技术领域technical field
本发明涉及一种基于车联网的交通路口分布式车辆碰撞预警方法，属于交通安全技术领域。The invention relates to a vehicle network-based distributed vehicle collision early warning method at a traffic intersection, which belongs to the technical field of traffic safety.
背景技术Background technique
车联网是一种特殊的移动自组织网络，而车联网技术则在智能化交通管理方面有着广泛的应用。当前，随着汽车普及程度的加大，车辆之间发生碰撞的交通事故也在变多，而这其中交通路口处由于其车流量大路况复杂已经成为车辆发生高频率碰撞的区域，因此实现该区域的车辆碰撞预警也是保证人们驾驶安全最重要的应用之一。The Internet of Vehicles is a special mobile ad hoc network, and the Internet of Vehicles technology has a wide range of applications in intelligent traffic management. At present, with the increasing popularity of automobiles, traffic accidents involving collisions between vehicles are also increasing, and traffic intersections have become areas where vehicles collide frequently due to their large traffic volume and complex road conditions. Regional vehicle collision warning is also one of the most important applications to ensure people's driving safety.
作为车联网研究的前沿，人们对利用车联网技术实现车辆的碰撞预警做了大量的研究。根据美国以及日本的交通事故统计数据显示，半数以上的交通事故是发生在交叉路口处，而车联网技术被认为是避免交通路口处碰撞提高驾驶安全的有效手段，其中利用车联网技术实现对车辆的行驶轨迹的精确预测，从而达到车辆的碰撞预警便是一种有效的方法。我们对车辆的行驶轨迹预测的想法来源于现实道路弯道处的设计符合数学上的回旋曲线这一特点，得此灵感我们对车辆行驶的状态建立起相应轨迹模型从而实现交通路口处的车辆碰撞预警。As the forefront of Internet of Vehicles research, people have done a lot of research on the use of Internet of Vehicles technology to realize vehicle collision warning. According to the statistics of traffic accidents in the United States and Japan, more than half of traffic accidents occur at intersections, and Internet of Vehicles technology is considered to be an effective means to avoid collisions at traffic intersections and improve driving safety. It is an effective method to accurately predict the driving trajectory of the vehicle so as to achieve the collision warning of the vehicle. Our idea of predicting the trajectory of the vehicle comes from the fact that the design of the curves of the real road conforms to the mathematical clothoid curve, which inspired us to establish a corresponding trajectory model for the state of the vehicle to realize the vehicle collision at the traffic intersection. early warning.
当前，存在的一些基于雷达，红外线，摄像机技术的碰撞预警系统容易受到恶劣天气还有光线的影响，可见这种方式的碰撞预警精度受到了客观环境的制约。其次，现有存在的基于车联网的碰撞预警系统大多需要在路边部署路边单元，通过车辆与路边单元的通信方式来获知相关信息，但此种方式在每个路口处部署路边单元投资成本太大，同时由于车辆需要先通过与路边单元通信，这样一来随着通信车辆的增多还有通信次数的增加，路边单元的存储和通信性能将成为瓶颈，另一方面车辆获取信息的实时性也不能保持同步。最后，在一些基于车联网的碰撞预警系统中通常把车辆看成地图上的一个点来以此计算与邻居车辆的相对距离和相对时间差，但是车辆有自己的相关属性，这种方式忽略了车辆的长度和宽度所计算出来的结果有着较大的误差。有鉴于此，我们提出的一种基于车联网技术的分布式碰撞预警方法便能够很好的解决上述问题。At present, some collision warning systems based on radar, infrared, and camera technologies are vulnerable to bad weather and light. It can be seen that the accuracy of collision warning in this way is restricted by the objective environment. Secondly, most of the existing collision warning systems based on the Internet of Vehicles need to deploy roadside units on the roadside, and obtain relevant information through the communication between vehicles and roadside units, but this method deploys roadside units at each intersection. The investment cost is too high. At the same time, because the vehicle needs to communicate with the roadside unit first, the storage and communication performance of the roadside unit will become the bottleneck as the number of communication vehicles increases. On the other hand, the vehicle acquisition The real-time nature of information cannot be kept in sync. Finally, in some collision warning systems based on the Internet of Vehicles, the vehicle is usually regarded as a point on the map to calculate the relative distance and relative time difference with neighboring vehicles, but the vehicle has its own related attributes, which ignores the vehicle There is a large error in the calculated results of length and width. In view of this, a distributed collision warning method based on Internet of Vehicles technology that we propose can well solve the above problems.
发明内容Contents of the invention
本发明提供了一种基于车联网的交通路口分布式车辆碰撞预警方法，能够较准确的预测车辆行驶轨迹及最早碰撞点时间。The invention provides a distributed vehicle collision early warning method at a traffic intersection based on the Internet of Vehicles, which can more accurately predict vehicle running tracks and the earliest collision point time.
本发明的技术方案是：一种基于车联网的交通路口分布式车辆碰撞预警方法，所述方法的具体步骤如下：The technical solution of the present invention is: a distributed vehicle collision early warning method at a traffic intersection based on the Internet of Vehicles, the specific steps of the method are as follows:
Step1、车辆通过车载单元信息采集模块获知车辆自身属性信息和车辆的行驶信息；车辆的行驶信息包括车辆当前行驶的位置、速度、加速度、行驶方向信息；Step1. The vehicle obtains the vehicle's own attribute information and vehicle driving information through the on-board unit information collection module; the vehicle's driving information includes the current driving position, speed, acceleration, and driving direction information of the vehicle;
Step2、车辆通过车载单元信息采集模块获知上述相关信息后，再通过车载单元中通信模块周期性的以车联网中V2V通信方式开始与周边车辆进行广播通信，并将车载单元获知的车辆自身属性信息和车辆的行驶信息发送给周边车辆，同时车辆本身也将接收其他车辆发送过来的车辆自身属性信息和车辆的行驶信息；Step2. After the vehicle obtains the above-mentioned relevant information through the vehicle-mounted unit information collection module, it periodically starts broadcasting communication with surrounding vehicles in the V2V communication mode in the Internet of Vehicles through the communication module in the vehicle-mounted unit, and transmits the vehicle’s own attribute information obtained by the vehicle-mounted unit. and the driving information of the vehicle are sent to surrounding vehicles, and the vehicle itself will also receive the vehicle's own attribute information and vehicle driving information sent by other vehicles;
Step3、以交通路口处中心点为坐标原点建立直角坐标系，随后车辆车载单元的计算模块将车辆抽象为车辆的中心点，根据获知的车辆自身属性信息和车辆的行驶信息建立起车辆行驶的轨迹方程，并依据车上存储的交通路口处电子地图和车辆经过的位置的坐标来预判当车辆行驶通过交通路口时的轨迹；Step3. Establish a Cartesian coordinate system with the center point at the traffic intersection as the coordinate origin, and then the calculation module of the vehicle on-board unit abstracts the vehicle as the center point of the vehicle, and establishes the vehicle’s driving trajectory based on the acquired vehicle’s own attribute information and vehicle driving information Equation, and based on the electronic map of the traffic intersection stored on the vehicle and the coordinates of the vehicle passing position to predict the trajectory of the vehicle when driving through the traffic intersection;
Step4、车载单元计算模块首先将车辆抽象为车辆的中心点，随后根据车辆行驶的轨迹方程来计算出车辆轨迹曲线的交叉点，而此时的交叉点为两辆车重合的中心点，再考虑到车辆自身的属性，将车辆还原于原本的长度和宽度，通过情景还原的方法找到车辆发生碰撞的最早点，并根据车辆到达碰撞最早点的时间差是否小于相应的阈值来发出预警。Step4. The on-board unit calculation module first abstracts the vehicle as the center point of the vehicle, and then calculates the intersection point of the vehicle trajectory curve according to the trajectory equation of the vehicle. At this time, the intersection point is the center point where the two vehicles overlap, and then consider According to the attributes of the vehicle itself, the vehicle is restored to its original length and width, and the earliest point of vehicle collision is found through the method of scene restoration, and an early warning is issued according to whether the time difference between the vehicle's arrival at the earliest point of collision is less than the corresponding threshold.
所述Step3中，根据车辆自身属性信息和车辆的行驶信息建立起车辆行驶轨迹方程；其中，如图1，对于建立直行车辆行驶轨迹方程为：In the described Step3, set up the vehicle trajectory equation according to the vehicle's own attribute information and the driving information of the vehicle; Wherein, as shown in Figure 1, for setting up the straight vehicle trajectory equation is:
车辆Vehicle_2当前行驶的位置为(X2,Y2)，车载单元根据当前车辆的行驶信息对车辆通过交通路口的轨迹做出预测，Vehicle_2最终到达路口的必经点就是(X2',Y2')，根据数学中的两点确定一条直线的定理，得到Vehicle_2的直线行驶轨迹表达式：The current driving position of vehicle Vehicle_2 is (X2 , Y2 ), and the on-board unit predicts the trajectory of the vehicle through the traffic intersection according to the current driving information of the vehicle. The necessary point for Vehicle_2 to finally reach the intersection is (X2 ', Y2 '), according to the theorem of determining a straight line by two points in mathematics, the straight-line driving trajectory expression of Vehicle_2 is obtained:
直线斜率为：The slope of the line is:
根据公式(1)、(2)得到车辆直行行驶轨迹方程：According to the formulas (1) and (2), the equation of the vehicle's straight travel trajectory is obtained:
因而引入一次线性方程对车辆直线行驶轨迹进行描述：Therefore, a linear equation is introduced to describe the straight-line driving trajectory of the vehicle:
Y＝kX+b (4)Y=kX+b (4)
其中k表示方程的斜率，b为一般常数。Where k represents the slope of the equation and b is a general constant.
所述Step3中，根据车辆自身属性信息和车辆的行驶信息建立起车辆行驶轨迹方程；其中，对于建立转弯时车辆的行驶轨迹方程为：In the described Step3, the vehicle trajectory equation is established according to the vehicle's own attribute information and the vehicle's driving information; wherein, the vehicle's trajectory equation for establishing a turn is:
当车辆当前以v(Km/h)的速度行驶，驾驶人此时转动方向盘，假设方向盘转动的角度为ψ，而汽车前轮转动的角度为则有下面关系：When the vehicle is currently traveling at a speed of v (Km/h), the driver turns the steering wheel at this time, assuming that the angle of rotation of the steering wheel is ψ, and the angle of rotation of the front wheels of the car is Then there is the following relationship:
假设方向盘转动的角速度为ω，则t秒过后，汽车前轮转动的角度为：Assuming that the angular velocity of the steering wheel is ω, then after t seconds, the angle of rotation of the front wheels of the car is:
假设汽车的轴距为d，d为车辆的宽度，汽车前轮转动了角度之后，此时算得车辆行驶轨迹的半径r：Suppose the wheelbase of the car is d, d is the width of the car, and the front wheels of the car turn After the angle, the radius r of the vehicle trajectory is calculated at this time:
同时又l＝vt (8)At the same time l=vt (8)
则由上述公式(5)(6)(7)(8)联立则推得：Then by combining the above formulas (5) (6) (7) (8), it can be deduced:
其中l代表车辆行驶的路程，A2是车辆行驶的一个特征常数，r是车辆在转弯过程中的半径，为变量，而k,d是与车辆尺寸相关的参数，通过上面的公式得知在车辆保持转弯速度且驾驶人匀速转动方向盘的时候，车辆的行驶轨迹是符合数学上的回旋曲线的，而现代道路设计中在设计车辆过弯的时候使用的就是数学上的回旋曲线；Among them, l represents the distance traveled by the vehicle, A2 is a characteristic constant of the vehicle, r is the radius of the vehicle during the turning process, which is a variable, and k, d are parameters related to the size of the vehicle. It is known from the above formula that in When the vehicle maintains the turning speed and the driver turns the steering wheel at a constant speed, the vehicle's driving trajectory conforms to the mathematical clothoid curve, and the modern road design uses the mathematical clothoid curve when designing the vehicle to turn;
为了验证这一观点，在回旋曲线上任取一点P取微分单元，则有：In order to verify this point of view, take any point P on the clothoid curve to take the differential unit, then:
我们将公式(9)中得到的r＝A2/l代入公式(10)得：We substitute r=A2 /l obtained in formula (9) into formula (10):
我们将公式(11)积分可得：We integrate formula (11) to get:
代入公式(10)可得：Substitute into formula (10) to get:
对公式(13)积分并化简可有：Integrating and simplifying formula (13) can have:
由于以下公式化简可得：Due to the simplification of the following formula:
将公式(14)(15)用sinβ与cosβ的n阶泰勒展开式可得如下回旋线的直角坐标方程：Using the n-order Taylor expansion of sinβ and cosβ in formulas (14) and (15), the following Cartesian coordinate equation of the clothoid can be obtained:
观察公式(16)中的弧长l可以得出弧长l是在无限趋近于2x，所以l可以用2x来替换，则可以得到回旋曲线的近似三次抛物线方程式：Observing the arc length l in the formula (16), it can be concluded that the arc length l is infinitely approaching 2x, so l can be replaced by 2x, and the approximate cubic parabola equation of the clothoid curve can be obtained:
2rx＝A2 (17)2rx＝A2 (17)
整理即为：The arrangement is:
C为常数 (18) C is a constant (18)
而现实情况下车辆在路口的行驶轨迹并不会完全符合上述理论，因此在这里我们引入如下完整的三次曲线方程表达式(19)来对车辆转弯时的轨迹进行描述:In reality, the trajectory of the vehicle at the intersection does not fully conform to the above theory, so here we introduce the following complete cubic curve equation (19) to describe the trajectory of the vehicle when turning:
y＝a+bx+cx2+ex3 (19)y=a+bx+cx2 +ex3 (19)
其中a、b、c、e是用来对车辆转弯时的轨迹进行修正的常数。Among them, a, b, c, and e are constants used to correct the trajectory of the vehicle when turning.
所述Step4中，在判断车辆是否会发生碰撞的过程中考虑到车辆自身的属性，即车辆的长度和宽度，使得计算出的碰撞点和碰撞时间差更加精准，具体方法为：In said Step4, in the process of judging whether the vehicle will collide, the attributes of the vehicle itself, that is, the length and width of the vehicle, are considered, so that the calculated collision point and collision time difference are more accurate. The specific method is:
首先以交通路口处中心点为坐标原点建立直角坐标系，再将车辆以其中心点为基准抽象成地图中的一个点，从而得到车辆的轨迹方程，再由轨迹方程去获得车辆通过点的计算方式所求得的预测车辆碰撞点坐标，此时再来考虑车辆的车宽和车长，通过情景还原的方法，将这种碰撞情形的时间点慢慢前移，找到车辆最早发生碰撞的坐标点，再来以此计算到达最早碰撞点的时间差，并根据车辆到达碰撞最早点的时间差是否小于相应的阈值来发出预警。Firstly, a Cartesian coordinate system is established with the center point at the traffic intersection as the coordinate origin, and then the vehicle is abstracted into a point in the map based on its center point, so as to obtain the trajectory equation of the vehicle, and then the calculation of the vehicle passing point is obtained by the trajectory equation The coordinates of the predicted vehicle collision point obtained by the method, at this time, consider the vehicle width and length of the vehicle, and use the method of scene restoration to slowly move the time point of this collision situation forward to find the coordinate point where the vehicle first collided , and then calculate the time difference of reaching the earliest collision point, and issue an early warning according to whether the time difference of the vehicle reaching the earliest collision point is less than the corresponding threshold.
所述Step4中的情况中，判断车辆是否会发生碰撞，其碰撞类型分为以下三种场景：In the situation in Step4, it is judged whether the vehicle will collide, and its collision type is divided into the following three scenarios:
(1)直行车辆与直行车辆的碰撞：如图4，车辆A1与车辆B1在不同车道，车辆B1由东向西行驶，车辆A1由南向北行驶，假如两辆直行车辆之间会发生碰撞，此时根据公式(4)得到车辆A1与车辆B1的行驶轨迹曲线方程分别为和其中和是车辆A1与车辆B1的轨迹方程斜率，和是常数，则联立两个方程得：(1) Collision between straight-going vehicles and straight-going vehicles: as shown in Figure 4, vehicle A1 and vehicle B1 are in different lanes, vehicle B1 is traveling from east to west, and vehicle A1 is traveling from south to north. There will be a collision between them, at this time, according to formula (4), the trajectory curve equations of vehicle A1 and vehicle B1 are obtained as and in and is the slope of the trajectory equation of vehicle A1 and vehicle B1 , and is a constant, then combine the two equations to get:
(2)直行车辆与转弯车辆碰撞：如图5，车辆A2与车辆B2，在不同车道行驶，其中车辆A2由南向北直行，车辆B2准备左转行驶，假设直行车辆A2和左转车辆B2之间发生碰撞；而此时根据公式y＝a+bx+cx2+ex3和公式(4)便可得车辆A2与车辆B2的行驶轨迹曲线方程分别为和其中是车辆B2行驶轨迹方程中的一般常数，和分别是车辆A2的轨迹方程的斜率和能得到的常数，则联立两个方程得：(2) Collision between a straight-going vehicle and a turning vehicle: As shown in Figure 5, vehicle A2 and vehicle B2 are driving in different lanes, where vehicle A2 is going straight from south to north, and vehicle B2 is going to turn left, assuming that vehicle A2 is going straight and left-turning vehicle B2; and at this time, according to the formula y=a+bx+ cx2+ ex3 andformula (4) , the trajectory curve equations of vehicle A2 and vehicle B2 are respectively and in is a general constant in the trajectory equation of vehicleB2 , and are the slope of the trajectory equation of vehicle A2 and the constants that can be obtained, then the two equations are combined to get:
(3)转弯车辆与转弯车辆的碰撞：如图6，车辆A3与车辆B3在不同的车道行驶，车辆A3向左转，车辆B3也向左转，假设车辆A3与车辆B3之间会发生碰撞，此时根据公式y＝a+bx+cx2+ex3得到车辆A3与车辆B3的行驶轨迹曲线方程分别为和其中是车辆A3行驶轨迹方程中的常数，是车辆B3行驶轨迹方程中的常数，则联立两个方程得：(3) Collision between turning vehicles and turning vehicles: as shown in Figure 6, vehicle A3 and vehicle B3 are driving in different lanes, vehicle A3 turns left, and vehicle B3 also turns left, assuming that vehicle A3 and vehicle B3 will collide. At this time, according to the formula y=a+bx+cx2 +ex3 , the trajectory curve equations of vehicle A3 and vehicle B3 are respectively and in is a constant in the vehicle A3 trajectory equation, is a constant in the equation of vehicleB3 ’s driving trajectory, then the two equations are combined to get:
所述Step4中的情况中，车辆最早点碰撞时间差的计算分为下面三种情形：In the situation in Step4, the calculation of the collision time difference at the earliest point of the vehicle is divided into the following three situations:
(1)直行车辆与直行车辆碰撞的情形：如图4，当车辆A1与车辆B1慢慢进入到交通路口处，车载单元检测到两车有碰撞的趋势之后(其中，车载单元不断进行迭代检测，依据前面建立的碰撞方程，当发向对两辆车建立的方程有交点时就意味着两车有碰撞的趋势)，便开始迭代计算车辆A1与车辆B1分别到达预测的最早碰撞点的碰撞时间，在不考虑车辆长度和宽度的情况下发生碰撞，也就是两辆车的中心点重合，这时根据方程组(20)求出的预测中心碰撞点坐标为而在现实中这种情况是不可能发生的，所以如果按照此种方式计算便存在误差，根据情景还原，将这种碰撞情形的时间点前移，得到了预测的车辆最早发生碰撞的坐标点，假设车辆A1长度为宽度为车辆B1长度为宽度为车辆A1当前速度为车辆B1当前速度为和分别为车辆A1与车辆B1的瞬时加速度，此时求得预测的最早碰撞点的坐标为车辆A1与车辆B1从当前位置到达碰撞点的距离分别为和(1) Collision between a straight-going vehicle and a straight-going vehicle: as shown in Figure 4, when vehicle A1 and vehicle B1 slowly enter the traffic intersection, after the vehicle-mounted unit detects that the two vehicles have a tendency to collide (wherein, the vehicle-mounted unit continuously Iterative detection, according to the previously established collision equation, when there is an intersection point between the equations established by the two vehicles, it means that the two vehicles have a tendency to collide), then iteratively calculate the earliest predicted arrival of vehicle A1 and vehicle B1 The collision time of the collision point, the collision occurs without considering the length and width of the vehicle, that is, the center points of the two vehicles coincide. At this time, the coordinates of the predicted center collision point calculated according to the equation group (20) are In reality, this kind of situation is impossible to happen, so if the calculation is carried out in this way, there will be errors. According to the restoration of the scene, the time point of this collision situation is moved forward, and the predicted coordinate point of the earliest collision of the vehicle is obtained. , assuming that the lengthof vehicle A1 is width isThe length of vehicle B1 is width is The current speed of vehicle A1 is The current speed of vehicle B1 is and are the instantaneous accelerations of vehicle A1 and vehicle B1 respectively, and the coordinates of the earliest predicted collision point obtained at this time are Vehicle A1 and Vehicle B1 from current location The distance to the collision point is and
和代表车辆A1与车辆B1到达最早碰撞点的时间，则计算两辆车分别到达最早碰撞点时间为： and Represents the time when vehicle A1 and vehicle B1 arrive at the earliest collision point, then calculate the time when the two vehicles arrive at the earliest collision point respectively:
(2)直行车辆与转弯车辆碰撞的情形：如图5，车载单元检测到行驶到交通路口处直行的车辆A2与转弯的车辆B2在路口处有碰撞的趋势后，之后便开始迭代计算车辆A2和B2分别到达预测的最早碰撞点的碰撞时间，根据方程组(21)解得预测的中心碰撞点的坐标为假设车辆A2长度为宽度为车辆B2长度为宽度为车辆A2当前速度为车辆B2当前速度为和分别为车辆A1与车辆B1的瞬时加速度，可按上述情景(1)的计算方法得到预测的两车最早碰撞点坐标为车辆A2与车辆B2从当前位置到达碰撞点的距离分别为和(2) Collision between a straight-going vehicle and a turning vehicle: as shown in Figure 5, the on-board unit detects that there is a tendency of collision between the straight-going vehicle A2 and the turning vehicle B2 at the intersection, and then iterative calculations are started The collision time of vehicles A2 and B2 respectively arriving at the predicted earliest collision point, according to the solution of equation group (21), the coordinates of the predicted center collision point are Suppose the length of vehicle A2 is width is The length of vehicle B2 is width is The current speed of vehicle A2 is The current speed of vehicle B2 is and are the instantaneous accelerations of vehicle A1 and vehicle B1 respectively, and the predicted coordinates of the earliest collision point of the two vehicles can be obtained according to the calculation method of the above scenario (1): Vehicle A2 and Vehicle B2 from current location The distance to the collision point is and
和代表车辆A2与车辆B2到达最早碰撞点的时间,则计算两辆车分别到达最早碰撞点时间为： and Represents the time when vehicle A2 and vehicle B2 arrive at the earliest collision point, then calculate the time when the two vehicles arrive at the earliest collision point respectively:
(3)转弯车辆与转弯车辆碰撞的情形：如图6，车载单元检测到转弯的车辆A3与同样转弯的车辆B3在路口处有碰撞的趋势，之后路边单元开始计算车辆A3和B3分别到达预测的最早碰撞点的碰撞时间，根据方程组(22)得到预测的中心碰撞点的坐标同样假设车辆A3长度为宽度为车辆B3长度为宽度为车辆A3当前速度为车辆B3当前速度为和分别为车辆A3与车辆B3的瞬时加速度，同样可按上述情景(1)的计算方法算出预测的最早碰撞点坐标车辆A3与车辆B3从当前位置到达碰撞点的距离分别为和(3) Collision between a turning vehicle and a turning vehicle: as shown in Figure 6, the on-board unit detects that the turning vehicle A3 and the same turning vehicle B3 have a tendency to collide at the intersection, and then the roadside unit starts to calculate the vehicle A3 andB3 respectively arrive at the collision time of the predicted earliest collision point, and obtain the coordinates of the predicted central collision point according to the equation group (22) Also assume that the length of vehicle A3 is width is The length of vehicle B3 is width is The current speed of vehicle A3 is The current speed of vehicle B3 is and are the instantaneous accelerations of vehicle A3 and vehicle B3 respectively, and the predicted coordinates of the earliest collision point can also be calculated according to the calculation method of the above scenario (1) Vehicle A3 and Vehicle B3 from current location The distance to the collision point is and
和代表车辆A3与车辆B3到达最早碰撞点的时间,则计算两辆车分别到达最早碰撞点时间为： and Represents the time when vehicle A3 and vehicle B3 arrive at the earliest collision point, then the time when the two vehicles arrive at the earliest collision point is calculated as:
所述Step4的情况中，根据到达预测的最早碰撞点时间差是否小于相应的阈值来发生预警，当车辆行驶在交通路口处如果有碰撞的可能，车载单元计算模块会根据相应的碰撞情形计算出两辆车到达预测的最早碰撞点的时间差，再通过时间差是否小于相应的阈值来对驾驶人做出减速提醒以此来避免碰撞。In the case of Step4, an early warning is generated according to whether the time difference of the earliest predicted collision point is less than the corresponding threshold. The time difference between the arrival of the vehicle at the earliest predicted collision point, and whether the time difference is less than the corresponding threshold is used to remind the driver to slow down to avoid collisions.
本发明的有益效果是：The beneficial effects of the present invention are:
本发明车辆携带的车载单元可以实时的获知车辆当前行驶的位置、速度、加速度、行驶方向等信息，车载单元根据这些信息以及车辆自身属性信息建立起相应的轨迹模型，并根据这些模型的轨迹曲线方程提前预判未来一段时间的轨迹，大大减少了车辆在复杂交通路口处发生的碰撞，尤其是在一些恶劣天气驾驶人视线受阻的情况下，本发明提供的车辆轨迹预测技术与现有技术相比更能发挥作用。同时本发明提供的是一种分布式碰撞预警方法，利用了各个车辆自身携带的车载单元进行了存储和计算判断，分担了以往路边单元集中存储计算的负荷，从而提高了整个系统的可靠性、可用性和扩展性，而利用车联网中的V2V通信方式则保证了车辆获知信息的实时性。The vehicle-mounted unit carried by the vehicle of the present invention can know information such as the current position, speed, acceleration, and driving direction of the vehicle in real time. The equation predicts the trajectory of a certain period of time in advance, which greatly reduces the collision of vehicles at complex traffic intersections, especially in the case of some bad weather when the driver's sight is blocked. The vehicle trajectory prediction technology provided by the present invention is comparable to that of the prior art more effective than . At the same time, the present invention provides a distributed collision warning method, which utilizes the on-board units carried by each vehicle for storage and calculation judgment, and shares the load of centralized storage and calculation of roadside units in the past, thereby improving the reliability of the entire system , usability and scalability, and the use of the V2V communication method in the Internet of Vehicles ensures the real-time nature of the information obtained by the vehicle.
附图说明Description of drawings
图1是车辆在交通路口处运动模型图；Fig. 1 is a motion model diagram of a vehicle at a traffic intersection;
图2是车辆转弯轨迹模型图；Fig. 2 is a vehicle turning track model diagram;
图3是车辆行驶轨迹的回旋线与三次抛物线比较；Fig. 3 is a comparison between the clothoid of the vehicle trajectory and the cubic parabola;
图4是直行与直行车辆碰撞时间差计算模型图；Fig. 4 is a calculation model diagram of the collision time difference between straight-going vehicles and straight-going vehicles;
图5是左转与直行车辆碰撞时间差计算模型图；Fig. 5 is a calculation model diagram of the collision time difference between left-turning and straight-going vehicles;
图6是左转与左转车辆碰撞时间差计算模型图。Fig. 6 is a calculation model diagram of the collision time difference between a left-turning vehicle and a left-turning vehicle.
具体实施方式Detailed ways
实施例1：如图1-6所示，一种基于车联网的交通路口分布式车辆碰撞预警方法，所述方法的具体步骤如下：Embodiment 1: As shown in Figures 1-6, a distributed vehicle collision warning method at a traffic intersection based on the Internet of Vehicles, the specific steps of the method are as follows:
Step1、车辆通过车载单元信息采集模块获知车辆自身属性信息和车辆的行驶信息；车辆的行驶信息包括车辆当前行驶的位置、速度、加速度、行驶方向信息；Step1. The vehicle obtains the vehicle's own attribute information and vehicle driving information through the on-board unit information collection module; the vehicle's driving information includes the current driving position, speed, acceleration, and driving direction information of the vehicle;
Step2、车辆通过车载单元信息采集模块获知上述相关信息后，再通过车载单元中通信模块周期性的以车联网中V2V通信方式开始与周边车辆进行广播通信，并将车载单元获知的车辆自身属性信息和车辆的行驶信息发送给周边车辆，同时车辆本身也将接收其他车辆发送过来的车辆自身属性信息和车辆的行驶信息；Step2. After the vehicle obtains the above-mentioned relevant information through the vehicle-mounted unit information collection module, it periodically starts broadcasting communication with surrounding vehicles in the V2V communication mode in the Internet of Vehicles through the communication module in the vehicle-mounted unit, and transmits the vehicle’s own attribute information obtained by the vehicle-mounted unit. and the driving information of the vehicle are sent to surrounding vehicles, and the vehicle itself will also receive the vehicle's own attribute information and vehicle driving information sent by other vehicles;
Step3、以交通路口处中心点为坐标原点建立直角坐标系，随后车辆车载单元的计算模块将车辆抽象为车辆的中心点，根据获知的车辆自身属性信息和车辆的行驶信息建立起车辆行驶的轨迹方程，并依据车上存储的交通路口处电子地图和车辆经过的位置的坐标来预判当车辆行驶通过交通路口时的轨迹；Step3. Establish a Cartesian coordinate system with the center point at the traffic intersection as the coordinate origin, and then the calculation module of the vehicle on-board unit abstracts the vehicle as the center point of the vehicle, and establishes the vehicle’s driving trajectory based on the acquired vehicle’s own attribute information and vehicle driving information Equation, and based on the electronic map of the traffic intersection stored on the vehicle and the coordinates of the vehicle passing position to predict the trajectory of the vehicle when driving through the traffic intersection;
Step4、车载单元计算模块首先将车辆抽象为车辆的中心点，随后根据车辆行驶的轨迹方程来计算出车辆轨迹曲线的交叉点，而此时的交叉点为两辆车重合的中心点，再考虑到车辆自身的属性，将车辆还原于原本的长度和宽度，通过情景还原的方法找到车辆发生碰撞的最早点，并根据车辆到达碰撞最早点的时间差是否小于相应的阈值来发出预警。Step4. The on-board unit calculation module first abstracts the vehicle as the center point of the vehicle, and then calculates the intersection point of the vehicle trajectory curve according to the trajectory equation of the vehicle. At this time, the intersection point is the center point where the two vehicles overlap, and then consider According to the attributes of the vehicle itself, the vehicle is restored to its original length and width, and the earliest point of vehicle collision is found through the method of scene restoration, and an early warning is issued according to whether the time difference between the vehicle's arrival at the earliest point of collision is less than the corresponding threshold.
所述Step3中，根据车辆自身属性信息和车辆的行驶信息建立起车辆行驶轨迹方程；其中，如图1，对于建立直行车辆行驶轨迹方程为：In the described Step3, set up the vehicle trajectory equation according to the vehicle's own attribute information and the driving information of the vehicle; Wherein, as shown in Figure 1, for setting up the straight vehicle trajectory equation is:
车辆Vehicle_2当前行驶的位置为(X2,Y2)，车载单元根据当前车辆的行驶信息对车辆通过交通路口的轨迹做出预测，Vehicle_2最终到达路口的必经点就是(X2',Y2')，根据数学中的两点确定一条直线的定理，得到Vehicle_2的直线行驶轨迹表达式：The current driving position of vehicle Vehicle_2 is (X2 , Y2 ), and the on-board unit predicts the trajectory of the vehicle through the traffic intersection according to the current driving information of the vehicle. The necessary point for Vehicle_2 to finally reach the intersection is (X2 ', Y2 '), according to the theorem of determining a straight line by two points in mathematics, the straight-line driving trajectory expression of Vehicle_2 is obtained:
直线斜率为：The slope of the line is:
根据公式(1)、(2)得到车辆直行行驶轨迹方程：According to the formulas (1) and (2), the equation of the vehicle's straight travel trajectory is obtained:
因而引入一次线性方程对车辆直线行驶轨迹进行描述：Therefore, a linear equation is introduced to describe the straight-line driving trajectory of the vehicle:
Y＝kX+b (4)Y=kX+b (4)
其中k表示方程的斜率，b为一般常数。Where k represents the slope of the equation and b is a general constant.
所述Step3中，根据车辆自身属性信息和车辆的行驶信息建立起车辆行驶轨迹方程；其中，对于建立转弯时车辆的行驶轨迹方程为：In the described Step3, the vehicle trajectory equation is established according to the vehicle's own attribute information and the vehicle's driving information; wherein, the vehicle's trajectory equation for establishing a turn is:
当车辆当前以v(Km/h)的速度行驶，驾驶人此时转动方向盘，假设方向盘转动的角度为ψ，而汽车前轮转动的角度为则有下面关系：When the vehicle is currently traveling at a speed of v (Km/h), the driver turns the steering wheel at this time, assuming that the angle of rotation of the steering wheel is ψ, and the angle of rotation of the front wheels of the car is Then there is the following relationship:
假设方向盘转动的角速度为ω，则t秒过后，汽车前轮转动的角度为：Assuming that the angular velocity of the steering wheel is ω, then after t seconds, the angle of rotation of the front wheels of the car is:
假设汽车的轴距为d，d为车辆的宽度，汽车前轮转动了角度之后，此时算得车辆行驶轨迹的半径r：Suppose the wheelbase of the car is d, d is the width of the car, and the front wheels of the car turn After the angle, the radius r of the vehicle trajectory is calculated at this time:
同时又l＝vt (8)At the same time l=vt (8)
则由上述公式(5)(6)(7)(8)联立则推得：Then by combining the above formulas (5) (6) (7) (8), it can be deduced:
其中l代表车辆行驶的路程，A2是车辆行驶的一个特征常数，r是车辆在转弯过程中的半径，为变量，而k,d是与车辆尺寸相关的参数，通过上面的公式得知在车辆保持转弯速度且驾驶人匀速转动方向盘的时候，车辆的行驶轨迹是符合数学上的回旋曲线的，而现代道路设计中在设计车辆过弯的时候使用的就是数学上的回旋曲线；Among them, l represents the distance traveled by the vehicle, A2 is a characteristic constant of the vehicle, r is the radius of the vehicle during the turning process, which is a variable, and k, d are parameters related to the size of the vehicle. It is known from the above formula that in When the vehicle maintains the turning speed and the driver turns the steering wheel at a constant speed, the vehicle's driving trajectory conforms to the mathematical clothoid curve, and the modern road design uses the mathematical clothoid curve when designing the vehicle to turn;
为了验证这一观点，在回旋曲线上任取一点P取微分单元，则有：In order to verify this point of view, take any point P on the clothoid curve to take the differential unit, then:
我们将公式(9)中得到的r＝A2/l代入公式(10)得：We substitute r=A2 /l obtained in formula (9) into formula (10):
我们将公式(11)积分可得：We integrate formula (11) to get:
代入公式(10)可得：Substitute into formula (10) to get:
对公式(13)积分并化简可有：Integrating and simplifying formula (13) can have:
由于以下公式化简可得：Due to the simplification of the following formula:
将公式(14)(15)用sinβ与cosβ的n阶泰勒展开式可得如下回旋线的直角坐标方程：Using the n-order Taylor expansion of sinβ and cosβ in formulas (14) and (15), the following Cartesian coordinate equation of the clothoid can be obtained:
观察公式(16)中的弧长l可以得出弧长l是在无限趋近于2x，所以l可以用2x来替换，则可以得到回旋曲线的近似三次抛物线方程式：Observing the arc length l in the formula (16), it can be concluded that the arc length l is infinitely approaching 2x, so l can be replaced by 2x, and the approximate cubic parabola equation of the clothoid curve can be obtained:
2rx＝A2 (17)2rx＝A2 (17)
整理即为：The arrangement is:
C为常数 (18) C is a constant (18)
而现实情况下车辆在路口的行驶轨迹并不会完全符合上述理论，因此在这里我们引入如下完整的三次曲线方程表达式(19)来对车辆转弯时的轨迹进行描述:In reality, the trajectory of the vehicle at the intersection does not fully conform to the above theory, so here we introduce the following complete cubic curve equation (19) to describe the trajectory of the vehicle when turning:
y＝a+bx+cx2+ex3 (19)y=a+bx+cx2 +ex3 (19)
其中a、b、c、e是用来对车辆转弯时的轨迹进行修正的常数。Among them, a, b, c, and e are constants used to correct the trajectory of the vehicle when turning.
所述Step4中，在判断车辆是否会发生碰撞的过程中考虑到车辆自身的属性，即车辆的长度和宽度，使得计算出的碰撞点和碰撞时间差更加精准，具体方法为：In said Step4, in the process of judging whether the vehicle will collide, the attributes of the vehicle itself, that is, the length and width of the vehicle, are considered, so that the calculated collision point and collision time difference are more accurate. The specific method is:
首先以交通路口处中心点为坐标原点建立直角坐标系，再将车辆以其中心点为基准抽象成地图中的一个点，从而得到车辆的轨迹方程，再由轨迹方程去获得车辆通过点的计算方式所求得的预测车辆碰撞点坐标，此时再来考虑车辆的车宽和车长，通过情景还原的方法，将这种碰撞情形的时间点慢慢前移，找到车辆最早发生碰撞的坐标点，再来以此计算到达最早碰撞点的时间差，并根据车辆到达碰撞最早点的时间差是否小于相应的阈值来发出预警。Firstly, a Cartesian coordinate system is established with the center point at the traffic intersection as the coordinate origin, and then the vehicle is abstracted into a point in the map based on its center point, so as to obtain the trajectory equation of the vehicle, and then the calculation of the vehicle passing point is obtained by the trajectory equation The coordinates of the predicted vehicle collision point obtained by the method, at this time, consider the vehicle width and length of the vehicle, and use the method of scene restoration to slowly move the time point of this collision situation forward to find the coordinate point where the vehicle first collided , and then calculate the time difference of reaching the earliest collision point, and issue an early warning according to whether the time difference of the vehicle reaching the earliest collision point is less than the corresponding threshold.
而如何具体的通过车辆轨迹预测来判断交通路口处车辆未来是否会发生碰撞可以由接下来的举例做进一步阐述：在通过路口处的时候车辆车载单元会每隔100ms获取自己的位置数据，并将这些数据发送给周边其他车辆。车辆的车载单元根据接收到的车辆的位置信息结合电子地图中该路口的关键点的信息进行曲线的插值运算得出车辆从当前位置到下一个路口关键点的曲线的数学表达式，然后与周围其他车辆发过来的轨迹曲线方程联立算得两轨迹曲线的交叉点，也就是两辆车重合的中心点。How to specifically judge whether a vehicle will collide at a traffic intersection in the future through vehicle trajectory prediction can be further elaborated by the following example: when passing through an intersection, the on-board unit of the vehicle will obtain its own position data every 100ms, and This data is sent to other vehicles in the surrounding area. The on-board unit of the vehicle performs the interpolation operation of the curve according to the received position information of the vehicle combined with the information of the key point of the intersection in the electronic map to obtain the mathematical expression of the curve of the vehicle from the current position to the key point of the next intersection, and then compares it with the surrounding The trajectory curve equations sent by other vehicles are combined to calculate the intersection point of the two trajectory curves, which is the center point where the two vehicles overlap.
见图1车辆电子地图存储了该路口处的每条车道的关键点的位置数据信息(图1中点N1,N2,N3,N4；S1,S2,S3,S4；W1,W2,W3,W4；E1,E2,E3,E4)这些关键点位于车道端位置中心处，是车辆行驶过路口之后必经的点。图中黑色三角形小点是车辆vehicle_1行驶过程中采集到的轨迹点，当车载单元判断出vehicle_1的左转趋向之后根据轨迹预测知道vehicle_1通过路口后的的最终点为N3。在这个过程中，我们就可以根据采集到得vehicle_1的轨迹数据并结合点N3的坐标插值出vehicle_1行驶的轨迹曲线的数学表达式。同理，直线行驶的vehicle_2的轨迹也可以得到。这样我们便计算出两条轨迹的交点。As shown in Figure 1, the vehicle electronic map stores the position data information of the key points of each lane at the intersection (points N1, N2, N3, N4 in Figure 1; S1, S2, S3, S4; W1, W2, W3, W4 ; E1, E2, E3, E4) These key points are located at the center of the lane end position, which is the point that the vehicle must pass after passing the intersection. The small black triangle points in the figure are the trajectory points collected during the driving of vehicle_1. When the on-board unit judges the left-turn tendency of vehicle_1, it is known from the trajectory prediction that the final point of vehicle_1 after passing the intersection is N3. In this process, we can interpolate the mathematical expression of the trajectory curve of vehicle_1 based on the collected trajectory data of vehicle_1 and the coordinates of point N3. Similarly, the trajectory of vehicle_2 traveling in a straight line can also be obtained. In this way we calculate the intersection point of the two trajectories.
见图1vehicle_1在行驶过程中形成了一系列的轨迹点，而这些轨迹点组成的轨迹曲线是连续的，且曲线的曲率也是连续变化的，于是我们可用三次曲线插值的方法来描述车辆的行驶轨迹。则vehicle_1轨迹的每一小段我们都可以用这样一个三次方程来描述：As shown in Figure 1, vehicle_1 forms a series of trajectory points during driving, and the trajectory curve composed of these trajectory points is continuous, and the curvature of the curve is also continuously changing, so we can use the cubic curve interpolation method to describe the driving trajectory of the vehicle . Then we can use such a cubic equation to describe each small segment of the vehicle_1 trajectory:
Si(x)＝ai+bi(x-xi)+ci(x-xi)2+di(x-xi)3,i＝1,2,…n-1Si (x)＝ai +bi (xxi )+ci (xxi )2 +di (xxi )3 , i＝1,2,...n-1
即有以下约束条件：That is, there are the following constraints:
①[xi,xi+1]中，有S(x)＝Si(x)，且S(x)是三次多项式①[xi ,xi+1 ], there is S(x)=Si (x), and S(x) is a cubic polynomial
②S(i)＝yi②S(i)=yi
③Si'(x)在[a,b]中连续，即S(x)是光滑的曲线③Si '(x) is continuous in [a,b], that is, S(x) is a smooth curve
由上述已知条件可推得：From the above known conditions, it can be deduced that:
Si(xi)＝yiSi (xi )=yi
Si(xi+1)＝yi+1Si (xi+1 )=yi+1
Si'(xi+1)＝Si'+1(xi+1)Si '(xi+1 )＝Si '+1 (xi+1 )
Si(x)＝ai+bi(x-xi)+ci(x-xi)2+di(x-xi)3Si (x)＝ai +bi (xxi )+ci (xxi )2 +di (xxi )3
Si'(x)＝bi+2ci(x-xi)+3di(x-xi)2Si '(x)＝bi +2ci (xxi )+3di (xxi )2
Si'(x)＝2ci+6di(x-xi)Si '(x)＝2ci +6di (xxi )
hi＝xi+1-xihi =xi+1 -xi
mi＝S”(xi)＝2cimi =S"(xi )=2ci
我们使用了自然边界条件来求解该方程，即增加了两个条件：We solve the equation using natural boundary conditions, that is, adding two conditions:
S'0'(x1)＝S1”(x1)S'0 '(x1 )＝S1 ”(x1 )
S'n'-2(xn-1)＝S'n'-1(xn-1)S'n '-2 (xn-1 )＝S'n '-1 (xn-1 )
另有：Wn+1的坐标为(X1',Y1')；In addition: the coordinates of Wn+1 are (X1 ', Y1 ');
使用上述已知条件可得解该方程的矩阵方程组。通过解得这个矩阵的系数方程，我们可以得到最后一个分段的三次曲线方程(见图1所示就是从当前位置点到点N3的轨迹曲线方程)，即车辆当前的位置到下一个路口关键点处的轨迹曲线方程。通过这个方程我们可以对车辆的行驶轨迹做出预测，再计算到达碰撞点时间差再将车辆长度和宽度还原并据此检测车辆是否会与其他车辆发生碰撞的可能。Using the known conditions above, the matrix equations to solve this equation can be obtained. By solving the coefficient equation of this matrix, we can get the cubic curve equation of the last segment (as shown in Figure1 is the trajectory curve equation from the current position point to point N3), that is, the current position of the vehicle to the next intersection Equations of trajectory curves at key points. Through this equation, we can predict the driving trajectory of the vehicle, calculate the time difference to the collision point, restore the length and width of the vehicle, and detect whether the vehicle will collide with other vehicles.
所述Step4中的情况中，判断车辆是否会发生碰撞，其碰撞类型分为以下三种场景：In the situation in Step4, it is judged whether the vehicle will collide, and its collision type is divided into the following three scenarios:
(1)直行车辆与直行车辆的碰撞：如图4，车辆A1与车辆B1在不同车道，车辆B1由东向西行驶，车辆A1由南向北行驶，假如两辆直行车辆之间会发生碰撞，此时根据公式(4)得到车辆A1与车辆B1的行驶轨迹曲线方程分别为和其中和是车辆A1与车辆B1的轨迹方程斜率，和是常数，则联立两个方程得：(1) Collision between straight-going vehicles and straight-going vehicles: as shown in Figure 4, vehicle A1 and vehicle B1 are in different lanes, vehicle B1 is traveling from east to west, and vehicle A1 is traveling from south to north. There will be a collision between them, at this time, according to formula (4), the trajectory curve equations of vehicle A1 and vehicle B1 are obtained as and in and is the slope of the trajectory equation of vehicle A1 and vehicle B1 , and is a constant, then combine the two equations to get:
(2)直行车辆与转弯车辆碰撞：如图5，车辆A2与车辆B2，在不同车道行驶，其中车辆A2由南向北直行，车辆B2准备左转行驶，假设直行车辆A2和左转车辆B2之间发生碰撞；而此时根据公式y＝a+bx+cx2+ex3和公式(4)便可得车辆A2与车辆B2的行驶轨迹曲线方程分别为和其中是车辆B2行驶轨迹方程中的一般常数，和分别是车辆A2的轨迹方程的斜率和能得到的常数，则联立两个方程得：(2) Collision between a straight-going vehicle and a turning vehicle: As shown in Figure 5, vehicle A2 and vehicle B2 are driving in different lanes, where vehicle A2 is going straight from south to north, and vehicle B2 is going to turn left, assuming that vehicle A2 is going straight and left-turning vehicle B2; and at this time, according to the formula y=a+bx+ cx2+ ex3 andformula (4) , the trajectory curve equations of vehicle A2 and vehicle B2 are respectively and in is a general constant in the trajectory equation of vehicleB2 , and are the slope of the trajectory equation of vehicle A2 and the constants that can be obtained, then the two equations are combined to get:
(3)转弯车辆与转弯车辆的碰撞：如图6，车辆A3与车辆B3在不同的车道行驶，车辆A3向左转，车辆B3也向左转，假设车辆A3与车辆B3之间会发生碰撞，此时根据公式y＝a+bx+cx2+ex3得到车辆A3与车辆B3的行驶轨迹曲线方程分别为和其中是车辆A3行驶轨迹方程中的常数，是车辆B3行驶轨迹方程中的常数，则联立两个方程得：(3) Collision between turning vehicles and turning vehicles: as shown in Figure 6, vehicle A3 and vehicle B3 are driving in different lanes, vehicle A3 turns left, and vehicle B3 also turns left, assuming that vehicle A3 and vehicle B3 will collide. At this time, according to the formula y=a+bx+cx2 +ex3 , the trajectory curve equations of vehicle A3 and vehicle B3 are respectively and in is a constant in the vehicle A3 trajectory equation, is a constant in the equation of vehicleB3 ’s driving trajectory, then the two equations are combined to get:
所述Step4中的情况中，车辆最早点碰撞时间差的计算分为下面三种情形：In the situation in Step4, the calculation of the collision time difference at the earliest point of the vehicle is divided into the following three situations:
(1)直行车辆与直行车辆碰撞的情形：如图4，当车辆A1与车辆B1慢慢进入到交通路口处，车载单元检测到两车有碰撞的趋势之后(其中，车载单元不断进行迭代检测，依据前面建立的碰撞方程，当发向对两辆车建立的方程有交点时就意味着两车有碰撞的趋势)，便开始迭代计算车辆A1与车辆B1分别到达预测的最早碰撞点的碰撞时间，在不考虑车辆长度和宽度的情况下发生碰撞，也就是两辆车的中心点重合，这时根据方程组(20)求出的预测中心碰撞点坐标为而在现实中这种情况是不可能发生的，所以如果按照此种方式计算便存在误差，根据情景还原，将这种碰撞情形的时间点前移，得到了预测的车辆最早发生碰撞的坐标点，假设车辆A1长度为宽度为车辆B1长度为宽度为车辆A1当前速度为车辆B1当前速度为和分别为车辆A1与车辆B1的瞬时加速度，此时求得预测的最早碰撞点的坐标为车辆A1与车辆B1从当前位置到达碰撞点的距离分别为和(1) Collision between a straight-going vehicle and a straight-going vehicle: as shown in Figure 4, when vehicle A1 and vehicle B1 slowly enter the traffic intersection, after the vehicle-mounted unit detects that the two vehicles have a tendency to collide (wherein, the vehicle-mounted unit continuously Iterative detection, according to the previously established collision equation, when there is an intersection point between the equations established by the two vehicles, it means that the two vehicles have a tendency to collide), then iteratively calculate the earliest predicted arrival of vehicle A1 and vehicle B1 The collision time of the collision point, the collision occurs without considering the length and width of the vehicle, that is, the center points of the two vehicles coincide. At this time, the coordinates of the predicted center collision point calculated according to the equation group (20) are In reality, this kind of situation is impossible to happen, so if the calculation is carried out in this way, there will be errors. According to the restoration of the scene, the time point of this collision situation is moved forward, and the predicted coordinate point of the earliest collision of the vehicle is obtained. , assuming that the lengthof vehicle A1 is width isThe length of vehicle B1 is width is The current speed of vehicle A1 is The current speed of vehicle B1 is and are the instantaneous accelerations of vehicle A1 and vehicle B1 respectively, and the coordinates of the earliest predicted collision point obtained at this time are Vehicle A1 and Vehicle B1 from current location The distance to the collision point is and
和代表车辆A1与车辆B1到达最早碰撞点的时间，则计算两辆车分别到达最早碰撞点时间为： and Represents the time when vehicle A1 and vehicle B1 arrive at the earliest collision point, then calculate the time when the two vehicles arrive at the earliest collision point respectively:
(2)直行车辆与转弯车辆碰撞的情形：如图5，车载单元检测到行驶到交通路口处直行的车辆A2与转弯的车辆B2在路口处有碰撞的趋势后，之后便开始迭代计算车辆A2和B2分别到达预测的最早碰撞点的碰撞时间，根据方程组(21)解得预测的中心碰撞点的坐标为假设车辆A2长度为宽度为车辆B2长度为宽度为车辆A2当前速度为车辆B2当前速度为和分别为车辆A1与车辆B1的瞬时加速度，可按上述情景(1)的计算方法得到预测的两车最早碰撞点坐标为车辆A2与车辆B2从当前位置到达碰撞点的距离分别为和(2) Collision between a straight-going vehicle and a turning vehicle: as shown in Figure 5, the on-board unit detects that there is a tendency of collision between the straight-going vehicle A2 and the turning vehicle B2 at the intersection, and then iterative calculations are started The collision time of vehicles A2 and B2 respectively arriving at the predicted earliest collision point, according to the solution of equation group (21), the coordinates of the predicted center collision point are Suppose the length of vehicle A2 is width is The length of vehicle B2 is width is The current speed of vehicle A2 is The current speed of vehicle B2 is and are the instantaneous accelerations of vehicle A1 and vehicle B1 respectively, and the predicted coordinates of the earliest collision point of the two vehicles can be obtained according to the calculation method of the above scenario (1): Vehicle A2 and Vehicle B2 from current location The distance to the collision point is and
和代表车辆A2与车辆B2到达最早碰撞点的时间,则计算两辆车分别到达最早碰撞点时间为： and Represents the time when vehicle A2 and vehicle B2 arrive at the earliest collision point, then calculate the time when the two vehicles arrive at the earliest collision point respectively:
(3)转弯车辆与转弯车辆碰撞的情形：如图6，车载单元检测到转弯的车辆A3与同样转弯的车辆B3在路口处有碰撞的趋势，之后路边单元开始计算车辆A3和B3分别到达预测的最早碰撞点的碰撞时间，根据方程组(22)得到预测的中心碰撞点的坐标同样假设车辆A3长度为宽度为车辆B3长度为宽度为车辆A3当前速度为车辆B3当前速度为和分别为车辆A3与车辆B3的瞬时加速度，同样可按上述情景(1)的计算方法算出预测的最早碰撞点坐标车辆A3与车辆B3从当前位置到达碰撞点的距离分别为和(3) Collision between a turning vehicle and a turning vehicle: as shown in Figure 6, the on-board unit detects that the turning vehicle A3 and the same turning vehicle B3 have a tendency to collide at the intersection, and then the roadside unit starts to calculate the vehicle A3 andB3 respectively arrive at the collision time of the predicted earliest collision point, and obtain the coordinates of the predicted central collision point according to the equation group (22) Also assume that the length of vehicle A3 is width is The length of vehicle B3 is width is The current speed of vehicle A3 is The current speed of vehicle B3 is and are the instantaneous accelerations of vehicle A3 and vehicle B3 respectively, and the predicted coordinates of the earliest collision point can also be calculated according to the calculation method of the above scenario (1) Vehicle A3 and Vehicle B3 from current location The distance to the collision point is and
和代表车辆A3与车辆B3到达最早碰撞点的时间,则计算两辆车分别到达最早碰撞点时间为： and Represents the time when vehicle A3 and vehicle B3 arrive at the earliest collision point, then the time when the two vehicles arrive at the earliest collision point is calculated as:
所述Step4的情况中，根据到达预测的最早碰撞点时间差是否小于相应的阈值来发生预警，当车辆行驶在交通路口处如果有碰撞的可能，车载单元计算模块会根据相应的碰撞情形计算出两辆车到达预测的最早碰撞点的时间差，再通过时间差是否小于相应的阈值来对驾驶人做出减速提醒以此来避免碰撞。In the case of Step4, an early warning is generated according to whether the time difference of the earliest predicted collision point is less than the corresponding threshold. The time difference between the arrival of the vehicle at the earliest predicted collision point, and whether the time difference is less than the corresponding threshold is used to remind the driver to slow down to avoid collisions.
上面结合附图对本发明的具体实施方式作了详细说明，但是本发明并不限于上述实施方式，在本领域普通技术人员所具备的知识范围内，还可以在不脱离本发明宗旨的前提下作出各种变化。The specific implementation of the present invention has been described in detail above in conjunction with the accompanying drawings, but the present invention is not limited to the above-mentioned implementation, within the knowledge of those of ordinary skill in the art, it can also be made without departing from the gist of the present invention. Variations.
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| CN201810282975.6ACN108597251A (en) | 2018-04-02 | 2018-04-02 | A kind of traffic intersection distribution vehicle collision prewarning method based on car networking |
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| CN201810282975.6ACN108597251A (en) | 2018-04-02 | 2018-04-02 | A kind of traffic intersection distribution vehicle collision prewarning method based on car networking |
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| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN109141460A (en)* | 2018-09-30 | 2019-01-04 | 佳都新太科技股份有限公司 | Vehicle running path optimization method, calculates equipment and storage medium at device |
| CN109557925A (en)* | 2018-12-29 | 2019-04-02 | 北京智行者科技有限公司 | Automatic driving vehicle barrier preventing collision method and device |
| CN109703558A (en)* | 2018-12-28 | 2019-05-03 | 吉林大学 | Internet of things-based car warning safety system |
| CN109859500A (en)* | 2018-12-17 | 2019-06-07 | 中电海康集团有限公司 | A kind of high speed merging area safe early warning method based on bus or train route collaboration |
| CN110533913A (en)* | 2019-09-17 | 2019-12-03 | 辰芯科技有限公司 | Method for early warning, device, vehicle and the storage medium of vehicle collision |
| CN111311918A (en)* | 2020-05-12 | 2020-06-19 | 南京云析科技有限公司 | Traffic management method and device based on visual analysis |
| CN111369811A (en)* | 2019-11-22 | 2020-07-03 | 杭州海康威视系统技术有限公司 | A kind of collision prediction method, device and electronic device |
| CN111402614A (en)* | 2020-03-27 | 2020-07-10 | 北京经纬恒润科技有限公司 | Vehicle driving decision adjustment method and device and vehicle-mounted terminal |
| CN111402601A (en)* | 2020-03-10 | 2020-07-10 | 合肥联宝信息技术有限公司 | Intelligent traffic control method and system |
| CN111524391A (en)* | 2020-04-29 | 2020-08-11 | 安徽江淮汽车集团股份有限公司 | Vehicle steering assist method, apparatus, storage medium, and device |
| CN111540236A (en)* | 2020-04-17 | 2020-08-14 | 淮阴工学院 | Prediction method of collision between left-turn motor vehicles and non-motor vehicles at intersections |
| TWI705016B (en)* | 2019-07-22 | 2020-09-21 | 緯創資通股份有限公司 | Driving alarm system, driving alarm method and electronic device using the same |
| CN111932881A (en)* | 2020-08-12 | 2020-11-13 | 广西科技大学 | Traffic intersection management method and device, terminal device and storage medium |
| CN111968395A (en)* | 2020-08-27 | 2020-11-20 | 深圳安智杰科技有限公司 | Vehicle motion relation determining method and device |
| CN112185132A (en)* | 2020-09-08 | 2021-01-05 | 大连理工大学 | Coordination method for vehicle intersection without traffic light |
| CN112215198A (en)* | 2020-10-28 | 2021-01-12 | 郑小云 | Self-adaptive man-machine interaction system and method based on big data |
| CN112486161A (en)* | 2019-09-11 | 2021-03-12 | 本田技研工业株式会社 | Vehicle control device, vehicle control method, and storage medium |
| CN112578792A (en)* | 2020-11-12 | 2021-03-30 | 东风汽车集团有限公司 | Crossroad auxiliary control method and storage medium |
| CN112767697A (en)* | 2021-01-19 | 2021-05-07 | 东南大学 | Traffic safety evaluation method, system and device based on traffic conflict prediction |
| CN112907986A (en)* | 2021-01-12 | 2021-06-04 | 浙江大学 | Dynamic time window crossing scheduling method based on digital twin scene and edge cloud |
| CN112908033A (en)* | 2021-01-13 | 2021-06-04 | 长安大学 | Internet vehicle cooperation collision avoidance early warning system and method under non-signal control intersection environment |
| CN113022488A (en)* | 2019-12-25 | 2021-06-25 | 北京宝沃汽车股份有限公司 | Vehicle and control method and device thereof |
| CN113095393A (en)* | 2021-04-06 | 2021-07-09 | 兰州交通大学 | High-income taxi driver and extraction method, equipment and storage medium of experience track of taxi driver |
| CN113096411A (en)* | 2021-03-17 | 2021-07-09 | 武汉大学 | Vehicle collision early warning method at intersection based on Internet of vehicles environmental system |
| CN113361148A (en)* | 2021-08-09 | 2021-09-07 | 中汽研汽车检验中心（天津）有限公司 | Method for recognizing vehicle frontal collision type and judging severity |
| CN113375685A (en)* | 2021-03-31 | 2021-09-10 | 福建工程学院 | Urban intersection center identification and intersection turning rule extraction method based on sub-track intersection |
| CN113537606A (en)* | 2021-07-22 | 2021-10-22 | 上汽通用五菱汽车股份有限公司 | Accident prediction method, accident prediction device and computer-readable storage medium |
| CN113692372A (en)* | 2019-04-12 | 2021-11-23 | 伟摩有限责任公司 | Exception handling for autonomous vehicles |
| CN114078326A (en)* | 2020-08-19 | 2022-02-22 | 北京万集科技股份有限公司 | Collision detection method, device, visual sensor and storage medium |
| CN114354216A (en)* | 2021-12-31 | 2022-04-15 | 信通院车联网创新中心(成都)有限公司 | High-precision positioning-based V2X collision warning real vehicle test system and method |
| CN114419926A (en)* | 2022-01-24 | 2022-04-29 | 浙江海康智联科技有限公司 | Intersection left-turning auxiliary early warning system and method based on vehicle-road cooperation |
| CN114655199A (en)* | 2022-03-29 | 2022-06-24 | 东风汽车集团股份有限公司 | Collision avoidance device for oncoming vehicle under left-turn working condition |
| WO2022151839A1 (en)* | 2021-01-12 | 2022-07-21 | 华为技术有限公司 | Vehicle turning route planning method and apparatus |
| CN115690196A (en)* | 2022-10-21 | 2023-02-03 | 袁世姣 | Highway driving vehicle collision point positioning application based on edge function and inpogon function |
| US11851053B2 (en) | 2020-09-14 | 2023-12-26 | International Business Machines Corporation | Collaborative accident prevention |
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN104537889A (en)* | 2014-12-30 | 2015-04-22 | 四川九洲电器集团有限责任公司 | Anti-collision method and system under different vehicle conditions |
| CN104882025A (en)* | 2015-05-13 | 2015-09-02 | 东华大学 | Crashing detecting and warning method based on vehicle network technology |
| CN105427669A (en)* | 2015-12-04 | 2016-03-23 | 重庆邮电大学 | Anti-collision early warning method based on DSRC vehicle-to-vehicle communication technology |
| CN105654779A (en)* | 2016-02-03 | 2016-06-08 | 北京工业大学 | Expressway construction area traffic flow coordination control method based on vehicle-road and vehicle-vehicle communication |
| CN105741609A (en)* | 2014-12-26 | 2016-07-06 | 本田技研工业株式会社 | Vehicle collision avoidance supporting apparatus and vehicle collision avoidance supporting method |
| CN106875747A (en)* | 2017-02-28 | 2017-06-20 | 同济大学 | A kind of real-time traffic conflict method for early warning based on high accuracy positioning and collaborative truck |
| CN107146412A (en)* | 2017-06-07 | 2017-09-08 | 江苏大学 | A method for constructing comprehensive variables of expressway vehicle anti-collision early warning based on Internet of Vehicles |
| CN107564336A (en)* | 2017-09-11 | 2018-01-09 | 哈尔滨工业大学 | A kind of signalized intersections left-hand rotation conflict early warning system and method for early warning |
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN105741609A (en)* | 2014-12-26 | 2016-07-06 | 本田技研工业株式会社 | Vehicle collision avoidance supporting apparatus and vehicle collision avoidance supporting method |
| CN104537889A (en)* | 2014-12-30 | 2015-04-22 | 四川九洲电器集团有限责任公司 | Anti-collision method and system under different vehicle conditions |
| CN104882025A (en)* | 2015-05-13 | 2015-09-02 | 东华大学 | Crashing detecting and warning method based on vehicle network technology |
| CN105427669A (en)* | 2015-12-04 | 2016-03-23 | 重庆邮电大学 | Anti-collision early warning method based on DSRC vehicle-to-vehicle communication technology |
| CN105654779A (en)* | 2016-02-03 | 2016-06-08 | 北京工业大学 | Expressway construction area traffic flow coordination control method based on vehicle-road and vehicle-vehicle communication |
| CN106875747A (en)* | 2017-02-28 | 2017-06-20 | 同济大学 | A kind of real-time traffic conflict method for early warning based on high accuracy positioning and collaborative truck |
| CN107146412A (en)* | 2017-06-07 | 2017-09-08 | 江苏大学 | A method for constructing comprehensive variables of expressway vehicle anti-collision early warning based on Internet of Vehicles |
| CN107564336A (en)* | 2017-09-11 | 2018-01-09 | 哈尔滨工业大学 | A kind of signalized intersections left-hand rotation conflict early warning system and method for early warning |
| Title |
|---|
| 李修琪: "基于车载自组织网络的车辆路口碰撞预警算法研究", 《中国优秀硕士学位论文全文数据库工程科技Ⅱ辑》* |
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN109141460A (en)* | 2018-09-30 | 2019-01-04 | 佳都新太科技股份有限公司 | Vehicle running path optimization method, calculates equipment and storage medium at device |
| CN109859500A (en)* | 2018-12-17 | 2019-06-07 | 中电海康集团有限公司 | A kind of high speed merging area safe early warning method based on bus or train route collaboration |
| CN109703558A (en)* | 2018-12-28 | 2019-05-03 | 吉林大学 | Internet of things-based car warning safety system |
| CN109557925A (en)* | 2018-12-29 | 2019-04-02 | 北京智行者科技有限公司 | Automatic driving vehicle barrier preventing collision method and device |
| CN109557925B (en)* | 2018-12-29 | 2021-12-10 | 北京智行者科技有限公司 | Obstacle avoiding method and device for automatic driving vehicle |
| CN113692372B (en)* | 2019-04-12 | 2022-09-13 | 伟摩有限责任公司 | Exception Handling for Autonomous Vehicles |
| US11709503B2 (en) | 2019-04-12 | 2023-07-25 | Waymo Llc | Exception handling for autonomous vehicles |
| CN113692372A (en)* | 2019-04-12 | 2021-11-23 | 伟摩有限责任公司 | Exception handling for autonomous vehicles |
| US12372971B2 (en) | 2019-04-12 | 2025-07-29 | Waymo Llc | Exception handling for autonomous vehicles |
| US12093058B2 (en) | 2019-04-12 | 2024-09-17 | Waymo Llc | Exception handling for autonomous vehicles |
| TWI705016B (en)* | 2019-07-22 | 2020-09-21 | 緯創資通股份有限公司 | Driving alarm system, driving alarm method and electronic device using the same |
| CN112486161A (en)* | 2019-09-11 | 2021-03-12 | 本田技研工业株式会社 | Vehicle control device, vehicle control method, and storage medium |
| CN110533913A (en)* | 2019-09-17 | 2019-12-03 | 辰芯科技有限公司 | Method for early warning, device, vehicle and the storage medium of vehicle collision |
| CN111369811A (en)* | 2019-11-22 | 2020-07-03 | 杭州海康威视系统技术有限公司 | A kind of collision prediction method, device and electronic device |
| CN113022488A (en)* | 2019-12-25 | 2021-06-25 | 北京宝沃汽车股份有限公司 | Vehicle and control method and device thereof |
| CN111402601A (en)* | 2020-03-10 | 2020-07-10 | 合肥联宝信息技术有限公司 | Intelligent traffic control method and system |
| CN111402614A (en)* | 2020-03-27 | 2020-07-10 | 北京经纬恒润科技有限公司 | Vehicle driving decision adjustment method and device and vehicle-mounted terminal |
| CN111540236A (en)* | 2020-04-17 | 2020-08-14 | 淮阴工学院 | Prediction method of collision between left-turn motor vehicles and non-motor vehicles at intersections |
| CN111524391A (en)* | 2020-04-29 | 2020-08-11 | 安徽江淮汽车集团股份有限公司 | Vehicle steering assist method, apparatus, storage medium, and device |
| CN111524391B (en)* | 2020-04-29 | 2021-08-27 | 安徽江淮汽车集团股份有限公司 | Vehicle steering assist method, apparatus, storage medium, and device |
| CN111311918B (en)* | 2020-05-12 | 2020-09-04 | 南京云析科技有限公司 | Traffic management method and device based on visual analysis |
| CN111311918A (en)* | 2020-05-12 | 2020-06-19 | 南京云析科技有限公司 | Traffic management method and device based on visual analysis |
| CN111932881A (en)* | 2020-08-12 | 2020-11-13 | 广西科技大学 | Traffic intersection management method and device, terminal device and storage medium |
| CN114078326A (en)* | 2020-08-19 | 2022-02-22 | 北京万集科技股份有限公司 | Collision detection method, device, visual sensor and storage medium |
| CN111968395A (en)* | 2020-08-27 | 2020-11-20 | 深圳安智杰科技有限公司 | Vehicle motion relation determining method and device |
| CN112185132A (en)* | 2020-09-08 | 2021-01-05 | 大连理工大学 | Coordination method for vehicle intersection without traffic light |
| US11851053B2 (en) | 2020-09-14 | 2023-12-26 | International Business Machines Corporation | Collaborative accident prevention |
| CN112215198A (en)* | 2020-10-28 | 2021-01-12 | 郑小云 | Self-adaptive man-machine interaction system and method based on big data |
| CN112578792A (en)* | 2020-11-12 | 2021-03-30 | 东风汽车集团有限公司 | Crossroad auxiliary control method and storage medium |
| CN112907986B (en)* | 2021-01-12 | 2022-10-21 | 浙江大学 | A dynamic time window intersection scheduling method based on digital twin scene and edge cloud |
| WO2022151839A1 (en)* | 2021-01-12 | 2022-07-21 | 华为技术有限公司 | Vehicle turning route planning method and apparatus |
| CN112907986A (en)* | 2021-01-12 | 2021-06-04 | 浙江大学 | Dynamic time window crossing scheduling method based on digital twin scene and edge cloud |
| CN112908033A (en)* | 2021-01-13 | 2021-06-04 | 长安大学 | Internet vehicle cooperation collision avoidance early warning system and method under non-signal control intersection environment |
| CN112767697B (en)* | 2021-01-19 | 2021-11-30 | 东南大学 | Traffic safety evaluation method, system and device based on traffic conflict prediction |
| CN112767697A (en)* | 2021-01-19 | 2021-05-07 | 东南大学 | Traffic safety evaluation method, system and device based on traffic conflict prediction |
| CN113096411A (en)* | 2021-03-17 | 2021-07-09 | 武汉大学 | Vehicle collision early warning method at intersection based on Internet of vehicles environmental system |
| CN113375685A (en)* | 2021-03-31 | 2021-09-10 | 福建工程学院 | Urban intersection center identification and intersection turning rule extraction method based on sub-track intersection |
| CN113095393A (en)* | 2021-04-06 | 2021-07-09 | 兰州交通大学 | High-income taxi driver and extraction method, equipment and storage medium of experience track of taxi driver |
| CN113537606A (en)* | 2021-07-22 | 2021-10-22 | 上汽通用五菱汽车股份有限公司 | Accident prediction method, accident prediction device and computer-readable storage medium |
| CN113361148A (en)* | 2021-08-09 | 2021-09-07 | 中汽研汽车检验中心（天津）有限公司 | Method for recognizing vehicle frontal collision type and judging severity |
| CN113361148B (en)* | 2021-08-09 | 2021-10-12 | 中汽研汽车检验中心（天津）有限公司 | A method for identifying the type of vehicle collision and judging its severity |
| CN114354216A (en)* | 2021-12-31 | 2022-04-15 | 信通院车联网创新中心(成都)有限公司 | High-precision positioning-based V2X collision warning real vehicle test system and method |
| CN114419926A (en)* | 2022-01-24 | 2022-04-29 | 浙江海康智联科技有限公司 | Intersection left-turning auxiliary early warning system and method based on vehicle-road cooperation |
| CN114655199A (en)* | 2022-03-29 | 2022-06-24 | 东风汽车集团股份有限公司 | Collision avoidance device for oncoming vehicle under left-turn working condition |
| CN115690196A (en)* | 2022-10-21 | 2023-02-03 | 袁世姣 | Highway driving vehicle collision point positioning application based on edge function and inpogon function |
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