CN112526968B - Method for building automatic driving virtual test platform for mapping real world road conditions - Google Patents
Method for building automatic driving virtual test platform for mapping real world road conditions Download PDFInfo
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Abstract
The invention discloses a method for constructing an automatic driving virtual test platform for mapping real world road conditions, which comprises the following steps: acquiring real world road condition information; establishing a road environment module for mapping real world road conditions; constructing an automatic driving vehicle module and an interactive vehicle module; the road environment module, the automatic driving vehicle module and the interactive vehicle module are integrated, information interconnection is achieved, and an automatic driving virtual test platform capable of mapping real world road conditions is built. The invention can quickly test the running state of the automatic driving vehicle under real world road conditions, and conveniently adjust the interactive vehicle running track, the automatic driving perception sensor model and the automatic driving function model so as to meet the requirements of different automatic driving test working conditions.
Description
Technical Field
The invention belongs to the technical field of automatic driving virtual testing, and relates to a method for building an automatic driving virtual testing platform for mapping real world road conditions.
Background
Compared with an automatic driving virtual test method, the test field based on the real vehicle or the open road test method has the technical bottlenecks of long test period, uncontrollable test scene, limitation of manpower and material resources and incapability of guaranteeing test safety. Therefore, virtual simulation testing becomes an important alternative means for automatically driving real physical testing under the constraint economic condition, and a virtual testing platform is also an important tool for carrying out automatic driving related research.
The road traffic system is a nonlinear complex system with mutually coupled human-vehicle-road-traffic environment elements, the key research and development direction of the automatic driving virtual test platform is to provide a road traffic scene close to real world road conditions, and particularly to pay attention to dynamic interaction behaviors between road environment modules and system elements, such as road-automatic driving vehicles and automatic driving vehicles-interactive vehicles. Furthermore, as research around autopilot technology continues to advance, a common consensus among many scholars and related policy makers for autopilot landing schemes is that existing lanes are directly demarcated or additional lanes are added as autodrive-specific lanes, corridors, or common lanes on existing roads, i.e., real-world established roads, such as at established jingzhong highways, autodrive-specific lanes, U.S. I-5 intercontinental roads, etc. Therefore, there is an urgent need to develop an automatic driving virtual test platform capable of restoring existing road conditions.
The existing automatic driving virtual test platform is investigated, and a single simulation platform cannot integrate complex road environment modules simultaneously, including accurate road alignment description, adjustable road surface friction coefficient, unevenness and rich weather environment information; the system comprises a professional vehicle dynamics model, a perception sensor model based on a physical model, a vehicle model capable of interacting with an automatic driving vehicle and an automatic driving function model, wherein an existing combined simulation platform cannot accurately construct a road environment module capable of mapping real world road conditions.
Disclosure of Invention
The purpose of the invention is as follows: the technical problem to be solved by the invention is as follows: the method for building the automatic driving virtual test platform for mapping real-world road conditions is beneficial to quickly testing the running state of an automatic driving vehicle under the real-world road conditions, and the driving track of the interactive vehicle, the automatic driving perception sensor model and the automatic driving function model are conveniently adjusted so as to meet the requirements of different automatic driving test working conditions.
In order to realize the purpose of the invention, the technical scheme adopted by the invention is as follows: an automatic driving virtual test platform building method for mapping real world road conditions comprises the following steps:
step 2, constructing a road environment module by using the real world road condition information acquired in the step 1, wherein the road environment module comprises a road linear model, a road surface friction coefficient, unevenness and weather environment information;
and 5, integrating the road environment module in the step 2, the automatic driving vehicle module in the step 3 and the interactive vehicle module in the step 4, realizing information interconnection, and building an automatic driving virtual test platform capable of mapping real world road conditions.
As a preferred scheme of the present invention, the road geometric form information in step 1 includes a road side line three-dimensional coordinate and a pile number; the road surface function condition information comprises a road surface friction coefficient and unevenness;
as a preferred embodiment of the present invention, the specific process of step 2 is as follows:
step 21, setting three-dimensional coordinates and pile numbers of a road sideline at fixed intervals from a road starting point to a road finishing point by using the geometric form information of the real world road obtained in the step 1, and constructing a road linear model;
step 22, matching the real world road function condition information acquired in the step 1 with the road geometric form information acquired in the step 21, and setting the road friction coefficient and the unevenness at fixed intervals;
as a preferred embodiment of the present invention, the specific process of step 3 is as follows:
step 31, setting expected traveling track information including an expected path and an expected speed by using the three-dimensional coordinates of the road boundary line input in the step 21 as a track constraint boundary;
step 32, setting a driver model, including preview time, reaction delay time, minimum vehicle running speed, maximum steering wheel angle and rotating speed;
step 33, setting a vehicle dynamic model, which comprises vehicle aerodynamic parameters, a transmission system, a braking system, a steering system, a suspension system and a tire model;
step 34, setting an automatic driving vehicle model, including vehicle type and vehicle body size;
step 35, setting a perception sensor model, wherein the perception sensor model comprises the types of perception sensors, installation positions and installation postures, and the types of perception sensors comprise a camera, a millimeter wave radar, a laser radar and an ultrasonic radar;
step 36, constructing an automatic driving function model, including automatic emergency braking, adaptive cruise control, lane keeping assistance and lane changing assistance;
step 37, the model information transmission and action mode in the automatic driving vehicle module is as follows:
step 371, transferring the expected path and the expected speed in the expected driving track information to the driver model;
step 372, outputting a steering wheel angle, a throttle opening and a braking force range to the automatic driving function model by the driver model;
step 373, the perception sensor model outputs the relative distance, the relative speed and the perception azimuth angle of the target object to the automatic driving function model;
step 374, outputting a steering wheel angle, a throttle opening and a braking force range to a vehicle dynamics model by the automatic driving function model, and feeding back the engine speed to the automatic driving function model by the vehicle dynamics model;
step 375, loading the vehicle dynamics model and the perception sensor model onto the automatic driving vehicle model, feeding back the driving speed and the yaw angular speed to the automatic driving function model by the automatic driving vehicle model, and feeding back the driving speed and the vehicle pose to the driver model;
as a preferred embodiment of the present invention, the specific process of step 4 is as follows:
step 41, setting expected traveling track information including an expected path and an expected speed by using the three-dimensional coordinates of the road boundary input in step 21 as a track constraint boundary and using the expected track information of the autonomous vehicle obtained in step 31 as a reference track;
step 42, setting a driver model, including preview time, reaction delay time, minimum vehicle running speed, maximum steering wheel angle and rotation speed;
step 43, setting a vehicle dynamic model, which comprises vehicle aerodynamic parameters, a transmission system, a braking system, a steering system, a suspension system and a tire model;
step 44, setting an interactive vehicle model, including vehicle type and vehicle body size;
step 45, the mode of model information transmission and action in the interactive vehicle module specifically comprises the following steps:
step 451, transmitting the expected path and the expected speed in the expected running track information to the driver model;
step 452, outputting a steering wheel angle, a throttle opening and a braking force range to a vehicle dynamics model by the driver model;
step 453, loading the vehicle dynamics model onto the interactive vehicle model, and feeding the driving speed and the vehicle pose back to the driver model by the interactive vehicle model;
as a preferred embodiment of the present invention, the specific process of step 5 is as follows:
step 51, integrating the road environment module with the autonomous vehicle module, specifically comprising:
step 511, transmitting the road surface friction coefficient and the unevenness degree to a vehicle dynamic model in an automatic driving vehicle module;
step 512, transmitting the road linear model and the weather environment information to a sensing sensor model;
step 52, integrating the interactive vehicle module with the autonomous vehicle module: and transmitting the vehicle pose obtained by the interactive vehicle to the sensing sensor model.
Compared with the prior art, the invention adopting the technical scheme has the following technical effects:
the invention combines the advantages of the independent simulation platform model, comprehensively constructs the simulation environment necessary for the automatic driving virtual test, and can simultaneously integrate a complex road environment module, an automatic driving vehicle module comprising a professional vehicle dynamics model, a perception sensor model based on a physical model and an automatic driving function model, and an interactive vehicle module capable of generating interactive behavior with the automatic driving vehicle. The platform disclosed by the invention is beneficial to quickly testing the running state of the automatic driving vehicle under real world road conditions, solves the current urgent need of developing an automatic driving test under the existing road conditions, and is convenient to adjust the interactive vehicle running track, the automatic driving perception sensor model and the automatic driving function model so as to meet the requirements of different automatic driving test working conditions.
Drawings
FIG. 1 is a diagram of the logical architecture provided by the present invention;
FIG. 2 is a road alignment model constructed in the example;
FIG. 3 is a diagram of desired paths for autonomous and interactive vehicles in an embodiment;
FIG. 4 is a side view of an embodiment of a long/short millimeter wave radar arrangement of an autonomous vehicle;
FIG. 5 is a plan view of a long/short millimeter wave radar arrangement of the autonomous vehicle in the embodiment;
FIG. 6 is a schematic plan view of an automated driving virtual test environment implemented by the disclosed method in an embodiment of the present invention;
in the figure, 1 is a starting point of a road pile number; 2 is the starting point of the interactive vehicle path; 3 is a highway lane sideline; 4, path information in the expected running track information of the automatic driving vehicle; 5 is a road pile number terminal point; 6 is the sensing range of the long-distance millimeter wave radar; 7 is short-range millimeter wave radar sensing range; 8 is an interactive vehicle; 9 is an autonomous vehicle; 10 is short-range millimeter wave radar; and 11 is a long-range millimeter wave radar.
Detailed Description
Reference will now be made in detail to embodiments of the present invention, examples of which are illustrated in the accompanying drawings. The embodiments described below with reference to the accompanying drawings are illustrative only for the purpose of explaining the present invention, and are not to be construed as limiting the present invention.
As shown in fig. 1, the method for constructing an automatic driving virtual test platform for mapping real world road conditions provided by the invention specifically comprises the following steps:
the road geometric form information is a road sideline three-dimensional coordinate and a pile number; the road surface function condition information comprises a road surface friction coefficient and unevenness;
step 2, constructing a road environment module by using the real world road condition information acquired in the step 1, wherein the road environment module comprises a road linear model, a road surface friction coefficient, unevenness and weather environment information; the specific method comprises the following steps:
step 21, setting three-dimensional coordinates and pile numbers of a road sideline at fixed intervals from a road starting point to a road finishing point by using the geometric form information of the real world road obtained in the step 1, and constructing a road linear model;
step 22, matching the real world road function condition information acquired in the step 1 with the road geometric form information acquired in the step 21, and setting the road friction coefficient and the unevenness at fixed intervals; (ii) a
step 31, setting expected traveling track information including an expected path and an expected speed by using the three-dimensional coordinates of the road boundary line input in the step 21 as a track constraint boundary;
step 32, setting a driver model, including preview time, reaction delay time, minimum vehicle running speed, maximum steering wheel angle and rotating speed;
step 33, setting a vehicle dynamic model, which comprises vehicle aerodynamic parameters, a transmission system, a braking system, a steering system, a suspension system and a tire model;
step 34, setting an automatic driving vehicle model, including vehicle type and vehicle body size;
step 35, setting a perception sensor model, wherein the perception sensor model comprises the types of perception sensors, installation positions and installation postures, and the types of perception sensors comprise a camera, a millimeter wave radar, a laser radar and an ultrasonic radar;
step 36, constructing an automatic driving function model, including automatic emergency braking, adaptive cruise control, lane keeping assistance and lane changing assistance;
step 37, the model information transmission and action mode in the automatic driving vehicle module is as follows:
step 371, transferring the expected path and the expected speed in the expected driving track information to the driver model;
step 372, outputting a steering wheel angle, a throttle opening and a braking force range to the automatic driving function model by the driver model;
step 373, the perception sensor model outputs the relative distance, the relative speed and the perception azimuth angle of the target object to the automatic driving function model;
step 374, outputting a steering wheel angle, a throttle opening and a braking force range to a vehicle dynamics model by the automatic driving function model, and feeding back the engine speed to the automatic driving function model by the vehicle dynamics model;
step 375, loading the vehicle dynamics model and the perception sensor model onto the automatic driving vehicle model, feeding back the driving speed and the yaw angular speed to the automatic driving function model by the automatic driving vehicle model, and feeding back the driving speed and the vehicle pose to the driver model;
step 41, setting expected traveling track information including an expected path and an expected speed by using the three-dimensional coordinates of the road boundary input in step 21 as a track constraint boundary and using the expected track information of the autonomous vehicle obtained in step 31 as a reference track;
step 42, setting a driver model, including preview time, reaction delay time, minimum vehicle running speed, maximum steering wheel angle and rotation speed;
step 43, setting a vehicle dynamic model, which comprises vehicle aerodynamic parameters, a transmission system, a braking system, a steering system, a suspension system and a tire model;
step 44, setting an interactive vehicle model, including vehicle type and vehicle body size;
step 45, the mode of model information transmission and action in the interactive vehicle module specifically comprises the following steps:
step 451, transmitting the expected path and the expected speed in the expected running track information to the driver model;
step 452, outputting a steering wheel angle, a throttle opening and a braking force range to a vehicle dynamics model by the driver model;
step 453, loading the vehicle dynamics model onto the interactive vehicle model, and feeding the driving speed and the vehicle pose back to the driver model by the interactive vehicle model;
step 51, integrating the road environment module with the autonomous vehicle module, specifically comprising:
step 511, transmitting the road surface friction coefficient and the unevenness degree to a vehicle dynamic model in an automatic driving vehicle module;
step 512, transmitting the road linear model and the weather environment information to a sensing sensor model;
step 52, integrating the interactive vehicle module with the autonomous vehicle module: and transmitting the vehicle pose obtained by the interactive vehicle to the sensing sensor model.
The embodiment is a method for constructing an automatic driving virtual test platform for mapping real world road conditions by using a visual simulation tool Simulink in Prescan, CarSim and MATLAB, and the method comprises the following steps:
extracting an active highway lane side line three-dimensional coordinate and pile number data by using high-precision vehicle-mounted laser radar point cloud data as road geometric form information; the road is measured to be an asphalt road by a road surface detection tool, the road surface is smooth, and a constant can be taken as the functional condition information of the road surface.
Step 2, constructing a road environment module by using the real world road condition information acquired in the step 1, wherein the road environment module comprises a road linear model, a road surface friction coefficient, unevenness and weather environment information, and the specific method comprises the following steps:
step 21, inputting three-dimensional coordinates and pile numbers of a road side line into CarSim from a road starting point to a road finishing point at fixed intervals of 4 meters by using the geometric form information of the real world road obtained in the step 1, and constructing a road linear model as shown in figure 2;
step 22, inputting the road surface friction coefficient of 0.85 and the unevenness of 0 to CarSim by using the real world road surface function condition information acquired in the step 1;
step 31, setting expected traveling track information in CarSim by using the three-dimensional coordinates of the road boundary line input in step 21 as a track constraint boundary, wherein the expected path is to keep the road to travel in the middle from the stake mark starting point, as shown in FIG. 3, and the expected speed is 80 km/h;
step 32, setting a driver model as a closed-loop driver model in CarSim, wherein the preview time is 1s, the reaction delay time is 0.1s, the minimum vehicle running speed is 10km/h, and the maximum steering wheel rotation angle and the rotation speed are 540 deg/s and 1200deg/s respectively;
step 33, setting a vehicle dynamic model, vehicle aerodynamic parameters, a transmission system, a braking system, a steering system, a suspension system and a tire model in the CarSim, and adopting default parameters of the CarSim vehicle dynamic model;
step 34, setting an automatic driving vehicle model in CarSim as F-Class Sedan, wherein the size of the vehicle body adopts default parameters;
step 35, setting the sensing sensor models as two millimeter wave radars, specifically a long-range millimeter wave radar and a short-range millimeter wave radar, in the Prescan, and installing the two millimeter wave radars at the central position of the front bumper of the vehicle, wherein the two millimeter wave radars are not provided with a pitch angle towards the right front and are respectively shown in fig. 4 and 5;
step 36, constructing an adaptive cruise control model in Simulink;
step 37, transmitting model information in the automatic driving vehicle module; the specific method comprises the following steps:
step 371, transferring the expected path and the expected speed in the expected driving track information to the driver model;
step 372, outputting a steering wheel angle, a throttle opening and a brake force range to the adaptive cruise control model by the driver model;
step 373, outputting the relative distance, the relative speed and the perception azimuth angle of the target object to the adaptive cruise control model by the perception sensor model;
step 374, outputting a steering wheel angle, a throttle opening and a braking force range to a vehicle dynamics model by the adaptive cruise control model, and feeding back the engine speed to the automatic driving function model by the vehicle dynamics model;
step 375, loading the vehicle dynamics model and the perception sensor model onto the automatic driving vehicle model, feeding back the driving speed and the yaw angular speed to the automatic driving function model by the automatic driving vehicle model, and feeding back the driving speed and the vehicle pose to the driver model;
step 41, setting interactive vehicle expected driving track information by using the three-dimensional coordinates of the road boundary line input in the step 21 as a track constraint boundary and using the expected track information of the automatic driving vehicle obtained in the step 31 as a reference track, wherein the expected path is a path starting from 20m away from the pile number starting point and keeping the road to be centered for driving, and the expected speed is 60km/h as shown in fig. 3;
step 42, setting a driver model as a closed-loop driver model in Prescan, wherein the preview time is 1s, the reaction delay time is 0.1s, the minimum vehicle running speed is 10km/h, and the maximum steering wheel rotation angle and the rotation speed are 540 deg/s and 1200deg/s respectively;
step 43, setting a vehicle dynamic model as a Simple dynamic model as a 3D Simple in Prescan, wherein the vehicle dynamic model comprises vehicle aerodynamic parameters, a transmission system, a braking system, a steering system, a suspension system and a tire model, and default parameters of the Prescan vehicle dynamic model are adopted;
step 44, setting the interactive vehicle model as BMW X5 SUV in Prescan, and adopting default parameters for the vehicle body size
And step 45, transmitting the model information in the interactive vehicle module, wherein the specific method comprises the following steps:
step 451, transmitting the expected path and the expected speed in the expected running track information to the driver model;
step 452, outputting a steering wheel angle, a throttle opening and a braking force range to a vehicle dynamics model by the driver model;
step 453, loading the vehicle dynamics model onto the interactive vehicle model, and feeding the driving speed and the vehicle pose back to the driver model by the interactive vehicle model;
transmitting the relevant models in the road environment module in the step 2, the automatic driving vehicle module in the step 3 and the interactive vehicle module in the step 4 to Simulink by using self-contained interfaces of CarSim and Prescan, referring to Table 1, connecting the models according to the following method:
TABLE 1 software for use in each module of autopilot virtual test platform
Step 51, integrating the road environment module with the autonomous vehicle module, specifically comprising:
step 511, transmitting the road surface friction coefficient and the unevenness degree to a vehicle dynamic model in an automatic driving vehicle module;
step 512, transmitting the road linear model and the weather environment information to a sensing sensor model;
step 52, integrating the interactive vehicle module with the autonomous vehicle module: and transmitting the vehicle pose obtained by the interactive vehicle to the sensing sensor model.
The plane schematic of the automatic driving virtual test environment realized by the method disclosed by the invention is shown in FIG. 6.
Claims (6)
1. A method for building an automatic driving virtual test platform for mapping real world road conditions is characterized by comprising the following steps:
step 1, acquiring real world road condition information; the road condition information comprises road geometric form information and road surface function condition information; the road geometric form information comprises a road sideline three-dimensional coordinate and a pile number; the road surface function condition information comprises a road surface friction coefficient and unevenness;
step 2, constructing a road environment module by using the real world road condition information in the step 1, wherein the specific method comprises the following steps:
step 21, setting road side line three-dimensional coordinates and pile numbers at fixed intervals from a road starting point to a road finishing point by using the real world road geometric form information acquired in the step 1, and constructing to obtain a road linear model;
step 22, matching the real world road function condition information acquired in the step 1 with the road geometric form information acquired in the step 21, and setting the road friction coefficient and the unevenness at fixed intervals;
step 23, setting weather environment information; the weather environment information comprises one or more of sunny days, cloudy days, rainy days, snowy days and foggy days;
step 3, constructing an automatic driving vehicle module;
step 4, constructing an interactive vehicle module; the method comprises the following steps of (1) obtaining expected running track information, a driver model, a vehicle dynamics model and an interactive vehicle model;
and 5, integrating the road environment module in the step 2, the automatic driving vehicle module in the step 3 and the interactive vehicle module in the step 4, realizing information interconnection, and building an automatic driving virtual test platform capable of mapping real world road conditions.
2. The method for building an automatic driving virtual test platform for mapping real-world road conditions according to claim 1, characterized in that: the specific process of constructing the autonomous vehicle module in step 3 is as follows:
step 31, setting expected traveling track information by using the three-dimensional coordinates of the road boundary line in the step 21 as a track constraint boundary, wherein the expected traveling track information comprises an expected path and an expected speed;
step 32, setting a driver model, including preview time, reaction delay time, minimum vehicle running speed, maximum steering wheel angle and rotating speed;
step 33, setting a vehicle dynamic model, which comprises vehicle aerodynamic parameters, a transmission system, a braking system, a steering system, a suspension system and a tire model;
step 34, setting an automatic driving vehicle model, including vehicle type and vehicle body size;
step 35, setting a perception sensor model, including the type, installation position and installation posture of a perception sensor; the sensing sensor types comprise a camera, a millimeter wave radar, a laser radar and an ultrasonic radar;
step 36, constructing an automatic driving function model, including automatic emergency braking, adaptive cruise control, lane keeping assistance and lane changing assistance;
step 37, model information in the autonomous vehicle module is communicated.
3. The method for building the automatic driving virtual test platform for mapping real-world road conditions according to claim 2, wherein the method comprises the following steps: step 37, transferring model information in the autonomous vehicle module, specifically comprising the following steps:
step 371, transmitting the expected path and the expected speed in the expected running track information to the driver model;
step 372, outputting a steering wheel angle, a throttle opening and a braking force range to the automatic driving function model by the driver model;
step 373, the perception sensor model outputs the relative distance, the relative speed and the perception azimuth angle of the target object to the automatic driving function model;
step 374, outputting a steering wheel angle, a throttle opening and a braking force range to a vehicle dynamics model by the automatic driving function model, and feeding back the engine speed to the automatic driving function model by the vehicle dynamics model;
and step 375, loading the vehicle dynamics model and the perception sensor model onto the automatic driving vehicle model, feeding back the driving speed and the yaw rate to the automatic driving function model by the automatic driving vehicle model, and feeding back the driving speed and the vehicle pose to the driver model.
4. The method for building the automatic driving virtual test platform for mapping real-world road conditions according to claim 3, wherein the method comprises the following steps: the specific process of constructing the interactive vehicle module in the step 4 is as follows:
step 41, setting the expected travel track information by using the three-dimensional coordinates of the road boundary input in step 21 as a track constraint boundary and using the expected track information of the automatic driving vehicle obtained in step 31 as a reference track; the expected running track information comprises an expected path and an expected speed;
step 42, setting a driver model, including preview time, reaction delay time, minimum vehicle running speed, maximum steering wheel angle and rotation speed;
step 43, setting a vehicle dynamic model, which comprises vehicle aerodynamic parameters, a transmission system, a braking system, a steering system, a suspension system and a tire model;
step 44, setting an interactive vehicle model, including vehicle type and vehicle body size;
and step 45, transmitting the model information in the interactive vehicle module.
5. The method for building the automatic driving virtual test platform for mapping real-world road conditions according to claim 4, wherein the method comprises the following steps: step 45, transmitting the model information in the interactive vehicle module, which comprises the following specific steps:
step 451, transmitting the expected path and the expected speed in the expected running track information to the driver model;
step 452, outputting a steering wheel angle, a throttle opening and a braking force range to a vehicle dynamics model by the driver model;
and 453, loading the vehicle dynamics model to the interactive vehicle model, and feeding the running speed and the vehicle pose back to the driver model by the interactive vehicle model.
6. The method for constructing an automatic driving virtual test platform for mapping real-world road conditions according to claim 5, wherein: step 5, integrating the road environment module in step 2, the automatic driving vehicle module in step 3 and the interactive vehicle module in step 4, and realizing the specific process of information interconnection as follows:
step 51, integrating the road environment module with the autonomous vehicle module, the method specifically comprising:
step 511, transmitting the road surface friction coefficient and the unevenness degree to a vehicle dynamic model in an automatic driving vehicle module;
step 512, transmitting the road linear model and the weather environment information to a sensing sensor model;
step 52, integrating the interactive vehicle module with the autonomous vehicle module: and transmitting the vehicle pose obtained in the interactive vehicle module to the sensing sensor model.
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