CN112124308A - Adaptive cruise system based on 5G grading decision - Google Patents
Adaptive cruise system based on 5G grading decision Download PDFInfo
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- B—PERFORMING OPERATIONS; TRANSPORTING
- B60—VEHICLES IN GENERAL
- B60W—CONJOINT CONTROL OF VEHICLE SUB-UNITS OF DIFFERENT TYPE OR DIFFERENT FUNCTION; CONTROL SYSTEMS SPECIALLY ADAPTED FOR HYBRID VEHICLES; ROAD VEHICLE DRIVE CONTROL SYSTEMS FOR PURPOSES NOT RELATED TO THE CONTROL OF A PARTICULAR SUB-UNIT
- B60W30/00—Purposes of road vehicle drive control systems not related to the control of a particular sub-unit, e.g. of systems using conjoint control of vehicle sub-units
- B60W30/14—Adaptive cruise control
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Abstract
The invention discloses a self-adaptive cruise system based on 5G grading decision, which comprises a cloud end, an edge cloud and an end, wherein the edge cloud is provided with an MEC platform, the edge cloud is arranged close to the end, the cloud end is arranged far away from the end, and the cloud end carries out data interaction with the end part through the edge cloud; the edge cloud is equipped with an MEC platform. The cloud end of the invention has wide coverage range and the most powerful computing capability, but the transmission time delay is relatively larger because the distance from the cloud end is far. The edge cloud is provided with an MEC platform, has strong computing power, can store dynamic high-precision maps, and provides high-precision positioning service for a vehicle end. And all the roadside equipment uploads the results to the edge cloud, so that a digital basis is provided for the decision of the edge cloud. The time delay of the vehicle-end equipment is small, the structural design of the whole system is reasonable, the driving safety of a driver is ensured, and accidents are avoided.
Description
Technical Field
The invention relates to the technical field of adaptive cruise, in particular to an adaptive cruise system based on 5G grading decision.
Background
ACC is developed from a constant speed Cruise (CCS), and a conventional adaptive Cruise System detects a front obstacle by using a camera and a millimeter wave radar, and automatically performs acceleration and deceleration of a vehicle according to a Cruise speed and a following distance (collision time) set by a driver based on the front obstacle detection. Because each sensor has own weakness, in the traditional adaptive cruise system, research personnel can cooperate with each sensor to use the weakness, namely, the sensor fusion technology, to provide front obstacle information for the ACC system together. The conventional ACC can accurately identify the front obstacle and perform vehicle following driving when the vehicle is driven in a straight line.
However, conventional ACCs still face the following problems:
a) the vehicle merges into a side lane: if the ACC is not withdrawn in the lane changing process, the system recognizes that the front vehicle is not obstructed, and the acceleration action is suddenly carried out.
When the ACC vehicle following vehicle 1 is turned on, the driver performs a lane change operation without an obstacle in front, and the vehicle suddenly performs an acceleration operation, as shown in fig. 1.
After the lane change is completed, the vehicle 2 is recognized, and if the time is less than the set collision time, the vehicle suddenly performs a deceleration operation, as shown in fig. 2.
b) The vehicle is turned at a large angle:
the sensor can not scan the front vehicle of the lane, the vehicle can move forward according to the preset speed, and the collision danger exists between the vehicle and the front vehicle;
when the sensor scans that a vehicle turns around in an adjacent lane, the vehicle is mistaken to be a vehicle in front of the lane, the following distance is judged to be insufficient, and the vehicle is decelerated;
when the ACC vehicle is started to enter a curve, the vehicle 1 will scan the vehicle 2 and suddenly decelerate after turning, and if the vehicle 2 is not available, the vehicle will suddenly accelerate after following the vehicle 1 to turn, as shown in FIG. 3;
c) urban road conditions: the driver is required to actively exit the ACC mode because the traffic light information in front cannot be identified.
The situations can cause the driver to have anxiety, and hidden danger is buried for accidents.
The C-V2X is a vehicular wireless communication technology (Vehicle to evolution) formed based on Cellular (Cellular) communication evolution, meanwhile, different C-V2X puts various different requirements on network environment in the aspects of time delay, bandwidth, computing capacity and the like, and according to the requirements, a hierarchical decision framework based on 5G is formed, and Vehicle-road-cloud cooperative interaction and hierarchical decision are formed by means of the fusion concept of MEC and C-V2X.
In summary, the invention relates to an adaptive cruise system based on 5G hierarchical decision.
Disclosure of Invention
Aiming at the defects in the prior art, the invention aims to provide a 5G-based hierarchical decision-making self-adaptive cruise system which is reasonable in structural design, ensures the driving safety of a driver and avoids accidents.
In order to achieve the purpose, the invention is realized by the following technical scheme: the self-adaptive cruise system based on the 5G grading decision comprises a cloud end, an edge cloud and an end, wherein the edge cloud is provided with an MEC platform, the edge cloud is arranged close to the end, the cloud end is arranged far away from the end, and the cloud end carries out data interaction with the end through the edge cloud; the edge cloud is equipped with an MEC platform.
The edge cloud: the system is used for detecting information analysis and environment dynamic prediction, can bear the application of the system (the self-adaptive cruise system based on 5G grading decision), is close to a vehicle end, and can perform data interaction (within 10ms of time delay) with a vehicle and road side equipment in real time.
The end comprises road side end equipment and vehicle end equipment, and the road side end equipment mainly comprises a camera, a millimeter wave radar and an intelligent traffic light; uploading results to the edge cloud by all the roadside equipment to provide a digital basis for the decision of the edge cloud; the vehicle-end equipment performs vehicle control and decision closely related to vehicle safety; acquiring data information of a vehicle-mounted sensor (a millimeter wave radar/a camera and the like); and carrying out data interaction with the edge cloud in real time to obtain decision information issued by the edge cloud.
The invention has the beneficial effects that: the cloud end of the invention has wide coverage range and the most powerful computing capability, but the transmission time delay is relatively larger because the distance from the cloud end is far. The edge cloud is provided with an MEC platform, has strong computing power, can store dynamic high-precision maps, and provides high-precision positioning service for a vehicle end. And all the roadside equipment uploads the results to the edge cloud, so that a digital basis is provided for the decision of the edge cloud. The time delay of the vehicle-end equipment is small, the structural design of the whole system is reasonable, the driving safety of a driver is ensured, and accidents are avoided.
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The invention is described in detail below with reference to the drawings and the detailed description;
FIGS. 1-2 are schematic views of a vehicle merging into a side lane according to the background art of the present invention;
FIG. 3 is a schematic diagram of a vehicle turning at a large angle in the background art of the present invention;
FIG. 4 is a system architecture diagram of the present invention;
FIG. 5 is a vehicle end equipment architecture diagram of the present invention;
FIG. 6 is a view of a straight-line following scene in embodiment 1 of the present invention;
FIG. 7 is a flowchart of example 1 of the present invention;
FIG. 8 is a view of the scene of the driver actively changing lanes according to embodiment 2 of the present invention;
FIG. 9 is a flowchart of example 2 of the present invention;
FIG. 10 is a flowchart of a vehicle making a wide-angle turn according to embodiment 3 of the present invention;
FIG. 11 is a flowchart of example 3 of the present invention;
fig. 12 is a scene diagram of the urban road conditions in embodiment 4 of the present invention.
Detailed Description
In order to make the technical means, the creation characteristics, the achievement purposes and the effects of the invention easy to understand, the invention is further described with the specific embodiments.
Referring to fig. 4 to 5, the following technical solutions are adopted in the present embodiment: the self-adaptive cruise system based on the 5G grading decision comprises a cloud end, an edge cloud and an end, wherein the edge cloud is provided with an MEC platform, the edge cloud is arranged close to the end, the cloud end is arranged far away from the end, and the cloud end carries out data interaction with the end through the edge cloud; the edge cloud is equipped with an MEC platform.
The edge cloud: the system is used for detecting information analysis and environment dynamic prediction, can bear the application of the system (the self-adaptive cruise system based on 5G grading decision), is close to a vehicle end, and can perform data interaction (within 10ms of time delay) with a vehicle and road side equipment in real time.
The end comprises road side end equipment and vehicle end equipment, and the road side end equipment mainly comprises a camera, a millimeter wave radar and an intelligent traffic light; uploading results to the edge cloud by all the roadside equipment to provide a digital basis for the decision of the edge cloud; the vehicle-end equipment performs vehicle control and decision closely related to vehicle safety; acquiring data information of a vehicle-mounted sensor (a millimeter wave radar/a camera and the like); and carrying out data interaction with the edge cloud in real time to obtain decision information issued by the edge cloud.
Example 1: the straight-line following scene refers to fig. 6 and 7, and the process is as follows:
1. the driver turns on the ACC function;
2. the vehicle-mounted equipment uploads the current state (such as the speed, the position, the course, the obstacle information detected by a sensor and the like, and the collision event of the driver equipment and the like) information of the vehicle to the edge cloud; the front vehicle uploads the same information;
3, calculating by the edge cloud according to the information, the vehicle model state and the like to obtain a vehicle acceleration value and transmitting the vehicle acceleration value to vehicle-end equipment;
4. and the vehicle-end equipment completes vehicle speed control according to the acceleration value.
After the edge cloud of the embodiment acquires the gps information of the vehicle, in order to reduce the calculation amount, the longitude and latitude are converted into a vehicle coordinate system, the center of the rear axle of the vehicle is taken as the origin, the advancing direction is the direction of the X axis of the vehicle, and the direction of the copilot pointing to the driver is the direction of the Y axis of the vehicle, as shown in fig. 6; then, the distance and the like are calculated (1)
) And comparing the vehicle speed with a preset collision distance of a driver, simultaneously inquiring vehicle information uploaded by road side equipment and information such as speed and position uploaded by other vehicles under a high-precision map by taking a course angle as a vehicle advancing direction, and fusing the information with sensor detection information uploaded by the vehicle to ensure the accuracy of the speed and direction information of an obstacle in front of a lane where the vehicle is located.
Example 2: the driver actively changes the lane scene, referring to fig. 8 and 9, the process is as follows:
1. the driver starts to turn the steering wheel;
2. the vehicle-mounted equipment collects intention (steering wheel rotation angle) of a driver and self speed, position and course information and uploads the information to the edge cloud;
3. the edge cloud calculates the specific lane where the vehicle is located and the adjacent vehicle lane according to the information; the roadside device detecting vehicle data of adjacent lanes; uploading self speed and position information on an adjacent lane;
4. calculating the acceleration value of the vehicle at the moment and issuing the acceleration value to vehicle-end equipment by taking the nearest vehicle to enter the lane as a following vehicle;
5. and the vehicle-end equipment executes the acceleration value and controls the vehicle to advance.
According to the embodiment, the obstacle information of the vehicle advancing direction can be accurately known by the edge cloud by means of the road side equipment, the vehicle positioning information and the high-precision map, and the problem that the vehicle is accelerated and decelerated suddenly when a driver changes lanes is solved.
Example 3: the vehicle makes a large-angle turn, and referring to fig. 10 and 11, the flow is as follows:
1. entering a bend, and starting to turn a steering wheel by a driver;
2. the vehicle-mounted equipment collects intention (steering wheel rotation angle) of a driver and self speed, position and course information and uploads the information to the edge cloud;
3. the edge cloud calculates a specific lane (curve) where the vehicle is located and an adjacent lane according to the information; the roadside device detecting vehicle data of adjacent lanes; uploading self speed and position information on an adjacent lane;
4. the edge cloud virtualizes the information of the vehicle on the current lane and the front lane behind the vehicle, judges that the current vehicle speed is enough to exceed the maximum vehicle speed of a curve, calculates the deceleration of the vehicle and sends the deceleration to the vehicle-end equipment;
5. and the vehicle-end equipment executes the acceleration value and controls the vehicle to advance.
According to the method, the edge cloud carries out curve virtualization according to the road width and the curve curvature radius of the high-precision map, the schematic diagram is as shown in FIG. 10, and meanwhile, the arc length between vehicles is accurately calculated according to the curve curvature radius, so that the following vehicle distance is obtained:
d = n (central angle degree) × pi (1) × r (radius)/180 (angle system).
Example 4: as shown in fig. 12, the flow of the urban road condition is as follows:
1. the driver turns on the ACC function;
2. the vehicle-mounted equipment uploads the current state (speed, position, course, obstacle information detected by a sensor and the like, and collision time set by a driver and the like) information of the vehicle to the edge cloud; the roadside traffic light uploads the current traffic light and the traffic light state in a future period of time;
3. and the edge cloud calculates whether the vehicle passes through the traffic light closest to the vehicle heading direction or not (if the vehicle cannot pass through the traffic light), obtains the vehicle acceleration and sends the vehicle acceleration to the vehicle-mounted equipment.
4. And the vehicle-end equipment completes vehicle speed control according to the acceleration value.
The foregoing shows and describes the general principles and broad features of the present invention and advantages thereof. It will be understood by those skilled in the art that the present invention is not limited to the embodiments described above, which are described in the specification and illustrated only to illustrate the principle of the present invention, but that various changes and modifications may be made therein without departing from the spirit and scope of the present invention, which fall within the scope of the invention as claimed. The scope of the invention is defined by the appended claims and equivalents thereof.
Claims (3)
1. The self-adaptive cruise system based on the 5G grading decision is characterized by comprising a cloud end, an edge cloud and an end, wherein the edge cloud is provided with an MEC platform, the edge cloud is arranged close to the end, the cloud end is arranged far away from the end, and the cloud end carries out data interaction with the end part through the edge cloud; the edge cloud is equipped with an MEC platform.
2. The adaptive cruise system based on 5G hierarchical decision according to claim 1, characterized in that said edge cloud: the system is used for detecting information analysis and environment dynamic prediction, can bear the application of the system, is close to a vehicle end, and performs data interaction with a vehicle and road side equipment in real time.
3. The adaptive cruise system based on 5G grading decision according to claim 1, wherein the end comprises a roadside end device and a vehicle end device, the roadside end device mainly comprises a camera, a millimeter wave radar and an intelligent traffic light; uploading results to the edge cloud by all the roadside equipment to provide a digital basis for the decision of the edge cloud; the vehicle-end equipment performs vehicle control and decision closely related to vehicle safety; acquiring data information of a vehicle-mounted sensor; and carrying out data interaction with the edge cloud in real time to obtain decision information issued by the edge cloud.
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| CN202011289238.2A CN112124308A (en) | 2020-11-18 | 2020-11-18 | Adaptive cruise system based on 5G grading decision |
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| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN114401502A (en) * | 2022-01-21 | 2022-04-26 | 中国联合网络通信集团有限公司 | Configuration method, configuration device, electronic equipment and storage medium |
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| CN111367292A (en) * | 2020-03-20 | 2020-07-03 | 特路（北京）科技有限公司 | Intelligent road system for automatically driving automobile |
| CN111845754A (en) * | 2020-07-27 | 2020-10-30 | 扬州大学 | Decision prediction method of automatic driving vehicle based on edge calculation and crowd-sourcing algorithm |
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- 2020-11-18 CN CN202011289238.2A patent/CN112124308A/en active Pending
Patent Citations (5)
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| US20190079659A1 (en) * | 2018-09-25 | 2019-03-14 | Intel Corporation | Computer-assisted or autonomous driving vehicles social network |
| WO2020126438A1 (en) * | 2018-12-20 | 2020-06-25 | Volkswagen Aktiengesellschaft | Method for operating a vehicle when transferring processing power from the vehicle to at least one edge cloud computer |
| CN110979332A (en) * | 2019-12-24 | 2020-04-10 | 奇瑞汽车股份有限公司 | Control method and device of intelligent automobile and storage medium |
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| Publication number | Priority date | Publication date | Assignee | Title |
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| CN114401502A (en) * | 2022-01-21 | 2022-04-26 | 中国联合网络通信集团有限公司 | Configuration method, configuration device, electronic equipment and storage medium |
| CN114401502B (en) * | 2022-01-21 | 2023-07-18 | 中国联合网络通信集团有限公司 | Configuration method, configuration device, electronic equipment and storage medium |
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Application publication date: 20201225 |