CN114360243A - Comfort-based vehicle optimization method and system - Google Patents
Comfort-based vehicle optimization method and system Download PDFInfo
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- CN114360243A CN114360243A CN202111566619.5A CN202111566619A CN114360243A CN 114360243 A CN114360243 A CN 114360243A CN 202111566619 A CN202111566619 A CN 202111566619A CN 114360243 A CN114360243 A CN 114360243A
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Abstract
The invention relates to a comfort-based vehicle optimization method and a comfort-based vehicle optimization system, wherein the method comprises the following steps: s1: obtaining road surface data of a road section to be optimized and historical vehicle data of a vehicle running on the road section to be optimized; s2: extracting historical vehicle data at different speeds, and acquiring an upper limit value and a lower limit value of objective comfort at different speeds according to the historical vehicle data and road surface data; s3: acquiring historical subjective comfort data of passengers driving on a road section to be optimized at different speeds; s4: comparing the historical subjective comfort data at different speeds with the upper and lower limit values of objective comfort to obtain the optimal speed range of the road section to be optimized; s5: and acquiring real-time speed information of the vehicle running on the road section to be optimized, and reminding the speed of the vehicle according to the optimal speed range. Compared with the prior art, the method has the advantages of high accuracy, good optimization effect, high efficiency and the like.
Description
Technical Field
The invention relates to the technical field of traffic planning, in particular to a comfort-based vehicle optimization method and system.
Background
With the rapid development of the economy of China, the income and the living standard of the nation are continuously improved, and the holding quantity of urban motor vehicles and the running quantity of residents are rapidly increased. In order to pursue higher traveling quality, people constantly improve riding comfort and timeliness, the existing data processing for evaluating the riding comfort is generally realized manually, namely, riding experience information recorded by trial riding personnel is collected, and a large amount of riding experience information is counted and analyzed in a manual mode to obtain the comfort of a vehicle.
However, the method for manually analyzing the comfort level has a large subjectivity, a data processing flow is tedious, the processing efficiency is not high, and meanwhile, with the development of social economy, people pursue efficient, convenient and comfortable riding experience, which needs to be effectively improved from the aspects of traffic operation, payment, scheduling and the like.
Disclosure of Invention
The present invention is directed to a method and system for comfort-based vehicle optimization to overcome the above-mentioned drawbacks of the prior art.
The purpose of the invention can be realized by the following technical scheme:
a comfort-based vehicle optimization method, comprising the steps of:
s1: obtaining road surface data of a road section to be optimized and historical vehicle data of a vehicle running on the road section to be optimized;
s2: extracting historical vehicle data at different speeds, and acquiring an upper limit value and a lower limit value of objective comfort at different speeds according to the historical vehicle data and road surface data;
s3: acquiring historical subjective comfort data of passengers driving on a road section to be optimized at different speeds;
s4: comparing the historical subjective comfort data at different speeds with the upper and lower limit values of objective comfort to obtain the optimal speed range of the road section to be optimized;
s5: and acquiring real-time speed information of the vehicle running on the road section to be optimized, and reminding the speed of the vehicle according to the optimal speed range.
Preferably, the objective comfort level upper limit value is a road flatness mean square deviation value:
wherein the content of the first and second substances,
the road flatness mean square deviation value is shown, v is the driving speed, L is the road surface wavelength, and A is the road surface amplitude.
Preferably, the lower limit value of the objective comfort is a weighted acceleration effective value of the vehicle:
wherein, awIs the weighted effective acceleration value of the vehicle, awxIs the X-axis acceleration root mean square value of the vehicle, awyIs the mean square root of the acceleration of the vehicle in the Y axis, awzIs the Z-axis acceleration RMS of the vehicle.
Preferably, the X-axis acceleration root mean square value is:
wherein, K1Is the weighting coefficient of the X axis, T is the vibration statistic time, awx(t) X-axis acceleration at time t;
the root mean square value of the acceleration of the Y axis is as follows:
wherein, K3Is a weighting coefficient of the Y axis, awy(t) is the Y-axis acceleration at time t;
the Z-axis acceleration root mean square value is:
wherein, K3Is a weighting coefficient of the Z axis, awz(t) is the Z-axis acceleration at time t.
Preferably, in step S3, the subjective comfort feedback result of the passenger is counted by the MaaS system.
Preferably, the specific step of step S4 includes:
s41: acquiring subjective comfort values of different speeds of historical subjective comfort data;
s42: according to the proportion that the subjective comfort value is lower than the objective comfort lower limit value, belongs to the space between the objective comfort upper limit value and is higher than the objective comfort upper limit value;
s43: and judging the optimal speed range according to the proportional distribution of the subjective comfort value.
Preferably, the step S43 specifically includes:
s431: carrying out comfort judgment on different vehicle speeds, judging whether the proportion of the subjective comfort value higher than the objective comfort upper limit value is larger than a first threshold value or not, if so, judging that the current vehicle speed is too high, otherwise, entering a step S432;
s432: judging whether the ratio of the subjective comfort value to the objective comfort lower limit value is greater than a second threshold value or not, if so, judging that the current vehicle speed can be increased, and otherwise, judging that the current vehicle speed is a proper vehicle speed;
s433: and repeating the steps S431 to S432 until the judgment of different vehicle speeds is completed, and selecting a range of a proper vehicle speed as an optimal speed range.
Preferably, the step S5 specifically includes:
acquiring real-time speed information of vehicles running on a road section to be optimized;
and judging whether the real-time speed information is in the optimal speed range, if the real-time speed information exceeds the optimal speed range, sending deceleration reminding to the vehicle, and if the real-time speed information is lower than the optimal speed range, sending speed-up reminding to the vehicle.
Preferably, the historical vehicle data includes X-axis, Y-axis and Z-axis acceleration and GPS information data while traveling on the road section.
A comfort-based vehicle optimization system comprises a data acquisition module, an objective comfort calculation module, a subjective comfort extraction module, an optimal speed range acquisition module and a reminding module,
the data acquisition module is used for acquiring road surface data of a road section to be optimized and historical vehicle data of a vehicle when the vehicle runs on the road section to be optimized, wherein the historical vehicle data comprises acceleration of an X axis, an acceleration of a Y axis and an acceleration of a Z axis and GPS information data when the vehicle runs on the road section;
the objective comfort degree calculation module is used for extracting historical vehicle data at different speeds and acquiring an upper limit value and a lower limit value of objective comfort at different speeds according to the historical vehicle data and road surface data;
the subjective comfort level extraction module is used for acquiring historical subjective comfort level data of passengers driving at different speeds on a road section to be optimized;
the optimal speed range acquisition module is used for comparing the historical subjective comfort data at different speeds with the upper and lower limit values of objective comfort to acquire the optimal speed range of the road section to be optimized;
the reminding module is used for acquiring real-time speed information of vehicles running on the road section to be optimized and reminding the speed of the vehicles according to the optimal speed range.
Compared with the prior art, the invention has the following advantages:
(1) the invention can effectively acquire the vehicle data of the vehicle, acquire objective comfort level based on the vehicle data, acquire the optimal speed range of the road section by comparing and judging with subjective comfort level, guide and optimize the speed of the running vehicle, effectively improve the riding comfort level of passengers, integrally improve the travel satisfaction degree of public transportation, improve the good experience of green travel of the public and meet the travel demand of consumers;
(2) the method accurately judges the comfort of different vehicle speeds based on the ratio comparison of the subjective comfort value and the objective comfort value, does not need manual statistical analysis, effectively improves the optimization efficiency and reduces the labor cost;
(3) according to the invention, the upper limit value of the objective comfort degree adopts a road flatness mean square difference value, and the lower limit value of the objective comfort degree adopts a weighted acceleration effective value of the vehicle, so that the driving comfort degree condition of the vehicle can be accurately and efficiently reflected, the data processing difficulty is reduced, and the optimization accuracy is improved.
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FIG. 1 is a flow chart of the present invention.
Detailed Description
The invention is described in detail below with reference to the figures and specific embodiments. Note that the following description of the embodiments is merely a substantial example, and the present invention is not intended to be limited to the application or the use thereof, and is not limited to the following embodiments.
Example 1
A comfort-based vehicle optimization method, as shown in fig. 1, comprising the steps of:
s1: and obtaining road surface data of the road section to be optimized and historical vehicle data of the vehicle running on the road section to be optimized, wherein the road surface data comprises road surface wavelength and road surface amplitude of the road section to be optimized, and the road surface wavelength and the road surface amplitude are measured in advance by a road surface spectrum measuring vehicle. The value range of L is from 0.1 to 100m, and the value range of A is generally from 1 to 200 mm. The historical vehicle data includes X-axis, Y-axis, and Z-axis accelerations and GPS information data while traveling over the road segment.
S2: historical vehicle data under different speeds are extracted, and the upper limit value and the lower limit value of objective comfort under different speeds are obtained according to the historical vehicle data and the road surface data. Specifically, the upper and lower limits are:
the objective comfort upper limit value is a road flatness mean square deviation value:
wherein the content of the first and second substances,
the road flatness mean square deviation value is shown, v is the driving speed, L is the road surface wavelength, and A is the road surface amplitude.
The lower limit value of the objective comfort is the weighted acceleration effective value of the vehicle:
wherein, awIs the weighted effective acceleration value of the vehicle, awxIs the X-axis acceleration root mean square value of the vehicle, awyIs the mean square root of the acceleration of the vehicle in the Y axis, awzIs the Z-axis acceleration rms of the vehicle and, further,
the X-axis acceleration root mean square value is:
wherein, K1Is the weighting coefficient of the X axis, T is the vibration statistic time, awx(t) X-axis acceleration at time t;
the root mean square value of the acceleration of the Y axis is as follows:
wherein, K3Is a weighting coefficient of the Y axis, awy(t) is the Y-axis acceleration at time t;
the Z-axis acceleration root mean square value is:
wherein, K3Is a weighting coefficient of the Z axis, awz(t) is the Z-axis acceleration at time t.
S2 obtains the upper and lower limit values of objective comfort degree of different speeds, the speed value can be 10km/h, 20km/h, 30km/h and 40km/h … … according to the historical vehicle data with 10km/h as the interval, and the speed of 10km/h, 11km/h, 12km/h and 13km/h … … can also be selected with 1 as the interval.
S3: and counting the subjective comfort feedback result of the passenger through the MaaS system, and acquiring historical subjective comfort data of the passenger driving the vehicle at different speeds of the road section to be optimized.
The MaaS system in this embodiment includes a core service layer, a service support layer, and a service expansion layer. The core business layer comprises MaaS operation service providers, traffic operators, passengers and government management departments. The business support layer comprises an ICT service provider, a back-end technical service provider, a payment and identity authentication service provider, a travel matching service provider and a spatial information service provider. The business expansion layer comprises insurance service providers and consumption service platform operators. An integrated travel service system integrating multiple transportation modes is constructed through the MaaS ecological service system, and the passenger travel comfort is effectively improved.
S4: comparing the historical subjective comfort data at different speeds with the upper and lower limit values of objective comfort to obtain the optimal speed range of the road section to be optimized;
the specific steps of step S4 include:
s41: acquiring subjective comfort values of different speeds of historical subjective comfort data;
s42: according to the proportion that the subjective comfort value is lower than the objective comfort lower limit value, belongs to the space between the objective comfort upper limit value and is higher than the objective comfort upper limit value;
s43: and judging the optimal speed range according to the proportional distribution of the subjective comfort value.
Step S43 specifically includes:
s431: carrying out comfort judgment on different vehicle speeds, judging whether the proportion of the subjective comfort value higher than the objective comfort upper limit value is larger than a first threshold value or not, if so, judging that the current vehicle speed is too high, otherwise, entering a step S432;
s432: judging whether the ratio of the subjective comfort value to the objective comfort lower limit value is greater than a second threshold value or not, if so, judging that the current vehicle speed can be increased, and otherwise, judging that the current vehicle speed is a proper vehicle speed;
s433: and repeating the steps S431 to S432 until the judgment of different vehicle speeds is completed, and selecting a range of a proper vehicle speed as an optimal speed range.
In this embodiment, if comfort determination is performed for a certain vehicle speed V1, an objective comfort upper limit value of the vehicle speed V1 is obtained
Lower limit value awAnd (V1), acquiring a proportion a of the subjective comfort value of the vehicle speed, which is lower than the objective comfort lower limit value, a proportion b of the subjective comfort value, which belongs to the objective comfort upper limit value, and a proportion C of the subjective comfort value, which is higher than the objective comfort upper limit value, wherein the corresponding first threshold value is C, and the second threshold value is A.
And judging whether C is larger than C, if so, judging whether the current vehicle speed V1 is too high, otherwise, judging whether a is larger than A, if so, judging that the current vehicle speed V1 can be increased, and otherwise, judging that the current vehicle speed V1 is proper. And judging all the vehicle speeds, and selecting proper vehicle speeds to form an optimal speed range.
S5: and acquiring real-time speed information of the vehicle running on the road section to be optimized, and reminding the speed of the vehicle according to the optimal speed range. And judging whether the real-time speed information is in the optimal speed range, if the real-time speed information exceeds the optimal speed range, sending deceleration reminding to the vehicle, and if the real-time speed information is lower than the optimal speed range, sending speed-up reminding to the vehicle.
By repeating the optimization method, historical data can be continuously acquired to optimize the existing most speed range, and the optimization effect is improved.
Example 2
The invention also provides a comfort-based vehicle optimization system, which comprises a data acquisition module, an objective comfort degree calculation module, a subjective comfort degree extraction module, an optimal speed range acquisition module and a reminding module,
the data acquisition module is used for acquiring road surface data of a road section to be optimized and historical vehicle data of a vehicle when the vehicle runs on the road section to be optimized, wherein the historical vehicle data comprises acceleration of an X axis, an acceleration of a Y axis and an acceleration of a Z axis and GPS information data when the vehicle runs on the road section;
the objective comfort degree calculation module is used for extracting historical vehicle data at different speeds and acquiring an upper limit value and a lower limit value of objective comfort at different speeds according to the historical vehicle data and road surface data;
the subjective comfort level extraction module is used for acquiring historical subjective comfort level data of passengers driving at different speeds on a road section to be optimized;
the optimal speed range acquisition module is used for comparing the historical subjective comfort data at different speeds with the upper and lower limit values of objective comfort to acquire the optimal speed range of the road section to be optimized;
the reminding module is used for acquiring real-time speed information of vehicles running on a road section to be optimized and reminding the speed of the vehicles according to the optimal speed range.
A livestock diet monitoring system based on visual recognition disclosed in this embodiment corresponds to a livestock diet monitoring method based on visual recognition, and please refer to embodiment 1 for the implementation method of this embodiment, which is not described herein again.
The above embodiments are merely examples and do not limit the scope of the present invention. These embodiments may be implemented in other various manners, and various omissions, substitutions, and changes may be made without departing from the technical spirit of the present invention.
Claims (10)
1. A comfort-based vehicle optimization method, comprising the steps of:
s1: obtaining road surface data of a road section to be optimized and historical vehicle data of a vehicle running on the road section to be optimized;
s2: extracting historical vehicle data at different speeds, and acquiring an upper limit value and a lower limit value of objective comfort at different speeds according to the historical vehicle data and road surface data;
s3: acquiring historical subjective comfort data of passengers driving on a road section to be optimized at different speeds;
s4: comparing the historical subjective comfort data at different speeds with the upper and lower limit values of objective comfort to obtain the optimal speed range of the road section to be optimized;
s5: and acquiring real-time speed information of the vehicle running on the road section to be optimized, and reminding the speed of the vehicle according to the optimal speed range.
2. A comfort-based vehicle optimization method according to claim 1, wherein the objective comfort upper limit value is a road flatness mean square deviation value:
wherein the content of the first and second substances,
the road flatness mean square deviation value is shown, v is the driving speed, L is the road surface wavelength, and A is the road surface amplitude.
3. A comfort-based vehicle optimization method according to claim 1, wherein the lower limit value of the objective comfort is a weighted acceleration effective value of the vehicle:
wherein, awIs the weighted effective acceleration value of the vehicle, awxIs the X-axis acceleration root mean square value of the vehicle, awyIs the mean square root of the acceleration of the vehicle in the Y axis, awzIs the Z-axis acceleration RMS of the vehicle.
4. A comfort-based vehicle optimization method as claimed in claim 3, wherein the X-axis acceleration rms value is:
wherein, K1Is the weighting coefficient of the X axis, T is the vibration statistic time, awx(t) X-axis acceleration at time t;
the root mean square value of the acceleration of the Y axis is as follows:
wherein, K3Is a weighting coefficient of the Y axis, awy(t) is the Y-axis acceleration at time t;
the Z-axis acceleration root mean square value is:
wherein, K3Is a weighting coefficient of the Z axis, awz(t) is the Z-axis acceleration at time t.
5. The comfort-based vehicle optimization method of claim 1, wherein the step S3 is implemented by counting subjective comfort feedback results of passengers through a MaaS system.
6. The comfort-based vehicle optimization method of claim 1, wherein the step S4 includes the following steps:
s41: acquiring subjective comfort values of different speeds of historical subjective comfort data;
s42: according to the proportion that the subjective comfort value is lower than the objective comfort lower limit value, belongs to the space between the objective comfort upper limit value and is higher than the objective comfort upper limit value;
s43: and judging the optimal speed range according to the proportional distribution of the subjective comfort value.
7. The comfort-based vehicle optimization method of claim 6, wherein the step S43 specifically comprises:
s431: carrying out comfort judgment on different vehicle speeds, judging whether the proportion of the subjective comfort value higher than the objective comfort upper limit value is larger than a first threshold value or not, if so, judging that the current vehicle speed is too high, otherwise, entering a step S432;
s432: judging whether the ratio of the subjective comfort value to the objective comfort lower limit value is greater than a second threshold value or not, if so, judging that the current vehicle speed can be increased, and otherwise, judging that the current vehicle speed is a proper vehicle speed;
s433: and repeating the steps S431 to S432 until the judgment of different vehicle speeds is completed, and selecting a range of a proper vehicle speed as an optimal speed range.
8. The comfort-based vehicle optimization method according to claim 1, wherein the step S5 specifically includes:
acquiring real-time speed information of vehicles running on a road section to be optimized;
and judging whether the real-time speed information is in the optimal speed range, if the real-time speed information exceeds the optimal speed range, sending deceleration reminding to the vehicle, and if the real-time speed information is lower than the optimal speed range, sending speed-up reminding to the vehicle.
9. The comfort-based vehicle optimization method of claim 1, wherein the historical vehicle data includes X-axis, Y-axis, and Z-axis acceleration and GPS information data while traveling over the road segment.
10. A comfort-based vehicle optimization system is characterized by comprising a data acquisition module, an objective comfort degree calculation module, a subjective comfort degree extraction module, an optimal speed range acquisition module and a reminding module,
the data acquisition module is used for acquiring road surface data of a road section to be optimized and historical vehicle data of a vehicle when the vehicle runs on the road section to be optimized, wherein the historical vehicle data comprises acceleration of an X axis, an acceleration of a Y axis and an acceleration of a Z axis and GPS information data when the vehicle runs on the road section;
the objective comfort degree calculation module is used for extracting historical vehicle data at different speeds and acquiring an upper limit value and a lower limit value of objective comfort at different speeds according to the historical vehicle data and road surface data;
the subjective comfort level extraction module is used for acquiring historical subjective comfort level data of passengers driving at different speeds on a road section to be optimized;
the optimal speed range acquisition module is used for comparing the historical subjective comfort data at different speeds with the upper and lower limit values of objective comfort to acquire the optimal speed range of the road section to be optimized;
the reminding module is used for acquiring real-time speed information of vehicles running on the road section to be optimized and reminding the speed of the vehicles according to the optimal speed range.
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Cited By (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN115795943A (en) * | 2022-11-10 | 2023-03-14 | 哈尔滨工业大学 | Fine evaluation method for driving comfort of highway bridge |
Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| DE102012011181A1 (en) * | 2012-06-06 | 2013-12-12 | Bayerische Motoren Werke Aktiengesellschaft | Method for operating drive device of wheel of motor car, involves reducing actual torque on wheels to predetermined maximum torque which is less than target torque in comfort mode |
| CN104792937A (en) * | 2015-04-02 | 2015-07-22 | 同济大学 | Bridge head bump detection evaluation method based on vehicle-mounted gravitational acceleration sensor |
| CN109311478A (en) * | 2016-12-30 | 2019-02-05 | 同济大学 | A kind of automatic Pilot method for controlling driving speed based on comfort level |
| CN112896186A (en) * | 2021-01-30 | 2021-06-04 | 同济大学 | Automatic driving longitudinal decision control method under cooperative vehicle and road environment |
-
2021
- 2021-12-20 CN CN202111566619.5A patent/CN114360243B/en active Active
Patent Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| DE102012011181A1 (en) * | 2012-06-06 | 2013-12-12 | Bayerische Motoren Werke Aktiengesellschaft | Method for operating drive device of wheel of motor car, involves reducing actual torque on wheels to predetermined maximum torque which is less than target torque in comfort mode |
| CN104792937A (en) * | 2015-04-02 | 2015-07-22 | 同济大学 | Bridge head bump detection evaluation method based on vehicle-mounted gravitational acceleration sensor |
| CN109311478A (en) * | 2016-12-30 | 2019-02-05 | 同济大学 | A kind of automatic Pilot method for controlling driving speed based on comfort level |
| CN109415043A (en) * | 2016-12-30 | 2019-03-01 | 同济大学 | A kind of automatic Pilot traveling planing method based on comfort level |
| CN112896186A (en) * | 2021-01-30 | 2021-06-04 | 同济大学 | Automatic driving longitudinal decision control method under cooperative vehicle and road environment |
Non-Patent Citations (1)
| Title |
|---|
| 杜艳花: "基于路面乘车舒适性的振动加速度评价方法的探讨", 《市政技术》 * |
Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN115795943A (en) * | 2022-11-10 | 2023-03-14 | 哈尔滨工业大学 | Fine evaluation method for driving comfort of highway bridge |
| CN115795943B (en) * | 2022-11-10 | 2023-06-13 | 哈尔滨工业大学 | Highway bridge driving comfort refined evaluation method |
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