CN115440027A - Multi-scene-oriented intelligent vehicle formation function evaluation method and device - Google Patents
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Abstract
The invention provides a multi-scene-oriented intelligent vehicle formation function evaluation method and device, wherein different quantitative evaluation indexes are constructed on the basis of three formation driving scenes of formation acceleration, formation deceleration and self-adaptive formation, the defects that the indexes of the existing evaluation method are single in one surface and difficult to quantitatively describe are effectively overcome, the performance of intelligent vehicle formation functions can be accurately and quantitatively evaluated, and the method and device are suitable for the requirements of increasingly updated and developed intelligent vehicle formation technologies and industries.
Description
Technical Field
The invention relates to the technical field of road tests and tests of intelligent driving automobiles, in particular to a multi-scene-oriented intelligent vehicle formation function evaluation method and device.
Background
With the explosive increase of road mileage, the road capacity is rapidly increased. According to statistics, more than 70% of the passenger transportation volume is born by road transportation at present. However, the road traffic accidents and the economic losses associated therewith are difficult to ignore. The factors causing road traffic accidents are various, wherein the ratio of human factors reaches about 90%. In addition, in a serious accident, the loss caused by a large transport vehicle is more than 80 percent. Therefore, in order to reduce the bad or abnormal driving behaviors of the driver, the intelligent driving technology which adopts the informatization technology to assist in intervening or controlling the driving behaviors of the automobile has become the key point of domestic and foreign research. The intelligent vehicle formation with the intelligent driving technology is regarded as a method capable of effectively solving the problem of safe and efficient transportation of large road transport vehicles on the expressway.
The intelligent vehicle formation maintains the vehicle-to-vehicle interval of vehicles running in the same direction at a small safe fixed value by means of technologies such as intelligent sensing and cooperative communication, so that a uniformly organized running queue is formed. The vehicle at the forefront plays a role of a pilot, and the rear vehicle automatically follows to run so as to realize nearly synchronous control of acceleration, deceleration, steering, braking and the like of the formation vehicles. As a road traffic transportation mode with more informatization and intellectualization, intelligent vehicle formation has important significance and application prospects in improving driving safety, improving transportation efficiency, slowing down traffic jam and reducing energy emission. Road transport vehicles, especially large trucks, have gradually become the main application object of intelligent vehicle formation technology. The vehicle has the characteristics of large vehicle volume, heavy carrying mass, long driving time, more vision blind areas and the like, is easier to have traffic accidents compared with other vehicles, and has the possibility of causing group death and group injury once the accidents happen. Therefore, comprehensive, effective and reliable testing of intelligent vehicle formation functions in multiple scenes is indispensable through vehicle evaluation means.
The method for evaluating the formation function specified in test procedures (trial) for auto-driving functions of intelligent networked automobiles published in 2018 is widely accepted and applied. In the test procedure, three formation driving scenes, namely formation acceleration, formation deceleration and self-adaptive formation, are defined, and related test requirements and indexes are defined respectively for different scenes. Even so, the existing research on the intelligent formation function evaluation system is still relatively deficient, and the only evaluation systems are mainly evaluated from a macroscopic perspective, which mainly show the following two problems: 1. the index description is single, and actual evaluation requirements under different scenes are difficult to effectively cover; 2. the index description is mainly qualitative, and effective and objective quantitative description indexes are lacked if collision does not occur, the vehicle speed is controlled and the like. In conclusion, the existing indexes can not objectively and accurately describe the performance of the intelligent vehicle formation operation process, and the requirements of the intelligent vehicle formation technology and the increasingly updated and developed industry are difficult to meet.
Disclosure of Invention
The invention solves the problems that the performance of the intelligent vehicle formation operation process cannot be objectively and accurately described by the existing intelligent formation function evaluation index, and the requirement of the increasingly updated and developed intelligent vehicle formation technology and industry is difficult to meet.
In order to solve the problems, the invention provides a multi-scene-oriented intelligent vehicle formation function evaluation method, which comprises the following steps: controlling the intelligent vehicles to carry out formation test driving according to a pre-established intelligent vehicle formation performance test scene; the intelligent vehicle formation performance test scene comprises the following steps: a formation acceleration scene, a formation deceleration scene and a self-adaptive formation scene; acquiring driving parameters of the intelligent vehicles in the formation test driving process; respectively calculating a formation acceleration evaluation index, a formation deceleration evaluation index and a self-adaptive formation evaluation index according to the driving parameters; the formation accelerated evaluation index comprises the following steps: the method comprises the following steps of maintaining accuracy of queue speed and maintaining accuracy of queue vehicle distance, wherein a formation deceleration evaluation index comprises a brake deceleration consistency index, and the self-adaptive formation evaluation index comprises the following steps: finishing the single vehicle yaw stability index of the lane-changing vehicle and the safety distance allowance of two adjacent vehicles; and quantitatively evaluating the intelligent vehicle formation function according to the formation acceleration evaluation index, the formation deceleration evaluation index and the self-adaptive formation evaluation index.
Optionally, the safety distance margin of the two adjacent vehicles is the shortest distance between the left front top point or the right front top point of the rear vehicle and the front vehicle.
Optionally, the respectively calculating the adaptive formation evaluation indexes according to the driving parameters includes calculating safety distance margins of two adjacent vehicles according to the driving parameters as follows:
calculating the vertex coordinates of four vertexes of each vehicle in the formation; a. The 21 、A 22 、A 23 、A 24 Respectively representing four vertices of the vehicle, with coordinates:
wherein l and w are the length and width of the vehicle body, respectively, (x) 2 ,y 2 ,
) As a location point S 2 The coordinate and course angle of (1), delta l and delta w are respectively the distance from the positioning point to the head and the side of the vehicle, alpha 21 、α 22 、α 23 、α 24 Are respectively a connecting line S 2 A 21 、S 2 A 22 、S 2 A 23 、S 2 A 24 The included angle with the vehicle heading angle is as follows:
according to the vertex coordinates of each vehicle, calculating a body curve equation gamma of each vehicle 1 、Γ 2 、…Γ n ；
For the ith vehicle (i > 1) in the queue, defining the safety distance margin at the moment k as the minimum value of the curve distance from two vertexes at the front end of the vehicle to the front vehicle as follows:
d i (k)＝min(A i1 (k)Γ i-1 (k),A i2 (k)Γ i-1 (k))
in the formula, A i1 (k)Γ i-1 (k) Front end vertex A representing the time k of the i-th vehicle i1 (k) Curve gamma to the front vehicle i-1 (k) A distance of (A) i2 (k)Γ i-1 (k) Front end vertex A representing the k time of the ith vehicle i2 (k) Curve gamma to front vehicle i-1 (k) The distance of (a);
and (3) the safety distance allowance in the whole sampling process is as follows:
Dsafe＝min(d i (k))。
optionally, the respectively calculating the adaptive formation evaluation indexes according to the driving parameters includes calculating a lane-changing yaw stability index of the lane-changing vehicle according to the driving parameters as follows:
the lane change yaw stability is defined as:
in the formula, σ iMSE The quantized value representing the stability of the lane-changing yaw represents the degree of the vehicle lane change executed by the ith vehicle in the queue, s represents the number of sampling points in the test process, and omega represents the number of the sampling points i (k) For the yaw rate of the vehicle at time k,
the expected value of the yaw rate at the time k is represented by the following calculation formula:
in the formula, the curvature radius calculation formula is as follows:
in the formula, variable B i1 ，B i2 ，B i3 ，B i4 Respectively as follows:
wherein the content of the first and second substances,
respectively representing the east and north speed of the ith vehicle k in the queue,
respectively representing the east and north positions of the ith vehicle k in the queue at that time.
Optionally, the respectively calculating adaptive formation evaluation indexes according to the driving parameters includes calculating queue speed maintaining accuracy according to the driving parameters as follows:
the queue speed holding accuracy calculation formula is as follows:
in the formula, xi v Representing queue speed maintaining precision under the formation acceleration scene, n representing the total number of formation vehicles, s representing the number of sampling points in the test process, v i (k) Indicating the speed of the ith vehicle in the queue at time k,
the average speed of all vehicles in the queue at the moment k is represented by the following calculation formula:
optionally, the respectively calculating adaptive formation evaluation indexes according to the driving parameters includes calculating a queue vehicle distance maintaining precision according to the driving parameters as follows:
the calculation formula of the maintaining precision of the queue vehicle distance is as follows:
in the formula, xi D Vehicle distance protection for queue under formation acceleration sceneMaintaining accuracy, n represents the total number of the vehicles in the formation, s represents the number of sampling points in the test process, deltad represents the vehicle distance set by the formation running, and deltad i (k) The distance between the ith vehicle and the preceding vehicle in the queue is represented by the following calculation formula:
in the above-mentioned formula, the compound has the following structure,
respectively representing the east position components of the ith and (i-1) th vehicles in the queue,
indicating the north position component of the ith and (i-1) th vehicles in the queue and l indicating the length of the convoy vehicle.
Optionally, the formation acceleration scene includes at least 3 or more intelligent vehicles, and a leading intelligent vehicle is in a manual driving mode, and other subsequent vehicles are in an automatic driving mode;
and in the formation acceleration scene, the first intelligent vehicle is accelerated linearly from a static state to a preset speed per hour and then keeps running at a constant speed.
Optionally, the formation deceleration scene includes at least 3 or more intelligent vehicles, each intelligent vehicle is already in a formation driving state and is driving at a constant speed, and the first intelligent vehicle starts to brake and decelerate to a stop at a preset time.
Optionally, the adaptive formation scene includes at least 3 or more intelligent vehicles, the intelligent vehicles except for the target intelligent vehicle are already in a formation driving state and are driven at a constant speed, and the target intelligent vehicle is driven on an adjacent lane;
and starting merging and switching between the target intelligent vehicles in the self-adaptive formation scene at a preset time and the two intelligent vehicles in the formation driving state, and continuing the formation driving of the target intelligent vehicles after merging and switching.
The embodiment of the invention provides a multi-scene-oriented intelligent vehicle formation function evaluation device, which comprises: the formation driving module is used for controlling the intelligent vehicles to perform formation test driving according to a pre-established intelligent vehicle formation performance test scene; the intelligent vehicle formation performance test scene comprises the following steps: a formation acceleration scene, a formation deceleration scene and a self-adaptive formation scene; the parameter acquisition module is used for acquiring driving parameters of the intelligent vehicles in the formation test driving process; the index calculation module is used for respectively calculating a formation acceleration evaluation index, a formation deceleration evaluation index and a self-adaptive formation evaluation index according to the driving parameters; the formation accelerated evaluation index comprises the following steps: the method comprises the following steps of maintaining accuracy of queue speed and maintaining accuracy of queue vehicle distance, wherein a formation deceleration evaluation index comprises a brake deceleration consistency index, and the self-adaptive formation evaluation index comprises the following steps: finishing the single vehicle yaw stability index of the lane-changing vehicle and the safety distance allowance of two adjacent vehicles; and the quantitative evaluation module is used for quantitatively evaluating the intelligent vehicle formation function according to the formation acceleration evaluation index, the formation deceleration evaluation index and the self-adaptive formation evaluation index.
The embodiment of the invention constructs different quantitative evaluation indexes based on three formation driving scenes of formation acceleration, formation deceleration and self-adaptive formation, effectively overcomes the defects of single index and difficulty in quantitative description of the conventional evaluation method, can accurately and quantitatively evaluate the performance of the intelligent vehicle formation function, and is suitable for the requirements of the increasingly updated and developed intelligent vehicle formation technology and industry.
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In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the embodiments or the prior art will be briefly described below, it is obvious that the drawings in the following description are only embodiments of the present invention, and for those skilled in the art, other drawings can be obtained according to the provided drawings without creative efforts.
FIG. 1 is a schematic flow chart of a multi-scenario-oriented intelligent vehicle formation function evaluation method in an embodiment of the invention;
FIG. 2 is a diagram illustrating a queuing acceleration test scenario according to an embodiment of the present invention;
FIG. 3 is a diagram illustrating a formation slowdown test scenario in an embodiment of the present invention;
FIG. 4 is a diagram illustrating an adaptive formation test scenario in an embodiment of the present invention;
FIG. 5 is a schematic diagram illustrating calculation of coordinates of vertexes of formation vehicles according to an embodiment of the present invention;
fig. 6 is a schematic structural diagram of an intelligent vehicle formation function evaluation device for multiple scenes in the embodiment of the invention.
Detailed Description
In order to make the aforementioned objects, features and advantages of the present invention comprehensible, embodiments accompanied with figures are described in detail below. It should be understood that the specific embodiments described herein are merely illustrative of the invention and do not limit the invention.
Based on the analysis of the intelligent driving function evaluation research, the three formation driving scenes of formation acceleration, formation deceleration and self-adaptive formation are oriented by referring to the scene requirements of the existing standard, the embodiment of the invention constructs a more comprehensive and delicate intelligent vehicle formation evaluation method, and effectively makes up the defects that the index of the existing evaluation method is single and is difficult to quantitatively describe.
Fig. 1 is a schematic flow chart of a multi-scenario-oriented intelligent vehicle formation function evaluation method in an embodiment of the present invention, where the method includes:
and S102, controlling the intelligent vehicles to carry out formation test driving according to a pre-established intelligent vehicle formation performance test scene.
Wherein, intelligent vehicle formation performance test scene includes: a formation acceleration scene, a formation deceleration scene and a self-adaptive formation scene.
Specifically, the formation acceleration scene comprises at least more than 3 intelligent vehicles, the first intelligent vehicle is in a manual driving mode, and other subsequent vehicles are in an automatic driving mode; and in the formation acceleration scene, the first intelligent vehicle is linearly accelerated from a static state to a preset speed per hour and then keeps running at a constant speed. The formation deceleration scene comprises at least more than 3 intelligent vehicles, each intelligent vehicle is in a formation driving state and drives at a constant speed, and the first intelligent vehicle starts to brake and decelerate to stop at a preset moment. The self-adaptive formation scene comprises at least more than 3 intelligent vehicles, the intelligent vehicles except the target intelligent vehicle are in a formation running state and run at a constant speed, and the target intelligent vehicle runs on an adjacent lane; and starting merging and switching the target intelligent vehicles into the space between two intelligent vehicles in the formation driving state at a preset time in the self-adaptive formation scene, and continuing the formation driving of the target intelligent vehicles after merging and switching.
Illustratively, the intelligent vehicle formation performance test scenario is established in the present embodiment as follows:
(1) Formation acceleration scenario
The test road is a long straight road at least comprising one lane, and the test fleet is composed of 3 or more test vehicles. During testing, the Vehicle 1 is in a manual driving mode, other subsequent vehicles are in an automatic driving mode, a V2V (Vehicle-to-Vehicle communication) function is started, and all vehicles in a formation meet the interconnection requirement. The vehicle 1 accelerates from a standstill to 60km/h and keeps running at a constant speed. FIG. 2 shows a diagram of a formation accelerated test scenario.
(2) Formation deceleration scenario
The test road is a long straight road at least comprising one lane, the test vehicle team consists of 3 or more test vehicles, the test vehicles are in a team driving state and drive at a constant speed, at a certain moment, the vehicle 1 starts to brake and decelerate to stop, and the braking deceleration is 2m/s 2 ～4m/s 2 . FIG. 3 shows a diagram of a formation slowdown test scenario.
(3) Adaptive formation scenarios
The test road is a long straight road at least comprising two lanes. The test vehicle team consists of 3 or more test vehicles, the test vehicles are in a formation driving state and drive at a constant speed, and the target vehicle cuts into the space between the vehicle 1 and the vehicle 2 from the adjacent lanes. During testing, the test vehicle is in a formation driving state and drives at a constant speed of 60km/h, the target vehicle starts to cut into the space between the vehicle 1 and the vehicle 2 at a certain moment, and the cut-in target vehicle drives at a constant speed of 60km/h along with the vehicle 1. FIG. 4 shows a schematic diagram of an adaptive formation test scenario.
And S104, acquiring the driving parameters of the intelligent vehicles in the formation test driving process.
In the process of driving the intelligent vehicles in the formation test, the driving parameters of the intelligent vehicles can be obtained based on the sampling of the preset period. The driving parameters may include: the speed of the vehicle, the acceleration of the vehicle, the yaw rate of the vehicle, the position of the vehicle, the total number of vehicles, the distance between two adjacent vehicles, and the like.
And S106, respectively calculating a formation acceleration evaluation index, a formation deceleration evaluation index and a self-adaptive formation evaluation index according to the driving parameters.
Specifically, the formation accelerated evaluation index comprises: the queue speed maintaining precision and the queue vehicle distance maintaining precision, the formation deceleration evaluation indexes comprise a brake deceleration consistency index, and the self-adaptive formation evaluation indexes comprise: and finishing the single vehicle yaw stability index of the lane-changing vehicle and the safety distance allowance of two adjacent vehicles. The safety distance allowance of two adjacent vehicles is the shortest distance between the left front vertex or the right front vertex of the rear vehicle and the front vehicle.
The traditional safety distance allowance is the distance between the rearmost end of the central axis of the front vehicle and the foremost end of the central axis of the rear vehicle, but considering that the distance between the rearmost end of the central axis of the front vehicle and the foremost end of the central axis of the rear vehicle cannot accurately represent the safety distance allowance when the self-adaptive formation process is completed on a curve, the shortest distance between the left front vertex or the right front vertex of the rear vehicle and the front vehicle needs to be considered at the moment, and therefore the shortest distance between the left front vertex or the right front vertex of the rear vehicle and the front vehicle of two adjacent vehicles is adopted as the safety distance allowance of the two adjacent vehicles in the embodiment. For safety margin distance indexes in the self-adaptive formation scene, the influence of the rectangular outline of the vehicle body is considered, so that the evaluation is more accurate.
And S108, quantitatively evaluating the intelligent vehicle formation function according to the formation acceleration evaluation index, the formation deceleration evaluation index and the self-adaptive formation evaluation index.
The formation acceleration evaluation index, the formation deceleration evaluation index and the self-adaptive formation evaluation index which are obtained based on the calculation are quantitative data, and the intelligent vehicle formation function can be further quantitatively evaluated.
According to the multi-scene-oriented intelligent vehicle formation function evaluation method, different quantitative evaluation indexes are constructed on the basis of three formation driving scenes of formation acceleration, formation deceleration and self-adaptive formation, the defects that the indexes of the existing evaluation method are single in one surface and difficult to quantitatively describe are effectively overcome, the performance of intelligent vehicle formation functions can be accurately and quantitatively evaluated, and the method is suitable for the requirements of increasingly updated and developed intelligent vehicle formation technologies and industries.
Particularly, for safety margin distance indexes in a self-adaptive formation scene, the influence of a rectangular outline of a vehicle body is considered, a formation running safety margin measurement model is established to calculate coordinates of four vertexes of each vehicle, the distance between the front end vertex of a rear vehicle and a vehicle curve of a front vehicle can be calculated, and the minimum value of the distance in the whole sampling process is used as the safety distance margin, so that the evaluation is more accurate.
Illustratively, the formation evaluation indexes under different scenes are constructed in the embodiment as follows:
(1) Formation acceleration evaluation index
On the basis of the existing standard requirement (namely, the distance keeping range of the train distance), in order to describe the formation acceleration performance more finely and quantitatively, the embodiment of the invention constructs two indexes of the queue speed keeping precision and the queue train distance keeping precision in the formation driving process to evaluate the formation acceleration performance, and the calculation method of the queue speed keeping precision comprises the following steps:
in the formula, xi v Representing queue speed maintaining precision under the formation acceleration scene, n representing the total number of formation vehicles, s representing the number of sampling points in the test process, v i (k) Representing the speed of the ith vehicle in the queue at time k,
the average speed of all vehicles in the queue at the time k is represented, and the calculation method comprises the following steps:
meanwhile, the queue vehicle distance keeping precision calculation method comprises the following steps:
in the formula, xi D For maintaining the precision of the queue vehicle distance under the formation acceleration scene, similarly, n represents the total number of the formation vehicles, s represents the number of sampling points in the test process, deltad represents the vehicle distance set by the formation running, and deltad i (k) The distance between the ith vehicle and the preceding vehicle in the queue is represented, and the calculation method comprises the following steps:
in the above formula, the first and second carbon atoms are,
respectively representing the east position components of the ith and (i-1) th vehicles in the queue,
representing the north position components of the ith vehicle and the (i-1) th vehicle in the queue, and l representing the length of the formation vehicle;
(2) Formation deceleration experiment evaluation index
On the basis of standard existing requirements (namely no collision), the embodiment of the invention evaluates the response speed of the train braking by comparing the Brake deceleration Consistency indexes of the vehicles in the train in the deceleration experiment, and the Brake deceleration Consistency indexes are recorded as train Braking Consistency (BC):
wherein n represents the total number of the formation vehicles, s represents the number of sampling points in the test process, a i (k) Indicating the acceleration of the ith vehicle in the queue at time k,
and (3) representing the average acceleration of all vehicles in the queue at the moment k, and calculating the formula as follows:
(3) Self-adaptive team experiment evaluation index
According to the above definition of the test scenario, in the adaptive formation scenario, the experimental vehicle can be divided into three parts: on the basis of standard existing indexes (namely safety distance), the self-adaptive formation performance of the vehicle to be formed, the vehicle to be added in front of the position and the vehicle to be added in back of the position are evaluated as follows:
firstly, vehicles to be formed complete the lane change task in the adaptive formation experiment, so that the safety of the vehicles in the lane change process is evaluated by independently adopting a single-vehicle yaw stability index, and the single-vehicle lane change yaw stability can be defined as:
in the formula, σ iMSE The quantized value representing the yaw stability of the single vehicle lane change reflects the aggressive degree of the ith vehicle in the queue to execute lane change, so that the safety in the lane change process is evaluated, s represents the number of sampling points in the test process, and omega represents the number of sampling points in the test process i (k) For the yaw rate of the vehicle at time k,
the expected value of the yaw rate at the time k is represented by the following calculation formula:
in the formula, σ iMSE ，ω i (k)，
The units of (a) are radians per second, and the curvature radius calculation formula is as follows:
in the formula, variable B i1 ，B i2 ，B i3 ，B i4 Respectively as follows:
wherein the content of the first and second substances,
respectively representing the east and north speed of the ith vehicle k in the queue,
respectively representing the east and north positions of the ith vehicle k in the queue at that time.
Then, in the self-adaptive formation process, the safety distance allowance of each vehicle is an important evaluation parameter, and the traditional safety distance allowance refers to the distance between the rearmost end of the central axis of the front vehicle and the foremost end of the central axis of the rear vehicle. However, when the self-adaptive formation process is completed on a curve, the distance between the rearmost end of the central axis of the front vehicle and the foremost end of the central axis of the rear vehicle cannot accurately represent the safety distance margin, and at this time, the shortest distance between the left front vertex or the right front vertex of the rear vehicle and the front vehicle needs to be considered, so that a formation driving safety margin measurement model is built in the embodiment of the invention, the positions of four vertexes of each vehicle are calculated, and fig. 5 shows a schematic diagram of calculation of vertex coordinates of formation vehicles.
Taking the second vehicle as an example, l and w are the length and width of the vehicle body, respectively, (x) 2 ,y 2 ,
) As a location point S 2 Is the distance between the locating point and the head and the side of the vehicle, A 21 、A 22 、A 23 、A 24 Respectively representing the four vertices of the vehicle with the coordinates:
in the formula, alpha 21 、α 22 、α 23 、α 24 Are respectively a connecting line S 2 A 21 、S 2 A 22 、S 2 A 23 、S 2 A 24 Included angle with vehicle course angle:
therefore, the vertex coordinates of all vehicles in the queue are obtained, and the body curve equation gamma of each vehicle can be further calculated 1 、Γ 2 、…Γ n To the ith vehicle (i) in the queue>1) Defining the safety distance allowance at the moment k as the minimum value of the distance from two vertexes at the front end of the vehicle to the curve of the vehicle in front:
d i (k)＝min(A i1 (k)Γ i-1 (k),A i2 (k)Γ i-1 (k)) (13)
in the formula, A i1 (k)Γ i-1 (k) Front end vertex A representing the time k of the i-th vehicle i1 (k) Curve gamma to front vehicle i-1 (k) A distance of (A) i2 (k)Γ i-1 (k) Front end vertex A representing the time k of the i-th vehicle i2 (k) Curve gamma to front vehicle i-1 (k) The distance of (c).
And finally, solving the minimum value of the safety margin in the whole sampling process as the safety distance margin:
Dsafe＝min(d i (k)) (14)
and finally, evaluating the re-formation effect of all the vehicles subjected to the self-adaptive formation by adopting the formation speed maintaining precision and the formation vehicle distance maintaining precision in the formation acceleration evaluation index.
Fig. 6 is a schematic structural diagram of an intelligent vehicle formation function evaluation device for multiple scenes in one embodiment of the invention, including:
the formation driving module 601 is used for controlling the intelligent vehicles to perform formation test driving according to a pre-established intelligent vehicle formation performance test scene; the intelligent vehicle formation performance test scene comprises the following steps: a formation acceleration scene, a formation deceleration scene and a self-adaptive formation scene;
the parameter obtaining module 602 is configured to obtain driving parameters of the intelligent vehicles during the formation test driving process;
the index calculation module 603 is configured to calculate a formation acceleration evaluation index, a formation deceleration evaluation index and a self-adaptive formation evaluation index according to the driving parameters; the formation accelerated evaluation index comprises the following steps: the method comprises the following steps of maintaining accuracy of queue speed and maintaining accuracy of queue vehicle distance, wherein a formation deceleration evaluation index comprises a brake deceleration consistency index, and the self-adaptive formation evaluation index comprises the following steps: finishing the yaw stability index of the single vehicle of the lane-changing vehicle and the safety distance allowance of two adjacent vehicles;
and the quantitative evaluation module 604 is used for quantitatively evaluating the intelligent vehicle formation function according to the formation acceleration evaluation index, the formation deceleration evaluation index and the self-adaptive formation evaluation index.
The multi-scene-oriented intelligent vehicle formation function evaluation device provided by the embodiment of the invention constructs different quantitative evaluation indexes based on three formation driving scenes of formation acceleration, formation deceleration and self-adaptive formation, effectively overcomes the defects of single index and difficulty in quantitative description of the conventional evaluation method, can accurately and quantitatively evaluate the performance of the intelligent vehicle formation function, and is suitable for the increasingly updated and developed requirements of intelligent vehicle formation technology and industry.
Optionally, the safety distance margin between two adjacent vehicles is the shortest distance between the left front vertex or the right front vertex of the rear vehicle and the front vehicle.
Optionally, the safe distance margin between two adjacent vehicles is calculated according to the running parameters, as follows:
calculating the vertex coordinates of four vertexes of each vehicle in the formation; a. The 21 、A 22 、A 23 、A 24 Respectively representing four vertices of the vehicle, with coordinates:
wherein l and w are the length and width of the vehicle body, respectively, (x) 2 ,y 2 ,
) As a location point S 2 Is the distance between the locating point and the head and the side of the vehicle, alpha 21 、α 22 、α 23 、α 24 Are respectively a connecting line S 2 A 21 、S 2 A 22 、S 2 A 23 、S 2 A 24 The included angle between the vehicle and the heading angle of the vehicle is as follows:
according to the vertex coordinates of each vehicle, calculating the body curve equation gamma of each vehicle 1 、Γ 2 、…Γ n ；
For the ith vehicle (i > 1) in the queue, defining the safety distance margin at the moment k as the minimum value of the curve distance from two vertexes at the front end of the vehicle to the front vehicle as follows:
d i (k)＝min(A i1 (k)Γ i-1 (k),A i2 (k)Γ i-1 (k))
in the formula, A i1 (k)Γ i-1 (k) Front end vertex A representing the time k of the i-th vehicle i1 (k) Curve gamma to the front vehicle i-1 (k) A distance of (A) i2 (k)Γ i-1 (k) Front end vertex A representing the time k of the i-th vehicle i2 (k) Curve gamma to front vehicle i-1 (k) The distance of (d);
and (3) the safety distance allowance in the whole sampling process is as follows:
Dsafe＝min(d i (k))。
optionally, a lane change yaw stability indicator of the lane change completed vehicle is calculated according to the driving parameters as follows:
lane change yaw stability is defined as:
in the formula, σ iMSE The quantized value representing the stability of the lane-changing yaw represents the degree of the vehicle lane change executed by the ith vehicle in the queue, s represents the number of sampling points in the test process, and omega i (k) The yaw rate of the vehicle at time k,
the expected value of the yaw rate at the time k is represented by the following formula:
in the formula, the curvature radius calculation formula is as follows:
in the formula, variable B i1 ，B i2 ，B i3 ，B i4 Respectively as follows:
wherein, the first and the second end of the pipe are connected with each other,
respectively representing the east and north speed of the ith vehicle k in the queue,
respectively representing the east and north positions of the ith vehicle k in the queue at that time.
Optionally, calculating a queue speed holding accuracy from the driving parameters as follows:
the queue speed holding accuracy calculation formula is as follows:
in the formula, xi v Representing queue speed maintaining precision under the formation acceleration scene, n representing the total number of formation vehicles, s representing the number of sampling points in the test process, v i (k) Representing the speed of the ith vehicle in the queue at time k,
the average speed of all vehicles in the queue at the moment k is represented by the following calculation formula:
optionally, the queue distance keeping accuracy is calculated according to the driving parameters as follows:
the calculation formula of the maintaining precision of the queue vehicle distance is as follows:
in the formula, xi D Maintaining precision for the queue vehicle distance under the formation acceleration scene, wherein n represents the total number of formation vehicles, s represents the number of sampling points in the test process, deltad represents the vehicle distance set by the formation running, and deltad i (k) Indicating the ith vehicle distance in the queueThe distance of the front vehicle is calculated by the following formula:
in the above formula, the first and second carbon atoms are,
respectively representing the east position components of the ith and (i-1) th vehicles in the queue,
indicating the north position component of the ith and (i-1) th vehicles in the queue, and l indicating the length of the convoy vehicle.
Optionally, the formation acceleration scene includes at least 3 or more intelligent vehicles, and a leading intelligent vehicle is in a manual driving mode, and other subsequent vehicles are in an automatic driving mode; and in the formation acceleration scene, the first intelligent vehicle is accelerated linearly from a static state to a preset speed per hour and then keeps running at a constant speed.
Optionally, the formation deceleration scene includes at least 3 or more intelligent vehicles, each intelligent vehicle is already in a formation driving state and is driving at a constant speed, and the first intelligent vehicle starts to brake and decelerate to stop at a preset time.
Optionally, the adaptive formation scene includes at least 3 or more intelligent vehicles, the intelligent vehicles except for a target intelligent vehicle are already in a formation driving state and are driven at a constant speed, and the target intelligent vehicle is driven in an adjacent lane; and starting merging and switching between the target intelligent vehicles in the self-adaptive formation scene at a preset time and the two intelligent vehicles in the formation driving state, and continuing the formation driving of the target intelligent vehicles after merging and switching.
The multi-scene-oriented intelligent vehicle formation function evaluation device provided by the embodiment can realize each process in the embodiment of the multi-scene-oriented intelligent vehicle formation function evaluation method, and is not repeated here for avoiding repetition.
The embodiment of the invention also provides a computer-readable storage medium, wherein a computer program is stored on the computer-readable storage medium, and when being executed by a processor, the computer program realizes each process of the multi-scenario-oriented intelligent vehicle formation function evaluation method embodiment, can achieve the same technical effect, and is not repeated here to avoid repetition. The computer-readable storage medium may be a Read-Only Memory (ROM), a Random Access Memory (RAM), a magnetic disk or an optical disk.
Of course, those skilled in the art will understand that all or part of the processes in the methods of the above embodiments may be implemented by instructing the control device to perform operations through a computer, and the programs may be stored in a computer-readable storage medium, and when executed, the programs may include the processes of the above method embodiments, where the storage medium may be a memory, a magnetic disk, an optical disk, and the like.
In this document, relational terms such as first and second, and the like may be used solely to distinguish one entity or action from another entity or action without necessarily requiring or implying any actual such relationship or order between such entities or actions. Also, the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or apparatus. Without further limitation, an element defined by the phrases "comprising one of 8230; \8230;" 8230; "does not exclude the presence of additional like elements in a process, method, article, or apparatus that comprises the element.
In the present specification, the embodiments are described in a progressive manner, each embodiment focuses on differences from other embodiments, and the same and similar parts among the embodiments are referred to each other.
The previous description of the disclosed embodiments is provided to enable any person skilled in the art to make or use the present invention. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the generic principles defined herein may be applied to other embodiments without departing from the spirit or scope of the invention. Thus, the present invention is not intended to be limited to the embodiments shown herein but is to be accorded the widest scope consistent with the principles and novel features disclosed herein.
Claims (10)
1. A multi-scene-oriented intelligent vehicle formation function evaluation method is characterized by comprising the following steps:
controlling the intelligent vehicles to carry out formation test driving according to a pre-established intelligent vehicle formation performance test scene; the intelligent vehicle formation performance test scene comprises the following steps: a formation acceleration scene, a formation deceleration scene and a self-adaptive formation scene;
acquiring driving parameters of the intelligent vehicles in the formation test driving process;
respectively calculating a formation acceleration evaluation index, a formation deceleration evaluation index and a self-adaptive formation evaluation index according to the driving parameters; the formation accelerated evaluation index comprises the following steps: the method comprises the following steps of maintaining queue speed and queue vehicle distance, wherein the formation deceleration evaluation index comprises a brake deceleration consistency index, and the self-adaptive formation evaluation index comprises the following steps: finishing the yaw stability index of the single vehicle of the lane-changing vehicle and the safety distance allowance of two adjacent vehicles;
and quantitatively evaluating the intelligent vehicle formation function according to the formation acceleration evaluation index, the formation deceleration evaluation index and the self-adaptive formation evaluation index.
2. The method of claim 1, wherein the safety distance margin between two adjacent vehicles is the shortest distance between the left or right front vertex of the rear vehicle and the front vehicle.
3. The method of claim 2, wherein the calculating the adaptive formation evaluation index according to the driving parameters respectively comprises calculating a safe distance margin between two adjacent vehicles according to the driving parameters as follows:
calculating the vertex coordinates of four vertexes of each vehicle in the formation; a. The 21 、A 22 、A 23 、A 24 Respectively representing four vertices of the vehicle, with coordinates:
wherein l and w are the length and width of the vehicle body, respectively,
as anchor points S 2 Is the distance between the locating point and the head and the side of the vehicle, alpha 21 、α 22 、α 23 、α 24 Are respectively a connecting line S 2 A 21 、S 2 A 22 、S 2 A 23 、S 2 A 24 The included angle with the vehicle heading angle is as follows:
according to the vertex coordinates of each vehicle, calculating a body curve equation gamma of each vehicle 1 、Γ 2 、…Γ n ；
For the ith vehicle (i > 1) in the queue, defining the safety distance allowance at the moment k as the minimum value of the curve distance from two vertexes at the front end of the vehicle to the front vehicle as follows:
d i (k)＝min(A i1 (k)Γ i-1 (k),A i2 (k)Γ i-1 (k))
in the formula, A i1 (k)Γ i-1 (k) Front end vertex A representing the time k of the i-th vehicle i1 (k) Curve gamma to the front vehicle i-1 (k) A distance of (A) i2 (k)Γ i-1 (k) Front end vertex A representing the k time of the ith vehicle i2 (k) Curve gamma to front vehicle i-1 (k) The distance of (a);
and (3) the safety distance allowance in the whole sampling process is as follows:
Dsafe＝min(d i (k))。
4. the method of claim 2, wherein the separately calculating an adaptive fleet assessment indicator based on the driving parameters comprises calculating a lane change yaw stability indicator for a completed lane change vehicle based on the driving parameters as follows:
lane change yaw stability is defined as:
in the formula, σ iMSE The quantized value representing the stability of the lane-changing yaw represents the degree of the vehicle lane change executed by the ith vehicle in the queue, s represents the number of sampling points in the test process, and omega i (k) For the yaw rate of the vehicle at time k,
the expected value of the yaw rate at the time k is represented by the following calculation formula:
in the formula, the curvature radius calculation formula is as follows:
in the formula, variable B i1 ，B i2 ，B i3 ，B i4 Respectively as follows:
wherein the content of the first and second substances,
respectively representing the east and north speed of the ith vehicle k in the queue,
respectively representing the east and north positions of the ith vehicle k in the queue at that time.
5. The method of claim 1, wherein the separately calculating an adaptive formation evaluation index based on the driving parameters comprises calculating a queue speed maintenance accuracy based on the driving parameters as follows:
the queue speed holding accuracy calculation formula is as follows:
in the formula, xi v Representing queue speed maintaining precision under the formation acceleration scene, n representing the total number of formation vehicles, s representing the number of sampling points in the test process, v i (k) Indicating the speed of the ith vehicle in the queue at time k,
the average speed of all vehicles in the queue at the moment k is represented by the following calculation formula:
6. the method according to claim 5, wherein the calculating the adaptive formation evaluation index according to the running parameters respectively comprises calculating a queue distance keeping precision according to the running parameters as follows:
the calculation formula of the maintaining precision of the queue vehicle distance is as follows:
in the formula, xi D Maintaining precision for the queue vehicle distance under the formation acceleration scene, wherein n represents the total number of formation vehicles, s represents the number of sampling points in the test process, deltad represents the vehicle distance set by the formation running, and deltad i (k) The distance between the ith vehicle and the preceding vehicle in the queue is represented by the following calculation formula:
in the above-mentioned formula, the compound has the following structure,
respectively representing the east position components of the ith and (i-1) th vehicles in the queue,
indicating the north position component of the ith and (i-1) th vehicles in the queue, and l indicating the length of the convoy vehicle.
7. The method according to any one of claims 1-6, wherein the formation acceleration scenario includes at least 3 or more of the smart vehicles, and a first smart vehicle is in a manual driving mode and other subsequent vehicles are in an automatic driving mode;
and in the formation acceleration scene, the first intelligent vehicle is accelerated linearly from a static state to a preset speed per hour and then keeps running at a constant speed.
8. The method according to any one of claims 1-6, wherein the platooning deceleration scenario comprises at least 3 or more of the smart vehicles, each of the smart vehicles has been in a platooning state and is traveling at a constant speed, and a leading smart vehicle starts to brake and decelerate to a stop at a preset time.
9. The method according to any one of claims 1-6, wherein the adaptive formation scenario includes at least 3 or more of the smart vehicles, the smart vehicles other than the target smart vehicle having been in a formation driving state and driving at a constant speed, the target smart vehicle driving in an adjacent lane;
and starting merging and switching between two intelligent vehicles in the formation driving state at a preset moment by the target intelligent vehicle in the self-adaptive formation scene, and continuing to form and drive the target intelligent vehicle after merging and switching.
10. The utility model provides an intelligent vehicle formation function evaluation device towards many scenes which characterized in that includes:
the formation driving module is used for controlling the intelligent vehicles to perform formation test driving according to a pre-established intelligent vehicle formation performance test scene; the intelligent vehicle formation performance test scene comprises the following steps: a formation acceleration scene, a formation deceleration scene and a self-adaptive formation scene;
the parameter acquisition module is used for acquiring driving parameters of the intelligent vehicles in the formation test driving process;
the index calculation module is used for respectively calculating a formation acceleration evaluation index, a formation deceleration evaluation index and a self-adaptive formation evaluation index according to the driving parameters; the formation accelerated evaluation index comprises the following steps: the method comprises the following steps of maintaining accuracy of queue speed and maintaining accuracy of queue vehicle distance, wherein a formation deceleration evaluation index comprises a brake deceleration consistency index, and the self-adaptive formation evaluation index comprises the following steps: finishing the yaw stability index of the single vehicle of the lane-changing vehicle and the safety distance allowance of two adjacent vehicles;
and the quantitative evaluation module is used for quantitatively evaluating the intelligent vehicle formation function according to the formation acceleration evaluation index, the formation deceleration evaluation index and the self-adaptive formation evaluation index.
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|---|---|---|---|---|
| CN117217422A (en) * | 2023-11-07 | 2023-12-12 | 国汽(北京)智能网联汽车研究院有限公司 | Vehicle motion control capability assessment method, system, device and medium thereof |
Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN110987463A (en) * | 2019-11-08 | 2020-04-10 | 东南大学 | Multi-scene-oriented intelligent driving autonomous lane change performance test method |
| CN111595592A (en) * | 2020-05-21 | 2020-08-28 | 东南大学 | Performance evaluation method of adaptive cruise control system |
| CN111780981A (en) * | 2020-05-21 | 2020-10-16 | 东南大学 | Intelligent vehicle formation lane change performance evaluation method |
| CN112629883A (en) * | 2020-12-28 | 2021-04-09 | 东南大学 | Test evaluation method for intelligent vehicle queue driving performance |
-
2022
- 2022-07-21 CN CN202210862539.2A patent/CN115440027A/en active Pending
Patent Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN110987463A (en) * | 2019-11-08 | 2020-04-10 | 东南大学 | Multi-scene-oriented intelligent driving autonomous lane change performance test method |
| CN111595592A (en) * | 2020-05-21 | 2020-08-28 | 东南大学 | Performance evaluation method of adaptive cruise control system |
| CN111780981A (en) * | 2020-05-21 | 2020-10-16 | 东南大学 | Intelligent vehicle formation lane change performance evaluation method |
| CN112629883A (en) * | 2020-12-28 | 2021-04-09 | 东南大学 | Test evaluation method for intelligent vehicle queue driving performance |
Non-Patent Citations (1)
| Title |
|---|
| 王培宇: "基于多源信息融合的车辆编队功能测评技术研究" * |
Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN117217422A (en) * | 2023-11-07 | 2023-12-12 | 国汽(北京)智能网联汽车研究院有限公司 | Vehicle motion control capability assessment method, system, device and medium thereof |
| CN117217422B (en) * | 2023-11-07 | 2024-03-22 | 国汽(北京)智能网联汽车研究院有限公司 | Vehicle motion control capability assessment method, system, device and medium thereof |
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