WO2020168745A1 - 基于激光雷达的无人车辆的测试方法及装置 - Google Patents

基于激光雷达的无人车辆的测试方法及装置 Download PDF

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Publication number
WO2020168745A1
WO2020168745A1 PCT/CN2019/115963 CN2019115963W WO2020168745A1 WO 2020168745 A1 WO2020168745 A1 WO 2020168745A1 CN 2019115963 W CN2019115963 W CN 2019115963W WO 2020168745 A1 WO2020168745 A1 WO 2020168745A1
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Prior art keywords
vehicle
road
test
information
action
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PCT/CN2019/115963
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English (en)
French (fr)
Inventor
钱鹏程
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苏州风图智能科技有限公司
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Priority to EP19915647.2A priority Critical patent/EP3929556A4/en
Publication of WO2020168745A1 publication Critical patent/WO2020168745A1/zh
Priority to US17/407,053 priority patent/US11994592B2/en

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    • G—PHYSICS
    • G05—CONTROLLING; REGULATING
    • G05D—SYSTEMS FOR CONTROLLING OR REGULATING NON-ELECTRIC VARIABLES
    • G05D1/00—Control of position, course, altitude or attitude of land, water, air or space vehicles, e.g. using automatic pilots
    • G05D1/02—Control of position or course in two dimensions
    • G05D1/021—Control of position or course in two dimensions specially adapted to land vehicles
    • G05D1/0231—Control of position or course in two dimensions specially adapted to land vehicles using optical position detecting means
    • G05D1/0238—Control of position or course in two dimensions specially adapted to land vehicles using optical position detecting means using obstacle or wall sensors
    • G05D1/024—Control of position or course in two dimensions specially adapted to land vehicles using optical position detecting means using obstacle or wall sensors in combination with a laser
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01C—MEASURING DISTANCES, LEVELS OR BEARINGS; SURVEYING; NAVIGATION; GYROSCOPIC INSTRUMENTS; PHOTOGRAMMETRY OR VIDEOGRAMMETRY
    • G01C21/00—Navigation; Navigational instruments not provided for in groups G01C1/00 - G01C19/00
    • G01C21/26—Navigation; Navigational instruments not provided for in groups G01C1/00 - G01C19/00 specially adapted for navigation in a road network
    • G01C21/34—Route searching; Route guidance
    • G01C21/36—Input/output arrangements for on-board computers
    • G01C21/3697—Output of additional, non-guidance related information, e.g. low fuel level
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01M—TESTING STATIC OR DYNAMIC BALANCE OF MACHINES OR STRUCTURES; TESTING OF STRUCTURES OR APPARATUS, NOT OTHERWISE PROVIDED FOR
    • G01M17/00—Testing of vehicles
    • G01M17/007—Wheeled or endless-tracked vehicles
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01S—RADIO DIRECTION-FINDING; RADIO NAVIGATION; DETERMINING DISTANCE OR VELOCITY BY USE OF RADIO WAVES; LOCATING OR PRESENCE-DETECTING BY USE OF THE REFLECTION OR RERADIATION OF RADIO WAVES; ANALOGOUS ARRANGEMENTS USING OTHER WAVES
    • G01S17/00—Systems using the reflection or reradiation of electromagnetic waves other than radio waves, e.g. lidar systems
    • G01S17/86—Combinations of lidar systems with systems other than lidar, radar or sonar, e.g. with direction finders
    • G—PHYSICS
    • G05—CONTROLLING; REGULATING
    • G05D—SYSTEMS FOR CONTROLLING OR REGULATING NON-ELECTRIC VARIABLES
    • G05D1/00—Control of position, course, altitude or attitude of land, water, air or space vehicles, e.g. using automatic pilots
    • G05D1/02—Control of position or course in two dimensions
    • G05D1/021—Control of position or course in two dimensions specially adapted to land vehicles
    • G05D1/0268—Control of position or course in two dimensions specially adapted to land vehicles using internal positioning means
    • G05D1/0274—Control of position or course in two dimensions specially adapted to land vehicles using internal positioning means using mapping information stored in a memory device
    • G—PHYSICS
    • G05—CONTROLLING; REGULATING
    • G05D—SYSTEMS FOR CONTROLLING OR REGULATING NON-ELECTRIC VARIABLES
    • G05D1/00—Control of position, course, altitude or attitude of land, water, air or space vehicles, e.g. using automatic pilots
    • G05D1/20—Control system inputs
    • G05D1/24—Arrangements for determining position or orientation
    • G05D1/247—Arrangements for determining position or orientation using signals provided by artificial sources external to the vehicle, e.g. navigation beacons
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T7/00—Image analysis
    • G06T7/20—Analysis of motion
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01S—RADIO DIRECTION-FINDING; RADIO NAVIGATION; DETERMINING DISTANCE OR VELOCITY BY USE OF RADIO WAVES; LOCATING OR PRESENCE-DETECTING BY USE OF THE REFLECTION OR RERADIATION OF RADIO WAVES; ANALOGOUS ARRANGEMENTS USING OTHER WAVES
    • G01S17/00—Systems using the reflection or reradiation of electromagnetic waves other than radio waves, e.g. lidar systems
    • G01S17/88—Lidar systems specially adapted for specific applications
    • G01S17/89—Lidar systems specially adapted for specific applications for mapping or imaging
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01S—RADIO DIRECTION-FINDING; RADIO NAVIGATION; DETERMINING DISTANCE OR VELOCITY BY USE OF RADIO WAVES; LOCATING OR PRESENCE-DETECTING BY USE OF THE REFLECTION OR RERADIATION OF RADIO WAVES; ANALOGOUS ARRANGEMENTS USING OTHER WAVES
    • G01S17/00—Systems using the reflection or reradiation of electromagnetic waves other than radio waves, e.g. lidar systems
    • G01S17/88—Lidar systems specially adapted for specific applications
    • G01S17/93—Lidar systems specially adapted for specific applications for anti-collision purposes
    • G01S17/931—Lidar systems specially adapted for specific applications for anti-collision purposes of land vehicles
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T2207/00—Indexing scheme for image analysis or image enhancement
    • G06T2207/10—Image acquisition modality
    • G06T2207/10028—Range image; Depth image; 3D point clouds

Definitions

  • the present disclosure relates to the technical field of vehicle testing, and in particular to a testing method and device for unmanned vehicles based on lidar.
  • Vehicles are becoming more and more popular, demand for vehicles is still growing, and buyers are paying more and more attention to the quality of vehicles, which makes vehicle manufacturers pay more and more attention to improving vehicle performance, such as handling performance and durability.
  • vehicle testing is an important part of testing vehicle performance or durability, and has important guiding significance for the subsequent production of vehicles. In order to ensure that the vehicles put on the market are high-quality vehicles, vehicle testing becomes more and more important.
  • the current vehicle tests are all carried out by manual driving, which not only consumes manpower, but may also have defects such as incomplete test items and inaccurate tests.
  • the present disclosure proposes a method and device for testing unmanned vehicles based on lidar.
  • a method for testing an unmanned vehicle based on lidar including:
  • the vehicle motion information includes the motion of the vehicle during the driving process
  • If the action of the vehicle is a preset action, obtain the road information of the test road where the vehicle is located according to the point cloud data and map;
  • the preset action includes one or more of the following: speed-up, deceleration, emergency braking, and steering;
  • the performance index includes one or more of the following: power index, emergency braking index, and control stability index.
  • the vehicle motion information further includes vehicle motion parameters
  • the determining the vehicle performance test result according to the vehicle action information and the performance index includes:
  • the vehicle action parameter includes one or more of the following: the start time of the preset action, the end time of the preset action, the speed corresponding to the start time, the speed corresponding to the end time, the start The driving distance, vehicle steering angle, and vehicle steering radius from time to end.
  • obtaining the road information of the road where the vehicle is located according to the point cloud data and the map includes:
  • the point cloud data and the map determine the position of the vehicle on the test road, and obtain road information corresponding to the position;
  • the corresponding relationship between the road information and the location on the test road is preset; the road information includes road types.
  • the method further includes:
  • the vehicle pose is determined.
  • the method further includes:
  • the vehicle is located and the fault information is recorded.
  • the method further includes:
  • the unmanned vehicle uses lidar to drive on the test road; or,
  • the unmanned vehicle runs on the test road according to the pre-configured action information
  • the pre-configured action information includes the speed and steering angle corresponding to each position on the test road, or the pre-configured action information includes the speed and steering angle corresponding to each time period in the test.
  • test device for an unmanned vehicle based on lidar, the device comprising:
  • the first acquisition module is used to acquire vehicle motion information and point cloud data collected by lidar; wherein, the vehicle motion information includes the motion of the vehicle during the running of the vehicle;
  • the road information acquisition module is used to acquire road information of the test road on which the vehicle is located according to the point cloud data and the map if the action of the vehicle is a preset action;
  • the performance index acquisition module is used to acquire the performance index of the vehicle according to the preset action and the road information
  • the first determining module is used to determine the vehicle performance test result according to the vehicle action information and the performance index.
  • the preset action includes one or more of the following: speed-up, deceleration, emergency braking, and steering;
  • the performance index includes one or more of the following: power index, emergency braking index, and control stability index.
  • the vehicle motion information further includes vehicle motion parameters;
  • the first determining module includes:
  • the first determining unit is configured to determine the performance parameters of the vehicle according to the vehicle action parameters
  • the second determining unit is configured to determine that the vehicle performance test result is qualified if the performance parameters of the vehicle meet the performance index;
  • the vehicle action parameter includes one or more of the following: the start time of the preset action, the end time of the preset action, the speed corresponding to the start time, the speed corresponding to the end time, the start The driving distance, vehicle steering angle, and vehicle steering radius from time to end.
  • the road information acquisition module includes:
  • a road information acquisition unit configured to determine the position of the vehicle on the test road according to the point cloud data and the map, and acquire road information corresponding to the position;
  • the corresponding relationship between the road information and the location on the test road is preset; the road information includes road types.
  • the device further includes:
  • the vehicle pose determination module is used to determine the vehicle pose according to the point cloud data and the map.
  • the device further includes:
  • the positioning and fault recording module is used to locate the vehicle and record the fault information if the vehicle fault is determined according to the vehicle pose.
  • the device further includes:
  • the wear data acquisition module is used to acquire the wear data of the vehicle if the driving mileage of the vehicle reaches the preset mileage;
  • the second determination module is used to determine the durability test result of the vehicle according to the wear data, the fault information and the vehicle performance test result.
  • the lidar is installed on a vehicle, and there are one or more lidars;
  • the angle between the laser direction of the lidar and the horizontal plane is 0-50 degrees.
  • the laser direction of the lidar is inclined downward by 10 degrees with respect to the horizontal plane.
  • the unmanned vehicle uses lidar to drive on the test road; or,
  • the unmanned vehicle runs on the test road according to the pre-configured action information
  • the pre-configured action information includes the speed and steering angle corresponding to each position on the test road, or the pre-configured action information includes the speed and steering angle corresponding to each time period in the test.
  • a lidar-based test device for unmanned vehicles including: a processor; a memory for storing executable instructions of the processor; wherein the processor is configured to execute the above method.
  • a non-volatile computer-readable storage medium having computer program instructions stored thereon, wherein the computer program instructions implement the above method when executed by a processor.
  • Unmanned automatic testing saves manpower and can ensure the comprehensiveness and accuracy of testing.
  • Fig. 1 shows a flowchart of an unmanned vehicle testing method based on lidar according to an embodiment of the present disclosure.
  • Fig. 2 shows a flowchart of step S14 according to an embodiment of the present disclosure.
  • Fig. 3 shows a flow chart of an unmanned vehicle testing method based on lidar according to an embodiment of the present disclosure.
  • Fig. 4 shows a flow chart of an unmanned vehicle testing method based on lidar according to an embodiment of the present disclosure.
  • Fig. 5 shows a flow chart of an unmanned vehicle testing method based on lidar according to an embodiment of the present disclosure.
  • Fig. 6 shows a block diagram of an unmanned vehicle testing device based on lidar according to an embodiment of the present disclosure.
  • Fig. 7 shows a block diagram of an unmanned vehicle testing device based on lidar according to an embodiment of the present disclosure.
  • Fig. 8 shows a block diagram of an unmanned vehicle testing device based on lidar according to an embodiment of the present disclosure.
  • Fig. 9 is a block diagram showing an unmanned vehicle testing device 800 based on lidar according to an exemplary embodiment.
  • Fig. 10 is a block diagram showing an unmanned vehicle testing device 1900 based on lidar according to an exemplary embodiment.
  • Fig. 1 shows a flowchart of an unmanned vehicle testing method based on lidar according to an embodiment of the present disclosure. As shown in Figure 1, the method may include:
  • Step S11 acquiring vehicle motion information and point cloud data collected by lidar.
  • the vehicle test can be carried out in a vehicle test proving ground.
  • the vehicle test proving ground can include test roads for vehicle testing.
  • the test roads can be preset, for example, based on existing vehicle durability tests.
  • the test roads are set up in the vehicle test proving ground in advance.
  • the present disclosure does not limit the test road, as long as it can be used to test vehicles.
  • the vehicle test may include vehicle performance test and vehicle durability test.
  • the vehicle test may be an unmanned vehicle test, that is, the vehicle test does not require manual labor, and the vehicle is in an unmanned driving state.
  • one or more lidars can be installed on the unmanned vehicle, and a camera or other sensing devices can also be installed to achieve functions such as avoiding obstacles or positioning.
  • the position of the lidar, camera or other sensing equipment on the vehicle is not limited, as long as the smooth driving of unmanned driving can be ensured.
  • the vehicle motion information may include the motion of the vehicle during the running of the vehicle, and the motion of the vehicle may include the operation motion of the vehicle, for example, sudden braking, turning at right angles, toggle direction lights, etc., and may also include the body motion of the vehicle, For example, the vibration of the car body.
  • the lidar on the vehicle can collect point cloud data, for example, the point cloud data of the environment or the point cloud data of the vehicle, etc., and the test platform (or can be called the test control system) can obtain the point cloud data from the lidar
  • the point cloud data is acquired, and vehicle motion information can be acquired in real time.
  • the test platform may be a computer, etc.
  • the test platform may be set in a monitoring room of a vehicle test proving ground, and the test platform may communicate with the lidar and the vehicle in a wireless manner.
  • Step S12 If the action of the vehicle is a preset action, obtain the road information of the test road where the vehicle is located according to the point cloud data and the map.
  • the preset action may be preset according to the test target, for example, the test target may be the power of the test vehicle, and the preset action may be speed increase
  • the map may be a map of a vehicle test field, and the map may be described by using point cloud data.
  • the test platform can determine whether the vehicle's action is a preset action, for example, speeding up. If the vehicle's action is a preset action, it can find a match in the map according to the obtained point cloud data, and obtain the position in the map corresponding to the point cloud data , The location is the location of the test road where the vehicle is located.
  • the test road in the vehicle test test site can be preset, for example, different road types are set at different positions of the test road, such as undulating roads, mountain roads, ramps, loops, etc., that is, the location and road in the test road Correspondence of types already exists.
  • the test platform can obtain the road information of the test road where the vehicle is located according to the location of the test road where the vehicle is located. For example, if the location of the test road where the vehicle is located is within the range of a mountain road, it can obtain the road information of the test road where the vehicle is located as a mountain road.
  • Step S13 Acquire performance indicators of the vehicle according to the preset action and the road information.
  • the performance index of the vehicle may refer to an index that can describe the performance of the vehicle, such as power, safety, and comfort.
  • the corresponding relationship between the preset actions, road information, and vehicle performance indicators can be preset before the test. For example, it can be written into a database and saved by professionals according to the performance indicators of different models of vehicles. For example, the corresponding relationship can be Save it in the form of Table 1 below.
  • Model Default action Road information Vehicle performance indicators A Speed up Mountain road 10 seconds/100 kilometers acceleration A Speed up high way 7 seconds/100 kilometers acceleration A Emergency braking high way 3-5 meters B Turn to Loop Turning radius a, turning angle b
  • Table 1 is only an example of the corresponding relationship between the preset actions, road information, and vehicle performance indicators, which is not limited in the present disclosure, and the tester can make settings according to the items to be tested.
  • the test platform can obtain the performance index of the vehicle by looking up Table 1 above according to the preset action and road information to which the vehicle action information belongs.
  • Step S14 Determine the vehicle performance test result according to the vehicle action information and the performance index.
  • the vehicle motion information may also include the motion parameters of the vehicle, for example, the acceleration time of the vehicle from 100 kilometers, the intensity of vehicle vibration, and the like.
  • the test platform can determine the vehicle performance test result according to the vehicle action information and the performance index. For example, when testing model A, if the vehicle's movement is to increase speed and the road information is a highway, the performance index can be obtained by looking up the table as 7 seconds. If the acceleration time per 100 kilometers in the vehicle movement information is 7 seconds , It can be determined that the vehicle performance test results are qualified. The test result is only for this test result. The final vehicle performance test result will be obtained by the statistical test results of multiple vehicle performance tests during the test process.
  • test platform can also obtain parameters related to vehicle comfort or vehicle handling performance, and test the vehicle's comfort performance, handling performance (vehicle response to handling), etc.
  • the testing method for the unmanned vehicle based on the lidar of the embodiment of the present disclosure can be realized Unmanned automatic testing of vehicles saves manpower and can ensure the comprehensiveness and accuracy of testing.
  • the preset action includes one or more of the following: speed increase, deceleration, emergency braking, steering, etc.;
  • the performance index includes one or more of the following: power index, emergency braking index, control stability index, etc.
  • the lidar may be installed on a vehicle, and there may be one or more lidars;
  • the angle between the laser direction of the lidar and the horizontal plane may be 0-50 degrees.
  • the laser direction of the lidar is inclined downward by 10 degrees with respect to the horizontal plane. This can test the obstacles on the road well and ensure the smooth driving of the vehicle.
  • the unmanned vehicle may use lidar to drive on the test road; for example, the unmanned vehicle may use the lidar to avoid obstacles on the test road and complete the driving on the test road.
  • the unmanned vehicle may drive on the test road according to pre-configured action information
  • the pre-configured action information includes the speed and steering angle corresponding to each position on the test road, or the pre-configured action information includes the speed and steering angle corresponding to each time period (time point) in the test.
  • the unmanned vehicle Before the test, the unmanned vehicle can be configured with action information.
  • the action information can be stored on the unmanned vehicle, such as the speed, steering angle, acceleration, etc. of each position in the test road.
  • the unmanned vehicle is testing the road.
  • the control module of the unmanned vehicle can obtain matching action information from the stored action information according to the current position, and control the unmanned measurement drive according to the action information.
  • the unmanned vehicle can be configured with a test mileage or test duration before the test, and the unmanned vehicle control module can control the unmanned vehicle to stop the test according to the test mileage or test duration.
  • Fig. 2 shows a flowchart of step S14 according to an embodiment of the present disclosure.
  • the vehicle motion information further includes vehicle motion parameters.
  • the step S14 may include:
  • Step S141 Determine the performance parameters of the vehicle according to the vehicle action parameters.
  • the vehicle action parameter may include one or more of the following: the start time of the preset action, the end time of the preset action, the speed corresponding to the start time, the speed corresponding to the end time, the start time The driving distance, vehicle steering angle, and vehicle steering radius within the end time.
  • the present disclosure does not limit the vehicle action parameters, as long as the vehicle action parameters can be used to calculate the performance of the vehicle.
  • the performance of the vehicle may include vehicle power performance, emergency braking performance, handling performance, safety performance, comfort performance, etc.
  • the performance parameters of the vehicle may refer to parameters that characterize the performance of the vehicle, and the performance parameters of the vehicle may include vehicle dynamic performance parameters, emergency braking performance parameters, handling performance parameters, safety performance parameters, and comfort performance. Parameters etc.
  • the movement of the vehicle is speed increase (acceleration to 100 kilometers)
  • the corresponding vehicle motion parameters may include the start time t1 of the acceleration of 100 kilometers, the end time t2 of the acceleration of 100 kilometers, and the vehicle speed at the start time of 0-10km/h
  • the vehicle speed at the end time is 100km/h
  • the test platform can determine that the vehicle's dynamic performance parameters are 100km acceleration time t2-t1.
  • Step S142 If the performance parameters of the vehicle meet the performance index, it is determined that the vehicle performance test result is qualified.
  • the test platform can determine whether the t2-t1 is within 7 seconds, if it is within 7 seconds, it can be determined that the vehicle performance test is qualified, if it is not within 7 seconds, the vehicle performance test can be determined Unqualified.
  • the test platform can record the results of each vehicle performance test.
  • Fig. 3 shows a flow chart of an unmanned vehicle testing method based on lidar according to an embodiment of the present disclosure.
  • the step S12 may include:
  • Step S121 Determine the position of the vehicle on the test road according to the point cloud data and the map, and obtain road information corresponding to the position;
  • the road information includes road types, that is, multiple road types are set on the test road for testing vehicles, and which locations are set accordingly Which road types are known in advance.
  • the road types may include highways, gravel roads, fish-scale pit roads, washboard roads, Belgian roads, undulating roads, swing roads, damaged roads, square pits, standard ramps, loops, etc.
  • the test platform can determine the location of the vehicle on the test road according to the point cloud data and the map, and can obtain road information corresponding to the location according to the corresponding relationship between the road information and the location on the test road.
  • Fig. 4 shows a flow chart of an unmanned vehicle testing method based on lidar according to an embodiment of the present disclosure. As shown in Figure 4, in a possible implementation manner, the method may further include:
  • Step S15 Determine the vehicle pose according to the point cloud data and the map
  • step S16 if a vehicle fault is determined according to the vehicle pose, the vehicle is located and the fault information is recorded.
  • the test platform needs to obtain the measurement status in real time to ensure the normal driving of the measurement and avoid missing the problems in the vehicle testing process.
  • the test platform can also determine the vehicle pose, such as tilt, rollover, etc., according to the point cloud data and the map. If the vehicle fault is determined according to the vehicle pose, the vehicle can be located and the fault information can be recorded.
  • the fault information may include faulty components, fault severity, etc., and the fault information may be used to determine the durability test result.
  • step S15 can be performed after the point cloud data is obtained in step S11, and is not affected by other steps, and it can be executed as long as the point cloud data is obtained.
  • Fig. 5 shows a flow chart of an unmanned vehicle testing method based on lidar according to an embodiment of the present disclosure. As shown in Figure 5, in a possible implementation manner, the method may further include:
  • Step S17 if the mileage of the vehicle reaches the preset mileage, obtain the wear data of the vehicle.
  • the preset mileage may be set by the tester to control the unmanned vehicle to stop when it reaches the preset mileage on the test road.
  • the test platform can determine whether the driving range of the unmanned vehicle reaches a preset mileage, and if it reaches the preset mileage, the unmanned vehicle can be controlled to stop driving to end the vehicle test. After stopping the vehicle test, the test platform can obtain the wear data of the vehicle, such as the degree of wear of various parts of the vehicle.
  • Step S18 Determine the durability test result of the vehicle according to the wear data, the fault information and the vehicle performance test result.
  • the test platform can determine the durability test result of the vehicle according to the wear data, the fault information and the vehicle performance test result. For example, it can determine the durability test result of the vehicle, such as the life of the vehicle. How many years or tens of thousands of kilometers.
  • the wear data and failure information can be used to determine the degree of fatigue of each component
  • the test process can include multiple vehicle performance test results
  • the multiple vehicle performance test results can be used to determine the performance change of the vehicle, For example, whether the power performance of the vehicle is getting worse, whether the increase in fuel consumption of the vehicle is greater than a threshold, and so on.
  • the test platform can determine the vehicle durability test result according to the fatigue degree of each component of the vehicle and the performance change of the vehicle.
  • the durability test result may also include more detailed content, for example, the life of a certain component, etc., which may be set according to the purpose of the vehicle test, which is not limited in the present disclosure.
  • Fig. 6 shows a block diagram of an unmanned vehicle testing device based on lidar according to an embodiment of the present disclosure.
  • the test device may refer to the above-mentioned test platform (test control system), or the test device may also refer to a device in the test platform that specifically executes the unmanned vehicle test method. As shown in Figure 6, the device may include:
  • the first acquisition module 11 is used to acquire vehicle motion information and point cloud data collected by lidar; wherein, the vehicle motion information includes the motion of the vehicle during the driving process;
  • the road information acquisition module 12 is configured to acquire road information of the test road on which the vehicle is located according to the point cloud data and the map if the action of the vehicle is a preset action;
  • the performance index obtaining module 13 is used to obtain the performance index of the vehicle according to the preset action and the road information;
  • the first determining module 14 is configured to determine the vehicle performance test result according to the vehicle action information and the performance index.
  • the testing device for the unmanned vehicle based on the lidar of the embodiment of the present disclosure can realize Unmanned automatic testing of vehicles saves manpower and can ensure the comprehensiveness and accuracy of testing.
  • the preset action may include one or more of the following: speed increase, deceleration, emergency braking, steering, etc.;
  • the performance index may include one or more of the following: power index, emergency braking index, control stability index, etc.
  • the lidar may be installed on a vehicle, and there are one or more lidars;
  • the angle between the laser direction of the lidar and the horizontal plane is 0-50 degrees.
  • the laser direction of the lidar is inclined downward by 10 degrees with respect to the horizontal plane.
  • the unmanned vehicle uses lidar to drive on the test road; or,
  • the unmanned vehicle runs on the test road according to the pre-configured action information
  • the pre-configured action information includes the speed and steering angle corresponding to each position on the test road, or the pre-configured action information includes the speed and steering angle corresponding to each time period in the test.
  • Fig. 7 shows a block diagram of an unmanned vehicle testing device based on lidar according to an embodiment of the present disclosure.
  • the vehicle action information may also include vehicle action parameters; as shown in FIG. 7, in a possible implementation manner, the first determining module 14 includes:
  • the first determining unit 141 is configured to determine the performance parameters of the vehicle according to the vehicle action parameters
  • the second determining unit 142 is configured to determine that the vehicle performance test result is qualified if the performance parameters of the vehicle meet the performance index;
  • the vehicle action parameter includes one or more of the following: the start time of the preset action, the end time of the preset action, the speed corresponding to the start time, the speed corresponding to the end time, the start The driving distance, vehicle steering angle, and vehicle steering radius from time to end.
  • the road information acquiring module 12 may include:
  • the road information obtaining unit 121 is configured to determine the position of the vehicle on the test road according to the point cloud data and the map, and obtain road information corresponding to the position;
  • the corresponding relationship between the road information and the location on the test road is preset; the road information includes road types.
  • Fig. 8 shows a block diagram of an unmanned vehicle testing device based on lidar according to an embodiment of the present disclosure.
  • the apparatus may further include:
  • the vehicle pose determination module 15 is used to determine the vehicle pose according to the point cloud data and map;
  • the positioning and fault recording module 16 is used to locate the vehicle and record the fault information if the vehicle is determined to be faulty according to the vehicle pose.
  • the apparatus may further include:
  • the wear data acquisition module 17 is used to acquire the wear data of the vehicle if the mileage of the vehicle reaches the preset mileage;
  • the second determining module 18 is configured to determine the durability test result of the vehicle according to the wear data, the fault information and the vehicle performance test result.
  • Fig. 9 is a block diagram showing an unmanned vehicle testing device 800 based on lidar according to an exemplary embodiment.
  • the device 800 may be a mobile phone, a computer, a digital broadcasting terminal, a messaging device, a game console, a tablet device, a medical device, a fitness device, a personal digital assistant, etc.
  • the device 800 may include one or more of the following components: a processing component 802, a memory 804, a power supply component 806, a multimedia component 808, an audio component 810, an input/output (I/O) interface 812, a sensor component 814, And the communication component 816.
  • a processing component 802 a memory 804, a power supply component 806, a multimedia component 808, an audio component 810, an input/output (I/O) interface 812, a sensor component 814, And the communication component 816.
  • the processing component 802 generally controls the overall operations of the device 800, such as operations associated with display, telephone calls, data communications, camera operations, and recording operations.
  • the processing component 802 may include one or more processors 820 to execute instructions to complete all or part of the steps of the foregoing method.
  • the processing component 802 may include one or more modules to facilitate the interaction between the processing component 802 and other components.
  • the processing component 802 may include a multimedia module to facilitate the interaction between the multimedia component 808 and the processing component 802.
  • the memory 804 is configured to store various types of data to support operations in the device 800. Examples of these data include instructions for any application or method operating on the device 800, contact data, phone book data, messages, pictures, videos, etc.
  • the memory 804 can be implemented by any type of volatile or nonvolatile storage device or a combination thereof, such as static random access memory (SRAM), electrically erasable programmable read-only memory (EEPROM), erasable Programmable Read Only Memory (EPROM), Programmable Read Only Memory (PROM), Read Only Memory (ROM), Magnetic Memory, Flash Memory, Magnetic Disk or Optical Disk.
  • SRAM static random access memory
  • EEPROM electrically erasable programmable read-only memory
  • EPROM erasable Programmable Read Only Memory
  • PROM Programmable Read Only Memory
  • ROM Read Only Memory
  • Magnetic Memory Flash Memory
  • Magnetic Disk Magnetic Disk or Optical Disk.
  • the power supply component 806 provides power to various components of the device 800.
  • the power supply component 806 may include a power management system, one or more power supplies, and other components associated with generating, managing, and distributing power for the device 800.
  • the multimedia component 808 includes a screen that provides an output interface between the device 800 and the user.
  • the screen may include a liquid crystal display (LCD) and a touch panel (TP). If the screen includes a touch panel, the screen may be implemented as a touch screen to receive input signals from the user.
  • the touch panel includes one or more touch sensors to sense touch, sliding, and gestures on the touch panel. The touch sensor may not only sense the boundary of a touch or slide action, but also detect the duration and pressure related to the touch or slide operation.
  • the multimedia component 808 includes a front camera and/or a rear camera. When the device 800 is in an operation mode, such as a shooting mode or a video mode, the front camera and/or the rear camera can receive external multimedia data. Each front camera and rear camera can be a fixed optical lens system or have focal length and optical zoom capabilities.
  • the audio component 810 is configured to output and/or input audio signals.
  • the audio component 810 includes a microphone (MIC), and when the device 800 is in an operation mode, such as a call mode, a recording mode, and a voice recognition mode, the microphone is configured to receive external audio signals.
  • the received audio signal may be further stored in the memory 804 or transmitted via the communication component 816.
  • the audio component 810 further includes a speaker for outputting audio signals.
  • the I/O interface 812 provides an interface between the processing component 802 and a peripheral interface module.
  • the peripheral interface module may be a keyboard, a click wheel, a button, and the like. These buttons may include but are not limited to: home button, volume button, start button, and lock button.
  • the sensor component 814 includes one or more sensors for providing the device 800 with various aspects of status assessment.
  • the sensor component 814 can detect the on/off status of the device 800 and the relative positioning of the components.
  • the component is the display and the keypad of the device 800.
  • the sensor component 814 can also detect the position change of the device 800 or a component of the device 800. , The presence or absence of contact between the user and the device 800, the orientation or acceleration/deceleration of the device 800, and the temperature change of the device 800.
  • the sensor component 814 may include a proximity sensor configured to detect the presence of nearby objects when there is no physical contact.
  • the sensor component 814 may also include a light sensor, such as a CMOS or CCD image sensor, for use in imaging applications.
  • the sensor component 814 may also include an acceleration sensor, a gyroscope sensor, a magnetic sensor, a pressure sensor or a temperature sensor.
  • the communication component 816 is configured to facilitate wired or wireless communication between the device 800 and other devices.
  • the device 800 can access a wireless network based on a communication standard, such as WiFi, 2G, or 3G, or a combination thereof.
  • the communication component 816 receives a broadcast signal or broadcast related information from an external broadcast management system via a broadcast channel.
  • the communication component 816 further includes a near field communication (NFC) module to facilitate short-range communication.
  • the NFC module can be implemented based on radio frequency identification (RFID) technology, infrared data association (IrDA) technology, ultra-wideband (UWB) technology, Bluetooth (BT) technology and other technologies.
  • RFID radio frequency identification
  • IrDA infrared data association
  • UWB ultra-wideband
  • Bluetooth Bluetooth
  • the apparatus 800 may be implemented by one or more application specific integrated circuits (ASIC), digital signal processors (DSP), digital signal processing equipment (DSPD), programmable logic devices (PLD), field programmable A gate array (FPGA), controller, microcontroller, microprocessor, or other electronic components are implemented to implement the above methods.
  • ASIC application specific integrated circuits
  • DSP digital signal processors
  • DSPD digital signal processing equipment
  • PLD programmable logic devices
  • FPGA field programmable A gate array
  • controller microcontroller, microprocessor, or other electronic components are implemented to implement the above methods.
  • a non-volatile computer-readable storage medium such as the memory 804 including computer program instructions, which can be executed by the processor 820 of the device 800 to complete the foregoing method.
  • Fig. 10 is a block diagram showing an unmanned vehicle testing device 1900 based on lidar according to an exemplary embodiment.
  • the device 1900 may be provided as a server.
  • the apparatus 1900 includes a processing component 1922, which further includes one or more processors, and a memory resource represented by a memory 1932, for storing instructions that can be executed by the processing component 1922, such as application programs.
  • the application program stored in the memory 1932 may include one or more modules each corresponding to a set of instructions.
  • the processing component 1922 is configured to execute instructions to perform the above-described methods.
  • the device 1900 may also include a power component 1926 configured to perform power management of the device 1900, a wired or wireless network interface 1950 configured to connect the device 1900 to the network, and an input output (I/O) interface 1958.
  • the device 1900 can operate based on an operating system stored in the memory 1932, such as Windows ServerTM, Mac OS XTM, UnixTM, LinuxTM, FreeBSDTM or the like.
  • a non-volatile computer-readable storage medium such as the memory 1932 including computer program instructions, which can be executed by the processing component 1922 of the device 1900 to complete the foregoing method.
  • the present disclosure may be a system, method, and/or computer program product.
  • the computer program product may include a computer-readable storage medium loaded with computer-readable program instructions for enabling a processor to implement various aspects of the present disclosure.
  • the computer-readable storage medium may be a tangible device that can hold and store instructions used by the instruction execution device.
  • the computer-readable storage medium may be, for example, but not limited to, an electrical storage device, a magnetic storage device, an optical storage device, an electromagnetic storage device, a semiconductor storage device, or any suitable combination of the foregoing.
  • Computer-readable storage media include: portable computer disks, hard disks, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM) Or flash memory), static random access memory (SRAM), portable compact disk read-only memory (CD-ROM), digital versatile disk (DVD), memory stick, floppy disk, mechanical encoding device, such as a printer with instructions stored thereon
  • RAM random access memory
  • ROM read-only memory
  • EPROM erasable programmable read-only memory
  • flash memory flash memory
  • SRAM static random access memory
  • CD-ROM compact disk read-only memory
  • DVD digital versatile disk
  • memory stick floppy disk
  • mechanical encoding device such as a printer with instructions stored thereon
  • the computer-readable storage medium used here is not interpreted as a transient signal itself, such as radio waves or other freely propagating electromagnetic waves, electromagnetic waves propagating through waveguides or other transmission media (for example, light pulses through fiber optic cables), or through wires Transmission of electrical signals.
  • the computer-readable program instructions described herein can be downloaded from a computer-readable storage medium to various computing/processing devices, or downloaded to an external computer or external storage device via a network, such as the Internet, a local area network, a wide area network, and/or a wireless network.
  • the network may include copper transmission cables, optical fiber transmission, wireless transmission, routers, firewalls, switches, gateway computers, and/or edge servers.
  • the network adapter card or network interface in each computing/processing device receives computer-readable program instructions from the network, and forwards the computer-readable program instructions for storage in the computer-readable storage medium in each computing/processing device .
  • the computer program instructions used to perform the operations of the present disclosure may be assembly instructions, instruction set architecture (ISA) instructions, machine instructions, machine-related instructions, microcode, firmware instructions, status setting data, or in one or more programming languages.
  • Source code or object code written in any combination, the programming language includes object-oriented programming languages such as Smalltalk, C++, etc., and conventional procedural programming languages such as "C" language or similar programming languages.
  • Computer-readable program instructions can be executed entirely on the user's computer, partly on the user's computer, executed as a stand-alone software package, partly on the user's computer and partly executed on a remote computer, or entirely on the remote computer or server carried out.
  • the remote computer can be connected to the user's computer through any kind of network, including a local area network (LAN) or a wide area network (WAN), or it can be connected to an external computer (for example, using an Internet service provider to access the Internet connection).
  • LAN local area network
  • WAN wide area network
  • an electronic circuit such as a programmable logic circuit, a field programmable gate array (FPGA), or a programmable logic array (PLA), can be customized by using the status information of the computer-readable program instructions.
  • the computer-readable program instructions are executed to realize various aspects of the present disclosure.
  • These computer-readable program instructions can be provided to the processor of a general-purpose computer, a special-purpose computer, or other programmable data processing device, thereby producing a machine such that when these instructions are executed by the processor of the computer or other programmable data processing device , A device that implements the functions/actions specified in one or more blocks in the flowchart and/or block diagram is produced. It is also possible to store these computer-readable program instructions in a computer-readable storage medium. These instructions make computers, programmable data processing apparatuses, and/or other devices work in a specific manner, so that the computer-readable medium storing instructions includes An article of manufacture, which includes instructions for implementing various aspects of the functions/actions specified in one or more blocks in the flowchart and/or block diagram.
  • each block in the flowchart or block diagram may represent a module, program segment, or part of an instruction, and the module, program segment, or part of an instruction contains one or more functions for implementing the specified logical function.
  • Executable instructions may also occur in a different order from the order marked in the drawings. For example, two consecutive blocks can actually be executed in parallel, or they can sometimes be executed in the reverse order, depending on the functions involved.
  • each block in the block diagram and/or flowchart, and the combination of the blocks in the block diagram and/or flowchart can be implemented by a dedicated hardware-based system that performs the specified functions or actions Or it can be realized by a combination of dedicated hardware and computer instructions.

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Abstract

一种基于激光雷达的无人车辆的测试方法及装置，方法包括：获取车辆动作信息以及激光雷达采集的点云数据；其中，车辆动作信息包括车辆行驶过程中车辆的动作；若车辆的动作为预设动作，根据点云数据和地图，获取车辆所处测试道路的道路信息；根据预设动作和道路信息，获取车辆的性能指标；根据车辆动作信息和性能指标，确定车辆性能测试结果。通过该无人车辆的测试方式代替传统人工进行车辆测试的方式，以及基于激光雷达保证车辆测试中的正常行驶，可以实现车辆无人自动测试，节约人力，并且可以保证测试的全面性和准确性。

Description

基于激光雷达的无人车辆的测试方法及装置 技术领域
本公开涉及车辆测试技术领域，尤其涉及一种基于激光雷达的无人车辆的测试方法及装置。
背景技术
车辆越来越普及，车辆的需求量还在日益增长，并且购买者越来越关注车辆的质量，这使得车辆生产厂家越来越注重提高车辆的性能，例如操控性能和耐久性能。一般车辆需要在测试通过后，才能投入市场，因此，车辆的测试是检验车辆性能或耐久性的一个重要环节，对车辆的后续生产有重要的指导意义。为了保证投放到市场的车辆为高质量的车辆，车辆的测试显得越来越重要。
目前的车辆测试均通过人工驾驶的方式进行，不仅耗费人力，也可能存在测试项目不全面、测试不准确的缺陷。
发明内容
有鉴于此，本公开提出了一种基于激光雷达的无人车辆的测试方法及装置。
根据本公开的一方面，提供了一种基于激光雷达的无人车辆的测试方法，所述方法包括：
获取车辆动作信息以及激光雷达采集的点云数据；其中，所述车辆动作信息包括车辆行驶过程中车辆的动作；
若车辆的动作为预设动作，根据所述点云数据和地图，获取车辆所处测试道路的道路信息；
根据所述预设动作和所述道路信息，获取车辆的性能指标；
根据所述车辆动作信息和所述性能指标，确定车辆性能测试结果。
在一种可能的实现方式中，所述预设动作包括以下中的一种或多种：提速、减速、紧急制动、转向；
所述性能指标包括以下中的一种或多种：动力指标、紧急制动指标、操控稳定性指标。
在一种可能的实现方式中，所述车辆动作信息还包括车辆动作参数；
所述根据所述车辆动作信息和所述性能指标，确定车辆性能测试结果，包括：
根据所述车辆动作参数，确定车辆的性能参数；
若车辆的性能参数满足所述性能指标，确定车辆性能测试结果为合格；
其中，所述车辆动作参数包括以下中的一种或多种：预设动作的开始时间、预设动作的结束时间、所述开始时间对应的速度、所述结束时间对应的速度、所述开始时间至结束时间内的行驶距离、车辆转向角度、车辆转向半径。
在一种可能的实现方式中，根据所述点云数据和地图，获取车辆所处道路的道路信息，包括：
根据所述点云数据和地图，确定车辆在测试道路中的位置，并获取与所述位置对应的道路信息；
其中，所述道路信息与测试道路中的位置对应关系为预先设置的；所述道路信息包括道路类型。
在一种可能的实现方式中，所述方法还包括：
根据所述点云数据与地图，确定车辆位姿。
在一种可能的实现方式中，所述方法还包括：
若根据所述车辆位姿确定车辆故障，对车辆进行定位，并记录故障信息。
在一种可能的实现方式中，所述方法还包括：
若车辆的行驶里程达到预设里程，获取车辆的磨损数据；
根据所述磨损数据、所述故障信息和所述车辆性能测试结果，确定车辆的耐久性测试结果。
在一种可能的实现方式中，所述无人车辆利用激光雷达在测试道路中行驶；或者，
所述无人车辆根据预先配置的动作信息在测试道路中行驶；
其中，所述预先配置的动作信息包括测试道路中每一个位置对应的速度和转向角度，或者，所述预先配置的动作信息包括测试中每一个时间段对应的速度和转向角度。
根据本公开的另一方面，提供了一种基于激光雷达的无人车辆的测试装置，所述装置包括：
第一获取模块，用于获取车辆动作信息以及激光雷达采集的点云数据；其中，所述车辆动作信息包括车辆行驶过程中车辆的动作；
道路信息获取模块，用于若车辆的动作为预设动作，根据所述点云数据和地图，获取车辆所处测试道路的道路信息；
性能指标获取模块，用于根据所述预设动作和所述道路信息，获取车辆的性能指标；
第一确定模块，用于根据所述车辆动作信息和所述性能指标，确定车辆性能测试结果。
在一种可能的实现方式中，所述预设动作包括以下中的一种或多种：提速、减速、紧急制动、转向；
所述性能指标包括以下中的一种或多种：动力指标、紧急制动指标、操控稳定性指标。
在一种可能的实现方式中，所述车辆动作信息还包括车辆动作参数；所述第一确定模块包括：
第一确定单元，用于根据所述车辆动作参数，确定车辆的性能参数；
第二确定单元，用于若车辆的性能参数满足所述性能指标，确定车辆性能测试结果为合格；
其中，所述车辆动作参数包括以下中的一种或多种：预设动作的开始时间、预设动作的结束时间、所述开始时间对应的速度、所述结束时间对应的 速度、所述开始时间至结束时间内的行驶距离、车辆转向角度、车辆转向半径。
在一种可能的实现方式中，所述道路信息获取模块，包括：
道路信息获取单元，用于根据所述点云数据和地图，确定车辆在测试道路中的位置，并获取与所述位置对应的道路信息；
其中，所述道路信息与测试道路中的位置对应关系为预先设置的；所述道路信息包括道路类型。
在一种可能的实现方式中，所述装置还包括：
车辆位姿确定模块，用于根据所述点云数据与地图，确定车辆位姿。
在一种可能的实现方式中，所述装置还包括：
定位与故障记录模块，用于若根据所述车辆位姿确定车辆故障，对车辆进行定位，并记录故障信息。
在一种可能的实现方式中，所述装置还包括：
磨损数据获取模块，用于若车辆的行驶里程达到预设里程，获取车辆的磨损数据；
第二确定模块，用于根据所述磨损数据、所述故障信息和所述车辆性能测试结果，确定车辆的耐久性测试结果。
在一种可能的实现方式中，所述激光雷达设置在车辆上，所述激光雷达为一个或多个；
其中，所述激光雷达的激光方向与水平面的夹角为0-50度。
在一种可能的实现方式中，所述激光雷达的激光方向相对水平面向下倾斜10度。
在一种可能的实现方式中，所述无人车辆利用激光雷达在测试道路中行驶；或者，
所述无人车辆根据预先配置的动作信息在测试道路中行驶；
其中，所述预先配置的动作信息包括测试道路中每一个位置对应的速度和转向角度，或者，所述预先配置的动作信息包括测试中每一个时间段对应 的速度和转向角度。
根据本公开的另一方面，提供了一种基于激光雷达的无人车辆的测试装置，包括：处理器；用于存储处理器可执行指令的存储器；其中，所述处理器被配置为执行上述方法。
根据本公开的另一方面，提供了一种非易失性计算机可读存储介质，其上存储有计算机程序指令，其中，所述计算机程序指令被处理器执行时实现上述方法。
通过无人车辆的测试方式代替传统人工进行车辆测试的方式，以及基于激光雷达保证车辆测试中的正常行驶，根据本公开实施例的基于激光雷达的无人车辆的测试方法及装置，可以实现车辆无人自动测试，节约人力，并且可以保证测试的全面性和准确性。
根据下面参考附图对示例性实施例的详细说明，本公开的其它特征及方面将变得清楚。
附图说明
包含在说明书中并且构成说明书的一部分的附图与说明书一起示出了本公开的示例性实施例、特征和方面，并且用于解释本公开的原理。
图1示出根据本公开一实施例的基于激光雷达的无人车辆测试方法的流程图。
图2示出根据本公开一实施例的步骤S14的流程图。
图3示出根据本公开一实施例的基于激光雷达的无人车辆测试方法的流程图。
图4示出根据本公开一实施例的基于激光雷达的无人车辆测试方法的流程图。
图5示出根据本公开一实施例的基于激光雷达的无人车辆测试方法的流程图。
图6示出根据本公开一实施例的基于激光雷达的无人车辆的测试装置的 框图。
图7示出根据本公开一实施例的基于激光雷达的无人车辆的测试装置的框图。
图8示出根据本公开一实施例的基于激光雷达的无人车辆的测试装置的框图。
图9是根据一示例性实施例示出的一种基于激光雷达的无人车辆的测试装置800的框图。
图10是根据一示例性实施例示出的一种基于激光雷达的无人车辆的测试装置1900的框图。
具体实施方式
以下将参考附图详细说明本公开的各种示例性实施例、特征和方面。附图中相同的附图标记表示功能相同或相似的元件。尽管在附图中示出了实施例的各种方面，但是除非特别指出，不必按比例绘制附图。
在这里专用的词“示例性”意为“用作例子、实施例或说明性”。这里作为“示例性”所说明的任何实施例不必解释为优于或好于其它实施例。
另外，为了更好的说明本公开，在下文的具体实施方式中给出了众多的具体细节。本领域技术人员应当理解，没有某些具体细节，本公开同样可以实施。在一些实例中，对于本领域技术人员熟知的方法、手段、元件和电路未作详细描述，以便于凸显本公开的主旨。
图1示出根据本公开一实施例的基于激光雷达的无人车辆测试方法的流程图。如图1所示，所述方法可以包括：
步骤S11，获取车辆动作信息以及激光雷达采集的点云数据。
车辆测试可以在车辆测试试验场中进行，所述车辆测试试验场中可以包括用于车辆测试的测试道路，所述测试道路可以是预先设置的，例如，可以是根据现有车辆耐久性测试用的测试道路，预先在车辆测试试验场设置测试道路。本公开对测试道路不作限定，只要能够用于测试车辆即可。其中，所述车辆测试可以包括车辆性能测试和车辆耐久性测试等。
所述车辆测试可以是无人车辆的测试，即车辆的测试不需要人工，车辆处于无人驾驶状态。为了保证无人车辆在测试道路上顺利行驶，可以在无人车辆上安装一颗或多颗激光雷达，也可以安装摄像头或其它传感设备以实现躲避障碍物或定位等功能。对于激光雷达、摄像头或其它传感设备在车辆上的位置不作限定，只要能够保证无人驾驶的顺利行驶即可。
所述车辆动作信息可以包括车辆行驶过程中车辆的动作，所述车辆的动作可以包括车辆的操作动作，例如，急刹、直角转弯、拨动方向灯等等，也可以包括车辆的车身动作，比如，车身的振动等。
在车辆的行驶过程中，车辆上的激光雷达可以采集点云数据，例如，环境的点云数据或车辆的点云数据等，测试平台(或者可以称为测试控制系统)可以从所述激光雷达获取所述点云数据，并可以实时获取车辆动作信息。其中，所述测试平台可以是计算机等，所述测试平台可以设置在车辆测试试验场的监控室内，测试平台可以通过无线的方式与激光雷达和车辆进行通信。
步骤S12，若车辆的动作为预设动作，根据所述点云数据和地图，获取车辆所处测试道路的道路信息。
所述预设动作可以是根据测试目标预先设置的，例如，测试目标可以是测试车辆的动力，预设动作可以是提速
所述地图可以是车辆测试试验场的地图，所述地图可以利用点云数据描述。
测试平台可以判断车辆的动作是否为预设动作，比如，提速，若车辆的动作为预设动作，可以根据获取的点云数据，在地图中查找匹配，获取点云数据对应的地图中的位置，该位置即为车辆所处测试道路的位置。车辆测试试验场中的测试道路可以是预先设置的，例如，测试道路的不同位置设置不同道路类型的道路，比如，起伏路、山路、坡道、环路等，即测试道路中的位置与道路类型的对应关系已预先存在。
测试平台可以根据车辆所处测试道路的位置，获取车辆所处测试道路的道路信息，例如，车辆所处测试道路的位置在山路的范围内，可以获取车辆 所处测试道路的道路信息为山路。
步骤S13，根据所述预设动作和所述道路信息，获取车辆的性能指标。
所述车辆的性能指标可以指能够描述车辆性能的动力、安全、舒适等的指标。
所述预设动作、道路信息、车辆的性能指标的对应关系，可以在测试前预先设置，例如，可以由专业人员根据不同型号的车辆的性能指标写入数据库保存，例如，所述对应关系可以通过下表1方式保存。
表1
车型 预设动作 道路信息 车辆的性能指标
A 提速 山路 10秒/百公里加速
A 提速 高速路 7秒/百公里加速
A 紧急制动 高速路 3-5米
B 转向 环路 转向半径a、转向角度b
上述表1仅仅是所述预设动作、道路信息、车辆的性能指标的对应关系的示例，本公开对此不作限定，测试人员可以根据想要测试的项目进行设置。
测试平台可以根据车辆动作信息所属的预设动作和道路信息，通过查找上述表1，获取车辆的性能指标。
步骤S14，根据所述车辆动作信息和所述性能指标，确定车辆性能测试结果。
所述车辆动作信息还可以包括车辆的动作参数，例如，车辆百公里加速时长、车辆振动强度等。
测试平台可以根据所述车辆动作信息和所述性能指标，确定车辆性能测试结果。例如，对车型A进行测试时，若获取到车辆的动作为提速、道路信息为高速路，通过查表，可以得出性能指标为7秒，若车辆动作信息中的百公里加速时长为7秒，则可以确定车辆性能测试结果合格。该测试结果仅仅是针对此次的测试结果，最终车辆性能测试结果要到测试结束，统计测试过程中的多个车辆性能测试结果得出。
需要说明的是，测试平台还可以获取车辆舒适性能有关的参数或获取车辆操控性能有关的参数，对车辆的舒适性能、操控性能(车辆对操控的响应)等进行测试。
通过本公开的无人车辆的测试方式代替传统人工进行车辆测试的方式，以及基于激光雷达保证车辆测试中的正常行驶，根据本公开实施例的基于激光雷达的无人车辆的测试方法，可以实现车辆无人自动测试，节约人力，并且可以保证测试的全面性和准确性。
在一种可能的实现方式中，所述预设动作包括以下中的一种或多种：提速、减速、紧急制动、转向等；
所述性能指标包括以下中的一种或多种：动力指标、紧急制动指标、操控稳定性指标等。
在一种可能的实现方式中，所述激光雷达可以设置在车辆上，所述激光雷达可以为一个或多个；
其中，所述激光雷达的激光方向与水平面的夹角可以为0-50度。
在一种可能的实现方式中，所述激光雷达的激光方向相对水平面向下倾斜10度。这样可以很好的测试路面上的障碍物，保证车辆的顺利行驶。
在一种可能的实现方式中，所述无人车辆可以利用激光雷达在测试道路中行驶；例如，无人车辆在测试道路，可以根据激光雷达实现避障，完成在测试道路上的行驶。
或者，所述无人车辆可以根据预先配置的动作信息在测试道路中行驶；
其中，所述预先配置的动作信息包括测试道路中每一个位置对应的速度和转向角度，或者，所述预先配置的动作信息包括测试中每一个时间段(时间点)对应的速度和转向角度。
所述无人车辆在测试前，可以被配置动作信息，例如，可以在无人车辆上存储动作信息，比如，测试道路中每一个位置的速度、转向角度、加速度等，无人车辆在测试道路上行驶时，无人车辆的控制模块可以根据当前位置，从所述存储的动作信息获取匹配的动作信息，根据该动作信息控制无人测量 行驶。
可选地，无人车辆在测试前可以被配置测试里程或测试时长，无人车辆控制模块可以根据测试里程或测试时长，控制无人车辆停止测试。
图2示出根据本公开一实施例的步骤S14的流程图。如图2所示，在一种可能的实现方式中，所述车辆动作信息还包括车辆动作参数。所述步骤S14可以包括：
步骤S141，根据所述车辆动作参数，确定车辆的性能参数。
所述车辆动作参数可以包括以下中的一种或多种：预设动作的开始时间、预设动作的结束时间、所述开始时间对应的速度、所述结束时间对应的速度、所述开始时间至结束时间内的行驶距离、车辆转向角度、车辆转向半径。本公开对车辆动作参数不作限定，只要车辆动作参数可以用于计算车辆的性能即可。
所述车辆的性能可以包括车辆动力性能、紧急制动性能、操控性能、安全性能、舒适性能等。相应地，所述车辆的性能参数可以是指表征所述车辆的性能的参数，所述车辆的性能参数可以包括车辆动力性能参数、紧急制动性能参数、操控性能参数、安全性能参数、舒适性能参数等
举例来说，车辆的动作为提速(百公里加速)，相应的车辆动作参数可以包括百公里加速的开始时间t1、百公里加速的结束时间t2、所述开始时间的车速为0-10km/h、所述结束时间的车速为100km/h，测试平台可以确定车辆的动力性能参数为百公里加速时长t2-t1。
步骤S142，若车辆的性能参数满足所述性能指标，确定车辆性能测试结果为合格。
如果通过查表得到性能指标为7秒，测试平台可以判断所述t2-t1是否在7秒内，如果在7秒内，可以确定车辆性能测试合格，如果不在7秒内，可以确定车辆性能测试不合格。
可选地，测试平台可以对每次的车辆性能测试结果进行记录。
图3示出根据本公开一实施例的基于激光雷达的无人车辆测试方法的流 程图。如图3所示，在一种可能的实现方式中，所述步骤S12可以包括：
步骤S121，根据所述点云数据和地图，确定车辆在测试道路中的位置，并获取与所述位置对应的道路信息；
其中，所述道路信息与测试道路中的位置对应关系为预先设置的，所述道路信息包括道路类型，即测试道路中设置了多种道路类型以用于测试车辆，并且具体哪些位置相应设置了哪些道路类型都是预先知道的。所述道路类型可以包括高速路、卵石路、鱼鳞坑路、搓板路、比利时路、起伏路、摇摆路、破损路、方坑、标准坡道、环路等。
测试平台可以根据所述点云数据和地图，确定车辆在测试道路中的位置，并可以根据所述道路信息与测试道路中的位置对应关系，获取与所述位置对应的道路信息。
图4示出根据本公开一实施例的基于激光雷达的无人车辆测试方法的流程图。如图4所示，在一种可能的实现方式中，所述方法还可以包括：
步骤S15，根据所述点云数据与地图，确定车辆位姿；
步骤S16，若根据所述车辆位姿确定车辆故障，对车辆进行定位，并记录故障信息。
在无人车辆的测试过程中，测试平台需要实时获取测量的状态以保证测量的正常行驶以及避免遗漏车辆测试过程中出现的问题等。测试平台在获取到点云数据后，还可以根据所述点云数据与地图，确定车辆位姿，例如倾斜、侧翻等。若根据所述车辆位姿确定车辆故障，可以对车辆进行定位，并可以记录故障信息。所述故障信息可以包括故障部件、故障严重程度等，所述故障信息可以用于确定耐久性测试结果。
需要说明的是，所述步骤S15可以在步骤S11获取所述点云数据之后进行，不受其它步骤的影响，只要获取点云数据就可执行。
图5示出根据本公开一实施例的基于激光雷达的无人车辆测试方法的流程图。如图5所示，在一种可能的实现方式中，所述方法还可以包括：
步骤S17，若车辆的行驶里程达到预设里程，获取车辆的磨损数据。
所述预设里程可以是测试人员设置的，以控制无人车辆在测试道路中行驶至预设里程时停止。
所述测试平台可以判断无人车辆的行驶里程是否达到预设里程，若达到预设里程，可以控制无人车辆停止行驶以结束车辆测试。在停止车辆测试后，测试平台可以获取车辆的磨损数据，例如车辆各部件的磨损程度。
步骤S18，根据所述磨损数据、所述故障信息和所述车辆性能测试结果，确定车辆的耐久性测试结果。
测试平台可以根据所述磨损数据、所述故障信息和所述车辆性能测试结果，确定车辆的耐久性测试结果，例如，可以确定车辆的整车耐久性测试结果，例如车辆的寿命等，比如可以行驶多少年或多少万公里。
其中，所述磨损数据和故障信息可以用于确定各部件的疲劳程度，所述测试过程中可以包括多个车辆性能测试结果，所述多个车辆性能测试结果可以用于确定车辆的性能变化，例如，车辆的动力性能是否变差、车辆的油耗增值是否大于阈值等。测试平台可以根据所述车辆各部件的疲劳程度和车辆的性能变化，确定车辆的整车耐久性测试结果。
需要说明的是，所述耐久性测试结果也可以包括更加详细的内容，例如，某个部件的寿命等，这些可以根据车辆测试目的而设置，本公开对此不作限定。
图6示出根据本公开一实施例的基于激光雷达的无人车辆的测试装置的框图。所述测试装置可以是指上述测试平台(测试控制系统)，或者，所述测试装置也可以是指所述测试平台中具体执行所述无人车辆测试方法的装置。如图6所示，所述装置可以包括：
第一获取模块11，用于获取车辆动作信息以及激光雷达采集的点云数据；其中，所述车辆动作信息包括车辆行驶过程中车辆的动作；
道路信息获取模块12，用于若车辆的动作为预设动作，根据所述点云数据和地图，获取车辆所处测试道路的道路信息；
性能指标获取模块13，用于根据所述预设动作和所述道路信息，获取车 辆的性能指标；
第一确定模块14，用于根据所述车辆动作信息和所述性能指标，确定车辆性能测试结果。
通过本公开的无人车辆的测试方式代替传统人工进行车辆测试的方式，以及基于激光雷达保证车辆测试中的正常行驶，根据本公开实施例的基于激光雷达的无人车辆的测试装置，可以实现车辆无人自动测试，节约人力，并且可以保证测试的全面性和准确性。
在一种可能的实现方式中，所述预设动作可以包括以下中的一种或多种：提速、减速、紧急制动、转向等；
所述性能指标可以包括以下中的一种或多种：动力指标、紧急制动指标、操控稳定性指标等。
在一种可能的实现方式中，所述激光雷达可以设置在车辆上，所述激光雷达为一个或多个；
其中，所述激光雷达的激光方向与水平面的夹角为0-50度。
在一种可能的实现方式中，所述激光雷达的激光方向相对水平面向下倾斜10度。
在一种可能的实现方式中，所述无人车辆利用激光雷达在测试道路中行驶；或者，
所述无人车辆根据预先配置的动作信息在测试道路中行驶；
其中，所述预先配置的动作信息包括测试道路中每一个位置对应的速度和转向角度，或者，所述预先配置的动作信息包括测试中每一个时间段对应的速度和转向角度。
图7示出根据本公开一实施例的基于激光雷达的无人车辆的测试装置的框图。所述车辆动作信息还可以包括车辆动作参数；如图7所示，在一种可能的实现方式中，所述第一确定模块14包括：
第一确定单元141，用于根据所述车辆动作参数，确定车辆的性能参数；
第二确定单元142，用于若车辆的性能参数满足所述性能指标，确定车 辆性能测试结果为合格；
其中，所述车辆动作参数包括以下中的一种或多种：预设动作的开始时间、预设动作的结束时间、所述开始时间对应的速度、所述结束时间对应的速度、所述开始时间至结束时间内的行驶距离、车辆转向角度、车辆转向半径。
如图7所示，在一种可能的实现方式中，所述道路信息获取模块12，可以包括：
道路信息获取单元121，用于根据所述点云数据和地图，确定车辆在测试道路中的位置，并获取与所述位置对应的道路信息；
其中，所述道路信息与测试道路中的位置对应关系为预先设置的；所述道路信息包括道路类型。
图8示出根据本公开一实施例的基于激光雷达的无人车辆的测试装置的框图。如图8所示，在一种可能的实现方式中，所述装置还可以包括：
车辆位姿确定模块15，用于根据所述点云数据与地图，确定车辆位姿；
定位与故障记录模块16，用于若根据所述车辆位姿确定车辆故障，对车辆进行定位，并记录故障信息。
如图8所示，在一种可能的实现方式中，所述装置还可以包括：
磨损数据获取模块17，用于若车辆的行驶里程达到预设里程，获取车辆的磨损数据；
第二确定模块18，用于根据所述磨损数据、所述故障信息和所述车辆性能测试结果，确定车辆的耐久性测试结果。
图9是根据一示例性实施例示出的一种基于激光雷达的无人车辆的测试装置800的框图。例如，装置800可以是移动电话，计算机，数字广播终端，消息收发设备，游戏控制台，平板设备，医疗设备，健身设备，个人数字助理等。
参照图9，装置800可以包括以下一个或多个组件：处理组件802，存储器804，电源组件806，多媒体组件808，音频组件810，输入/输出(I/O)的 接口812，传感器组件814，以及通信组件816。
处理组件802通常控制装置800的整体操作，诸如与显示，电话呼叫，数据通信，相机操作和记录操作相关联的操作。处理组件802可以包括一个或多个处理器820来执行指令，以完成上述的方法的全部或部分步骤。此外，处理组件802可以包括一个或多个模块，便于处理组件802和其他组件之间的交互。例如，处理组件802可以包括多媒体模块，以方便多媒体组件808和处理组件802之间的交互。
存储器804被配置为存储各种类型的数据以支持在装置800的操作。这些数据的示例包括用于在装置800上操作的任何应用程序或方法的指令，联系人数据，电话簿数据，消息，图片，视频等。存储器804可以由任何类型的易失性或非易失性存储设备或者它们的组合实现，如静态随机存取存储器(SRAM)，电可擦除可编程只读存储器(EEPROM)，可擦除可编程只读存储器(EPROM)，可编程只读存储器(PROM)，只读存储器(ROM)，磁存储器，快闪存储器，磁盘或光盘。
电源组件806为装置800的各种组件提供电力。电源组件806可以包括电源管理系统，一个或多个电源，及其他与为装置800生成、管理和分配电力相关联的组件。
多媒体组件808包括在所述装置800和用户之间的提供一个输出接口的屏幕。在一些实施例中，屏幕可以包括液晶显示器(LCD)和触摸面板(TP)。如果屏幕包括触摸面板，屏幕可以被实现为触摸屏，以接收来自用户的输入信号。触摸面板包括一个或多个触摸传感器以感测触摸、滑动和触摸面板上的手势。所述触摸传感器可以不仅感测触摸或滑动动作的边界，而且还检测与所述触摸或滑动操作相关的持续时间和压力。在一些实施例中，多媒体组件808包括一个前置摄像头和/或后置摄像头。当装置800处于操作模式，如拍摄模式或视频模式时，前置摄像头和/或后置摄像头可以接收外部的多媒体数据。每个前置摄像头和后置摄像头可以是一个固定的光学透镜系统或具有焦距和光学变焦能力。
音频组件810被配置为输出和/或输入音频信号。例如，音频组件810包括一个麦克风(MIC)，当装置800处于操作模式，如呼叫模式、记录模式和语音识别模式时，麦克风被配置为接收外部音频信号。所接收的音频信号可以被进一步存储在存储器804或经由通信组件816发送。在一些实施例中，音频组件810还包括一个扬声器，用于输出音频信号。
I/O接口812为处理组件802和外围接口模块之间提供接口，上述外围接口模块可以是键盘，点击轮，按钮等。这些按钮可包括但不限于：主页按钮、音量按钮、启动按钮和锁定按钮。
传感器组件814包括一个或多个传感器，用于为装置800提供各个方面的状态评估。例如，传感器组件814可以检测到装置800的打开/关闭状态，组件的相对定位，例如所述组件为装置800的显示器和小键盘，传感器组件814还可以检测装置800或装置800一个组件的位置改变，用户与装置800接触的存在或不存在，装置800方位或加速/减速和装置800的温度变化。传感器组件814可以包括接近传感器，被配置用来在没有任何的物理接触时检测附近物体的存在。传感器组件814还可以包括光传感器，如CMOS或CCD图像传感器，用于在成像应用中使用。在一些实施例中，该传感器组件814还可以包括加速度传感器，陀螺仪传感器，磁传感器，压力传感器或温度传感器。
通信组件816被配置为便于装置800和其他设备之间有线或无线方式的通信。装置800可以接入基于通信标准的无线网络，如WiFi，2G或3G，或它们的组合。在一个示例性实施例中，通信组件816经由广播信道接收来自外部广播管理系统的广播信号或广播相关信息。在一个示例性实施例中，所述通信组件816还包括近场通信(NFC)模块，以促进短程通信。例如，在NFC模块可基于射频识别(RFID)技术，红外数据协会(IrDA)技术，超宽带(UWB)技术，蓝牙(BT)技术和其他技术来实现。
在示例性实施例中，装置800可以被一个或多个应用专用集成电路(ASIC)、数字信号处理器(DSP)、数字信号处理设备(DSPD)、可编程逻辑器件(PLD)、现场可编程门阵列(FPGA)、控制器、微控制器、微处理 器或其他电子元件实现，用于执行上述方法。
在示例性实施例中，还提供了一种非易失性计算机可读存储介质，例如包括计算机程序指令的存储器804，上述计算机程序指令可由装置800的处理器820执行以完成上述方法。
图10是根据一示例性实施例示出的一种基于激光雷达的无人车辆的测试装置1900的框图。例如，装置1900可以被提供为一服务器。参照图10，装置1900包括处理组件1922，其进一步包括一个或多个处理器，以及由存储器1932所代表的存储器资源，用于存储可由处理组件1922的执行的指令，例如应用程序。存储器1932中存储的应用程序可以包括一个或一个以上的每一个对应于一组指令的模块。此外，处理组件1922被配置为执行指令，以执行上述方法。
装置1900还可以包括一个电源组件1926被配置为执行装置1900的电源管理，一个有线或无线网络接口1950被配置为将装置1900连接到网络，和一个输入输出(I/O)接口1958。装置1900可以操作基于存储在存储器1932的操作系统，例如Windows ServerTM，Mac OS XTM，UnixTM,LinuxTM，FreeBSDTM或类似。
在示例性实施例中，还提供了一种非易失性计算机可读存储介质，例如包括计算机程序指令的存储器1932，上述计算机程序指令可由装置1900的处理组件1922执行以完成上述方法。
本公开可以是系统、方法和/或计算机程序产品。计算机程序产品可以包括计算机可读存储介质，其上载有用于使处理器实现本公开的各个方面的计算机可读程序指令。
计算机可读存储介质可以是可以保持和存储由指令执行设备使用的指令的有形设备。计算机可读存储介质例如可以是――但不限于――电存储设备、磁存储设备、光存储设备、电磁存储设备、半导体存储设备或者上述的任意合适的组合。计算机可读存储介质的更具体的例子(非穷举的列表)包括：便携式计算机盘、硬盘、随机存取存储器(RAM)、只读存储器(ROM)、 可擦式可编程只读存储器(EPROM或闪存)、静态随机存取存储器(SRAM)、便携式压缩盘只读存储器(CD-ROM)、数字多功能盘(DVD)、记忆棒、软盘、机械编码设备、例如其上存储有指令的打孔卡或凹槽内凸起结构、以及上述的任意合适的组合。这里所使用的计算机可读存储介质不被解释为瞬时信号本身，诸如无线电波或者其他自由传播的电磁波、通过波导或其他传输媒介传播的电磁波(例如，通过光纤电缆的光脉冲)、或者通过电线传输的电信号。
这里所描述的计算机可读程序指令可以从计算机可读存储介质下载到各个计算/处理设备，或者通过网络、例如因特网、局域网、广域网和/或无线网下载到外部计算机或外部存储设备。网络可以包括铜传输电缆、光纤传输、无线传输、路由器、防火墙、交换机、网关计算机和/或边缘服务器。每个计算/处理设备中的网络适配卡或者网络接口从网络接收计算机可读程序指令，并转发该计算机可读程序指令，以供存储在各个计算/处理设备中的计算机可读存储介质中。
用于执行本公开操作的计算机程序指令可以是汇编指令、指令集架构(ISA)指令、机器指令、机器相关指令、微代码、固件指令、状态设置数据、或者以一种或多种编程语言的任意组合编写的源代码或目标代码，所述编程语言包括面向对象的编程语言—诸如Smalltalk、C++等，以及常规的过程式编程语言—诸如“C”语言或类似的编程语言。计算机可读程序指令可以完全地在用户计算机上执行、部分地在用户计算机上执行、作为一个独立的软件包执行、部分在用户计算机上部分在远程计算机上执行、或者完全在远程计算机或服务器上执行。在涉及远程计算机的情形中，远程计算机可以通过任意种类的网络—包括局域网(LAN)或广域网(WAN)—连接到用户计算机，或者，可以连接到外部计算机(例如利用因特网服务提供商来通过因特网连接)。在一些实施例中，通过利用计算机可读程序指令的状态信息来个性化定制电子电路，例如可编程逻辑电路、现场可编程门阵列(FPGA)或可编程逻辑阵列(PLA)，该电子电路可以执行计算机可读程序指令，从而 实现本公开的各个方面。
这里参照根据本公开实施例的方法、装置(系统)和计算机程序产品的流程图和/或框图描述了本公开的各个方面。应当理解，流程图和/或框图的每个方框以及流程图和/或框图中各方框的组合，都可以由计算机可读程序指令实现。
这些计算机可读程序指令可以提供给通用计算机、专用计算机或其它可编程数据处理装置的处理器，从而生产出一种机器，使得这些指令在通过计算机或其它可编程数据处理装置的处理器执行时，产生了实现流程图和/或框图中的一个或多个方框中规定的功能/动作的装置。也可以把这些计算机可读程序指令存储在计算机可读存储介质中，这些指令使得计算机、可编程数据处理装置和/或其他设备以特定方式工作，从而，存储有指令的计算机可读介质则包括一个制造品，其包括实现流程图和/或框图中的一个或多个方框中规定的功能/动作的各个方面的指令。
也可以把计算机可读程序指令加载到计算机、其它可编程数据处理装置、或其它设备上，使得在计算机、其它可编程数据处理装置或其它设备上执行一系列操作步骤，以产生计算机实现的过程，从而使得在计算机、其它可编程数据处理装置、或其它设备上执行的指令实现流程图和/或框图中的一个或多个方框中规定的功能/动作。
附图中的流程图和框图显示了根据本公开的多个实施例的系统、方法和计算机程序产品的可能实现的体系架构、功能和操作。在这点上，流程图或框图中的每个方框可以代表一个模块、程序段或指令的一部分，所述模块、程序段或指令的一部分包含一个或多个用于实现规定的逻辑功能的可执行指令。在有些作为替换的实现中，方框中所标注的功能也可以以不同于附图中所标注的顺序发生。例如，两个连续的方框实际上可以基本并行地执行，它们有时也可以按相反的顺序执行，这依所涉及的功能而定。也要注意的是，框图和/或流程图中的每个方框、以及框图和/或流程图中的方框的组合，可以用执行规定的功能或动作的专用的基于硬件的系统来实现，或者可以用专 用硬件与计算机指令的组合来实现。
以上已经描述了本公开的各实施例，上述说明是示例性的，并非穷尽性的，并且也不限于所披露的各实施例。在不偏离所说明的各实施例的范围和精神的情况下，对于本技术领域的普通技术人员来说许多修改和变更都是显而易见的。本文中所用术语的选择，旨在最好地解释各实施例的原理、实际应用或对市场中的技术的技术改进，或者使本技术领域的其它普通技术人员能理解本文披露的各实施例。

Claims (20)

  1. 一种基于激光雷达的无人车辆的测试方法，其特征在于，所述方法包括：
    获取车辆动作信息以及激光雷达采集的点云数据；其中，所述车辆动作信息包括车辆行驶过程中车辆的动作；
    若车辆的动作为预设动作，根据所述点云数据和地图，获取车辆所处测试道路的道路信息；
    根据所述预设动作和所述道路信息，获取车辆的性能指标；
    根据所述车辆动作信息和所述性能指标，确定车辆性能测试结果。
  2. 根据权利要求1所述的方法，其特征在于，所述预设动作包括以下中的一种或多种：提速、减速、紧急制动、转向；
    所述性能指标包括以下中的一种或多种：动力指标、紧急制动指标、操控稳定性指标。
  3. 根据权利要求1所述的方法，其特征在于，所述车辆动作信息还包括车辆动作参数；
    所述根据所述车辆动作信息和所述性能指标，确定车辆性能测试结果，包括：
    根据所述车辆动作参数，确定车辆的性能参数；
    若车辆的性能参数满足所述性能指标，确定车辆性能测试结果为合格；
    其中，所述车辆动作参数包括以下中的一种或多种：预设动作的开始时间、预设动作的结束时间、所述开始时间对应的速度、所述结束时间对应的速度、所述开始时间至结束时间内的行驶距离、车辆转向角度、车辆转向半径。
  4. 根据权利要求1所述的方法，其特征在于，根据所述点云数据和地图，获取车辆所处道路的道路信息，包括：
    根据所述点云数据和地图，确定车辆在测试道路中的位置，并获取与所述位置对应的道路信息；
    其中，所述道路信息与测试道路中的位置对应关系为预先设置的，所述道路信息包括道路类型。
  5. 根据权利要求1所述的方法，其特征在于，所述方法还包括：
    根据所述点云数据与地图，确定车辆位姿。
  6. 根据权利要求1所述的方法，其特征在于，所述方法还包括：
    若根据所述车辆位姿确定车辆故障，对车辆进行定位，并记录故障信息。
  7. 根据权利要求6所述的方法，其特征在于，所述方法还包括：
    若车辆的行驶里程达到预设里程，获取车辆的磨损数据；
    根据所述磨损数据、所述故障信息和所述车辆性能测试结果，确定车辆的耐久性测试结果。
  8. 根据权利要求1所述的方法，其特征在于，所述无人车辆利用激光雷达在测试道路中行驶；或者，
    所述无人车辆根据预先配置的动作信息在测试道路中行驶；
    其中，所述预先配置的动作信息包括测试道路中每一个位置对应的速度和转向角度，或者，所述预先配置的动作信息包括测试中每一个时间段对应的速度和转向角度。
  9. 一种基于激光雷达的无人车辆的测试装置，其特征在于，包括：
    第一获取模块，用于获取车辆动作信息以及激光雷达采集的点云数据；其中，所述车辆动作信息包括车辆行驶过程中车辆的动作；
    道路信息获取模块，用于若车辆的动作为预设动作，根据所述点云数据和地图，获取车辆所处测试道路的道路信息；
    性能指标获取模块，用于根据所述预设动作和所述道路信息，获取车辆的性能指标；
    第一确定模块，用于根据所述车辆动作信息和所述性能指标，确定车辆性能测试结果。
  10. 根据权利要求9所述的装置，其特征在于，所述预设动作包括以下中的一种或多种：提速、减速、紧急制动、转向；
    所述性能指标包括以下中的一种或多种：动力指标、紧急制动指标、操控稳定性指标。
  11. 根据权利要求9所述的装置，其特征在于，所述车辆动作信息还包括车辆动作参数；所述第一确定模块包括：
    第一确定单元，用于根据所述车辆动作参数，确定车辆的性能参数；
    第二确定单元，用于若车辆的性能参数满足所述性能指标，确定车辆性能测试结果为合格；
    其中，所述车辆动作参数包括以下中的一种或多种：预设动作的开始时间、预设动作的结束时间、所述开始时间对应的速度、所述结束时间对应的速度、所述开始时间至结束时间内的行驶距离、车辆转向角度、车辆转向半径。
  12. 根据权利要求9所述的装置，其特征在于，所述道路信息获取模块， 包括：
    道路信息获取单元，用于根据所述点云数据和地图，确定车辆在测试道路中的位置，并获取与所述位置对应的道路信息；
    其中，所述道路信息与测试道路中的位置对应关系为预先设置的；所述道路信息包括道路类型。
  13. 根据权利要求9所述的装置，其特征在于，所述装置还包括：
    车辆位姿确定模块，用于根据所述点云数据与地图，确定车辆位姿。
  14. 根据权利要求13所述的装置，其特征在于，所述装置还包括：
    定位与故障记录模块，用于若根据所述车辆位姿确定车辆故障，对车辆进行定位，并记录故障信息。
  15. 根据权利要求14所述的装置，其特征在于，所述装置还包括：
    磨损数据获取模块，用于若车辆的行驶里程达到预设里程，获取车辆的磨损数据；
    第二确定模块，用于根据所述磨损数据、所述故障信息和所述车辆性能测试结果，确定车辆的耐久性测试结果。
  16. 根据权利要求9所述的装置，其特征在于，所述激光雷达设置在车辆上，所述激光雷达为一个或多个；
    其中，所述激光雷达的激光方向与水平面的夹角为0-50度。
  17. 根据权利要求16所述的装置，其特征在于，所述激光雷达的激光方向相对水平面向下倾斜10度。
  18. 根据权利要求9所述的装置，其特征在于，所述无人车辆利用激光雷达在测试道路中行驶；或者，
    所述无人车辆根据预先配置的动作信息在测试道路中行驶；
    其中，所述预先配置的动作信息包括测试道路中每一个位置对应的速度和转向角度，或者，所述预先配置的动作信息包括测试中每一个时间段对应的速度和转向角度。
  19. 一种基于激光雷达的无人车辆的测试装置，其特征在于，包括：
    处理器；
    用于存储处理器可执行指令的存储器；
    其中，所述处理器被配置为：
    执行所述可执行指令以实现权利要求1-8任一项所述的方法。
  20. 一种非易失性计算机可读存储介质，其上存储有计算机程序指令，其特征在于，所述计算机程序指令被处理器执行时实现权利要求1至8中任意一项所述的方法。
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