WO2024251300A1 - 一种基于坐标变换的车路协同换道风险评估方法 - Google Patents

一种基于坐标变换的车路协同换道风险评估方法 Download PDF

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WO2024251300A1
WO2024251300A1 PCT/CN2024/103767 CN2024103767W WO2024251300A1 WO 2024251300 A1 WO2024251300 A1 WO 2024251300A1 CN 2024103767 W CN2024103767 W CN 2024103767W WO 2024251300 A1 WO2024251300 A1 WO 2024251300A1
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Prior art keywords
vehicle
lane
changing
risk
coordinate system
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French (fr)
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吴红兰
张跃
郭旭周
孙有朝
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Nanjing University of Aeronautics and Astronautics
Nanjing Panda Electronics Co Ltd
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Nanjing University of Aeronautics and Astronautics
Nanjing Panda Electronics Co Ltd
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Publication of WO2024251300A1 publication Critical patent/WO2024251300A1/zh
Priority to US19/075,794 priority Critical patent/US12412476B2/en
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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
    • G08G1/167—Driving aids for lane monitoring, lane changing, e.g. blind spot detection
    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F30/00—Computer-aided design [CAD]
    • G06F30/20—Design optimisation, verification or simulation
    • G—PHYSICS
    • G08—SIGNALLING
    • G08G—TRAFFIC CONTROL SYSTEMS
    • G08G1/00—Traffic control systems for road vehicles
    • G08G1/16—Anti-collision systems
    • G08G1/166—Anti-collision systems for active traffic, e.g. moving vehicles, pedestrians, bikes
    • Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
    • Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
    • Y02T—CLIMATE CHANGE MITIGATION TECHNOLOGIES RELATED TO TRANSPORTATION
    • Y02T10/00—Road transport of goods or passengers
    • Y02T10/10—Internal combustion engine [ICE] based vehicles
    • Y02T10/40—Engine management systems

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  • the present invention belongs to the field of risk assessment of vehicle-road cooperative systems, and specifically relates to a vehicle-road cooperative lane changing risk assessment method based on coordinate transformation.
  • a vehicle-road collaborative lane changing risk assessment method based on coordinate transformation is proposed.
  • the two-dimensional coordinate transformation is unified, and a risk assessment model is established.
  • the factors affecting the risk of the vehicle during the lane changing process are analyzed to ensure the safety and effectiveness of the vehicle during the lane changing process, which provides a new idea for lane changing logic judgment and lane changing path planning.
  • the present invention provides a vehicle-road cooperative lane change risk assessment method based on coordinate transformation, comprising the following steps:
  • the lane-changing vehicle receives and analyzes the vehicle-road cooperative vehicle grouping information and obtains the driving data of the related vehicles;
  • step S3 converting the geographical coordinates of the relevant vehicles analyzed in step S1 into a spatial coordinate system respectively, and mapping them into the two-dimensional coordinate system of step S2, thereby completing the transformation from the geographical coordinate system of the relevant vehicles to the two-dimensional coordinate system;
  • step S4 constructing a vehicle lane-changing risk assessment model based on the two-dimensional coordinate system established in step S3;
  • step S5 inputting the relevant vehicle driving parameters analyzed in step S1 into the risk assessment model established in step S4, and performing calculation simulation;
  • step S2 establishes a two-dimensional coordinate system with the lane-changing vehicle P0 as the center point, with due north as the positive direction of the Y axis and due east as the positive direction of the X axis, which is consistent with the direction of the earth's space coordinate system, facilitating subsequent translation conversion of the geographic coordinate system.
  • step S3 the vehicle geographic coordinate point information parsed in step S1 is converted into a two-dimensional coordinate system by using the positioning base station to reduce positioning errors, thereby completing the mapping of the vehicle point position to the two-dimensional coordinate system.
  • the specific transformation process is:
  • A1 According to the standard longitude and latitude errors of the positioning base station, the longitude and latitude of the vehicle are uniformly corrected to reduce error interference and obtain the actual coordinates p'i (L' i , B' i , H' i ).
  • A2 Based on the corrected vehicle positioning longitude and latitude information p'i , calculate the distance l i0 between the vehicles in the formation and the lane-changing vehicle p'0 (L' 0 , B'0 , H'0 ).
  • l i0 is the distance between vehicle i and the lane-changing vehicle
  • R is the radius of the earth
  • PI is the pi
  • A3 Translate the coordinates of the center of the earth to the center of the lane-changing vehicle, keep the directions of the X and Y axes unchanged, ignore the Z axis, project the coordinates of the intervening vehicle into the two-dimensional coordinate system, and obtain the transformed coordinates A i (A ix ,A iy ) of the intervening vehicle.
  • the specific model building steps are:
  • B1 Calculate the coordinates of the vehicle after ⁇ T time according to the instantaneous driving parameters of the vehicle.
  • ⁇ T is the time required for the expected lane change. Since ⁇ T is short, the uniform acceleration motion formula is used.
  • the coordinates after movement are calculated to be A' i (A' ix ,A' iy ).
  • A'0x is the X-axis coordinate of the lane-changing vehicle after ⁇ T time
  • A'ix is the X-axis coordinate of the intervening vehicle i
  • is the lateral distance between the two vehicles
  • Sx is the expected minimum lateral lane-changing distance.
  • A'0y is the Y-axis coordinate of the lane-changing vehicle after ⁇ T time
  • A'iy is the Y-axis coordinate of the intervening vehicle i
  • is the longitudinal distance between the two vehicles
  • Sy is the expected minimum longitudinal lane-changing distance.
  • R l Max(RI x ,RI y ).
  • R l ⁇ RI 0 RI 0 is the critical risk value, and a collision due to lane changing will occur 100%.
  • R l ⁇ RI 0 the lane can be changed, and the probability of a collision due to lane changing is low.
  • S x and S y are both pre-set values, namely, safe lane changing distances.
  • step S5 Use the vehicle grouping data analyzed in step S1 to group the relevant vehicles into lanes and The risk analysis model calculates the risk index of changing lanes to the left and right respectively.
  • step S5 the relevant vehicle driving parameters analyzed in step S5 are input into the risk assessment model established in step S4, and calculation simulation is performed to derive the risks brought by the vehicle changing lanes under various complex conditions, and further derive the most critical influencing factors.
  • the present invention introduces the lane changing risk index RI, and constructs a vehicle-road collaborative lane changing risk assessment method based on coordinate transformation.
  • This method obtains and parses the vehicle formation information of the vehicle-road collaborative platform through linkage with the vehicle-road collaborative platform, performs correction calculation on the vehicle geographic coordinates, and maps the geographic coordinates to a two-dimensional coordinate system to complete the coordinate system conversion.
  • a risk assessment model is established, and the formation vehicle information is used as an input parameter to conduct simulation experiments to obtain the factors that have a greater impact on the lane changing risk, thereby ensuring the safety of the vehicle during driving.
  • the present invention combines the precise data of the vehicle-road collaborative platform, utilizes coordinate transformation, establishes a unified two-dimensional coordinate system, and constructs a risk assessment model for simulation.
  • the objects of simulation are not only the lane-changing vehicle and the front and rear vehicles, but also the vehicles in the entire formation.
  • the results are more accurate and reliable, and can judge the risks of lane changes in the left and right lanes, as well as longitudinal and lateral lanes. It is suitable for various road environments, ensures the safety of vehicle lane changes, and improves lane change efficiency.
  • Fig. 1 is a flow chart of the method of the present invention
  • FIG2 is a diagram of a vehicle grouping data receiving process of a vehicle-road cooperative platform of the present invention
  • FIG3 is a schematic diagram of vehicle grouping information of the present invention.
  • FIG4 is a schematic diagram of a vehicle lane changing process according to the present invention.
  • FIG5 is a schematic diagram of the end of a vehicle lane change according to the present invention.
  • FIG. 6 is a diagram showing a vehicle coordinate mapping relationship according to the present invention.
  • the present invention provides a vehicle-road cooperative lane-changing risk assessment method based on coordinate transformation, as shown in FIG1 , comprising the following steps:
  • the lane-changing vehicle receives and analyzes the vehicle-road cooperative vehicle grouping information and obtains the driving data of the related vehicles;
  • step S3 converting the geographical coordinates of the relevant vehicles analyzed in step S1 into a spatial coordinate system respectively, and mapping them into the two-dimensional coordinate system of step S2, thereby completing the transformation from the geographical coordinate system of the relevant vehicles to the two-dimensional coordinate system;
  • step S4 constructing a vehicle lane-changing risk assessment model based on the two-dimensional coordinate system established in step S3;
  • step S5 inputting the relevant vehicle driving parameters analyzed in step S1 into the risk assessment model established in step S4, and performing calculation simulation;
  • Step S2 establishes a two-dimensional coordinate system with the lane-changing vehicle P0 as the center point, with due north as the positive direction of the Y axis and due east as the positive direction of the X axis, which is consistent with the direction of the earth's space coordinate system to facilitate subsequent translation and conversion of the geographic coordinate system.
  • step S3 the vehicle geographic coordinate point information parsed in step S1 is converted into a two-dimensional coordinate system by using the positioning base station to reduce positioning errors, thereby completing the mapping of the vehicle point position to the two-dimensional coordinate system.
  • the specific transformation process is:
  • A1 According to the standard longitude and latitude errors of the positioning base station, the longitude and latitude of the vehicle are uniformly corrected to reduce error interference and obtain the actual coordinates p'i (L' i , B'i , H'i ).
  • A2 Based on the corrected vehicle positioning longitude and latitude information p'i , calculate the distance l i0 between the vehicles in the formation and the lane-changing vehicle p'0 (L' 0 , B'0 , H'0 ).
  • l i0 is the distance between vehicle i and the lane-changing vehicle
  • R is the radius of the earth
  • PI is the pi
  • A3 Translate the coordinates of the center of the earth to the center of the lane-changing vehicle, keep the directions of the X and Y axes unchanged, ignore the Z axis, project the coordinates of the intervening vehicle into the two-dimensional coordinate system, and obtain the transformed coordinates A i (A ix ,A iy ) of the intervening vehicle.
  • the specific model building steps are:
  • B1 Calculate the coordinates of the vehicle after ⁇ T time according to the instantaneous driving parameters of the vehicle.
  • ⁇ T is the time required for the expected lane change. Since ⁇ T is short, the uniform acceleration motion formula is used.
  • the coordinates after movement are calculated to be A' i (A' ix ,A' iy ).
  • A'0x is the X-axis coordinate of the lane-changing vehicle after ⁇ T time
  • A'ix is the X-axis coordinate of the intervening vehicle i
  • is the lateral distance between the two vehicles
  • Sx is the expected minimum lateral lane-changing distance.
  • A'0y is the Y-axis coordinate of the lane-changing vehicle after ⁇ T time
  • A'iy is the Y-axis coordinate of the intervening vehicle i
  • is the longitudinal distance between the two vehicles
  • Sy is the expected minimum longitudinal lane-changing distance.
  • R l Max(RI x ,RI y ).
  • R l ⁇ RI 0 RI 0 is the critical risk value. Collisions caused by lane changes will occur 100%.
  • R l ⁇ RI 0 lane changes are possible and the probability of collisions caused by lane changes is low.
  • step S1 The vehicle grouping data analyzed in step S1 is used to group the relevant vehicles into lanes, and the risk indexes of changing lanes to the left and right are calculated respectively according to the established risk analysis model.
  • step S5 the analyzed relevant vehicle driving parameters are input into the risk assessment model established in step S4, and calculation simulation is performed to obtain the risks brought by the vehicle changing lanes under various complex conditions, and further obtain the most critical influencing factors.
  • This embodiment also provides a vehicle-road cooperative lane changing risk assessment system based on coordinate transformation, which includes a network interface, a memory and a processor; wherein the network interface is used to realize the reception and transmission of signals during the process of sending and receiving information between other external network elements; the memory is used to store computer program instructions that can be run on the processor; the processor is used to execute the steps of the above-mentioned consensus method when running the computer program instructions.
  • the present embodiment also provides a computer storage medium, which stores a computer program, and the method described above can be implemented when the processor executes the computer program.
  • the computer readable medium can be considered to be tangible and non-temporary.
  • Non-limiting examples of non-temporary tangible computer-readable media include non-volatile memory circuits (such as flash memory circuits, erasable programmable read-only memory circuits or mask read-only memory circuits), volatile memory circuits (such as static random access memory circuits or dynamic random access memory circuits), magnetic storage media (such as analog or digital tapes or hard drives) and optical storage media (such as CDs, DVDs or Blu-ray discs), etc.
  • the computer program includes processor executable instructions stored on at least one non-temporary tangible computer-readable medium.
  • the computer program may also include or rely on stored data.
  • the computer program may include a basic input/output system (BIOS) that interacts with the hardware of a special-purpose computer, a device driver that interacts with a specific device of a special-purpose computer, one or more operating systems, user applications, background services, background applications, etc.
  • BIOS basic input/output system
  • the method of the present invention is applied as follows:
  • the vehicle grouping information in this embodiment includes vehicles C0 to C3, and the specific positions are shown in the figure; the specific implementation process is as follows:
  • the lane-changing vehicle C0 receives and analyzes the vehicle-road cooperative vehicle grouping information and obtains the driving data of the related vehicles (C1-C3);
  • step 3 the geographic coordinates of the relevant vehicles analyzed in step 1 are converted into a spatial coordinate system and mapped into the two-dimensional coordinate system of step 2 to complete the transformation from the geographic coordinate system of the relevant vehicles to the two-dimensional coordinate system;
  • step 3 Based on the two-dimensional coordinate system established in step 3, a vehicle lane-changing risk assessment model is constructed;
  • step 5 Input the relevant vehicle driving parameters analyzed in step 1 into the risk assessment model established in step 4, and perform calculation simulation;
  • the lane-changing vehicle C0 executes lane changing according to the acquired lane-changing risk influencing factors, as shown in FIGS. 4 and 5 .
  • the factors affecting lane-changing risk include the speed, acceleration, steering angle of the vehicles in the formation, and the relative distance between the vehicles. Under the condition that the motion state of other vehicles remains unchanged, the faster the lane-changing vehicle is, the smaller the steering angle is, and the faster the acceleration is, the greater the risk is.

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Abstract

本发明公开了一种基于坐标变换的车路协同换道风险评估方法，包括解析车路协同平台车辆编队信息参数，获得干系车辆行驶数据；确定以换道车辆为中心坐标的二维坐标系；将地理坐标系变换为空间坐标系，并映射到二维坐标系之中，完成从地理坐标系到实时平面坐标系的变换；建立风险评估模型；将换道过程中干系车辆行驶数据和期望值输入到风险评估模型之中，进行运算分析和仿真；探究车辆行驶数据对换道风险的影响，得出车辆在换道过程中对风险影响最大的因素。本发明借助坐标变换算法和换道风险评估模型，计算车辆在行驶中变换车道带来的碰撞风险，量化风险指数，保证了车辆在换道过程中的安全性和有效性，为换道逻辑判断和换道路径规划提供新思路。

Description

一种基于坐标变换的车路协同换道风险评估方法 技术领域
本发明属于车路协同系统风险评估领域，具体涉及一种基于坐标变换的车路协同换道风险评估方法。
背景技术
随着车路协同技术的发展和智能网联汽车技术的突破和普及，车辆在行驶中变道行为带来的碰撞风险也逐渐成为研究的趋势。但由于车辆行驶环境、道路类型、道路路况等因素相对复杂且多样性，没有统一的方法对换道风险进行评估。在之前的研究中，尽管评估方法各不相同，但均以换道车辆本身所能采集到的数据作为评估参数来建立评估模型，无法准确识别周围车辆的行驶意图，对所得出的结论也大多时定性的。同时，存在人为因素的影响，驾驶员的主观随机驾驶行为，会使得一些评估参数产生较大的差异，导致参数无效，最终对评估结果产生较大的偏差。
现有研究成果中还未出现相关成果使用车路协同平台对车辆信息进行编队，使用统一方法来评估车辆换道风险，分析风险产生的影响因素，所以需要一个新的技术方案来实现。
发明内容
发明目的：为了克服现有技术中存在的车辆换道风险评估模型的不足，提出了一种基于坐标变换的车路协同换道风险评估方法，通过车辆地理坐标系变换映射，实现二维坐标变换统一化，建立风险评估模型，分析车辆在换道过程中影响风险的因素，保证车辆在换道过程中的安全性和有效性，为换道逻辑判断和换道路径规划提供了一条新思路。
技术方案：为实现上述目的，本发明提供一种基于坐标变换的车路协同换道风险评估方法，包括如下步骤：
S1：换道车辆接收并解析车路协同车辆编组信息，获取干系车辆行驶数据；
S2：确定以换道车辆为中心点的二维坐标系；
S3：分别将步骤S1中解析到的干系车辆地理坐标转为空间坐标系，并映射到步骤S2的二维坐标系中，完成干系车辆地理坐标系到二维坐标系的变换；
S4：基于步骤S3所建立的二维坐标系，构建车辆换道风险评估模型；
S5：将步骤S1所解析到的干系车辆行驶参数，输入至步骤S4建立的风险评估模型之中，并进行计算仿真；
S6：改变任意一个车辆的行驶数据并保持其他参数不变的情况下，进一步进行计算仿真，分析单个参数对换道风险的影响；
S7：对比计算仿真结果，得出对换道风险影响最大的因素。
进一步地，所述步骤S1中车辆编组信息是一组车辆的实时行驶数据集，使用D表示，车辆信息使用C表示，则D＝[C1,C2,C3,...,Cn]。
车辆信息Ci＝{pi,vi,ai,ri,Δpb,θi…}，其中，pi＝(Li,Bi,Hi)为车辆i的实时地理坐标信息(Li为经度，Bi为纬度，Hi为高程)，vi为车辆i的行驶速度，ai为车辆i的行驶加速度，ri为车辆i的所在车道，Δpb＝(ΔLb,ΔBb,ΔHb)为定位基准站的位置矫正信息，θi为车辆i的转向角度。
进一步地，所述步骤S2建立以换道车辆P0为中心点的二维坐标系，以正北为Y轴正方向，正东为X轴正方向，与地球空间坐标系方向保持一致，便于后续对地理坐标系进行平移转换。
进一步地，所述步骤S3中将步骤S1中解析到的车辆地理坐标点信息，利用定位基准站进行定位误差消减，进而转换成二维坐标系，完成车辆点位向二维坐标系的映射。具体的变换过程为：
A1：根据定位基准站标准经纬度误差，对车辆经纬度进行统一矫正处理，降低误差干扰，得到实际坐标p'i(L'i,B'i,H'i)。
由于在同一时刻，换道车辆与编组车辆的相对距离较近，其所处路段的高程信息变化不大，因此，在后续的坐标变换过程中，忽略了高程信息对换道风险的影响，只使用L'i，B'i，进行计算。
A2：根据矫正后的车辆定位经纬度信息p'i，分别计算编组内车辆距离换道车辆p'0(L'0,B'0,H'0)之间的距离li0。
其中，li0为车辆i距离换道车辆的距离，R为地球半径，PI为圆周率。
A3：将地球中心点坐标平移到换道车辆中心，保持X和Y轴方向不变，忽略Z轴，将干系车辆的坐标投影到二维坐标系中，得到干系车辆的变换后的坐标Ai(Aix,Aiy)。
进一步地，所述步骤S4使用基于步骤S3所建立的二维坐标系，构建车辆换道风险 评估模型RI，RI＝(Rl,Rr)，Rl，Rr分别表示向左和向右变道的风险指标，Rl,Rr＝Max(RIx,RIy)，其中，RIx为横向(变换车道过程中X轴方向的车辆间距)换道风险，RIy为纵向(变换车道过程中Y轴方向的车辆间距)换道风险。具体模型建立步骤为：
B1：根据车辆瞬时行驶参数，计算ΔT时间后的车辆的坐标点位，ΔT为期望换道所用时间，由于ΔT时间较短，使用匀加速运动公式计算得到运动后的坐标为A'i(A'ix,A'iy)。
B2：分别结算车辆i与换道车辆之间横向距离
B3：建立RIx横向换道风险模型，表达式为：
其中，A'0x为换道车辆经ΔT时间后得到的X轴坐标，A'ix为干系车辆i的X轴坐标，|A'0x-A'ix|为两车之间横向距离，Sx为期望的最小横向换道距离。
B4：同理，建立RIy横向换道风险模型，表达式为：
其中，A'0y为换道车辆经ΔT时间后得到的Y轴坐标，A'iy为干系车辆i的Y轴坐标，|A'0y-A'iy|为两车之间纵向距离，Sy为期望的最小纵向换道距离。
当换道车辆向左变道时，Rl＝Max(RIx,RIy)，当Rl≥RI0时，RI0为临界风险值，换道发生的碰撞将百分百发生，当Rl<RI0时，可以变道，变道产生的碰撞概率较低。
Sx和Sy均为预先设定值，即安全换道距离。
B5：通过步骤S1解析的车辆编组数据，对干系车辆进行车道分组，按照建立的风 险分析模型分别计算向左和向右变道的风险指数。
进一步地，所述步骤S5中将解析到的干系车辆行驶参数，输入至步骤S4建立的风险评估模型之中，并进行计算仿真，得出车辆在各种复杂条件下变换车道带来的风险，进一步得出最关键的影响因素。
本发明为了量化车辆在换道过程中存在的风险等级，引入了换道风险指数RI，并构建了一种基于坐标变换的车路协同换道风险评估方法。该方法通过与车路协同平台联动，获取并解析车路协同平台车辆编组信息，对车辆地理坐标进行矫正计算，并将地理坐标映射到二维坐标系中，完成坐标系转换。同时建立风险评估模型，将编组车辆信息作为输入参数，进行仿真实验，得出对换道风险影响较大的因素，进而保证车辆在行驶过程中的安全性。
有益效果：本发明与现有技术相比，结合车路协同平台精准数据的同时，利用坐标变换，建立统一二维坐标系，构建了风险评估模型进行仿真，仿真的对象不仅仅是换道车辆与前后车，而是整个编组内的车辆，结果更加准确和可靠，能够判断左右车道，以及纵向和横向的变道风险，适用于各种道路环境，保障车辆换道安全，提高换道效率。
附图说明
图1为本发明的方法流程图；
图2为本发明车路协同平台车辆编组数据接收过程图；
图3为本发明车辆编组信息示意图；
图4为本发明车辆变道过程示意图；
图5为本发明车辆变道结束示意图；
图6为本发明车辆坐标映射关系图。
具体实施方式
下面结合附图和具体实施例，进一步阐明本发明，应理解这些实施例仅用于说明本发明而不用于限制本发明的范围，在阅读了本发明之后，本领域技术人员对本发明的各种等价形式的修改均落于本申请所附权利要求所限定的范围。
本发明提供一种基于坐标变换的车路协同换道风险评估方法，如图1所示，包括如下步骤：
S1：换道车辆接收并解析车路协同车辆编组信息，获取干系车辆行驶数据；
S2：确定以换道车辆为中心点的二维坐标系；
S3：分别将步骤S1中解析到的干系车辆地理坐标转为空间坐标系，并映射到步骤S2的二维坐标系中，完成干系车辆地理坐标系到二维坐标系的变换；
S4：基于步骤S3所建立的二维坐标系，构建车辆换道风险评估模型；
S5：将步骤S1所解析到的干系车辆行驶参数，输入至步骤S4建立的风险评估模型之中，并进行计算仿真；
S6：改变任意一个车辆的行驶数据并保持其他参数不变的情况下，进一步进行计算仿真，分析单个参数对换道风险的影响；
S7：对比计算仿真结果，得出对换道风险影响最大的因素。
如图2所示，步骤S1中车辆编组信息是一组车辆的实时行驶数据集，使用D表示，车辆信息使用C表示，则D＝[C1,C2,C3,…,Cn]。
车辆信息Ci＝{pi,vi,ai,ri,Δpb,θi…}，其中，pi＝(Li,Bi,Hi)为车辆i的实时地理坐标信息(Li为经度，Bi为纬度，Hi为高程)，vi为车辆i的行驶速度，ai为车辆i的行驶加速度，ri为车辆i的所在车道，Δpb＝(ΔLb,ΔBb,ΔHb)为定位基准站的位置矫正信息，θi为车辆i的转向角度。
步骤S2建立以换道车辆P0为中心点的二维坐标系，以正北为Y轴正方向，正东为X轴正方向，与地球空间坐标系方向保持一致，便于后续对地理坐标系进行平移转换。
步骤S3中将步骤S1中解析到的车辆地理坐标点信息，利用定位基准站进行定位误差消减，进而转换成二维坐标系，完成车辆点位向二维坐标系的映射。具体的变换过程为：
A1：根据定位基准站标准经纬度误差，对车辆经纬度进行统一矫正处理，降低误差干扰，得到实际坐标p'i(L'i,B'i,H'i)。
由于在同一时刻，换道车辆与编组车辆的相对距离较近，其所处路段的高程信息变化不大，因此，在后续的坐标变换过程中，忽略了高程信息对换道风险的影响，只使用L'i，B'i，进行计算。
A2：根据矫正后的车辆定位经纬度信息p'i，分别计算编组内车辆距离换道车辆p'0(L'0,B'0,H'0)之间的距离li0。
其中，li0为车辆i距离换道车辆的距离，R为地球半径，PI为圆周率。
A3：将地球中心点坐标平移到换道车辆中心，保持X和Y轴方向不变，忽略Z轴，将干系车辆的坐标投影到二维坐标系中，得到干系车辆的变换后的坐标Ai(Aix,Aiy)。
步骤S4使用基于步骤S3所建立的二维坐标系，构建车辆换道风险评估模型RI，RI＝(Rl,Rr)，Rl，Rr分别表示向左和向右变道的风险指标，Rl,Rr＝Max(RIx,RIy)，其中，RIx为横向(变换车道过程中X轴方向的车辆间距)换道风险，RIy为纵向(变换车道过程中Y轴方向的车辆间距)换道风险。具体模型建立步骤为：
B1：根据车辆瞬时行驶参数，计算ΔT时间后的车辆的坐标点位，ΔT为期望换道所用时间，由于ΔT时间较短，使用匀加速运动公式计算得到运动后的坐标为A'i(A'ix,A'iy)。
B2：分别结算车辆i与换道车辆之间横向距离
B3：建立RIx横向换道风险模型，表达式为：
其中，A'0x为换道车辆经ΔT时间后得到的X轴坐标，A'ix为干系车辆i的X轴坐标，|A'0x-A'ix|为两车之间横向距离，Sx为期望的最小横向换道距离。
B4：同理，建立RIy横向换道风险模型，表达式为：
其中，A'0y为换道车辆经ΔT时间后得到的Y轴坐标，A'iy为干系车辆i的Y轴坐标，|A'0y-A'iy|为两车之间纵向距离，Sy为期望的最小纵向换道距离。
当换道车辆向左变道时，Rl＝Max(RIx,RIy)，当Rl≥RI0时，RI0为临界风险值， 换道发生的碰撞将百分百发生，当Rl<RI0时，可以变道，变道产生的碰撞概率较低。
B5：通过步骤S1解析的车辆编组数据，对干系车辆进行车道分组，按照建立的风险分析模型分别计算向左和向右变道的风险指数。
步骤S5中将解析到的干系车辆行驶参数，输入至步骤S4建立的风险评估模型之中，并进行计算仿真，得出车辆在各种复杂条件下变换车道带来的风险，进一步得出最关键的影响因素。
本实施例还提供一种基于坐标变换的车路协同换道风险评估系统，该系统包括网络接口、存储器和处理器；其中，网络接口，用于在与其他外部网元之间进行收发信息过程中，实现信号的接收和发送；存储器，用于存储能够在所述处理器上运行的计算机程序指令；处理器，用于在运行计算机程序指令时，执行上述共识方法的步骤。
本实施例还提供一种计算机存储介质，该计算机存储介质存储有计算机程序，在处理器执行所述计算机程序时可实现以上所描述的方法。所述计算机可读介质可以被认为是有形的且非暂时性的。非暂时性有形计算机可读介质的非限制性示例包括非易失性存储器电路(例如闪存电路、可擦除可编程只读存储器电路或掩膜只读存储器电路)、易失性存储器电路(例如静态随机存取存储器电路或动态随机存取存储器电路)、磁存储介质(例如模拟或数字磁带或硬盘驱动器)和光存储介质(例如CD、DVD或蓝光光盘)等。计算机程序包括存储在至少一个非暂时性有形计算机可读介质上的处理器可执行指令。计算机程序还可以包括或依赖于存储的数据。计算机程序可以包括与专用计算机的硬件交互的基本输入/输出系统(BIOS)、与专用计算机的特定设备交互的设备驱动程序、一个或多个操作系统、用户应用程序、后台服务、后台应用程序等。
基于上述内容，为了验证本发明方法的有效性，本实施例中将本发明方法进行实例应用，具体如下：
如图3所示，本实施例中车辆编组信息包括车辆C0～C3，具体位置如图所示；具体的实施过程为：
1)换道车辆C0接收并解析车路协同车辆编组信息，获取干系车辆(C1～C3)行驶数据；
2)确定以换道车辆C0为中心点的二维坐标系；
3)参照图6，分别将步骤1中解析到的干系车辆地理坐标转为空间坐标系，并映射到步骤2的二维坐标系中，完成干系车辆地理坐标系到二维坐标系的变换；
4)基于步骤3所建立的二维坐标系，构建车辆换道风险评估模型；
5)将步骤1所解析到的干系车辆行驶参数，输入至步骤4建立的风险评估模型之中，并进行计算仿真；
6)改变任意一个车辆的行驶数据并保持其他参数不变的情况下，进一步进行计算仿真，分析单个参数对换道风险的影响；
7)对比计算仿真结果，得出对换道风险影响最大的因素。
换道车辆C0根据获取的换道风险影响因素，执行换道，如图4和图5所示。
换道风险影响因素包括编组内车辆的行驶速度、加速度、转向角度，以及车辆之间的相对距离。在其他车辆运动状态不变情况下，换道车辆速度越快、转向角度越小，加速度越快，风险越大。

Claims (10)

  1. 一种基于坐标变换的车路协同换道风险评估方法，其特征在于，包括如下步骤：
    S1：换道车辆接收并解析车路协同车辆编组信息，获取干系车辆行驶数据；
    S2：确定以换道车辆为中心点的二维坐标系；
    S3：分别将步骤S1中解析到的干系车辆地理坐标转为空间坐标系，并映射到步骤S2的二维坐标系中，完成干系车辆地理坐标系到二维坐标系的变换；
    S4：基于步骤S3所建立的二维坐标系，构建车辆换道风险评估模型；
    S5：将步骤S1所解析到的干系车辆行驶参数，输入至步骤S4建立的风险评估模型之中，并进行计算仿真；
    S6：改变任意一个车辆的行驶数据并保持其他参数不变的情况下，进一步进行计算仿真，分析单个参数对换道风险的影响；
    S7：对比计算仿真结果，得出对换道风险影响最大的因素。
  2. 根据权利要求1所述的一种基于坐标变换的车路协同换道风险评估方法，其特征在于，所述步骤S1中车辆编组信息是一组车辆的实时行驶数据集，使用D表示，车辆信息使用C表示，则D＝[C1,C2,C3,…,Cn]；
    车辆信息Ci＝{pi,vi,ai,ri,Δpb,θi...}，其中，pi＝(Li,Bi,Hi)为车辆i的实时地理坐标信息，其中，Li为经度，Bi为纬度，Hi为高程，vi为车辆i的行驶速度，ai为车辆i的行驶加速度，ri为车辆i的所在车道，Δpb＝(ΔLb,ΔBb,ΔHb)为定位基准站的位置矫正信息，θi为车辆i的转向角度。
  3. 根据权利要求1所述的一种基于坐标变换的车路协同换道风险评估方法，其特征在于，所述步骤S2建立以换道车辆P0为中心点的二维坐标系，以正北为Y轴正方向，正东为X轴正方向，与地球空间坐标系方向保持一致。
  4. 根据权利要求2所述的一种基于坐标变换的车路协同换道风险评估方法，其特征在于，所述步骤S3中将步骤S1中解析到的车辆地理坐标点信息，利用定位基准站进行定位误差消减，进而转换成二维坐标系，完成车辆点位向二维坐标系的映射；
    具体的变换过程为：
    A1：根据定位基准站标准经纬度误差，对车辆经纬度进行统一矫正处理，得到实际坐标p'i(L'i,B'i,H'i)；
    在后续的坐标变换过程中，忽略了高程信息对换道风险的影响，只使用L'i，B'i，进行计算；
    A2：根据矫正后的车辆定位经纬度信息p'i，分别计算编组内车辆距离换道车辆p'0(L'0,B'0,H'0)之间的距离li0
    其中，li0为车辆i距离换道车辆的距离，R为地球半径，PI为圆周率；
    A3：将地球中心点坐标平移到换道车辆中心，保持X和Y轴方向不变，忽略Z轴，将干系车辆的坐标投影到二维坐标系中，得到干系车辆的变换后的坐标Ai(Aix,Aiy)
  5. 根据权利要求4所述的一种基于坐标变换的车路协同换道风险评估方法，其特征在于，所述步骤S4使用基于步骤S3所建立的二维坐标系，构建车辆换道风险评估模型RI，RI＝(Rl,Rr)，Rl，Rr分别表示向左和向右变道的风险指标，Rl,Rr＝Max(RIx,RIy)，其中，RIx为横向(变换车道过程中X轴方向的车辆间距)换道风险，RIy为纵向(变换车道过程中Y轴方向的车辆间距)换道风险。
  6. 根据权利要求5所述的一种基于坐标变换的车路协同换道风险评估方法，其特征在于，所述步骤S4根据车辆瞬时行驶参数，计算ΔT时间后的车辆的坐标点位，ΔT为期望换道所用时间，由于ΔT时间较短，使用匀加速运动公式计算得到运动后的坐标为A'i(A'ix,A'iy)。
  7. 根据权利要求6所述的一种基于坐标变换的车路协同换道风险评估方法，其特征在于，所述步骤S4建立RIx横向换道风险模型，表达式为：
    其中，A'0x为换道车辆经ΔT时间后得到的X轴坐标，A'ix为干系车辆i的X轴坐标，|A'0x-A'ix|为两车之间横向距离，Sx为期望的最小横向换道距离。
  8. 根据权利要求7所述的一种基于坐标变换的车路协同换道风险评估方法，其特征在于，所述步骤S4分别结算车辆i与换道车辆之间横向距离
  9. 根据权利要求6所述的一种基于坐标变换的车路协同换道风险评估方法，其特征在于，所述步骤S4建立RIy纵向换道风险模型，表达式为：
    其中，A'0y为换道车辆经ΔT时间后得到的Y轴坐标，A'iy为干系车辆i的Y轴坐标，|A'0y-A'iy|为两车之间纵向距离，Sy为期望的最小纵向换道距离。
  10. 根据权利要求7～9中任一项所述的一种基于坐标变换的车路协同换道风险评估方法，其特征在于，所述步骤S4通过步骤S1解析的车辆编组数据，对干系车辆进行车道分组，按照建立的风险分析模型分别计算向左和向右变道的风险指数，当换道车辆向左变道时，Rl＝Max(RIx,RIy)，当Rl≥RI0时，RI0为临界风险值，换道发生的碰撞将百分百发生，当Rl<RI0时，可以变道，变道产生的碰撞概率较低。
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