CN101118500A - Software emulation method of self-determination driving vehicle running process - Google Patents
Software emulation method of self-determination driving vehicle running process Download PDFInfo
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Abstract
The present invention relates to a software imitating method during the running course of the vehicle self-driving, and the imitation of the running course of the vehicle self-driving can be conducted on a computer with the windows operation system. Firstly, the present invention selects a proper state variable and a description pattern of the expectation track, consequently, then sets up a figure display interface and displays the expectation track and the vehicle ichnography under the initial state, and then repeatedly executes the series of procedures of pre-aiming deviation seeking, front-wheel corner control volume seeking, state variable refreshing, and figure display refreshing. The present invention avoids the safety problem likely existing in the course of actual vehicle self-driving experiment and debugging, which can be realized conveniently, and no external equipment is needed; and can imitate the running course of the vehicle self-driving better, and plays an instruction role in the experiment and debugging of actual vehicle self-driving for the relevant science and technology personnel experiment.
Description
Technical Field
The invention relates to a software simulation method for the running process of an autonomous driving vehicle, which can simulate the running process of the autonomous driving vehicle on a computer of a window operating system and play a guiding role in the practical experimental debugging of the autonomous driving vehicle of related scientific and technical personnel.
Background
The automobile is one of the most effective and widely used transportation means in modern society, is an indispensable part for maintaining normal life and work of people, and along with the continuous development of society, more and more automobiles go into the visual field of people. However, the mass emergence of automobiles, whether buses, trucks, or more and more sedans, presents more challenges to traffic efficiency, traffic safety, and environmental protection. Increasingly frequent traffic jams and accidents seriously affect the convenience of people in daily life and the efficiency of work, and even harm the life safety of people. In addition, the exhausted automobile exhaust, especially the automobile exhaust exhausted in large amount during traffic jam, can aggravate the greenhouse effect, pollute the environment, and then harm people's health.
To address these problems, many measures have been taken. The occurrence of fatigue passenger carrying situation of a passenger car driver is reduced or even eliminated by monitoring and controlling the driving time of the passenger car driver; the development is vigorous and people are encouraged to take more public transportation; and constructing infrastructures such as roads. Such management-level methods alleviate serious traffic problems to some extent, but cannot fundamentally solve the traffic problems caused by human factors. In addition, measures to improve the traffic efficiency by adopting more public transportation also limit the degree of freedom of people in using traffic. Based on these shortcomings, it is very popular to develop technical measures, such as autonomous driving vehicles (or intelligent vehicles and unmanned vehicles) and further intelligent transportation systems, which can fundamentally exclude human adverse factors.
The research work of the autonomous driving vehicle is paid attention by a plurality of countries, and related research work is carried out in some countries several decades ago. Our country starts to research unmanned vehicles later, but at present, some colleges and universities research and manufacture autonomous driving vehicle sample vehicles (such as CyberC3 vehicles of Shanghai university of traffic, THMR-V vehicles of Qinghua university, and sample vehicles of Jilin university and national defense university) which can run in urban environment or gallop on expressways.
The main work of autonomous driving involves two aspects: pose data acquisition and vehicle control. The pose data acquisition means that the pose information of the vehicle and the pose information of external environments such as roads, roadside trees and the like are acquired through equipment such as a visual camera, a laser radar, a magnetic sensor, an ultrasonic sensor, an infrared sensor, a gyroscope, an encoder, a GPS and the like. The vehicle control generally refers to that the acquired vehicle pose and environment pose information is used for carrying out proper steering control and speed control on the vehicle so that the vehicle can run along an expected track as much as possible. The expected track can be an actual road mark, such as a white guiding line in the center of a road, a magnetic nail laid on the road, buildings, walls, trees, shrubs and the like on two sides of the road, or a planned and assumed track. The process of autonomous driving is a process of repeatedly and circularly finishing the two aspects of work. It can be seen that vehicle control is an important part of achieving autonomous driving of the vehicle.
For the relevant technologists, to verify the effect of a certain vehicle control relation, the most direct method is to perform experiments on actual vehicles. However, the experiment directly on the actual vehicle has some disadvantages, especially when the stability of the vehicle control relation can not be determined, and the experiment can lead to the vehicle being out of control, causing personal injury and property loss. Therefore, at the initial stage of designing the vehicle control relational expression, the reliability and the performance of the control relational expression are verified through software simulation, hidden problems in the control relational expression can be conveniently and timely found, the success of further actual vehicle experiments is guaranteed, and the method has practical application significance. At present, no software simulation method for the driving process of the autonomous driving vehicle is disclosed.
Disclosure of Invention
The invention aims to provide a software simulation method for the running process of an autonomous driving vehicle aiming at the defects of the prior art, which is simple and convenient to realize, can better simulate the running process of the autonomous driving vehicle, avoids the safety problem possibly existing in the actual experimental debugging process of the autonomous driving vehicle, and provides a direct and effective test means for relevant science and technology workers to test the control effect of the designed control relation.
In order to achieve the purpose, the method firstly selects a proper state variable and a description form of an expected track, then establishes a graphical display interface and displays the expected track and a plan view of the vehicle in an initial state, and then repeatedly executes a series of steps of aiming deviation calculation, front wheel steering angle control quantity calculation, state variable refreshing and graphical display refreshing, so as to realize the simulation of the driving process of the autonomous driving vehicle.
The software simulation method for the driving process of the autonomous driving vehicle comprises the following specific steps:
And 2, describing the expected track of the vehicle running through discrete sampling points, namely sampling one sampling point at regular intervals on the expected track, and storing the position information of the sampling points.
In the invention, the size of the sample point distance can be flexibly determined according to the design requirement, the smaller the distance is obtained, the stronger the approximation capability of the sample point to the expected track is, but the larger the storage capacity for storing the sample point position information is. An appropriate spacing size may be selected based on a tradeoff between approximation capability and storage capacity.
Step 3, establishing a graphic display interface, displaying a plane graph of the vehicle in the initial state and all sampling points by using the variable value of the initial state and the position information of all the sampling points, and connecting adjacent sampling points by using a straight line; and taking the initial state variable value as the state variable value of the current control moment, and entering the simulation process of the driving process of the autonomous driving vehicle.
Step 4, selecting a preview distance, and calling a certain point, in which the distance between the center of the front of the vehicle and the center of the rear two wheels of the vehicle is equal to the preview distance, as a preview point; determining the position of a pre-aiming point by using the state variable value and the pre-aiming distance at the current control moment; and calculating two sampling points which are closest to the prealignment point in all the sampling points, and calculating the prealignment deviation, namely the distance between the prealignment point and the expected track according to the positions of the two sampling points and the position of the prealignment point.
And 5, establishing a control relation expression reflecting the relation between the preview deviation and the front wheel steering angle control quantity, and calculating the front wheel steering angle control quantity according to the control relation expression and the preview deviation.
It should be noted that, in this step, various specific control relational expressions can be adopted according to actual needs, and through simulation, a control relational expression that can well enable the autonomous driving vehicle to travel along an expected track can be found out to be used as a reference for experimental debugging of the actual autonomous driving vehicle. However, the present invention can be realized by using any specific control relation as long as the front wheel steering angle control amount is obtained from the preview deviation.
Step 6, establishing a vehicle dynamic model, and deriving a vehicle state dynamic relational expression reflecting the relationship between the state variable value of the next control moment, the state variable value of the current control moment and the front wheel steering angle control quantity; and calculating the state variable value of the next control time according to the state variable value of the current control time, the front wheel steering angle control quantity and the vehicle state dynamic relational expression.
And 7, delaying for waiting for a period of time, and refreshing the display of the vehicle in the graphical interface by using the state variable value at the next control moment.
And 8, taking the state variable value of the next control moment as the state variable value of the current control moment of the new round, returning to the step 4, and entering the simulation process of the running process of the autonomous driving vehicle of the new round.
The invention avoids the safety problem possibly existing in the experimental debugging process of the actual autonomous driving vehicle, only needs to simulate on a computer of a window operating system, does not need to use any external equipment, is simple and convenient to realize, can better simulate the driving process of the autonomous driving vehicle, provides a direct and effective test means for the related science and technology workers to test the control effect of the designed control relation, and plays a guiding role in the actual experimental debugging of the autonomous driving vehicle of the related science and technology workers.
Drawings
FIG. 1 is a plan view of a vehicle and a desired trajectory of vehicle travel in a graphical display interface.
Fig. 2 is a schematic diagram of a simulation of the driving process of an autonomously driven vehicle.
In fig. 1 and 2, each unit length in the planar coordinate system corresponds to 10 meters in practice.
Detailed Description
The following detailed description is provided in conjunction with a specific embodiment with reference to the accompanying drawings in order to provide a further understanding of the object and technical solution of the present invention.
The specific implementation steps are as follows:
The vehicle position is represented by the symbols x, y (x represents the abscissa and y represents the ordinate), ψ represents the heading angle, and δ represents the front wheel turning angle. x, y, ψ, δ are state variables that constitute a description of the travel process of the autonomously driven vehicle, and in the present embodiment, the state variables are assigned initial values of x =0, y =0, ψ =0, δ =0.
And 2, describing the expected track of the vehicle running through discrete sampling points, namely sampling one sampling point at regular intervals on the expected track, and storing the position information of the sampling points.
The present invention employs a way to describe the desired trajectory by discrete sampling points. Specifically, sampling points are taken at regular intervals on the expected track, and the position information of the sampling points is stored. In the simulation process, the position of the non-sampling point on the expected track can be obtained according to the position of the sampling point in a linear interpolation mode. The smaller the pitch, the more closely the sample points approach the desired trajectory, but the greater the number of sample points that need to be stored. A proper distance size can be selected by making a balance between approximation capacity and storage capacity according to requirements.
In this embodiment, the desired trajectory of vehicle travel is composed of two parts: a straight line AB of 10 meters in length and a 90 degree arc BC with a radius of curvature of 10 meters. On the straight line, the straight line can be completely determined only by two sampling points, so that for the straight line AB, only two end points A and B of the straight line AB are taken as the sampling points. On the arc, many sampling points are needed to better reflect the shape of the arc, so for the arc BC, the sampling interval is only 1 meter, namely one tenth of the curvature radius of the arc. As shown in fig. 1.
Step 3, establishing a graphic display interface, displaying a plane graph of the vehicle and all sampling points in the initial state by using the variable values of the initial state and the position information of all the sampling points, and connecting adjacent sampling points by using straight lines; and taking the initial state variable value as the state variable value of the current control moment, and entering the simulation process of the driving process of the autonomous driving vehicle.
The display is performed by displaying a plane coordinate system in a display window, displaying all the samples representing the desired trajectory in the plane coordinate system, connecting and displaying adjacent samples with a straight line, and displaying a plan view of the vehicle in the plane coordinate system according to the initial state variable value. As in fig. 1.
Step 4, selecting a preview distance, and calling a certain point, in which the distance between the center of the front of the vehicle and the center of the rear two wheels of the vehicle is equal to the preview distance, as a preview point; determining the position of a pre-aiming point by using the state variable value and the pre-aiming distance at the current control moment; and calculating two sampling points which are closest to the prealignment point in all the sampling points, and calculating the prealignment deviation, namely the distance between the prealignment point and the expected track according to the positions of the two sampling points and the position of the prealignment point.
The specific method of the step is that firstly, the position of the pre-aiming point is calculated by adopting the following formula:
Px＝x(n)+Lcosψ(n)
Py＝y(n)+Lsinψ(n)
where px, py represent the location of the home point. x (n), y (n) are the vehicle position at the current control time, ψ (n) is the heading angle at the current control time, and L represents the pre-aim distance. In this embodiment, L =4 meters is taken.
And then two sampling points which are closest to the pre-aiming point in all the sampling points are obtained.
And finally, calculating the preview deviation by adopting the following formula:
where e denotes the home-address deviation, px, py denote the positions of home-address points, and (p 1x, p1 y) and (p 2x, p2 y) denote the positions of two samples closest to the home-address point among all the samples.
And 5, establishing a control relation expression reflecting the relation between the preview deviation and the front wheel steering angle control quantity, and calculating the front wheel steering angle control quantity according to the control relation expression and the preview deviation.
In the present embodiment, the control relation is in the following specific formula:
u＝2*e
where u represents the front wheel steering angle control amount.
It should be noted that the specific formula is not meant to limit the invention, and the specific formula is only used as an example to facilitate understanding. The front wheel steering angle control quantity is obtained through the preview deviation, and other control relational expressions in the prior art can be adopted according to actual needs.
Step 6, establishing a vehicle dynamic model, and deriving a vehicle state dynamic relational expression reflecting the relationship between the state variable value of the next control moment, the state variable value of the current control moment and the front wheel steering angle control quantity; and calculating the state variable value of the next control time according to the state variable value of the current control time, the front wheel steering angle control quantity and the vehicle state dynamic relational expression.
Establishing a vehicle dynamic model composed of a kinematic model and a first-order steering dynamic model
Where x, y represent the vehicle position, ψ represents the vehicle heading angle, δ represents the front wheel steering angle, l represents the vehicle front-rear wheel axle distance, v represents the running speed, T represents the steering motor response time constant, and u represents the front wheel steering angle control amount.
Discretizing the vehicle dynamic model described by the continuous differential equation set to obtain a vehicle state dynamic relational expression, wherein the vehicle state dynamic relational expression is as follows:
wherein l represents the distance between the front and rear wheel shafts of the vehicle; v represents a running speed; t represents a response time constant of the steering motor; t is 0 Which indicates a control cycle for controlling the vehicle. x (n), y (n), ψ (n), and δ (n) represent the current control timeThe value of the state variable of (c). x (n + 1), y (n + 1), ψ (n + 1), δ (n + 1) denote state variable values at the next control timing.
In this example, l =2 m, v =3 m/s, T =0.6 s, T 0 And =0.1 second.
And calculating the state variable value of the next control time according to the state variable value of the current control time, the front wheel steering angle control quantity and the vehicle state dynamic relation formula.
And 7, delaying for waiting for a period of time, and refreshing the display of the vehicle in the graphical interface by using the state variable value at the next control moment.
In this embodiment, the delay waiting time is equal to the control period, i.e., 0.1 second. The delay waiting time can also take other values, and if the delay waiting time is less than the control period, the simulated result is a 'quick release' effect; if the control period is longer than the control period, the effect of slow-down is achieved.
And 8, taking the state variable value of the next control moment as the state variable value of the current control moment of the new round, returning to the step 4, and entering the simulation process of the running process of the autonomous driving vehicle of the new round.
The simulation schematic diagram of the autonomous driving vehicle running process in the above specific implementation steps is shown in fig. 2, where fig. 2 is four pictures captured from the simulated animation, and shows the forms of the vehicle at four different times in the running process. By adopting the method provided by the invention, the driving process of the autonomous driving vehicle can be effectively simulated. In the above step 5, the effect of the simulated autonomous driving vehicle running is different depending on the specific control relation adopted by the designer. The designer can try various control relations, can timely find out which control relations can enable the autonomous driving vehicle to be deviated from an expected track in the driving process through simulation, and can avoid adopting the control relations when the actual autonomous driving vehicle is subjected to experimental debugging. And the control relation can be found out through simulation, so that the autonomous driving vehicle can well run along the expected track, and the success probability is higher when the control relation is adopted during the experimental debugging of the actual autonomous driving vehicle. Therefore, the software simulation method for the driving process of the autonomous driving vehicle can play a guiding role in carrying out actual autonomous driving vehicle experiment debugging on related scientific and technical personnel.
Claims (4)
1. A software simulation method for the driving process of an autonomous driving vehicle is characterized by comprising the following steps: 1) Taking the position, the course angle and the front wheel steering angle of the vehicle as state variables describing the running process of the autonomous driving vehicle, and setting initial state variable values;
2) Describing an expected running track of a vehicle through discrete sampling points, namely, sampling one sampling point at regular intervals on the expected track, and storing the position information of the sampling points;
3) Establishing a graphic display interface, displaying a plane graph of the vehicle in the initial state and all sampling points by using the variable value of the initial state and the position information of all the sampling points, and connecting adjacent sampling points by using a straight line; taking the initial state variable value as the state variable value of the current control moment, and entering the simulation process of the driving process of the autonomous driving vehicle;
4) Selecting a pre-aiming distance, and calling a certain point, in which the distance between the center of two wheels right in front of the vehicle and the center of the two wheels behind the vehicle is equal to the pre-aiming distance, as a pre-aiming point; determining the position of a pre-aiming point by using the state variable value and the pre-aiming distance at the current control moment; calculating two sampling points which are closest to the preview point in all the sampling points, and calculating preview deviation, namely the distance between the preview point and the expected track according to the positions of the two sampling points and the position of the preview point;
5) Establishing a control relation reflecting the relation between the preview deviation and the front wheel steering angle control quantity, and calculating the front wheel steering angle control quantity according to the control relation and the preview deviation;
6) Establishing a vehicle dynamic model, and deriving a vehicle state dynamic relational expression reflecting the relationship between the state variable value at the next control moment, the state variable value at the current control moment and the front wheel steering angle control quantity; calculating the state variable value of the next control time according to the state variable value of the current control time, the front wheel steering angle control quantity and the vehicle state dynamic relational expression;
7) Waiting for a period of time in a delayed manner, and refreshing the display of the vehicle in the graphical interface by using the state variable value at the next control moment;
8) And taking the state variable value of the next control moment as the state variable value of the current control moment of the new round, returning to the step 4), and entering the simulation process of the driving process of the autonomous driving vehicle of the new round.
2. The software simulation method for the driving process of an autonomously driven vehicle according to claim 1, characterized in that when determining the position of the preview point, the formula is used:
px＝x(n)+Lcosψ(n)
py＝y(n)+Lsinψ(n)
where px, py represent the position of the preview point, x (n), y (n) represent the vehicle position at the current control time, ψ (n) represents the heading angle at the current control time, and L represents the preview distance.
3. The software simulation method for the driving process of an autonomously driven vehicle according to claim 1, characterized in that when calculating the preview deviation, the formula is adopted:
where e denotes the preview deviation, px, py denote the positions of the preview points, and (p 1x, p1 y) and (p 2x, p2 y) denote the positions of two of all the sample points that are closest to the preview point.
4. The software simulation method of a running course of an autonomously driven vehicle according to claim 1, characterized in that when calculating the value of the state variable at the next control time, the vehicle state dynamic relation employed is:
wherein l represents the distance between the front and rear wheel shafts of the vehicle; v represents a running speed; t represents a response time constant of the steering motor; t is a unit of 0 The control cycle for controlling the vehicle is shown, x (n), y (n), ψ (n), and δ (n) show the values of the state variables at the current control time, and x (n + 1), y (n + 1), ψ (n + 1), and δ (n + 1) show the values of the state variables at the next control time.
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| CN105676674A (en) * | 2016-04-20 | 2016-06-15 | 北京航空航天大学 | Unmanned aerial vehicle front wheel steering control method based on instruction filter |
| CN105891454A (en) * | 2016-05-23 | 2016-08-24 | 桂仲成 | Hub-type robot and detecting method for system for autonomously detecting road surfaces |
| CN106909153A (en) * | 2017-03-21 | 2017-06-30 | 北京京东尚科信息技术有限公司 | Unmanned vehicle crosswise joint method and apparatus |
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Family Cites Families (1)
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| US4745572A (en) * | 1985-11-04 | 1988-05-17 | Step Engineering | Software editing instrument |
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| CN105891454A (en) * | 2016-05-23 | 2016-08-24 | 桂仲成 | Hub-type robot and detecting method for system for autonomously detecting road surfaces |
| CN106909153A (en) * | 2017-03-21 | 2017-06-30 | 北京京东尚科信息技术有限公司 | Unmanned vehicle crosswise joint method and apparatus |
| CN107153420B (en) * | 2017-05-25 | 2021-01-22 | 广州汽车集团股份有限公司 | Path tracking control method and device and intelligent automobile |
| CN107153420A (en) * | 2017-05-25 | 2017-09-12 | 广州汽车集团股份有限公司 | Path tracking control method, device and intelligent automobile |
| CN112236346B (en) * | 2018-05-31 | 2021-10-15 | 北美日产公司 | Method and apparatus for simulating autonomous driving |
| CN112236346A (en) * | 2018-05-31 | 2021-01-15 | 北美日产公司 | Time warping for autonomous driving simulation |
| WO2020000192A1 (en) * | 2018-06-26 | 2020-01-02 | Psa Automobiles Sa | Method for providing vehicle trajectory prediction |
| CN111665738A (en) * | 2019-03-05 | 2020-09-15 | 阿里巴巴集团控股有限公司 | In-loop simulation system and information processing method and device thereof |
| CN113495767A (en) * | 2020-03-20 | 2021-10-12 | 北京轻舟智航智能技术有限公司 | Interactive scene generation method and device and electronic equipment |
| CN113495767B (en) * | 2020-03-20 | 2023-08-22 | 北京轻舟智航智能技术有限公司 | Method and device for generating interaction scene and electronic equipment |
| CN111547066A (en) * | 2020-04-27 | 2020-08-18 | 中汽研(天津)汽车信息咨询有限公司 | Vehicle trajectory tracking method, device, equipment and storage medium |
| CN111547066B (en) * | 2020-04-27 | 2021-11-30 | 中汽信息科技(天津)有限公司 | Vehicle trajectory tracking method, device, equipment and storage medium |
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