大。

Description

基于V2X的无信号灯路口碰撞预警激活概率评估方法 技术领域：
本发明涉及车联网中无线通信技术领域，尤其涉及一种基于随机几何理论对车联网场景下的路口会车碰撞预警问题进行仿真建模的基于V2X的无信号灯路口碰撞预警激活概率评估方法。
背景技术：
车联网指的是通过装载在车辆上的电子标签、无线射频等识别技术，实现信息网络平台上所有车辆的属性信息和静、动态信息的提取和有效利用，并根据不同的功能需求对所有车辆的运行状态进行有效的监管和提供综合服务的***。现如今，车联网被赋予了更多更广的功能，它是指通过车辆上的传感器、车载终端及电子标签提供车辆信息，采用各种通信技术手段实现车与车、车与人、车与路以及车与网之间的互联互通，并在信息网络平台对信息进行提取、共享等有效利用，对车辆进行有效的管控和提供综合服务。
C-V2X(Cellular-Vehicle to Everything)是基于蜂窝网技术的3GPP车联网通信技术，它提供了两种通信接口，其中一种是LTE蜂窝网的Uu接口，Uu通信接口可以实现基站与车辆、行人以及路侧单元(RSU)之间的通信，可以实现长距离、大范围的可靠通信，工作于运营商蜂窝网络频段；而另一种是LTE-D2D(点对点)的PC5接口，该接口被称为Sidelink(侧行链路或直通链路)，PC5通信接口可以实现车辆与车辆、车辆与行人以及车辆与路侧单元之间的短距离直接通信，工作于专用频段。
相较于固定的格点模型，随机几何理论在仿真建模的过程中保留了空间节点分布上的随机特性，因而随机几何工具可以更精确的对C-V2X的空间网络节点进行建模。随机几何理论最初的应用场景是移动自组织网络以及无线传感器网络，它们的空间节点分布具有随机特性，而随机几何理论可以在空间上平均地进行撒点，随机的生成任意可能的网络节点分布情况。
发明内容：
针对上述问题，本发明提出了一种基于V2X的无信号灯路口碰撞预警激活概率评估方法，能够提醒驾驶员注意安全，并且向附近其他车辆广播该车辆进入路口信息，提示附近车辆减速慢行，避免路口会车碰撞事故的发生。
本发明是通过如下技术方案实现的：
本发明提供一种基于V2X的无信号灯路口碰撞预警激活概率评估方法，包括如下步骤：
首先基于泊松线过程的双重随机过程，建立城市道路的仿真模型；
在随机生成的模拟道路上撒下服从一维泊松点过程的双向随机车辆节点以及路侧单元的空间节点位置，同时，在这一区域内通过二维泊松点过程生成基站的空间节点位置；
接着根据被研究车辆节点的信干噪比小于其阈值γ 0的概率来定义车辆与车辆之间通信的中断概率，并对中断概率进行仿真分析其曲线走势，从而找到满足路口通信成功概率大于95％的信干噪比阈值γ 0；
当车辆与车辆之间通信成功，并且不发生路口会车碰撞时，不会产生路口会车的碰撞预警信息，而如果一定范围内每一辆车的都能够与优先进入路口的车辆成功通信，并且都不与优先进入路口的车辆发生会车碰撞，车载终端将不会向驾驶员进行路口会车碰撞预警，无论以上哪一点不满足要求，车载终端都会向驾驶员发送路口会车碰撞预警信息，提醒驾驶员注意安全，并且向附近其他车辆广播该车辆进入路口信息，提示附近车辆减速慢行，避免路口会车碰撞事故的发生。
具体地，本发明首先对城市道路进行建模，利用泊松线过程，生成密度为λ L的城市道路，于是就有了随机生成的一张城市道路的快照，然后在已经生成的城市道路上，利用一维泊松点过程，进行车辆以及路侧单元进行撒点，将每一条城市道路看成是两条反向的车道，然后分别在每一个车道上进行车辆以及路侧单元撒点，在此基础上，利用二维的泊松点过程随机地生成基站节点位置使其分布在已生成的城市道路图上，其中基站的密度为λ 1，左车道的车辆以及路侧单元的密度为λ l2，而右车道的车辆以及路侧单元的密度为λ r2，两车道上车辆以及路侧单元的密度之和为λ 2。
车辆C根据接收到的信号强度估计自身与A、B中车辆之间的距离。该方法源自于定位中的RSS算法，可以通过车辆收到其他车辆发送过来信号的能量来估计目标车辆节点与接收车辆节点之间的距离，接受信号的强度与传播的距离成反比。简单来说就是，如果信干噪比SINR的值越大，车辆节点与车辆节点之间的距离就越大，相反如果SINR的值越小，车辆节点与车辆节点之间的距离就越小。
车辆间通信中断概率：车辆节点i与车辆节点C通信是否成功取决于信干噪比的阈值γ 0，当车辆节点i和车辆节点C之间的信干噪比高于阈值γ 0时，车辆节点i与节点C通信成功，而当车辆节点i和节点C直接的信干噪比低于阈值γ 0时，两车的通信连接失败，因而定义车辆节点i与节点C的通信中断概率
Figure PCTCN2020131015-appb-000001
由以下公式表示：
Figure PCTCN2020131015-appb-000002
I表示车辆C受到的干扰量化值，P 2表示车辆的信号发射功率,G表示天线增益，h i,c表示车辆i和车辆C之间进行通信的功率衰落系数，h i,c服从均值为λ i,c的指数分布，d i,c表示车辆i与车辆c之间的距离，-4表示路径损耗参数，N 0表示热噪声功率，假设每辆车的信号发射功率相同，P m表示车辆的信号发射功率，P m＝P 2，
Figure PCTCN2020131015-appb-000003
表示干扰的拉普拉斯变换。
路口会车碰撞预警分析：车辆C预警路口发生碰撞的概率为相交道路上的所有车辆在成功通信的情况下，预警每一辆车是否将会与车辆C发生碰撞，如果每一辆车都与车辆C通信成功并且不发生碰撞，则它们的乘积的就是不发生碰撞的概率，其互补概率就是可能发生碰撞的概率，也就是路口会车碰撞预警概率，碰撞预警概率可以由以下公式表示：
Figure PCTCN2020131015-appb-000004
其中，
Figure PCTCN2020131015-appb-000005
表示车辆节点Ai与节点C的通信中断概率，
Figure PCTCN2020131015-appb-000006
表示车辆节点Bi与节点C的通信中断概率,Ai表示行驶在某一方向上的车辆集合中的一辆汽车，Bi表示行驶在相反方向上的车辆集合中的一辆汽车。P CA和P CB表示车辆C与交叉路两个不同方向上的车通信成功并且不发生碰撞的概率，
Figure PCTCN2020131015-appb-000007
和
Figure PCTCN2020131015-appb-000008
表示车辆C与交叉路两个不同方向上的车辆A i以及车辆B i不发生碰撞的概率。
本发明具有如下优点：本发明基于V2X的无信号灯路口碰撞预警激活概率评估方法，基于随机几何理论知识，利用提出以上的公式进行路口会车碰撞预警的测试，可以获得与蒙特卡洛仿真非常接近的数值解，为智能交通***设计提供理论支撑。
附图说明：
图1为本发明的车联网下路口会车示意图；
图2为本发明车与车之间通信中断概率曲线图；
图3为本发明车辆节点速度-路口会车碰撞预警概率曲线图；
图4为本发明城市道路线密度-路口会车碰撞预警概率曲线图；
图5为本发明车辆节点密度-路口会车碰撞预警概率曲线图。
具体实施方式
下面结合附图对本发明的较佳实施例进行详细阐述，以使本发明的优点和特征能更易被本领域人员理解，从而对本发明的保护范围做出更为清楚明确的界定。
本发明在MATLAB仿真平台上，使用蒙特卡洛的方法进行该模型的仿真。
首先基于泊松线过程的双重随机过程，建立城市道路的仿真模型，将每一条城市道路看成是两条反向的车道，然后分别在每一个车道上进行车辆以及路侧单元撒点，利用二维的泊松点过程随机地生成基站节点位置使其分布在已生成的城市道路图上，如图1所示。
设定城市道路的线密度λ L为15km/km 2，基站节点分布的密度λ 1为0.5nodes/km 2，发射车辆节点以及路侧单元的密度之和λ 2为40nodes/km，基站的发射功率P 1为50dBm，车辆以及路侧单元的发射功率P 2从13dBm取到43dBm，中间间隔10dBm，热噪声功率N 0为-33dBm。定向天线的主瓣增益G为15，旁瓣增益g为1，仿真循环次数为10000次。
通过采用本发明中所述的方法，其仿真效果如下：
首先对车辆间通信中断概率进行仿真，如图2所示。信干噪比的阈值γ 0取在-25dB到25dB之间，对于不同的阈值γ 0来说，车辆与车辆之间通信的中断概率不相同，并且在-25dB到25dB的区间内呈上升趋势，并且信干噪比的阈值γ 0越大，通信成功概率就越低，所以通信中断概率呈上升趋势符号预期的要求。
由于路口场景下车辆与车辆之间的通信成功概率要求至少在95％以上(P 2取13dBm除外)，根据车辆与车辆之间的中断概率仿真图2所示，信干噪比的阈值γ 0取值为-20dB。设定车辆C的速度为25m/s，调节A i和B i的车速为10m/s到40m/s,如图3所示，随着车速的增大，路口会车的碰撞预警概率也随之增大，因为与车辆C所在道路相交的其他道路上的车辆行驶速度越快，就越有可能与车辆C发生碰撞，因而路口会车预警碰撞概率也随之提高。
此外，设定车辆C的速度为25m/s，A i和B i的车速为30m/s,改变城市道路建模的线密度，使之由1km/km 2变化到10km/km 2,如图4所示，路口会车预警碰撞概率极小幅度提升，这是由于城市道路线密度的提高只会小幅提高车辆C进入的路口其他车道上车辆节点数的原因，而由于其他车道上车辆节点数对路口会车碰撞概率的影响较大，因而路口会车的碰撞预警概率增幅很小。
最后，设定车辆C的速度为25m/s，A i和B i的车速为30m/s,改变城市道路上的车辆节点密度由40nodes/km增加到100nodes/km，如图5所示，随着车辆节点密度的增加路口会车碰撞预警概率增大。总而言之，该***模型能很好的描述C-V2X场景下路口会车的情况，可以为城市道路上车辆行驶方案提供参考，对路口碰撞进行预警，然后提示其他车道上的车辆减缓行驶速度，可以大大减小路口碰撞的发生，从而提高车辆行驶的安全。
最后应说明的是：以上实施例，仅为本发明的具体实施方式，用以说明本发明的技术方案，而非对其限制，本发明的保护范围并不局限于此，尽管参照前述实施例对本发明进行了详细的说明，本领域的普通技术人员应当理解：任何熟悉本技术领域的技术人员在本发明揭露的技术范围内，其依然可以对前述实施例所记载的技术方案进行修改或可轻易想到变化，或者对其中部分技术特征进行等同替换；而这些修改、变化或者替换，并不使相应技术方案的本质脱离本发明实施例技术方案的精神和范围，都应涵盖在本发明的保护范围之内。因此，本发明的保护范围应以权利要求的保护范围为准。

Claims (6)

  1. 一种基于V2X的无信号灯路口碰撞预警激活概率评估方法，其特征在于，包括如下步骤：
    (1)首先基于泊松线过程的双重随机过程，建立城市道路的仿真模型；
    (2)在随机生成的模拟道路上撒下服从一维泊松点过程的双向随机车辆节点以及路侧单元的空间节点位置，同时，在这一区域内通过二维泊松点过程生成基站的空间节点位置；
    (3)接着根据被研究车辆节点的信干噪比小于其阈值的概率来定义车辆与车辆之间通信的中断概率，并对中断概率进行仿真分析其曲线走势，从而找到满足路口通信成功概率大于95％的信干噪比阈值；
    (4)当车辆与车辆之间通信成功，并且不发生路口会车碰撞时，不会产生路口会车的碰撞预警信息，而如果一定范围内每一辆车都能够与优先进入路口的车辆成功通信，并且都不与优先进入路口的车辆发生会车碰撞，车载终端将不会向驾驶员进行路口会车碰撞预警，无论以上哪一点不满足要求，车载终端都会向驾驶员发送路口会车碰撞预警信息，并且向附近其他车辆广播该车辆进入路口信息。
  2. 根据权利要求1所述的基于V2X的无信号灯路口碰撞预警激活概率评估方法，其特征在于，步骤(1)具体包括利用泊松线过程，生成密度为λ L的城市道路以及一张随机城市道路分布的快照。
  3. 根据权利要求2所述的基于V2X的无信号灯路口碰撞预警激活概率评估方法，其特征在于，步骤(2)具体包括利用一维泊松点过程，将每一条城市道路看成是两条反向的车道，然后分别在每一个车道上进行车辆以及路侧单元撒点，利用二维的泊松点过程随机地生成基站节点位置使其分布在已生成的城市道路图上，其中基站的密度为λ 1，左车道的车辆以及路侧单元的密度为λ l2，而右车道的车辆以及路侧单元的密度为λ r2，两车道上车辆以及路侧单元的密度之和为λ 2。
  4. 根据权利要求3所述的基于V2X的无信号灯路口碰撞预警激活概率评估方法，其特征在于，步骤(3)中，通过车辆收到其他车辆发送过来信号的能量来估计目标车辆节点与接收车辆节点之间的距离，接受信号的强度与传播的距离成反比，如果信干噪比的值越大，车辆节点与车辆节点之间的距离就越大，如果信干噪比的值越小，车辆节点与车辆节点之间的距离就越小。
  5. 根据权利要求4所述的基于V2X的无信号灯路口碰撞预警激活概率评估方法，其特征在于，车辆节点i与车辆节点C通信是否成功取决于信干噪比的阈值γ 0，当车辆节点i和车 辆节点C之间的信干噪比高于阈值γ 0时，车辆节点i与节点C通信成功，而当车辆节点i和节点C直接的信干噪比低于阈值γ 0时，两车的通信连接失败，定义车辆节点i与节点C的通信中断概率
    Figure PCTCN2020131015-appb-100001
    由以下公式表示：
    Figure PCTCN2020131015-appb-100002
  6. 根据权利要求1所述的基于V2X的无信号灯路口碰撞预警激活概率评估方法，其特征在于，步骤(4)中，车辆C预警路口发生碰撞的概率为相交道路上的所有车辆在成功通信的情况下，预警每一辆车是否将会与车辆C发生碰撞，如果每一辆车都与车辆C通信成功并且不发生碰撞，则它们的乘积的就是不发生碰撞的概率，其互补概率就是可能发生碰撞的概率，也就是路口会车碰撞预警概率，碰撞预警概率可以由以下公式表示：
    Figure PCTCN2020131015-appb-100003
    其中P CA和P CB表示车辆C与交叉路两个不同方向上的车通信成功并且不发生碰撞的概率，
    Figure PCTCN2020131015-appb-100004
    和
    Figure PCTCN2020131015-appb-100005
    表示车辆C与交叉路两个不同方向上的车辆A i以及车辆B i不发生碰撞的概率。
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