CN113327444A - Control method for cooperatively optimizing vehicle speed based on vehicle road cloud - Google Patents
Control method for cooperatively optimizing vehicle speed based on vehicle road cloud Download PDFInfo
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- 238000000034 method Methods 0.000 title claims abstract description 25
- 238000005457 optimization Methods 0.000 claims abstract description 27
- 238000005265 energy consumption Methods 0.000 claims abstract description 25
- 239000000295 fuel oil Substances 0.000 claims abstract description 18
- 238000005096 rolling process Methods 0.000 claims abstract description 10
- 230000008447 perception Effects 0.000 claims description 15
- 230000005540 biological transmission Effects 0.000 claims description 9
- 239000000446 fuel Substances 0.000 claims description 9
- 238000004364 calculation method Methods 0.000 claims description 6
- 230000001133 acceleration Effects 0.000 claims description 3
- 239000003638 chemical reducing agent Substances 0.000 claims description 3
- 230000004927 fusion Effects 0.000 claims description 3
- 230000005484 gravity Effects 0.000 claims description 3
- 239000000126 substance Substances 0.000 claims description 3
- 230000000694 effects Effects 0.000 description 4
- 230000002457 bidirectional effect Effects 0.000 description 2
- 238000007792 addition Methods 0.000 description 1
- 230000009286 beneficial effect Effects 0.000 description 1
- 125000004122 cyclic group Chemical group 0.000 description 1
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- G—PHYSICS
- G08—SIGNALLING
- G08G—TRAFFIC CONTROL SYSTEMS
- G08G1/00—Traffic control systems for road vehicles
- G08G1/09—Arrangements for giving variable traffic instructions
- G08G1/0962—Arrangements for giving variable traffic instructions having an indicator mounted inside the vehicle, e.g. giving voice messages
- G08G1/0967—Systems involving transmission of highway information, e.g. weather, speed limits
- G08G1/096708—Systems involving transmission of highway information, e.g. weather, speed limits where the received information might be used to generate an automatic action on the vehicle control
- G08G1/096725—Systems involving transmission of highway information, e.g. weather, speed limits where the received information might be used to generate an automatic action on the vehicle control where the received information generates an automatic action on the vehicle control
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- G—PHYSICS
- G08—SIGNALLING
- G08G—TRAFFIC CONTROL SYSTEMS
- G08G1/00—Traffic control systems for road vehicles
- G08G1/09—Arrangements for giving variable traffic instructions
- G08G1/0962—Arrangements for giving variable traffic instructions having an indicator mounted inside the vehicle, e.g. giving voice messages
- G08G1/0967—Systems involving transmission of highway information, e.g. weather, speed limits
- G08G1/096766—Systems involving transmission of highway information, e.g. weather, speed limits where the system is characterised by the origin of the information transmission
- G08G1/096775—Systems involving transmission of highway information, e.g. weather, speed limits where the system is characterised by the origin of the information transmission where the origin of the information is a central station
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- G—PHYSICS
- G08—SIGNALLING
- G08G—TRAFFIC CONTROL SYSTEMS
- G08G1/00—Traffic control systems for road vehicles
- G08G1/09—Arrangements for giving variable traffic instructions
- G08G1/0962—Arrangements for giving variable traffic instructions having an indicator mounted inside the vehicle, e.g. giving voice messages
- G08G1/0967—Systems involving transmission of highway information, e.g. weather, speed limits
- G08G1/096766—Systems involving transmission of highway information, e.g. weather, speed limits where the system is characterised by the origin of the information transmission
- G08G1/096783—Systems involving transmission of highway information, e.g. weather, speed limits where the system is characterised by the origin of the information transmission where the origin of the information is a roadside individual element
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- Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
- Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
- Y02T—CLIMATE CHANGE MITIGATION TECHNOLOGIES RELATED TO TRANSPORTATION
- Y02T10/00—Road transport of goods or passengers
- Y02T10/10—Internal combustion engine [ICE] based vehicles
- Y02T10/40—Engine management systems
Abstract
The invention relates to a control method for cooperatively optimizing vehicle speed based on vehicle road cloud, which specifically comprises the following steps: s1, calculating by the vehicle road cloud cooperative positioning module according to the multi-source positioning information to obtain the real-time position of the vehicle, and calculating the real-time distance from the vehicle to a signal lamp of a front intersection; s2, calculating a proper speed interval with a green signal light when the vehicle passes through a corresponding intersection by the vehicle road cloud cooperative sensing module according to the real-time position of the vehicle through multi-source sensing information; s3, the cloud prediction control module calculates to obtain a real-time optimal vehicle speed according to a suitable vehicle speed interval through a vehicle speed optimization target model based on fuel oil/energy consumption economy; and S4, feeding back the real-time optimal vehicle speed to the cloud-end controller, sending the real-time optimal vehicle speed to the vehicle-end controller by the cloud-end controller, controlling by the vehicle-end controller according to the real-time optimal vehicle speed, and repeating the steps S1-S3 to realize vehicle speed rolling optimization. Compared with the prior art, the invention has the advantages of reducing the fuel oil/energy consumption rate of vehicles, improving the traffic efficiency of traffic roads and the like.
Description
Technical Field
The invention relates to the technical field of road traffic control, in particular to a control method for cooperatively optimizing vehicle speed based on vehicle road cloud.
Background
At frequent traffic light intersections, vehicles need to continuously go through the cyclic processes of starting, accelerating and braking, so that the urban traffic efficiency is low, and the fuel oil/energy consumption rate of the vehicles is greatly increased. At present, in the field of road green wave effect, attention is generally paid to control of signal lamp phases, for example, chinese patent CN110047303B discloses a phase sequence adjustment method for improving green band bandwidth in bidirectional green wave control, which can reduce the total parking times of bidirectional vehicles on an intersection on a main road and improve the main road traffic efficiency of the main road. But the green wave effect of other road vehicles can not be guaranteed, causing the problem of considering the green wave effect. In addition, in the prior art, the control method for reducing the fuel consumption/energy consumption of the vehicle by the cooperation of the vehicle cloud and the green wave effect is less described.
Disclosure of Invention
The invention aims to overcome the defect that the fuel/energy consumption rate of a vehicle is high due to frequent starting and braking in the prior art, and provides a control method for cooperatively optimizing the vehicle speed based on vehicle road cloud.
The purpose of the invention can be realized by the following technical scheme:
a control method for cooperatively optimizing vehicle speed based on vehicle road cloud specifically comprises the following steps:
s1, calculating by the vehicle road cloud cooperative positioning module according to the obtained multi-source positioning information to obtain a real-time position of the vehicle, and calculating a real-time distance from the vehicle to a signal lamp of a front intersection;
s2, calculating a proper vehicle speed interval with a green signal light when the vehicle passes through a corresponding intersection according to the real-time position of the vehicle by the vehicle road cloud cooperative sensing module through the acquired multi-source sensing information;
s3, the cloud prediction control module calculates to obtain a real-time optimal vehicle speed according to a suitable vehicle speed interval through a vehicle speed optimization target model based on fuel oil/energy consumption economy;
and S4, feeding the real-time optimal vehicle speed back to the cloud end controller, sending the real-time optimal vehicle speed to the vehicle end controller by the cloud end controller, controlling the vehicle by the vehicle end controller according to the real-time optimal vehicle speed, and repeating the steps S1-S3 to realize vehicle speed rolling optimization.
The multi-source positioning information comprises vehicle end positioning information and road end positioning information, the vehicle end positioning information comprises vehicle surrounding environment characteristic information, vehicle motion information and GPS positioning information, and the road end positioning information comprises road identification information and road end relative position information.
Further, the vehicle surrounding environment characteristic information, the vehicle motion information and the GPS positioning information are acquired by the vehicle-end controller through a camera, a laser radar, a wheel speed meter, a steering wheel corner sensor and an RTK-GPS sensor.
Further, the road identification information and the relative position information of the road end are acquired by the road end controller through a camera and a laser radar, and the relative position information of the road end is specifically the position information of the vehicle relative to the road end equipment.
Further, the vehicle road cloud cooperative positioning module is connected with a cloud end controller, a high-precision map is arranged in the cloud end controller, the cloud end controller matches the high-precision map according to the characteristic information of the surrounding environment of the vehicle to obtain first real-time position reference information, obtains second real-time position reference information according to the vehicle motion information and the GPS positioning information, and matches the high-precision map according to the relative position information of the road end to obtain third real-time position reference information.
Further, the cloud end controller performs fusion positioning according to the first real-time position reference information, the second real-time position reference information and the third real-time position reference information to obtain the real-time position of the vehicle.
The multi-source perception information comprises vehicle-end perception road information and road-end perception road information, the vehicle-end perception road information comprises vehicle flow average speed information, and the road-end perception road information comprises road condition information.
Further, the traffic flow average speed information and the road condition information are acquired by the vehicle end controller and the road end controller through the camera and the laser radar.
Further, the vehicle road cloud cooperative sensing module is connected with the cloud end controller, a high-precision map is arranged in the cloud end controller, the cloud end controller acquires signal lamp real-time state information and lane speed limit information in the high-precision map, green wave phase time of a signal lamp is obtained according to the signal lamp real-time state information, and a suitable vehicle speed interval of the signal lamp being a green lamp when the vehicle passes through a corresponding intersection is calculated according to the lane speed limit information, the vehicle flow average vehicle speed information and the road condition information by combining the real-time distance from the vehicle to the signal lamp of the front intersection.
And the target model of the vehicle speed optimization based on the fuel oil/energy consumption economy calculates to obtain the real-time optimal vehicle speed by adopting a model predictive control algorithm according to the suitable vehicle speed interval.
The real-time optimal vehicle speed is specifically the optimal engine output torque and the corresponding vehicle speed.
The vehicle speed optimization target model based on fuel oil/energy consumption economy comprises a vehicle speed prediction model and a fuel oil/energy consumption rate model, and the calculation formula is as follows:
wherein J is a target model, P is a prediction time domain, delta t is a step length predicted forward in each step in the prediction time domain, f is a fuel oil/energy consumption rate, i is an optimization turn, and k is a current automobile state.
Further, the vehicle speed prediction model is established based on stress information of the vehicle during running, the stress information comprises traction force, air resistance, rolling resistance and gradient resistance, and the fuel/energy consumption rate model is established based on the longitudinal vehicle speed of the vehicle and the output torque of the engine.
Further, the calculation formula of the vehicle speed prediction model is specifically as follows:
wherein the content of the first and second substances,
for the vehicle speed prediction model, T is the engine output torque, igIs the gear ratio of the transmission, i0Is the transmission ratio of the main reducer, eta is the mechanical efficiency of the transmission system, r is the wheel radius, ρ is the air density, A is the windward area of the vehicle, CDIs the air resistance coefficient, v is the vehicle speed, mu is the rolling resistance coefficient; m is the mass, including the sprung mass and the unsprung mass, and is the angle of the road slope, d is the real-time distance from the vehicle to the signal lamp at the front intersection, and g is the acceleration of gravity.
Compared with the prior art, the invention has the following beneficial effects:
1. according to the invention, the real-time optimal vehicle speed is calculated by the vehicle speed optimization target model based on fuel oil/energy consumption economy according to the real-time position of the vehicle and the proper vehicle speed interval with the green light as the signal lamp when the vehicle passes through the corresponding intersection, the vehicle-end controller ensures that the vehicle runs according to the real-time optimal vehicle speed, and the fuel oil/energy consumption rate of the vehicle is effectively reduced.
2. According to the invention, the vehicle road cloud cooperative positioning module and the vehicle road cloud cooperative sensing module are utilized to calculate the proper vehicle speed interval with the green light signal lamp when the vehicle passes through the corresponding intersection in real time, and the vehicle-end controller assists in adjusting the driving speed of the vehicle, so that the traffic efficiency of the traffic road is effectively improved.
Drawings
FIG. 1 is a schematic diagram of a control method according to an embodiment of the present invention.
Detailed Description
The invention is described in detail below with reference to the figures and specific embodiments. The present embodiment is implemented on the premise of the technical solution of the present invention, and a detailed implementation manner and a specific operation process are given, but the scope of the present invention is not limited to the following embodiments.
Examples
As shown in fig. 1, a control method for cooperatively optimizing a vehicle speed based on vehicle road cloud specifically includes the following steps:
s1, calculating by the vehicle road cloud cooperative positioning module according to the obtained multi-source positioning information to obtain a real-time position of the vehicle, and calculating a real-time distance from the vehicle to a signal lamp of a front intersection;
s2, calculating a proper vehicle speed interval with a green signal light when the vehicle passes through a corresponding intersection according to the real-time position of the vehicle by the vehicle road cloud cooperative sensing module through the acquired multi-source sensing information;
s3, the cloud prediction control module calculates to obtain a real-time optimal vehicle speed according to a suitable vehicle speed interval through a vehicle speed optimization target model based on fuel oil/energy consumption economy;
and S4, feeding back the real-time optimal vehicle speed to the cloud-end controller, sending the real-time optimal vehicle speed to the vehicle-end controller by the cloud-end controller, controlling the vehicle by the vehicle-end controller according to the real-time optimal vehicle speed, and repeating the steps S1-S3 to realize vehicle speed rolling optimization.
The multi-source positioning information comprises vehicle end positioning information and road end positioning information, the vehicle end positioning information comprises vehicle surrounding environment characteristic information, vehicle motion information and GPS positioning information, and the road end positioning information comprises road identification information and road end relative position information.
The vehicle surrounding environment characteristic information, the vehicle motion information and the GPS positioning information are acquired by a vehicle-end controller through a camera, a laser radar, a wheel speed meter, a steering wheel corner sensor and an RTK-GPS sensor.
The road identification information and the relative position information of the road end are acquired by the road end controller through the camera and the laser radar, and the relative position information of the road end is specifically the position information of the vehicle relative to the road end equipment.
The vehicle road cloud cooperative positioning module is connected with the cloud end controller, a high-precision map is arranged in the cloud end controller, the cloud end controller is matched with the high-precision map according to the characteristic information of the surrounding environment of the vehicle to obtain first real-time position reference information, second real-time position reference information is obtained according to the vehicle motion information and the GPS positioning information, and third real-time position reference information is obtained according to the high-precision map matched with the relative position information of the road end.
And the cloud end controller performs fusion positioning according to the first real-time position reference information, the second real-time position reference information and the third real-time position reference information to obtain the real-time position of the vehicle.
The multi-source perception information comprises vehicle-end perception road information and road-end perception road information, the vehicle-end perception road information comprises vehicle flow average speed information, and the road-end perception road information comprises road condition information.
The average vehicle speed information and the road condition information of the vehicle flow are acquired by the vehicle end controller and the road end controller through the camera and the laser radar.
The vehicle road cloud cooperation sensing module is connected with the cloud end controller, a high-precision map is arranged in the cloud end controller, the cloud end controller acquires signal lamp real-time state information and lane speed limit information in the high-precision map, signal lamp green wave phase time is obtained according to the signal lamp real-time state information, the real-time distance from a vehicle to a signal lamp of a front intersection is combined, and a proper vehicle speed interval with the signal lamp being a green lamp when the vehicle passes through the corresponding intersection is calculated according to the lane speed limit information, vehicle flow average vehicle speed information and road condition information, wherein the proper vehicle speed interval is a signal priority green wave vehicle speed interval.
The real-time optimal vehicle speed is specifically the optimal engine output torque and the corresponding vehicle speed.
The vehicle speed optimization target model based on fuel oil/energy consumption economy comprises a vehicle speed prediction model and a fuel oil/energy consumption rate model, and the calculation formula is as follows:
wherein J is a target model, P is a prediction time domain, delta t is a step length predicted forward in each step in the prediction time domain, f is a fuel oil/energy consumption rate, i is an optimization turn, and k is a current automobile state.
The vehicle speed prediction model is established based on stress information of the vehicle in the running process, the stress information comprises traction force, air resistance, rolling resistance and gradient resistance, and the fuel/energy consumption rate model is established based on the longitudinal vehicle speed of the vehicle and the output torque of an engine.
The calculation formula of the vehicle speed prediction model is specifically as follows:
wherein the content of the first and second substances,
for the vehicle speed prediction model, T is the engine output torque, igIs the gear ratio of the transmission, i0Is the transmission ratio of the main reducer, eta is the mechanical efficiency of the transmission system, r is the wheel radius, ρ is the air density, A is the windward area of the vehicle, CDIs the air resistance coefficient, v is the vehicle speed, mu is the rolling resistance coefficient; m is the mass, including the sprung mass and the unsprung mass, and is the angle of the road slope, d is the real-time distance from the vehicle to the signal lamp at the front intersection, and g is the acceleration of gravity.
In this embodiment, taking the consumption rate of fuel for an engine as an example, the fuel/consumption rate model is specifically as follows:
the vehicle speed optimization target model based on fuel oil/energy consumption economy adopts a model predictive control algorithm according to a proper vehicle speed interval to calculate to obtain a real-time optimal vehicle speed, and the cloud-end controller feeds an output torque value back to the vehicle-end controller through rolling optimization and real-time control, so that the vehicle realizes the optimal vehicle speed control of green wave passing and optimal fuel oil/energy consumption economy.
In addition, it should be noted that the specific embodiments described in the present specification may have different names, and the above descriptions in the present specification are only illustrations of the structures of the present invention. All equivalent or simple changes in the structure, characteristics and principles of the invention are included in the protection scope of the invention. Various modifications or additions may be made to the described embodiments or methods may be similarly employed by those skilled in the art without departing from the scope of the invention as defined in the appending claims.
Claims (10)
1. A control method for cooperatively optimizing vehicle speed based on vehicle road cloud is characterized by comprising the following steps:
s1, calculating by the vehicle road cloud cooperative positioning module according to the obtained multi-source positioning information to obtain a real-time position of the vehicle, and calculating a real-time distance from the vehicle to a signal lamp of a front intersection;
s2, calculating a proper vehicle speed interval with a green signal light when the vehicle passes through a corresponding intersection according to the real-time position of the vehicle by the vehicle road cloud cooperative sensing module through the acquired multi-source sensing information;
s3, the cloud prediction control module calculates to obtain a real-time optimal vehicle speed according to a suitable vehicle speed interval through a vehicle speed optimization target model based on fuel oil/energy consumption economy;
and S4, feeding the real-time optimal vehicle speed back to the cloud end controller, sending the real-time optimal vehicle speed to the vehicle end controller by the cloud end controller, controlling the vehicle by the vehicle end controller according to the real-time optimal vehicle speed, and repeating the steps S1-S3 to realize vehicle speed rolling optimization.
2. The vehicle-road-cloud-based collaborative optimization vehicle speed control method according to claim 1, wherein the multi-source positioning information comprises vehicle-end positioning information and road-end positioning information, the vehicle-end positioning information comprises vehicle surrounding environment characteristic information, vehicle motion information and GPS positioning information, and the road-end positioning information comprises road identification information and road-end relative position information.
3. The vehicle road cloud collaborative optimization vehicle speed control method according to claim 2 is characterized in that the vehicle road cloud collaborative positioning module is connected with a cloud end controller, a high-precision map is arranged in the cloud end controller, the cloud end controller matches the high-precision map according to vehicle surrounding environment feature information to obtain first real-time position reference information, obtains second real-time position reference information according to vehicle motion information and GPS positioning information, and matches the high-precision map according to road end relative position information to obtain third real-time position reference information.
4. The vehicle-road-cloud-collaborative-optimization-based vehicle speed control method according to claim 3, wherein the cloud-end controller performs fusion positioning according to the first real-time position reference information, the second real-time position reference information and the third real-time position reference information to obtain the real-time position of the vehicle.
5. The vehicle-road-cloud-based collaborative optimization vehicle speed control method according to claim 1, wherein the multi-source perception information comprises vehicle-end perception road information and road-end perception road information, the vehicle-end perception road information comprises vehicle flow average vehicle speed information, and the road-end perception road information comprises road condition information.
6. The vehicle-road-cloud-based collaborative optimization vehicle speed control method according to claim 5 is characterized in that the vehicle-road-cloud collaborative sensing module is connected with a cloud-end controller, a high-precision map is arranged in the cloud-end controller, the cloud-end controller acquires signal lamp real-time state information and lane speed limit information in the high-precision map, obtains signal lamp green wave phase time according to the signal lamp real-time state information, and calculates a suitable vehicle speed interval when a signal lamp is green when a vehicle passes through a corresponding intersection according to the lane speed limit information, vehicle flow average vehicle speed information and road condition information in combination with real-time distance from the vehicle to a signal lamp of the front intersection.
7. The vehicle-road-cloud-based collaborative optimization vehicle speed control method according to claim 1, wherein the real-time optimal vehicle speed is specifically an optimal engine output torque and a corresponding vehicle speed.
8. The vehicle speed control method based on vehicle road cloud collaborative optimization according to claim 1, wherein the target model for vehicle speed optimization based on fuel/energy consumption economy comprises a vehicle speed prediction model and a fuel/energy consumption rate model, and a calculation formula is as follows:
wherein J is a target model, P is a prediction time domain, delta t is a step length predicted forward in each step in the prediction time domain, f is a fuel oil/energy consumption rate, i is an optimization turn, and k is a current automobile state.
9. The vehicle speed control method based on vehicle road cloud collaborative optimization according to claim 8, characterized in that the vehicle speed prediction model is established based on stress information of a vehicle in a driving process, and the fuel/energy consumption rate model is established based on a longitudinal vehicle speed of the vehicle and an engine output torque.
10. The vehicle speed control method based on vehicle road cloud collaborative optimization according to claim 9, wherein a calculation formula of the vehicle speed prediction model is specifically as follows:
wherein the content of the first and second substances,
for the vehicle speed prediction model, T is the engine output torque, igIs the gear ratio of the transmission, i0Is the transmission ratio of the main reducer, eta is the mechanical efficiency of the transmission system, r is the wheel radius, ρ is the air density, A is the windward area of the vehicle, CDIs the air resistance coefficient, v is the vehicle speed, mu is the rolling resistance coefficient; m is the mass, alpha is the angle of the road gradient, d is the real-time distance from the vehicle to the signal lamp at the front intersection, and g is the acceleration of gravity.
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Application publication date: 20210831 |