CN112406891B - Vehicle performance quantitative detection method and device - Google Patents
Vehicle performance quantitative detection method and device Download PDFInfo
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Abstract
The invention relates to a vehicle performance quantitative detection method, which comprises the following steps: receiving signal data; determining tire road surface utilization based on the signal data; calculating a vehicle performance weighted distance count based on the tire road surface utilization; and obtaining a vehicle performance index based on the vehicle performance weighted distance count. The invention also provides a vehicle performance quantitative detection device. According to the vehicle performance quantitative detection method and device provided by the invention, the performance of the unmanned vehicle exerted under the extreme working condition can be quantized, whether the performance is close to the performance of the whole vehicle exerted by a professional driver trial, and the safety and the robustness of the unmanned vehicle under the extreme working condition are ensured.
Description
Technical Field
The invention relates to the field of automatic driving, in particular to a method and a device for quantitatively detecting performance of a base unmanned vehicle.
Background
The trajectory generation algorithm focuses on the normal driving scenario where none of the operations are considered to be extreme handling conditions. However, for an unmanned vehicle in a real environment, it is important to appropriately design automatic control to maintain the stability of the vehicle and successfully perform evasive action for challenging driving scenarios. The conventional path planning can prevent the uncontrolled path tracking dynamics, however, the existing unmanned vehicle control function, including the path planning algorithm, is designed for the normal operation condition, although the vehicle can be driven according to the planned path, the maximum utilization of the tire adhesion capacity is not considered, when the actual extreme operation condition, such as emergency avoidance, is encountered, the vehicle avoidance may fail, and the prior art does not have any function of quantitatively detecting the unmanned vehicle performance aiming at the extreme condition.
Autonomous manufacturers are working to develop new autonomous vehicles that, in extreme operating conditions, exceed the driver's average driving level, maintaining overall vehicle safety and robustness. In practice, the level of autonomous vehicles generally greatly exceeds the level of average attentive drivers, possibly reaching the level of professional racing car drivers.
Accordingly, the present invention is directed to a quantitative determination method and apparatus for evaluating the automatic control performance and reproducibility of an autonomous vehicle in comparison with the level of a professional racing driver.
Disclosure of Invention
Aiming at the problems in the prior art, the invention provides a method and a device for quantitatively detecting the performance of an unmanned vehicle, which can quantify the performance of the unmanned vehicle exerted under the limit working condition, detect whether the performance is close to the performance of the whole vehicle exerted by a professional driver trial when driving, and ensure the safety and the robustness of the unmanned vehicle under the limit working condition.
According to an embodiment of the present invention, there is provided a vehicle performance quantitative detection method including:
(a) Receiving signal data;
(b) Determining tire road surface utilization based on the signal data;
(c) Calculating a vehicle performance weighted distance count based on the tire road surface utilization; and
(d) A vehicle performance index is obtained based on the vehicle performance weighted distance count.
According to another embodiment of the present invention, there is provided a vehicle performance quantification detection apparatus including:
a signal receiving unit for receiving signal data;
a tire road surface utilization rate determining unit for determining a tire road surface utilization rate based on the signal data;
a vehicle performance weighted distance count calculation unit for calculating a vehicle performance weighted distance count based on the tire road surface utilization rate; and
a vehicle performance index obtaining unit for obtaining a vehicle performance index based on the vehicle performance weighted distance count.
According to the method and the device for quantitatively detecting the vehicle performance, the tire road surface utilization rate is determined according to signal data acquired under the driving condition of a professional; based on the tire road surface utilization rate, a vehicle performance weighted distance count is calculated, and thus a vehicle performance index of the vehicle under the condition that a professional drives the vehicle is obtained. Then, for the performance of the unmanned vehicle, the same steps of analyzing and determining the vehicle performance index by using the collected signal data of the vehicle under the condition that a professional is present are adopted, and the vehicle performance index under the unmanned condition is obtained. By comparing the vehicle performance index under the unmanned driving condition with the vehicle performance index under the condition that the vehicle is driven by a professional, the performance of the unmanned automobile can be obtained. Therefore, the performance of the unmanned vehicle exerted under the limit working condition can be quantified, whether the performance is close to the performance of the whole vehicle exerted by a professional driver trial is detected, and the safety and the robustness of the unmanned vehicle under the limit working condition are ensured.
For further clarity of explanation of the features and technical content of the present invention, reference should be made to the following detailed description of the present invention and accompanying drawings, which are provided for reference and description purposes only and are not intended to limit the present invention.
Drawings
Embodiments of the present invention are described below with reference to the drawings. In the drawings:
FIG. 1 is a schematic illustration of a vehicle according to an embodiment of the present invention.
Fig. 2 is a schematic diagram depicting the flow of information according to fig. 1, in accordance with an embodiment of the present invention.
FIG. 3 is a schematic illustration of a travel path utilized by the present invention, according to an embodiment of the present invention.
Fig. 4 is a schematic diagram of a method for quantitatively detecting the performance of an unmanned vehicle according to an embodiment of the present invention.
FIG. 5 is a schematic view of a vehicle friction circle for an overall "autonomous vehicle path" in accordance with an embodiment of the present invention.
FIG. 6 is a schematic diagram of calculating a vehicle performance index according to an embodiment of the invention.
Fig. 7 is a schematic diagram of calculating a tire road surface utilization performance index according to an embodiment of the present invention.
FIG. 8 is a schematic illustration of a driving path mapped to a fixed centerline according to an embodiment of the present invention.
Fig. 9 is a schematic diagram of calculating a vehicle performance index according to an embodiment of the invention.
Fig. 10 is a flowchart of a method for quantitatively detecting the performance of an unmanned vehicle according to an embodiment of the present invention.
Fig. 11 is a schematic diagram of an unmanned vehicle performance quantitative detection apparatus according to an embodiment of the present invention.
Detailed Description
The technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the drawings in the embodiments of the present application, and it is obvious that the described embodiments are some, but not all, embodiments of the present application. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present application.
In the description of the present disclosure, it is to be understood that the terms "center", "longitudinal", "lateral", "length", "width", "thickness", "upper", "lower", "front", "rear", "left", "right", "straight", "horizontal", "top", "bottom", "inner", "outer", "clockwise", "counterclockwise", and the like, indicate orientations or positional relationships based on those shown in the drawings, and are only for convenience of description and simplicity of description, but do not indicate or imply that the device or element being referred to must have a particular orientation, be constructed in a particular orientation, and be operated, and therefore should not be considered as limiting the present disclosure. Furthermore, the terms "first", "second" and "first" are used for descriptive purposes only and are not to be construed as indicating or implying relative importance or to implicitly indicate the number of technical features indicated. Thus, features defined as "first", "second", may explicitly or implicitly include one or more of the described features. In the description of the present disclosure, "a plurality" means two or more unless specifically limited otherwise.
Reference herein to "an embodiment" means that a particular feature, structure, or characteristic described in connection with the embodiment can be included in at least one embodiment of the application. The appearances of the phrase in various places in the specification are not necessarily all referring to the same embodiment, nor are separate or alternative embodiments mutually exclusive of other embodiments. It is explicitly and implicitly understood by one skilled in the art that the embodiments described herein may be combined with other embodiments.
In the embodiment of the invention, the performance of the unmanned vehicle under the limit working condition can be quantized and whether the performance is close to the performance of the whole vehicle exerted by a professional driver when the unmanned vehicle drives is detected by comparing the performance index and the reproducibility index of the whole vehicle respectively obtained by the unmanned vehicle and the professional driver when the unmanned vehicle drives the vehicle, so that the safety and the robustness of the unmanned vehicle under the limit working condition are ensured.
FIG. 1 is a schematic illustration of a vehicle according to an embodiment of the present invention. Fig. 2 is a schematic diagram depicting the flow of information according to fig. 1, in accordance with an embodiment of the present invention.
As shown in FIG. 1, a vehicle 10 includes wheels 12 in rolling frictional contact with a road surface 14. The vehicle 10 also includes a powertrain 20 having a plurality of associated subsystems. Vehicle subsystems include one or more torque-generative devices, e.g. comprising an electric machine (M) A ) 21 and an internal combustion engine (E) 13. In other embodiments of the vehicle 10, the internal combustion engine 13 may not be employed, or the internal combustion engine 13 may be used to power the wheels 12. Similarly, in various embodiments, the internal combustion engine 13 may be a gasoline-powered, diesel-powered, or fuel-powered engine,for example, the internal combustion engine may include a fuel cell.
It should be noted that fig. 1 is shown merely as one particular implementation for ease of description and understanding, and should not limit the scope of the present application to this particular implementation. In fact, other implementations that can be conceived by those skilled in the art are all within the scope covered by the present application as long as the objectives of the present invention are achieved.
As shown in FIG. 1, motor 21 drives motor torque (arrow T) to input 22 of transmission member (T) 24 M ). Subsequently, the transmission member 24 will output torque (arrow T) O ) The output 25 delivered to the transmission member 24 powers the wheels 12. In fig. 1, an internal combustion engine 13 transmits engine torque (arrow T) to a generator (G) 16 via a crankshaft 15 E ). The generator 16 generates current (arrow EE) sufficient to charge an Energy Storage System (ESS) 28 and/or directly power the electric machine 21.
The controller 50 receives input signal information (arrow CC) I ) And outputs a control signal (arrow CC) in response to the signal or a change in the signal O ) Each control module 64 shown in fig. 2 and each subsystem of the vehicle include a Transmission Control Module (TCM), an Engine Control Module (ECM), a Body Control Module (BCM), a Vehicle Integrated Control Module (VICM), and the like. The controller 50 is used in conjunction with a navigation system (NAV) 54, a geospatial mapping Database (DBS) 58 to output control signals (arrows CC, DBS) P ) As part of the following unmanned vehicle performance quantification detection method. In a vehicle integrated design, navigation system 54 and DBS 58 may be an integral part of controller 50, or navigation system 54 and DBS 58 may be in remote communication with vehicle 10 via a telephone or other portable device, or via a telematics unit of controller 50. Control signal (arrow CC) P ) And input signal information (arrow CC) I ) Is uploaded to the data logger 46. A controller (ECU) 48 receives data for executing the unmanned vehicle performance quantitative detection method. The controller (ECU) 48 includes the necessary memory (M) and processor (P), and associated hardware and software, such as an oscillator, high speed clock, and input/output circuitry. The memory (M) may comprise a computer readable medium or media including a sufficient amount of Read Only Memory (ROM), such as magnetic or optical storage, having recorded thereon computer readable instructions for performing a portion of the methods of the present invention as described below.
As shown in FIG. 1, the data logger 46 includes a signal processing module 47 for processing control signals (arrow CC) P ) And input signal information (arrow CC) I ). The controller 50 includes a path planning module 52 configured to plan, generate and display, either alone or in conjunction with a navigation system 54, the real-time location, speed, maximum target speed and time of the vehicle, and a desired path for evaluating a test runway of the unmanned vehicle. Signal processing module 47 generalOver input signal (DL) I ) In communication with the controller 48.
For example, fig. 2 shows a plurality of control modules 64, including, for example, a Transmission Control Module (TCM), an Engine Control Module (ECM), a Body Control Module (BCM), a Vehicle Integrated Control Module (VICM), and the like. As shown in fig. 2, a control signal (arrow CC) from the path planning module 52 P ) And the input signal information from the data source 66 (arrow CC) I ) Flows into the signal processing module 47. As part of the invention, the path planning module 52 sends information (arrow CC) O ) The controller 64 on board the vehicle 10 is used to regulate operation of the subsystem master and to receive control signal information (arrow CC) from the controller 64 O → for normalizing the operation of a series of brakes 60, such as, but not limited to, electric Power Steering (EPS), braking system, engine (E) 13, electric machine (M) A ) 21 or generator (G) 16. As used herein, "brake" refers to a device or another vehicle component that generates linear or rotational force along the driveline of the vehicle 10.
The path planning module 52 also receives information from various sensors 62, wherein the sensors 62 are configured to measure and report values for controlling the vehicle 10, including resolvers, temperature sensors, electronic sensors, front Camera Modules (FCMs), long-range radars (LRRs), short-range radars (SRRs), etc., which are disposed on the respective voltage buses.
A data source 66 is also in communication with path planning module 52 and signal processing module 47, data source 66 including mission requirements (MSN REQS), map data (MAPS data, MAPS) from DBS 58, and external sensor information, such as weather reports or road conditions. vehicle-to-X different remote sources, "V2X (vehicle-to-X)" information, including vehicle-to-vehicle (V2V) information, may also be provided to the path planning module 52 and the signal processing module 47.
The information gathered in fig. 2 includes a desired path for evaluating the test roadway of the unmanned vehicle, and the path planning module 52 calculates the desired travel path, analyzes the path, and outputs a recommended travel path (arrow RTE) to the navigation system 54, for display on the navigation system 54 or on another display screen device. Depending on the configuration of the vehicle 10, the driver may follow the recommended route, or in the case of unmanned, approve or confirm the route, along which the vehicle 10 then automatically controls operation.
FIG. 3 is a schematic illustration of a travel path utilized by the present invention, according to an embodiment of the present invention. As shown in FIG. 3, X 1 ,X 2 ,…,X j Representing a curved segment with a high path curvature, enabling the full force potential of the tire to be exploited. The path curvature for each curve segment may be calculated as:
where ρ is c In order to be a horizontal path curvature,
is yaw rate, and V x Is the longitudinal speed of the vehicle. X 1 ,X 2 ,…,X j Is such that ρ is c ≥ρ thr Where ρ is thr The preset path curvature threshold is, for example, 0.04. The highest path curvature corresponds to the smallest turning radius, and an architecture design can be provided for quantitatively estimating the reproduction performance of the unmanned vehicle.
Fig. 4 is a schematic diagram of a method for quantitatively detecting the performance of an unmanned vehicle according to an embodiment of the present invention. The mating of the various components shown in fig. 4 is as follows. First, the signal processing module 47 acquires signal data 402. The signal data includes, but is not limited to, vehicle longitudinal and lateral acceleration data, waypoint of vehicle origin data, time of lap, vehicle yaw rate data, vehicle longitudinal velocity data, and vehicle lateral distance to waypoint data. The vehicle dynamics module 403 uses the data from 402 to determine tire road surface utilization 404, a determination processAs will be shown in detail in fig. 6. Module 405 receives tire road surface utilization 404 and determines a tire road surface utilization performance indicator I p_t_r 406 and a vehicle performance weighted distance count 408, wherein the tire road surface utilization performance index I p_t_r And the vehicle performance weighted distance count, are shown in fig. 7 and 8, respectively. The module 409 uses the data 402 to determine a track error (CTE) 410, the process of which will be shown in fig. 9, and then determines a normalized mean dispersion Disp according to equations (10) and (11) _avr 412. The module 413 receives the vehicle performance weighted distance count 408 from the module 405 and the normalized average dispersion Disp from the module 409 _avr 412 to determine an unmanned vehicle performance index I perf 414 and driverless vehicle reproducibility index I rep The driverless vehicle performance index I will be described in detail with reference to FIG. 10 at 416 perf 414 and driverless vehicle reproducibility index I rep 416.
FIG. 5 is a schematic view of a vehicle friction circle for an overall "autonomous vehicle path" in accordance with an embodiment of the present invention. Referring to FIG. 5, the vehicle friction circle shown in FIG. 5 determines the vehicle operating conditions and the performance of the professional vehicle driver as well as the unmanned vehicle is evaluated by continuously recording the lateral and longitudinal accelerations along the path. The dashed line 514 represents the friction limit of the test vehicle under consideration of traction limits, effects of load transfer, suspension effects, etc. By normalizing the resultant longitudinal and lateral forces, the vehicle friction circle is expressed in units of gravitational acceleration. The arrow 512 indicates the longitudinal acceleration, which has a maximum value of a xmax_acc (arrow 524); arrow 514 indicates the longitudinal deceleration (braking), with a maximum value of-a xmax_brk (arrow 526). Arrow 510 indicates the lateral acceleration of the vehicle when turning to the right, with a maximum value of a ymax (arrow 528). Arrow 510 represents the lateral acceleration of the vehicle when turning to the left, with a maximum value of-a ymax (arrow 530).
According to fig. 5, point "a" represents an operating point (arrow 518) when the vehicle is traveling on a path, arrow 520 represents a longitudinal acceleration, arrow 516 represents a lateral acceleration, and arrow 522 represents a longitudinal acceleration a xA With transverse acceleration a yA Is shown as composite vector 522 as
Then, the tire road surface utilization rate R tu 404 is calculated as follows:
according to one embodiment, a xmax_acc ＝|a xmax_brk L =1g. According to another embodiment, a xmax_acc Limited by the tractive effort of the test road. For example, for front wheel drive vehicles, a xmax_acc The upper limit of (3) is 0.5g.
FIG. 6 is a schematic diagram of calculating a vehicle performance index according to an embodiment of the invention. As shown in FIG. 6, a tire road surface utilization performance index I is calculated based on the tire road surface utilization rate and the vehicle speed p_t_r The determination at 406 is as follows. Table 1 shows a description of the respective processing blocks and the corresponding functions in fig. 6.
TABLE 1
Wherein:
K ph1 when I vp The filter constant used when increasing, for example, when the sampling time is 100 milliseconds, is 0.989.
K ph2 When I vp The filter constant used when decreasing is, for example, 0.995 when the sampling time is 100 milliseconds.
K dh1 = when I vd The filter constants used being increased, e.g. when the sampling time is 100 millisecondsAnd was 0.990.
K dh2 When I vd The filter constant used when decreasing, for example, when the sampling time is 100 msec, is 0.994.
K W To set up I vp And I vd For vehicle performance index I p An arbitrary factor of the contribution ratio, for example 0.5, means that both contribute half.
K vp For speed-dependent weighting factors, for example:
K vd for speed-dependent weighting factors, for example:
referring to FIG. 6, a tire road surface utilization performance index I can be defined p_t_r 406 is represented in the form of a set of N-point arrays:
I p_t_r ＝{I p1 ,I p2 ,……,I pN }
wherein,
the total number of points, T, of the tire road surface utilization performance index calculated using the formula (7) _track Is the total travel time and Δ T is the sample time.
Fig. 7 is a schematic diagram of calculating a tire road surface utilization performance index according to an embodiment of the present invention. Let I p_t_r Decomposed into n ranges R i ,i＝1,…,n}。R i A range of tire road surface utilization performance indicators, wherein the value of the tire road surface utilization performance indicator is within a specific range, defined as:
R i ＝Range{I P_thr(i) ≤I p_t_r <I P_thr(i+1) }(8)
for example, i =1,r 1 ＝Range{0≤I p_t_r <0.5},i＝2,R 2 ＝Range{0.5≤I p_t_r <1}。
Definitions { Cnt (i), i =1, \ 8230;, n }, where Cnt (i) is attributed to R i The sum of the points of the tire road surface utilization performance index (c).
Vehicle performance weighted distance count C w 408 are calculated as follows:
w = { W (1), W (2), \8230;, W (n) } is a monotonically increasing weight vector, whose value, in one embodiment, for n =6, is: {0.5,1.0,1.5,2.5,3.5,4.5}.
FIG. 8 is a schematic illustration of a driving path mapped to a fixed centerline according to an embodiment of the present invention. Referring to fig. 8, the path taken by the driver (arrow 802) in each turn is mapped to the distance along the line centerline (arrow 801) and the trajectory error (CTE) epsilon 410 (arrow 803) from the line centerline (arrow 801).
As shown in fig. 8, such that E = { epsilon (i, l), i =1,m, l =1,p }, E denotes that the test path drives p turns, at the highest value ρ of the path curvature c Set of m p points for the absolute value of the measured CTE epsilon.
The mean and standard deviation at each point i in the p-circle were calculated:
calculate mean and standard deviation:
calculating normalized mean dispersion:
where | x | is the absolute value of x.
FIG. 9 is a schematic diagram of calculating a vehicle performance index according to an embodiment of the invention. As shown in FIG. 9, the weighted distance count C is displayed on the y-axis w And the time of one revolution is displayed on the x-axis. Y is target And t target Respectively representing the vehicle performance weighted distance count and the time taken to travel one revolution in the case of manned driving, theta target Is composed of (0, 0 and Y) in the case of human driving target ,t target ) The slope of the defined line. Y is veh And t veh Vehicle performance weighted distance count and time spent traveling one revolution, θ, in the driverless condition, respectively veh Is composed of (0, 0 and Y) in the case of no-man driving veh ,t veh ) The slope of the defined line.
Driverless vehicle performance index I perf 414 is calculated as follows:
equation (13) indicates that the higher the ratio of performance indices, the higher the driverless vehicle displays handling capability relative to the professional driver under extreme handling conditions. This may ensure safety and stability of the unmanned vehicle in all driving environments.
When unmanned vehicle performance index I perf Above 100%, it is indicative that the unmanned vehicle is performing better than a professional driver at extreme operating conditions. Or, if the driverless vehicle performance index I perf Well below 100% (e.g., 50%) indicates safety and stability of the unmanned vehicle during emergency maneuversThe performance of sex is unacceptable.
Driverless vehicle reproducibility index I rep 416 is calculated as follows:
according to equation (14), a high repeatability index (e.g., 3 or greater) indicates that the behavior of the unmanned vehicle during an emergency maneuver is repeatable.
At the same time, if
(e.g. ke rep_thr = 0.9), indicating that the unmanned vehicle performs more closely to the performance of the professional driver in the portion of the road having the highest path curvature, indicating that the unmanned vehicle exhibits safe and repeatable performance during an emergency maneuver, wherein I rep_target The index of reproducibility when a professional driver drives.
If it is not
Indicating that the unmanned vehicle performs equally or better than the performance of a professional driver in the portion of the roadway having the highest path curvature. Or, if the unmanned vehicle reproducibility index I rep Lower (e.g., less than 1), the ratio is lower (e.g.,
) It is shown that unmanned vehicles do not exhibit safe and repeatable performance during emergency maneuvers.
Based on the above description, the present invention provides a method for quantitatively detecting vehicle performance, as shown in fig. 10, which includes the following steps.
S1001, receiving signal data;
s1002, determining the road surface utilization rate of the tire based on the signal data;
s1003, calculating a vehicle performance weighted distance count based on the tire road surface utilization rate; and
and S1004, obtaining a vehicle performance index based on the vehicle performance weighted distance count.
Further, the method further comprises:
s1005, determining a track error based on the signal data;
s1006, calculating a normalized average dispersion based on the trajectory error; and
and S1007, obtaining the vehicle recurrence index according to the normalized average dispersion.
Wherein the signal data includes longitudinal acceleration, longitudinal deceleration, lateral acceleration when the vehicle is turning right, lateral acceleration when the vehicle is turning left, time it takes for the vehicle to make one turn with and without human driving, vehicle yaw rate data, vehicle lateral distance to the path data.
Wherein said determining tire road surface utilization based on said signal data comprises:
determining a tire road surface utilization rate based on the signal data using the following formula;
wherein R is tu Indicating the road surface utilization of the tire, a xmax_acc Representing the maximum value of longitudinal acceleration, -a xmax_brk Representing the maximum value of longitudinal deceleration, a ymax Representing the maximum value of the lateral acceleration when the vehicle is turning to the right, -a ymax A maximum value of lateral acceleration when the vehicle turns left; "A" represents an operation point when the vehicle travels on a route, a xA Represents the longitudinal acceleration of point A, a yA The lateral acceleration of point a is shown.
Wherein said obtaining a vehicle performance index based on the vehicle performance weighted distance count comprises:
based on the vehicle performance weighted distance count, obtaining a vehicle performance index using the following formula;
wherein, I perf Indicating the vehicle performance index, Y target And t target Respectively representing the vehicle performance weighted distance count and the time taken to travel one revolution, Y, in the case of a human driver veh And t veh Respectively representing the vehicle performance weighted distance count and the time taken to travel one revolution in the driverless case.
Wherein said calculating a normalized mean deviation based on said trajectory error comprises: such that E = { epsilon (i, l), i =1,m, l =1,p }, E denotes driving p turns of the test path at the highest value ρ of the path curvature c A set of m × p points for the absolute value of the next measured CTE ∈;
the mean and standard deviation at each point i in the p-circle were calculated:
calculate mean and standard deviation:
calculating a normalized mean dispersion:
where | μ | is the absolute value of μ.
Wherein obtaining a vehicle reproducibility index based on the normalized mean dispersion comprises:
wherein, disp _avr Denotes the normalized mean deviation, I rep Representing a vehicle reproducibility index.
The present invention also provides a vehicle performance quantitative detection apparatus, as shown in fig. 11, the apparatus including:
a signal receiving unit 1101 for receiving signal data;
a tire road surface utilization rate determining unit 1102 for determining a tire road surface utilization rate based on the signal data;
a vehicle performance weighted distance count calculation unit 1103 for calculating a vehicle performance weighted distance count based on the tire road surface utilization rate; and
a vehicle performance index obtaining unit 1104 for obtaining a vehicle performance index based on the vehicle performance weighted distance count.
Further, the above apparatus further comprises:
a trajectory error determination unit 1105 for determining a trajectory error based on the signal data;
a normalized mean deviation calculation unit 1106, configured to calculate a normalized mean deviation based on the trajectory error; and
a vehicle recurrence index obtaining unit 1107, configured to obtain a vehicle recurrence index according to the calculated normalized average dispersion.
Wherein the signal data includes longitudinal acceleration, longitudinal deceleration, lateral acceleration when the vehicle is turning right, lateral acceleration when the vehicle is turning left, time it takes for the vehicle to make one turn with and without human driving, vehicle yaw rate data, vehicle lateral distance to the path data.
Wherein said determining tire road surface utilization based on said signal data comprises:
determining a tire road surface utilization rate based on the signal data using the following formula;
wherein R is tu Indicating the road surface utilization of the tire, a xmax_acc Maximum value of longitudinal acceleration, -a xmax_brk Representing the maximum value of longitudinal deceleration, a ymax Representing the maximum value of the lateral acceleration when the vehicle is turning to the right, -a ymax A maximum value of lateral acceleration when the vehicle turns left; "A" represents an operation point when the vehicle travels on a route, a xA Represents the longitudinal acceleration of point A, a yA The lateral acceleration of point a is shown.
Wherein said obtaining a vehicle performance index based on said vehicle performance weighted distance count comprises:
based on the vehicle performance weighted distance count, obtaining a vehicle performance index using the following formula;
wherein, I perf Indicating the vehicle Performance index, Y target And t target Respectively representing the vehicle performance weighted distance count and the time taken to travel one revolution, Y, in the case of human driving veh And t veh Respectively representing the vehicle performance weighted distance count and the time taken to travel one revolution in the driverless case.
Wherein said calculating a normalized mean deviation based on said trajectory error comprises: such that E = { epsilon (i, l), i =1,m, l =1,p }, E denotes driving p turns of the test path at the highest value ρ of the path curvature c (ii) the set of m × p points of the absolute value of the measured CTE ∈;
the mean and standard deviation at each point i in the p-circle were calculated:
calculate mean and standard deviation:
calculating normalized mean dispersion:
where | μ | is the absolute value of μ.
Wherein obtaining a vehicle recurrence index according to the normalized mean dispersion comprises:
wherein, disp _avr Denotes the normalized mean deviation, I rep Representing a vehicle reproducibility index.
The invention provides a vehicle performance quantification detection method and a vehicle performance quantification detection device, which can quantify the performance of an unmanned vehicle exerted under the extreme working condition, detect whether the performance is close to the performance of a whole vehicle exerted by a professional driver trial, and ensure the safety and the robustness of the unmanned vehicle under the extreme working condition.
Finally, it should be noted that: although the present disclosure has been described in detail with reference to the foregoing embodiments, it will be apparent to those skilled in the art that modifications may be made to the embodiments described above, or equivalents may be substituted for elements thereof. Any modification, equivalent replacement, or improvement made within the spirit and principle of the present disclosure should be included in the protection scope of the present disclosure.
Claims (10)
1. A quantitative detection method for vehicle performance is characterized by comprising the following steps:
(a) Receiving signal data;
wherein the signal data includes a longitudinal acceleration, a longitudinal deceleration, a lateral acceleration when the vehicle turns right, a lateral acceleration when the vehicle turns left, a time taken for the vehicle to travel one turn with and without driving, respectively;
(b) Determining tire road surface utilization based on the signal data;
determining a tire road surface utilization rate based on the signal data using the following formula;
wherein R is tu Indicating the road surface utilization of the tire, a xmax_acc Representing the maximum value of longitudinal acceleration, a xmax_brk Representing the maximum value of longitudinal deceleration, a ymax Representing the maximum value of the lateral acceleration, a, when the vehicle is turning to the right ymax A maximum value of a lateral acceleration when the vehicle turns left; "A" represents an operation point when the vehicle travels on a route, a xA Represents the longitudinal acceleration of point A, a yA Represents the lateral acceleration of point a;
(c) Calculating a vehicle performance weighted distance count based on the tire road surface utilization;
calculating the tire pavement utilization performance index I according to the tire pavement utilization rate p_t_r Said tire road surface utilization performance index I p_t_r The calculation formula of (2) is as follows:
wherein, I vp (k) Is calculated byThe following were used:
inputting: tire road surface utilization ratio R tu And vehicle speed V x
Initialization: r tu_v (0)＝0，
I vp (0)＝0，I vd (0)＝0
At each time k, R tu_v (k)＝R tu (k)V x (k)，V x (k) = speed determination I of the vehicle at time k vp (k-1)-I vp (k-2)＜0？
If so, I vp (k)＝R tu_v (k)+K pth1 (I vp (k-1)-R tu_v (k))
If not, I vp (k)＝R tu_v (k)+K pth2 (I vp (k-1)-R tu_v (k))
I vd (k) The calculation process of (c) is as follows:
Judgment of
If the number of the data packets is more than the preset value,
if the result is no, then,
wherein, I p_t_r (k) For the tire road surface utilization performance index, vx is the vehicle speed, I vp (k) And I vd (k) For speed-related indices, K vp (V x ) And K vd (V x ) For the velocity-dependent weighting factor, K W To set up I vp (k) And I vd (k) Any factor to the vehicle performance index contribution rate; kpth1= filter constant used as Ivp increases; kpth2= filter constant used when Ivp decreases; kdth1= filter constant used as Ivd increases; kdth2= filter constant used as Ivd decreases;
the tire road surface utilization performance index I p_t_r In the range of RiR i ＝Range{I P_thr(i) ≤I p_t_r ＜I P_thr(i+1) }
The calculation formula of the vehicle performance weighted distance count is as follows:
wherein, C w Weighting the distance count for the vehicle performance, W = { W (1), W (2), \8230, W (n) } is a monotonically increasing weight vector, cnt (i) is attributed to R i The sum of points of the tire road surface utilization performance index;
(d) Obtaining a vehicle performance index based on the vehicle performance weighted distance count;
based on the vehicle performance weighted distance count, obtaining the vehicle performance index using the following formula;
wherein, I perf Representing said vehicle performance index, Y target And t target Respectively representing the vehicle performance weighted distance count and the time taken to travel one revolution, Y, in the case of a human driver veh And t veh Respectively representing the vehicle performance weighted distance count and the time taken to travel one revolution in the driverless case.
2. The method of claim 1, further comprising:
(e) Determining a trajectory error based on the signal data;
(f) Calculating a normalized mean deviation based on the trajectory errors; and
(g) And obtaining a vehicle reproducibility index according to the normalized average dispersion.
3. The method of claim 1, wherein the signal data further comprises vehicle yaw rate data, vehicle lateral distance to path data.
4. The method of claim 2, wherein calculating a normalized mean deviation based on the trajectory errors comprises: such that E = { epsilon (i, l), i =1,m, l =1,p }, E denotes driving p turns of the test path at the highest value ρ of the path curvature c (ii) the set of m × p points of the absolute value of the measured CTE ∈;
the mean and standard deviation at each point i in the p-circle were calculated:
calculate mean and standard deviation:
calculating a normalized mean dispersion:
where | μ | is the absolute value of μ.
5. The method of claim 4, wherein obtaining a vehicle repeatability index based on the normalized average dispersion comprises:
according to the normalized average dispersion, a formula is adopted
Obtaining a vehicle reproducibility index, wherein Disp _avr Denotes the normalized mean deviation, I rep Representing a vehicle reproducibility index.
6. A vehicle performance quantification detection apparatus, characterized by comprising:
a signal receiving unit for receiving signal data;
wherein the signal data includes a longitudinal acceleration, a longitudinal deceleration, a lateral acceleration when the vehicle turns right, a lateral acceleration when the vehicle turns left, a time taken for the vehicle to travel one turn with and without driving, respectively; a tire road surface utilization rate determining unit for determining a tire road surface utilization rate based on the signal data;
the tire road surface utilization rate determining unit determines the tire road surface utilization rate by adopting the following formula;
wherein R is tu Indicating the road surface utilization of the tire, a xmax_acc Represents the maximum value of the longitudinal acceleration, a xmax_brk Representing the maximum value of longitudinal deceleration, a ymax Represents the maximum value of the lateral acceleration of the vehicle when turning to the right, a ymax A maximum value of lateral acceleration when the vehicle turns left; "A" represents an operation point when the vehicle travels on a route, a xA Represents the longitudinal acceleration of point A, a yA Represents the lateral acceleration of point a; vehicle with a steering wheelA performance weighted distance count calculation unit for calculating a vehicle performance weighted distance count based on the tire road surface utilization rate;
the vehicle performance weighted distance counting calculation unit calculates a tire road surface utilization performance index I according to the tire road surface utilization rate p_t_r Said tire road surface utilization performance index I p_t_r The calculation formula of (c) is:
wherein, I vp (k) The calculation process of (c) is as follows:
inputting: road surface utilization rate R of tire tu And vehicle speed V x
Initialization: r is tu_v (0)＝0，
I vp (0)＝0，I vd (0)＝0
At each time k, R tu_v (k)＝R tu (k)V x (k)，V x (k) = speed of vehicle at time k
Judgment of I vp (k-1)-I vp (k-2)＜0？
If so, I vp (k)＝R tu_v (k)+K pth1 (I vp (k-1)-R tu_v (k))
If not, I vp (k)＝R tu_v (k)+K pth2 (I vp (k-1)-R tu_v (k))
I vd (k) The calculation process of (2) is as follows:
Judgment of
If it is usedIt is that,
if the result is no, then,
wherein, I p_t_r (k) For the tire road surface utilization performance index, vx is vehicle speed, I vp (k) And I vd (k) Is a speed-related index, K vp (V x ) And K vd (V x ) For the velocity-dependent weighting factor, K W To set up I vp (k) And I vd (k) Any factor to the vehicle performance index contribution rate; kpth1= filter constant used when Ivp increases; kpth2= filter constant used when Ivp decreases; kdth1= filter constant used as Ivd increases; kdth2= filter constant used when Ivd decreases;
the tire road surface utilization performance index I p_t_r In the range of Ri
R i ＝Range{I P_thr(i) ≤I p_t_r ＜I P_thr(i+1) }
The calculation formula of the vehicle performance weighted distance count is as follows:
wherein, C w Weighting the distance count for said vehicle performance W = { W (1), W (2), \8230, W (n) } is a monotonically increasing weight vector, cnt (i) is attributed to R i The sum of points of the tire road surface utilization performance index;
a vehicle performance index obtaining unit for obtaining a vehicle performance index based on the vehicle performance weighted distance count;
the vehicle performance index obtaining unit obtains the vehicle performance index by adopting the following formula;
wherein, I perf Representing said vehicle performance index, Y target And t target Respectively representing the vehicle performance weighted distance count and the time taken to travel one revolution, Y, in the case of a human driver veh And t veh Respectively representing the vehicle performance weighted distance count and the time taken to travel one revolution in the driverless case.
7. The apparatus of claim 6, further comprising:
a trajectory error determination unit for determining a trajectory error based on the signal data;
a normalized mean dispersion calculation unit for calculating a normalized mean dispersion based on the trajectory error; and
and the vehicle recurrence index obtaining unit is used for obtaining a vehicle recurrence index according to the normalized average dispersion.
8. The apparatus of claim 6, wherein the signal data further comprises vehicle yaw rate data, vehicle lateral distance to path data.
9. The apparatus of claim 7, wherein calculating a normalized mean deviation based on the trajectory errors comprises: such that E = { epsilon (i, l), i =1,m, l =1,p }, E denotes driving p turns of the test path at the highest value ρ of the path curvature c A set of m × p points for the absolute value of the next measured CTE ∈;
calculate the mean and standard deviation at each point i in the p-turns:
calculate mean and standard deviation:
calculating normalized mean dispersion:
where | μ | is the absolute value of μ.
10. The apparatus of claim 9, wherein obtaining a vehicle repeatability index based on the normalized mean dispersion comprises: according to the normalized average dispersion, a formula is adopted
Obtaining a vehicle repeatability index, wherein Disp _avr Denotes the normalized mean deviation, I rep Representing a vehicle reproducibility index.
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