WO2021207967A1 - 一种基于路面状况主动调节车辆悬架的方法及车辆 - Google Patents
一种基于路面状况主动调节车辆悬架的方法及车辆 Download PDFInfo
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- B—PERFORMING OPERATIONS; TRANSPORTING
- B60—VEHICLES IN GENERAL
- B60G—VEHICLE SUSPENSION ARRANGEMENTS
- B60G17/00—Resilient suspensions having means for adjusting the spring or vibration-damper characteristics, for regulating the distance between a supporting surface and a sprung part of vehicle or for locking suspension during use to meet varying vehicular or surface conditions, e.g. due to speed or load
- B60G17/015—Resilient suspensions having means for adjusting the spring or vibration-damper characteristics, for regulating the distance between a supporting surface and a sprung part of vehicle or for locking suspension during use to meet varying vehicular or surface conditions, e.g. due to speed or load the regulating means comprising electric or electronic elements
- B60G17/016—Resilient suspensions having means for adjusting the spring or vibration-damper characteristics, for regulating the distance between a supporting surface and a sprung part of vehicle or for locking suspension during use to meet varying vehicular or surface conditions, e.g. due to speed or load the regulating means comprising electric or electronic elements characterised by their responsiveness, when the vehicle is travelling, to specific motion, a specific condition, or driver input
- B60G17/0165—Resilient suspensions having means for adjusting the spring or vibration-damper characteristics, for regulating the distance between a supporting surface and a sprung part of vehicle or for locking suspension during use to meet varying vehicular or surface conditions, e.g. due to speed or load the regulating means comprising electric or electronic elements characterised by their responsiveness, when the vehicle is travelling, to specific motion, a specific condition, or driver input to an external condition, e.g. rough road surface, side wind
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- the invention relates to the field of vehicle suspension control, and more specifically, to a method and a vehicle for actively adjusting the vehicle suspension based on road conditions.
- the suspension system is how to accurately identify the driving conditions of the vehicle and actively control the suspension It is a hot spot in the research of future intelligent suspension systems.
- the current smarter active chassis when the current smarter active chassis is working, it is necessary to first act on the road surface interference on the front wheels of the vehicle in the driving process before collecting and analyzing road signals, and realizes the control of the rear wheel suspension through preview control.
- the active control and adjustment of the vehicle makes it impossible for the vehicle as a whole to make reasonable decisions and optimal control on the safety and comfort of driving in a timely manner. Therefore, the current research on intelligent suspension generally has problems such as low accuracy of vehicle road surface recognition, poor continuous and effective road signal acquisition, and difficulty in achieving optimal control of the suspension system.
- the technical problem to be solved by the present invention is to provide a method and a vehicle for actively adjusting the suspension of a vehicle based on road conditions in view of the above-mentioned defects of the prior art.
- the technical solution adopted by the present invention to solve its technical problems is: constructing a method for actively adjusting the vehicle suspension based on road conditions, including:
- the step S1 includes: S11, using a lidar to scan the road surface in the forward direction of the vehicle to obtain road surface scan information, and at the same time obtain the vehicle speed, vehicle acceleration, and At least one of the relative displacement of the suspension; wherein the vehicle speed is obtained by a speed sensor, the vehicle acceleration is obtained by an inertial measurement unit, and the relative displacement of the suspension is obtained by a relative displacement sensor;
- the step S3 includes: S31, adjusting the vehicle suspension according to the road surface condition information and the acquired vehicle speed, vehicle acceleration, and suspension relative displacement.
- the step S1 includes: S12, scanning the multiple laser channels of the lidar up and down and scanning left and right to obtain the road scan information,
- the road surface scanning information includes distance information between the lidar and each scanning point on the road surface;
- the step S3 includes: adjusting the damping force of the vehicle suspension according to the road surface condition information, so that the damping force position of the vehicle suspension is within a preset comfortable damping force interval.
- the step S2 includes:
- the step S21 includes:
- x represents the data on the X axis
- y represents the data on the Y axis
- i is a positive integer
- point[i] and point[i+1] are two adjacent scan points
- the method further includes:
- the step S22 includes:
- the feature points with positive and negative slopes k in the same laser channel are respectively connected to each other to form feature point clusters, and the feature point clusters are sorted according to a single direction;
- the method further includes:
- step S2246 is determined as a speed bump or bump, it further includes:
- the height of the speed bump or the depth and height of the concave-convex pit are obtained from the difference between the first z-axis average value and the second z-axis average value.
- the present invention also provides a vehicle including a lidar and a controller
- the lidar is used to scan the road surface in the forward direction of the vehicle to obtain road surface scanning information
- the controller adjusts the vehicle suspension using the method of actively adjusting the vehicle suspension based on the road surface condition as described above.
- the vehicle of the present invention further includes a speed sensor for acquiring the speed of the vehicle; and/or
- Inertial measurement unit for acquiring vehicle acceleration
- Relative displacement sensor used to obtain the relative displacement of the suspension
- the controller adjusts the vehicle suspension according to the road surface scanning information and the acquired vehicle speed, vehicle acceleration, and suspension relative displacement.
- the method and vehicle for actively adjusting vehicle suspension based on road surface conditions implemented in the present invention have the following beneficial effects: the present invention uses lidar to quickly and accurately obtain road surface information in the forward direction of the vehicle, and then actively adjust according to road surface control information
- the vehicle suspension improves the user's driving experience.
- FIG. 1 is a flowchart of a method for actively adjusting a vehicle suspension based on road conditions according to Embodiment 1;
- Embodiment 2 is a flowchart of a method for actively adjusting a vehicle suspension based on road conditions according to Embodiment 2;
- FIG. 3 is a flowchart of a method for actively adjusting a vehicle suspension based on road conditions according to Embodiment 3;
- FIGS. 4 and 5 are flowcharts of a method for actively adjusting a vehicle suspension based on road conditions according to Embodiment 4;
- FIG. 6 is a schematic diagram of the structure of a vehicle provided in Embodiment 5.
- FIG. 6 is a schematic diagram of the structure of a vehicle provided in Embodiment 5.
- the method for actively adjusting vehicle suspension based on road conditions of this embodiment is applied to vehicle suspension adjustment.
- Vehicles include but are not limited to fuel vehicles, electric vehicles, buses, trucks, etc., and the vehicle suspension requirements are controllable
- the suspension can be actively controlled to output different damping forces.
- the method includes the following steps:
- Step S1 of this embodiment includes: S12, multiple laser channels of the lidar are scanned up and down and left and right to obtain road surface scan information.
- the road scan information includes distance information between the lidar and each scanning point on the road surface. That is, each laser channel of the lidar will complete an up and down scan and a left and right scan. After scanning for one cycle, the road surface scanning information of each laser channel is obtained.
- the road condition can be judged by the distance information between the lidar and each scanning point on the road. .
- the distance measurement principle of the lidar will not be repeated here, and reference may be made to the prior art.
- the distance information between the lidar and each scan point on the road is processed according to the preset algorithm.
- the preset algorithm includes distance judgment models for various road conditions.
- the distance judgment models include but are not limited to the roughness model for judging the roughness of the road.
- Each model can find out whether there is a corresponding road condition in the road scan information according to their respective distance characteristics.
- Step S3 includes: adjusting the damping force of the vehicle suspension according to the road condition information, so that the damping force of the vehicle suspension is at a preset comfortable damping position. Within the power range to improve the user’s driving experience.
- the road surface conditions include but are not limited to road unevenness, road pits, road bumps, speed bumps, etc.
- the lidar is used to quickly and accurately obtain road surface condition information in the forward direction of the vehicle, and then actively adjust the vehicle suspension according to the road surface control condition information to improve the user's driving experience.
- the method for actively adjusting vehicle suspension based on road conditions in this embodiment is applied to the adjustment of vehicle suspension.
- Vehicles include, but are not limited to, fuel vehicles, electric vehicles, buses, trucks, etc.
- the vehicle suspension requirements are controllable
- the suspension can be actively controlled to output different damping forces.
- the method includes the following steps:
- the relative displacement of the suspension is obtained by a relative displacement sensor, which is installed on the vehicle suspension or the vehicle hub, and the relative displacement sensor can refer to the prior art.
- the lidar is installed at the front end of the vehicle, such as the front bumper position or the front air intake grille position or the roof of the vehicle, etc., and it can scan the road surface at the front end of the vehicle.
- the distance information between the lidar and each scan point on the road is processed according to the preset algorithm.
- the preset algorithm includes distance judgment models for various road conditions.
- the distance judgment models include but are not limited to the roughness model for judging the roughness of the road.
- Each model can find out whether there is a corresponding road condition in the road scan information according to their respective distance characteristics.
- S31 Adjust the vehicle suspension according to the road surface condition information and the obtained vehicle speed, vehicle acceleration, and relative displacement of the suspension. It is understandable that the requirements for the damping force output by the vehicle suspension are different under different road conditions. At the same time, considering that it will take a certain time for the vehicle to actually reach the scanned road in front, if it is adjusted in advance, it will affect the driving experience on the current road. The post-adjustment will not play the role of adjustment, so it should be adjusted as accurately as possible. In order to solve this problem, this application should collect the speed and acceleration of the vehicle, and estimate the time for the vehicle to reach the scanned road surface in front, and then accurately adjust the suspension so that the vehicle suspension outputs the corresponding damping force at an accurate position to ensure the user's driving. Take the experience.
- the current output damping force of the vehicle suspension is obtained, which provides a reference for the subsequent active adjustment of the damping force of the vehicle suspension.
- the laser radar is used to quickly and accurately obtain the road surface condition information in the forward direction of the vehicle, combined with the vehicle speed, vehicle acceleration, and suspension relative displacement information, and then based on the road surface condition information and the acquired vehicle speed, vehicle acceleration, and suspension
- the relative displacement adjusts the vehicle suspension to improve the user's driving experience.
- step S2 includes:
- step S21 Extracting feature points in the road surface scanning information according to a preset algorithm.
- the characteristics of different road conditions on the road are different. For example, road unevenness, road pits, road bumps, speed bumps, etc. have corresponding characteristics. These characteristics can be used to distinguish different road conditions, that is, the characteristic points refer to lasers.
- the points that meet certain characteristics in the scanning points of the radar can be used to judge the road surface condition through these characteristic points.
- step S21 includes:
- the rectangular coordinate system is a three-dimensional rectangular coordinate system, in which the X-axis of the rectangular coordinate system points to the front of the vehicle, the Y-axis points to the left of the vehicle, and the Z-axis points to the top of the vehicle to establish a coordinate system. Then, the distance information between the lidar and each scanning point on the road is converted into the parameters of each scanning point in a three-dimensional rectangular coordinate system.
- x represents the data on the X axis
- y represents the data on the Y axis
- i is a positive integer
- point[i] and point[i+1] are two adjacent scan points
- the scanning point point[i+1] is a feature point.
- the absolute value threshold of the slope k is different, and the slope of the speed bump or the edge of the bump can be detected. Alternatively, the absolute value of the slope k is 0.5.
- the preset feature model corresponding to the road condition setting.
- the preset feature models include, but are not limited to, an unevenness model for judging the unevenness of the road surface, a pit module for judging whether the road has pits, a convex model for judging whether the road is convex, and a bump model for judging the road surface. Whether there is a speed bump model for the speed bump, a tilt module for judging whether the road surface is inclined, etc.
- the feature points corresponding to each model are different. Conversely, the corresponding preset feature model can be determined according to the obtained feature points to achieve road conditions Of ok.
- the slope k is used to determine the feature points, and then the feature points are matched with a preset feature model to obtain road surface condition information.
- the method of actively adjusting the vehicle suspension based on the road surface condition of this embodiment further includes the judgment of the road surface roughness information after step S22:
- step S22 includes:
- a feature point cluster is a collection of feature points with the same or similar slopes and aligned in a single direction.
- the feature point clusters can be sorted from left to right, or from right to left.
- step S224 the method further includes:
- S2242 for the extracted point cloud of multiple laser channels that may be a speed bump or concave-convex pit, detect the length of the longest channel among them, and determine whether it is greater than a third preset threshold.
- the height of the speed bump or the depth of the bump pit may also be obtained, which specifically includes the following steps:
- This embodiment uses the slope k to determine the feature points, and then obtains feature point clusters based on the feature points, uses the feature point clusters to determine whether the road contains speed bumps or bumps, and then actively adjusts the vehicle suspension based on the road surface control information to improve the user’s driving Take the experience.
- the vehicles of this embodiment include but are not limited to fuel vehicles, electric vehicles, passenger cars, trucks, etc.
- the vehicle’s suspension requires a controllable suspension, which means that the suspension can be actively controlled to output different damping forces, and the suspension can be actively controlled.
- the vehicle includes a lidar and a controller.
- the lidar is installed at the front of the vehicle, such as the position of the front bumper or the position of the front air intake grille, etc., which can scan the road surface at the front of the vehicle.
- the lidar is used to scan the road surface in the forward direction of the vehicle to obtain road surface scanning information, and the controller uses the method of actively adjusting the vehicle suspension based on the road surface condition as in the above embodiment to adjust the vehicle suspension.
- a separate processor can be set up to execute the method of actively adjusting the vehicle suspension based on road conditions in the above embodiments, or the vehicle's own ECU can be used to execute the method of actively adjusting the vehicle suspension based on road conditions in the above embodiments. .
- the lidar is used to quickly and accurately obtain road surface condition information in the forward direction of the vehicle, and then actively adjust the vehicle suspension according to the road surface control condition information to improve the user's driving experience.
- the vehicle of some embodiments may also include a speed sensor for acquiring the speed of the vehicle.
- the speed sensor can be obtained from the vehicle's own speed sensor, which can be obtained from the CAN bus of the vehicle control system; of course, an independent vehicle can also be installed on the vehicle.
- the speed sensor can refer to the prior art for details.
- the vehicle of some embodiments further includes an inertial measurement unit for acquiring vehicle acceleration.
- the inertial measurement unit can flexibly select an installation position as required, and the inertial measurement unit can refer to the prior art.
- this application should collect the speed and acceleration of the vehicle, and estimate the time for the vehicle to reach the scanned road surface in front, and then accurately adjust the suspension so that the vehicle suspension outputs the corresponding damping force at an accurate position to ensure the user's driving. Take the experience.
- the vehicle of some embodiments further includes a relative displacement sensor for acquiring the relative displacement of the suspension.
- the relative displacement sensor is installed on the vehicle suspension or on the vehicle hub.
- the relative displacement sensor may refer to the prior art.
- the laser radar is used to quickly and accurately obtain the road surface condition information in the forward direction of the vehicle, combined with the vehicle speed, vehicle acceleration, and suspension relative displacement information, and then based on the road surface condition information and the acquired vehicle speed, vehicle acceleration, and suspension
- the relative displacement adjusts the vehicle suspension to improve the user's driving experience.
- the steps of the method or algorithm described in combination with the embodiments disclosed herein can be directly implemented by hardware, a software module executed by a processor, or a combination of the two.
- the software module can be placed in random access memory (RAM), internal memory, read-only memory (ROM), electrically programmable ROM, electrically erasable programmable ROM, registers, hard disks, removable disks, CD-ROMs, or all areas in the technical field. Any other known storage media.
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Abstract
本发明涉及一种基于路面状况主动调节车辆悬架的方法及车辆。该方法包括：S1、使用激光雷达扫描车辆前进方向的路面得到路面扫描信息；S2、处理路面扫描信息得到路面状况信息；S3、根据路面状况信息调节车辆悬架。本发明的车辆使用上述基于路面状况主动调节车辆悬架的方法调整车辆悬架。本发明使用激光雷达快速准确的获取车辆前进方向的路面状况信息，进而根据路面控制状况信息主动调节车辆悬架，提高用户的驾乘体验。
Description
本发明涉及车辆悬架控制领域，更具体地说，涉及一种基于路面状况主动调节车辆悬架的方法及车辆。
近年来驾乘者对于车辆行驶时的乘坐舒适性和操纵稳定性等性能要求不断提高，而悬架系统作为影响该性能的主要因素，如何准确识别车辆行驶的工况并对悬架进行主动控制是未来智能悬架系统研究的热点。
在传统的悬架控制研究中，一方面，由于汽车在不同等级路面行驶时，因对路面的异常状况，如异物、凹坑及凸起、减速带等形状及大小无法进行连续和准确的判断，难以对悬架系统进行预先控制，输出最优阻尼力来改善悬架系统的性能。另一方面，目前较为智能的主动式底盘在进行工作时，需要将路面干扰先作用于行驶过程中的车辆前轮后，才能采集和分析路面信号，并通过预瞄控制实现对后轮悬架的主动控制调节，使得车辆整体未能及时对行车的安全性及舒适性进行合理决策及最优控制。因此，当前有关智能悬架的研究普遍存在对车辆行驶路面识别准确度较低、连续有效的路面信号采集度较差、难以实现对悬架系统的最优控制等问题。
本发明要解决的技术问题在于，针对现有技术的上述缺陷，提供一种基于路面状况主动调节车辆悬架的方法及车辆。
本发明解决其技术问题所采用的技术方案是：构造一种基于路面状况主动调节车辆悬架的方法，包括：
S1、使用激光雷达扫描车辆前进方向的路面得到路面扫描信息；
S2、处理所述路面扫描信息得到路面状况信息；
S3、根据所述路面状况信息调节车辆悬架。
进一步，在本发明所述的基于路面状况主动调节车辆悬架的方法中，所述步骤S1包括：S11、使用激光雷达扫描车辆前进方向的路面得到路面扫描信息，同时获取车辆速度、车辆加速度和悬架相对位移中的至少一种；其中所述车辆速度通过速度传感器获得，所述车辆加速度通过惯性测量单元获得，所述悬架相对位移通过相对位移传感器得到；
所述步骤S3包括：S31、根据所述路面状况信息以及已获取的所述车辆速度、车辆加速度和悬架相对位移调节车辆悬架。
进一步，在本发明所述的基于路面状况主动调节车辆悬架的方法中，所述步骤S1包括：S12、所述激光雷达的多个激光通道进行上下扫描和左右扫描得到所述路面扫描信息，所述路面扫描信息包括所述激光雷达和路面上各个扫描点之间的距离信息；
所述步骤S3包括：根据所述路面状况信息调节车辆悬架的阻尼力，使所述车辆悬架的阻尼力位置在预设舒适阻尼力区间内。
进一步，在本发明所述的基于路面状况主动调节车辆悬架的方法中，所述步骤S2包括：
S21、根据预设算法提取所述路面扫描信息中的特征点；
S22、将特征点和预设特征模型进行匹配，得到所述路面状况信息。
进一步，在本发明所述的基于路面状况主动调节车辆悬架的方法中，所述步骤S21包括：
S211、将所述路面扫描信息转化为直角坐标系数据，其中所述直角坐标系的X轴指向车辆前方，Y轴指向车辆左方，Z轴指向车辆上方；
S212、计算同一激光通道所有相邻扫描点的斜率k：
k=(point[i+1].x-point[i].x)/(point[i+1].y-point[i].y)
其中x表示X轴上的数据，y表示Y轴上的数据，i为正整数，point[i]和point[i+1]为两个相邻扫描点；
S213、若所述斜率k的绝对值大于第一预设阈值，则扫描点point[i+1]为特征点。
进一步，在本发明所述的基于路面状况主动调节车辆悬架的方法中，在所述步骤S22之后还包括：
S221、对路面不平度信息的特征点进行提取和过滤后，通过路面与激光雷达之间的几何关系计算连续路面的相对高度变化，进而与预设标准等级路面信息的特征值进行匹配，以确定路面的不平度信息。
进一步，在本发明所述的基于路面状况主动调节车辆悬架的方法中，所述步骤S22包括：
S222、对特征点提取后，将同一激光通道内斜率k为正和负的特征点分别相互连接形成特征点簇，并按照单一方向对特征点簇进行排序；
S223、将第一特征点簇和最后一个特征点簇进行配对，通过点云补全配对特征点簇之间的点云，并用直线连接配对特征点簇外侧的两个特征点，进而在xy平面上判断补全后的点云与所述直线是否有交点或者交叉，其中所述点云为同一激光通道的扫描点；
S224、若没有交点或者交叉，则初步判断可能是减速带或者凹凸坑，并存储这一对所述特征点簇，清空这对所述特征点簇以及中间的特征点；
S225、若有交点或者交叉，则将第一特征点簇和倒数第二个特征点簇进行配对，以此类推，将第一个特征点簇和所有特征点簇配对后，开始新一轮配对第二个特征点簇，直到检索完成。
进一步，在本发明所述的基于路面状况主动调节车辆悬架的方法中，在所述步骤S224之后还包括：
S2241、对同一帧中可能包含减速带或凹凸坑的相邻激光通道进行归类，若检测出其中y位置重合度大于第二预设阈值的相邻通道的点云，则认定为可能属于同一条减速带或凹凸坑；
S2242、对于同一条可能是减速带或凹凸坑中的多个激光通道的提取出的点云，检测其中最长通道的长度，判断是否大于第三预设阈值；
S2243、若否，则认定不是减速带或凹凸坑；
S2244、若是，则判断连续3帧扫描数据是否在相同y值范围内出现至少两次，且两次中的平均x值减小；其中一帧扫描数据为一个激光通道完成一次上下左右周期扫描的数据；
S2245、若否，则判断不是减速带或凹凸坑；
S2246、若为是，则判断确定为减速带或凹凸坑。
进一步，在本发明所述的基于路面状况主动调节车辆悬架的方法中，在所述步骤S2246判断为减速带或凹凸坑后，还包括：
S2247、计算所述减速带或凹凸坑对应点云的外侧m个非特征点的第一z轴平均值，其中m为大于1的整数；去掉所述减速带或凹凸坑对应点云的首尾特征点，然后计算出剩余特征点对应坐标点的第二z轴平均值；
由所述第一z轴平均值和所述第二z轴平均值的差值得到所述减速带的高度或所述凹凸坑的深度和高度。
另外，本发明还提供一种车辆，包括激光雷达和控制器；
所述激光雷达用于扫描车辆前进方向的路面得到路面扫描信息；
所述控制器使用如上述的基于路面状况主动调节车辆悬架的方法调节车辆悬架。
进一步，本发明所述的车辆还包括用于获取车辆速度的速度传感器；和/或
用于获取车辆加速度的惯性测量单元；和/或
用于获取悬架相对位移的相对位移传感器；
所述控制器根据所述路面扫描信息以及已获取的所述车辆速度、车辆加速度和悬架相对位移调节车辆悬架。
实施本发明的一种基于路面状况主动调节车辆悬架的方法及车辆，具有以下有益效果：本发明使用激光雷达快速获准确的获取车辆前进方向的路面状况信息，进而根据路面控制状况信息主动调节车辆悬架，提高用户的驾乘体验。
下面将结合附图及实施例对本发明作进一步说明，附图中：
图1是实施例1提供的一种基于路面状况主动调节车辆悬架的方法的流程图；
图2是实施例2提供的一种基于路面状况主动调节车辆悬架的方法的流程图；
图3是实施例3提供的一种基于路面状况主动调节车辆悬架的方法的流程图；
图4和图5是实施例4提供的一种基于路面状况主动调节车辆悬架的方法的流程图；
图6是实施例5提供的一种车辆的结构示意图。
为了对本发明的技术特征、目的和效果有更加清楚的理解，现对照附图详细说明本发明的具体实施方式。
实施例1
参考图1，本实施例的基于路面状况主动调节车辆悬架的方法应用于车辆悬架的调整，车辆包括但不限于燃油汽车、电动汽车、客车、货车等，车辆的悬架要求为可控悬架，即可主动控制悬架以输出不同阻尼力，主动控制悬架可参考现有技术，本实施例不再赘述。该方法包括下述步骤：
S1、使用激光雷达扫描车辆前进方向的路面得到路面扫描信息。激光雷达安装在车辆的前端，例如前保险杠位置或前进气格栅位置或者车顶部等，能够扫描到车辆前端路面即可。本实施例的步骤S1包括：S12、激光雷达的多个激光通道进行上下扫描和左右扫描得到路面扫描信息，路面扫描信息包括激光雷达和路面上各个扫描点之间的距离信息。即激光雷达的每个激光通道都会完成一次上下扫描和左右扫描，扫描一个周期后得到每个激光通道的路面扫描信息，通过激光雷达和路面上各个扫描点之间的距离信息即可进行路况判断。其中激光雷达的测距原理在此不再赘述，可参考现有技术。
S2、处理路面扫描信息得到路面状况信息。根据预设算法处理激光雷达和路面上各个扫描点之间的距离信息，其中预设算法中包含各种路况的距离判断模型，距离判断模型包括但不限于用于判断路面不平度的不平度模型、用于判断路面是否有凹坑的凹坑模块、用于判断路面是否有凸起的凸起模型、用于判断路面是否有减速带的减速带模型、用于判断路面是否倾斜的倾斜模块等，每种模型都可根据各自的距离特征来找出路面扫描信息中是否有对应路况。通过这些距离判断模块处理激光雷达和路面上各个扫描点之间的距离信息，进而得到路面状况信息。
S3、根据路面状况信息调节车辆悬架。可以理解的，不同路况下对车辆悬架输出的阻尼力的要求是不同的，步骤S3包括：根据路面状况信息调节车辆悬架的阻尼力，使车辆悬架的阻尼力位置在预设舒适阻尼力区间内，以提高用户的驾乘体验。其中路面状况包括但不限于路面不平度、路面凹坑、路面凸起、减速带等。
本实施例使用激光雷达快速获准确的获取车辆前进方向的路面状况信息，进而根据路面控制状况信息主动调节车辆悬架，提高用户的驾乘体验。
实施例2
参考图2，本实施例的基于路面状况主动调节车辆悬架的方法应用于车辆悬架的调整，车辆包括但不限于燃油汽车、电动汽车、客车、货车等，车辆的悬架要求为可控悬架，即可主动控制悬架以输出不同阻尼力，主动控制悬架可参考现有技术，本实施例不再赘述。该方法包括下述步骤：
S11、使用激光雷达扫描车辆前进方向的路面得到路面扫描信息，同时获取车辆速度、车辆加速度和悬架相对位移中的至少一种；其中车辆速度通过速度传感器获得，速度传感器可使用车辆自带的速度传感器，即可通过车辆控制系统的CAN总线中获取；当然也可车辆上安装独立的速度传感器，速度传感器具体可参考现有技术。车辆加速度通过惯性测量单元获得，惯性测量单元可根据需要灵活选择安装位置，惯性测量单元可参考现有技术。悬架相对位移通过相对位移传感器得到，相对位移传感器安装在车辆悬架上或车辆轮毂上，相对位移传感器可参考现有技术。激光雷达安装在车辆的前端，例如前保险杠位置或前进气格栅位置或者车顶部等，能够扫描到车辆前端路面即可。
S2、处理路面扫描信息得到路面状况信息。根据预设算法处理激光雷达和路面上各个扫描点之间的距离信息，其中预设算法中包含各种路况的距离判断模型，距离判断模型包括但不限于用于判断路面不平度的不平度模型、用于判断路面是否有凹坑的凹坑模块、用于判断路面是否有凸起的凸起模型、用于判断路面是否有减速带的减速带模型、用于判断路面是否倾斜的倾斜模块等，每种模型都可根据各自的距离特征来找出路面扫描信息中是否有对应路况。通过这些距离判断模块处理激光雷达和路面上各个扫描点之间的距离信息，进而得到路面状况信息。
S31、根据路面状况信息以及已获取的车辆速度、车辆加速度和悬架相对位移调节车辆悬架。可以理解的，不同路况下对车辆悬架输出的阻尼力的要求是不同的，同时考虑到车辆实际到达前方已扫描路面还需要一定时间，若提前调节会影响当前路面的驾乘体验，若延后调节便起不到调节的作用，所以应在尽量在准确地方进行调节。本申请为解决这个问题，应采集车辆的速度和加速度，已估算出车辆达到前方已扫描路面的时间，进而准确调整悬架，使车辆悬架在准确位置输出对应的阻尼力，保证用户的驾乘体验。
进一步，通过获取悬架相对位移的当前相对位移，进而得出车辆悬架的当前输出阻尼力，为后续主动调节车辆悬架的阻尼力提供参考。
本实施例使用激光雷达快速获准确的获取车辆前进方向的路面状况信息，并结合车辆速度、车辆加速度和悬架相对位移信息，进而根据路面状况信息以及已获取的车辆速度、车辆加速度和悬架相对位移调节车辆悬架，提高用户的驾乘体验。
实施例3
参考图3，在上述实施例的基础上，在本实施例的基于路面状况主动调节车辆悬架的方法中，步骤S2包括：
S21、根据预设算法提取路面扫描信息中的特征点。可以理解，道路上不同路况的特征是不同的，例如路面不平度、路面凹坑、路面凸起、减速带等都有对应的特征，这些特征可以用于区分不同路况，即特征点是指激光雷达的扫描点中符合一定特征的点，通过这些特征点可以判断出路面状况。具体的，在本实施例的基于路面状况主动调节车辆悬架的方法中步骤S21包括：
S211、将路面扫描信息转化为直角坐标系数据，该直角坐标系为三维直角坐标系，其中直角坐标系的X轴指向车辆前方，Y轴指向车辆左方，Z轴指向车辆上方，建立坐标系后将激光雷达和路面上各个扫描点之间的距离信息转换为各个扫描点在三维直角坐标系中的参数。
S212、计算同一激光通道所有相邻扫描点的斜率k：
k=(point[i+1].x-point[i].x)/(point[i+1].y-point[i].y)
其中x表示X轴上的数据，y表示Y轴上的数据，i为正整数，point[i]和point[i+1]为两个相邻扫描点，通过该算法可快速处理所有扫描点，得到每个扫描点对应的斜率k。
S213、若斜率k的绝对值大于第一预设阈值，则扫描点point[i+1]为特征点。对斜率k绝对值阀值不同，可以检测减速带或凹凸坑边缘的坡度。作为选择，斜率k的绝对值取0.5。
S22、将特征点和预设特征模型进行匹配，得到路面状况信息。可以理解，道路上不同路况的特征是不同的，例如路面不平度、路面凹坑、路面凸起、减速带等都有对应的特征，这些特征可以用于区分不同路况，本实施例为每种路况设置对应的预设特征模型。其中预设特征模型包括但不限于用于判断路面不平度的不平度模型、用于判断路面是否有凹坑的凹坑模块、用于判断路面是否有凸起的凸起模型、用于判断路面是否有减速带的减速带模型、用于判断路面是否倾斜的倾斜模块等，每种模型对应的特征点不同，反过来，可根据已得到的特征点确定对应的预设特征模型，从而实现路况的确定。
本实施例使用斜率k确定特征点，进而将特征点和预设特征模型进行匹配，得到路面状况信息。
实施例4
在实施例3的基础上，本实施例的基于路面状况主动调节车辆悬架的方法中在步骤S22之后还包括对路面的不平度信息的判断：
S221、对路面不平度信息的特征点进行提取和过滤后，通过路面与激光雷达之间的几何关系计算连续路面的相对高度变化，进而与预设标准等级路面信息的特征值进行匹配，以确定路面的不平度信息。可以理解的，不同等级道路对应的路面不平度信息是不同的，例如高速公路、省级公路、市级公路、乡村公路等，各自之间的路面不平度相差较大。
参考图4，本实施例的基于路面状况主动调节车辆悬架的方法中还包括对减速带或者凹凸坑的判断，即步骤S22包括：
S222、对特征点提取后，将同一激光通道内斜率k为正和负的特征点分别相互连接形成特征点簇，即特征点簇是由斜率相同或相近的特征点的集合，并按照单一方向对特征点簇进行排序，可以从左到右，也可以从右到左等。
S223、将第一特征点簇和最后一个特征点簇进行配对，通过点云补全配对特征点簇之间的点云，并用直线连接配对特征点簇外侧的两个特征点，进而在xy平面（X轴和Y轴形成的坐标平面）上判断补全后的点云与直线是否有交点或者交叉，其中点云为同一激光通道的扫描点。
S224、若没有交点或者交叉，则初步判断可能是减速带或者凹凸坑，并存储这一对所述特征点簇，清空这对所述特征点簇以及中间的特征点。
S225、若有交点或者交叉，则判断不是减速带或者凹凸坑，将第一特征点簇和倒数第二个特征点簇进行配对，以此类推，将第一个特征点簇和所有特征点簇配对后，开始新一轮配对第二个特征点簇，直到检索完成。
进一步，参考图5，在步骤S224之后还包括：
S2241、对同一帧中可能包含减速带或凹凸坑的相邻激光通道进行归类，若检测出其中y位置重合度大于第二预设阈值的相邻通道的点云，则认定为可能属于同一条减速带或凹凸坑。
S2242、对于同一条可能是减速带或凹凸坑中的多个激光通道的提取出的点云，检测其中最长通道的长度，判断是否大于第三预设阈值。
S2243、若否，则认定不是减速带或凹凸坑。
S2244、若是，则判断连续3帧扫描数据是否在相同y值范围内出现至少两次，且两次中的平均x值减小；其中一帧扫描数据为一个激光通道完成一次上下左右周期扫描的数据。
S2245、若连续3帧扫描数据没有在相同y值范围内出现至少两次，则判断不是减速带或凹凸坑。
S2246、若连续3帧扫描数据在相同y值范围内出现至少两次，则判断确定为减速带或凹凸坑。
进一步，本实施例的基于路面状况主动调节车辆悬架的方法中在步骤S2246判断为减速带或凹凸坑后，还可以获取减速带的高度或凹凸坑的深度，具体包括下述步骤：
S2247、计算减速带或凹凸坑对应点云的外侧m个非特征点的第一z轴平均值，其中m为大于1的整数；去掉减速带或凹凸坑对应点云的首尾特征点，然后计算出剩余特征点对应坐标点的第二z轴平均值；由第一z轴平均值和第二z轴平均值的差值得到减速带的高度或凹凸坑的深度和高度。本实施例中不仅判断出前方路面有减速带或凹凸坑，还计算出减速带的高度或凹凸坑的深度和高度，进而实现更加准确调整悬架，使车辆悬架在准确位置输出对应的阻尼力，保证用户的驾乘体验。
本实施例使用斜率k确定特征点，进而根据特征点得到特征点簇，利用特征点簇判断出路面是否包含减速带或凹凸坑，进而根据路面控制状况信息主动调节车辆悬架，提高用户的驾乘体验。
实施例5
参考图6，本实施例的车辆包括但不限于燃油汽车、电动汽车、客车、货车等，车辆的悬架要求为可控悬架，即可主动控制悬架以输出不同阻尼力，主动控制悬架可参考现有技术，本实施例不再赘述。车辆包括激光雷达和控制器，激光雷达安装在车辆的前端，例如前保险杠位置或前进气格栅位置等，能够扫描到车辆前端路面即可。激光雷达用于扫描车辆前进方向的路面得到路面扫描信息，控制器使用如上述实施例的基于路面状况主动调节车辆悬架的方法调节车辆悬架。作为选择，可设置单独的处理器用于执行上述实施例的基于路面状况主动调节车辆悬架的方法，也可使用车辆本身自带的ECU执行上述实施例的基于路面状况主动调节车辆悬架的方法。
本实施例使用激光雷达快速获准确的获取车辆前进方向的路面状况信息，进而根据路面控制状况信息主动调节车辆悬架，提高用户的驾乘体验。
作为选择，一些实施例的车辆还包括用于获取车辆速度的速度传感器，速度传感器可使用车辆自带的速度传感器，即可通过车辆控制系统的CAN总线中获取；当然也可车辆上安装独立的速度传感器，速度传感器具体可参考现有技术。
作为选择，一些实施例的车辆还包括用于获取车辆加速度的惯性测量单元，惯性测量单元可根据需要灵活选择安装位置，惯性测量单元可参考现有技术。
可以理解的，不同路况下对车辆悬架输出的阻尼力的要求是不同的，同时考虑到车辆实际到达前方已扫描路面还需要一定时间，若提前调节会影响当前路面的驾乘体验，若延后调节便起不到调节的作用，所以应在尽量在准确地方进行调节。本申请为解决这个问题，应采集车辆的速度和加速度，已估算出车辆达到前方已扫描路面的时间，进而准确调整悬架，使车辆悬架在准确位置输出对应的阻尼力，保证用户的驾乘体验。
作为选择，一些实施例的车辆还包括用于获取悬架相对位移的相对位移传感器，相对位移传感器安装在车辆悬架上或车辆轮毂上，相对位移传感器可参考现有技术。通过获取悬架相对位移的当前相对位移，进而得出车辆悬架的当前输出阻尼力，为后续主动调节车辆悬架的阻尼力提供参考。
本实施例使用激光雷达快速获准确的获取车辆前进方向的路面状况信息，并结合车辆速度、车辆加速度和悬架相对位移信息，进而根据路面状况信息以及已获取的车辆速度、车辆加速度和悬架相对位移调节车辆悬架，提高用户的驾乘体验。
本说明书中各个实施例采用递进的方式描述，每个实施例重点说明的都是与其他实施例的不同之处，各个实施例之间相同相似部分互相参见即可。对于实施例公开的装置而言，由于其与实施例公开的方法相对应，所以描述的比较简单，相关之处参见方法部分说明即可。
专业人员还可以进一步意识到，结合本文中所公开的实施例描述的各示例的单元及算法步骤，能够以电子硬件、计算机软件或者二者的结合来实现，为了清楚地说明硬件和软件的可互换性，在上述说明中已经按照功能一般性地描述了各示例的组成及步骤。这些功能究竟以硬件还是软件方式来执行，取决于技术方案的特定应用和设计约束条件。专业技术人员可以对每个特定的应用来使用不同方法来实现所描述的功能，但是这种实现不应认为超出本发明的范围。
结合本文中所公开的实施例描述的方法或算法的步骤可以直接用硬件、处理器执行的软件模块，或者二者的结合来实施。软件模块可以置于随机存储器（RAM）、内存、只读存储器（ROM）、电可编程ROM、电可擦除可编程ROM、寄存器、硬盘、可移动磁盘、CD-ROM、或技术领域内所公知的任意其它形式的存储介质中。
以上实施例只为说明本发明的技术构思及特点，其目的在于让熟悉此项技术的人士能够了解本发明的内容并据此实施，并不能限制本发明的保护范围。凡跟本发明权利要求范围所做的均等变化与修饰，均应属于本发明权利要求的涵盖范围。
Claims (11)
- 一种基于路面状况主动调节车辆悬架的方法，其特征在于，包括：S1、使用激光雷达扫描车辆前进方向的路面得到路面扫描信息；S2、处理所述路面扫描信息得到路面状况信息；S3、根据所述路面状况信息调节车辆悬架。
- 根据权利要求1所述的基于路面状况主动调节车辆悬架的方法，其特征在于，所述步骤S1包括：S11、使用激光雷达扫描车辆前进方向的路面得到路面扫描信息，同时获取车辆速度、车辆加速度和悬架相对位移中的至少一种；其中所述车辆速度通过速度传感器获得，所述车辆加速度通过惯性测量单元获得，所述悬架相对位移通过相对位移传感器得到；所述步骤S3包括：S31、根据所述路面状况信息以及已获取的所述车辆速度、车辆加速度和悬架相对位移调节车辆悬架。
- 根据权利要求1或2所述的基于路面状况主动调节车辆悬架的方法，其特征在于，所述步骤S1包括：S12、所述激光雷达的多个激光通道进行上下扫描和左右扫描得到所述路面扫描信息，所述路面扫描信息包括所述激光雷达和路面上各个扫描点之间的距离信息；所述步骤S3包括：根据所述路面状况信息调节车辆悬架的阻尼力，使所述车辆悬架的阻尼力位置在预设舒适阻尼力区间内。
- 根据权利要求3所述的基于路面状况主动调节车辆悬架的方法，其特征在于，所述步骤S2包括：S21、根据预设算法提取所述路面扫描信息中的特征点；S22、将特征点和预设特征模型进行匹配，得到所述路面状况信息。
- 根据权利要求4所述的基于路面状况主动调节车辆悬架的方法，其特征在于，所述步骤S21包括：S211、将所述路面扫描信息转化为直角坐标系数据，其中所述直角坐标系的X轴指向车辆前方，Y轴指向车辆左方，Z轴指向车辆上方；S212、计算同一激光通道所有相邻扫描点的斜率k：k=(point[i+1].x-point[i].x)/(point[i+1].y-point[i].y)其中x表示X轴上的数据，y表示Y轴上的数据，i为正整数，point[i]和point[i+1]为两个相邻扫描点；S213、若所述斜率k的绝对值大于第一预设阈值，则扫描点point[i+1]为特征点。
- 根据权利要求5所述的基于路面状况主动调节车辆悬架的方法，其特征在于，在所述步骤S22之后还包括：S221、对路面不平度信息的特征点进行提取和过滤后，通过路面与激光雷达之间的几何关系计算连续路面的相对高度变化，进而与预设标准等级路面信息的特征值进行匹配，以确定路面的不平度信息。
- 根据权利要求5所述的基于路面状况主动调节车辆悬架的方法，其特征在于，所述步骤S22包括：S222、对特征点提取后，将同一激光通道内斜率k为正和负的特征点分别相互连接形成特征点簇，并按照单一方向对特征点簇进行排序；S223、将第一特征点簇和最后一个特征点簇进行配对，通过点云补全配对特征点簇之间的点云，并用直线连接配对特征点簇外侧的两个特征点，进而在xy平面上判断补全后的点云与所述直线是否有交点或者交叉，其中所述点云为同一激光通道的扫描点；S224、若没有交点或者交叉，则初步判断可能是减速带或者凹凸坑，并存储这一对所述特征点簇，清空这对所述特征点簇以及中间的特征点；S225、若有交点或者交叉，则将第一特征点簇和倒数第二个特征点簇进行配对，以此类推，将第一个特征点簇和所有特征点簇配对后，开始新一轮配对第二个特征点簇，直到检索完成。
- 根据权利要求7所述的基于路面状况主动调节车辆悬架的方法，其特征在于，在所述步骤S224之后还包括：S2241、对同一帧中可能包含减速带或凹凸坑的相邻激光通道进行归类，若检测出其中y位置重合度大于第二预设阈值的相邻通道的点云，则认定为可能属于同一条减速带或凹凸坑；S2242、对于同一条可能是减速带或凹凸坑中的多个激光通道的提取出的点云，检测其中最长通道的长度，判断是否大于第三预设阈值；S2243、若否，则认定不是减速带或凹凸坑；S2244、若是，则判断连续3帧扫描数据是否在相同y值范围内出现至少两次，且两次中的平均x值减小；其中一帧扫描数据为一个激光通道完成一次上下左右周期扫描的数据；S2245、若否，则判断不是减速带或凹凸坑；S2246、若为是，则判断确定为减速带或凹凸坑。
- 根据权利要求8所述的基于路面状况主动调节车辆悬架的方法，其特征在于，在所述步骤S2246判断为减速带或凹凸坑后，还包括：S2247、计算所述减速带或凹凸坑对应点云的外侧m个非特征点的第一z轴平均值，其中m为大于1的整数；去掉所述减速带或凹凸坑对应点云的首尾特征点，然后计算出剩余特征点对应坐标点的第二z轴平均值；由所述第一z轴平均值和所述第二z轴平均值的差值得到所述减速带的高度或所述凹凸坑的深度和高度。
- 一种车辆，其特征在于，包括激光雷达和控制器；所述激光雷达用于扫描车辆前进方向的路面得到路面扫描信息；所述控制器使用如权利要求1至9任一项所述的基于路面状况主动调节车辆悬架的方法调节车辆悬架。
- 根据权利要求10所述的车辆，其特征在于，还包括用于获取车辆速度的速度传感器；和/或用于获取车辆加速度的惯性测量单元；和/或用于获取悬架相对位移的相对位移传感器；所述控制器根据所述路面扫描信息以及已获取的所述车辆速度、车辆加速度和悬架相对位移调节车辆悬架。
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