CN111105614A - Traffic condition prediction method based on road social circle - Google Patents
Traffic condition prediction method based on road social circle Download PDFInfo
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Abstract
The invention relates to a traffic condition prediction method based on a road social circle, which comprises the steps of sending a social request to a vehicle networking platform; the Internet of vehicles platform acquires the driving information of the requested vehicle, wherein the driving information comprises the position, the driving direction and the driving speed of the vehicle; taking the vehicle position of the request vehicle as a center, taking a first preset value as a radius to establish a social circle, and regarding each vehicle in the coverage range of the social circle as a clustered vehicle; acquiring the driving information of each clustered vehicle, and mapping each clustered vehicle to a corresponding position of a map; acquiring the vehicle density and the average running speed of each road around the requested vehicle according to the running information of each clustered vehicle; when the vehicle density is greater than a first threshold value and the average running speed is less than a second threshold value, the road is considered to be a congestion area; the travel path of the requested vehicle is optimized. The prediction method can analyze the congestion area and the driving risk, provides reference opinions for drivers, is beneficial to improving traffic efficiency and reducing the accident occurrence probability.
Description
Technical Field
The invention relates to the technology of internet of things in the technical field of information, in particular to a traffic condition prediction method based on a road social circle.
Background
With the continuous development of science and technology, the high-degree automatic driving technology is mature day by day. When the highly automatic driving technology is developed to a certain degree, the vehicle can complete all driving actions and has the function of reminding a driver. That is, the driver does not need to monitor the driving environment all over, the free time during the driving of the vehicle is relatively increased, and both hands are also freed to enable the driver to perform some social or recreational activities in the vehicle. At present, most of social products used by drivers belong to mobile phone end operation applications, and vehicle end social products are generally ignored. Although some vehicle-end social products are also available in the market, most of the vehicle-end social products are gathered in point-to-point information interaction between vehicles and platforms or simple road information exchange between vehicles, and the vehicle-end social products are single in function and cannot bring good social experience to drivers. Most importantly, the road information obtained by only relying on the feedback of other vehicles is not comprehensive and accurate enough, even larger deviation occurs, and the driver is not helped to comprehensively know the road traffic conditions near the vehicle, so that the driving risk of the vehicle cannot be accurately evaluated, the optimal coping strategy cannot be made in advance, and the user experience effect is poor.
Disclosure of Invention
In order to solve the technical problem, the invention provides a traffic condition prediction method based on a road social circle, which is based on a vehicle networking platform and vehicle-mounted communication equipment and comprises the following steps:
sending a social request to the vehicle networking platform;
the Internet of vehicles platform acquires the driving information of the requested vehicle, wherein the driving information comprises the position, the driving direction and the driving speed of the vehicle;
taking the vehicle position of the request vehicle as a center, taking a first preset value as a radius to establish a social circle, and regarding each vehicle in the coverage range of the social circle as a clustered vehicle;
acquiring the driving information of each clustered vehicle, and mapping each clustered vehicle to a corresponding position of a map;
acquiring the vehicle density and the average running speed of each road around the requested vehicle according to the running information of each clustered vehicle;
when the vehicle density is greater than a first threshold value and the average running speed is less than a second threshold value, the road is considered to be a congestion area;
the travel path of the requested vehicle is optimized.
Further, the vehicle density obtaining process includes the following sub-steps:
carrying out position matching on the clustered vehicles and all roads around the request vehicle;
respectively counting the number of clustered vehicles on each road;
the number of clustered vehicles per unit length of each road is calculated as the vehicle density of the road.
Further, in the step of performing location matching on the clustered vehicles and the roads around the requested vehicle, when the shortest distance between the clustered vehicles and the roads is less than a third threshold, it is determined that the clustered vehicles are in location matching with the roads.
Further, the process of obtaining the average traveling speed includes the following sub-steps:
randomly extracting N clustered vehicles on a road;
solving the speed sum of N clustered vehicles;
the ratio of the speed sum to N is calculated as the average traveling speed.
Further, the second threshold value refers to an average traveling speed of the vehicle under a road smoothness condition.
Further, the running information further comprises overspeed times, rapid acceleration times, rapid deceleration times and rapid turning times; before the optimization of the driving path of the requested vehicle, the method further comprises a driving risk assessment step, wherein the driving risk assessment step comprises the following steps:
summing the overspeed times, the rapid acceleration times, the rapid deceleration times and the rapid turning times of each clustered vehicle within preset time;
comparing the sum with a fourth threshold, and if the sum is greater than the fourth threshold, determining that the personality of the driver is impatient;
counting the number of drivers with impatient characters on each road;
when the number of drivers with impatience type characters exceeds the fifth threshold, it is considered that there is a certain risk of traveling on the road, and the road is regarded as a dangerous road.
Further, in optimizing the travel path of the requested vehicle, not only the congested area needs to be avoided, but also the dangerous road needs to be kept away.
Further, after the step of comparing the sum with a fourth threshold, and if the sum is greater than the fourth threshold, considering the personality of the driver as impatient, the method further includes:
the vehicle net platform pushes light music to the driver with impatient characters so as to achieve the purpose of relieving the emotion of the driver.
Furthermore, the vehicle located in the coverage area of the social circle not only can realize information interaction with the Internet of vehicles platform, but also can realize information interaction with other vehicles in the coverage area of the social circle, and the limitation of the social circle can be broken through by mutual attention between the vehicle and the vehicle, so that long-term connection is established.
Further, before the step of optimizing the traveling path of the requested vehicle, the method further includes the step of information confirmation:
requesting the vehicles to send confirmation information to the clustered vehicles in the congested area, and acquiring feedback results of the clustered vehicles on the confirmation information in real time;
and when the feedback result of the clustered vehicles is congestion, the analysis of the congestion area is considered to be accurate.
A traffic condition prediction system is based on the traffic condition prediction method based on the road social circle, and comprises a vehicle networking platform and a plurality of vehicle-mounted communication devices arranged on different vehicles; each vehicle-mounted communication device is respectively in communication connection with the vehicle networking platform so as to realize information interaction with the vehicle networking platform, and each vehicle-mounted communication device can realize information interaction through the vehicle networking platform; the vehicle network platform is used for receiving the driving information sent by the vehicle-mounted communication equipment and analyzing the road traffic condition according to the driving information so as to optimize the driving path.
The invention has the following beneficial technical effects:
compared with the prior art, the invention discloses a traffic condition prediction method based on a road social circle, which is characterized in that the social circle is established through an internet of vehicles platform, the driving information of each clustered vehicle in the coverage range of the social circle is subjected to statistical analysis to obtain a congestion area, and a driving path is optimized for a driver according to the distribution position of the congestion area, so that the driver is prevented from blindly entering a congestion road section, the information gap is reduced, and the traffic efficiency is improved. In addition, the traffic condition prediction method can analyze the personality type of the driver through the overspeed times, the rapid acceleration times and the rapid deceleration times, evaluate the driving risk of the road according to the number of the drivers with impatient personality on the road, provide reference opinions for the drivers, cause the attention of the drivers, facilitate the reduction of the probability of traffic accidents and avoid unnecessary loss.
Drawings
Fig. 1 is a flowchart of a traffic condition prediction method based on a road social circle in embodiment 1.
Fig. 2 is a schematic diagram of a connection relationship between the car networking platform and a plurality of vehicle-mounted communication devices in embodiment 1.
The drawings are for illustrative purposes only and are not to be construed as limiting the patent; for the purpose of better illustrating the embodiments, certain features of the drawings may be omitted, enlarged or reduced, and do not represent the size of an actual product; it will be understood by those skilled in the art that certain well-known structures in the drawings and descriptions thereof may be omitted; the same or similar reference numerals correspond to the same or similar parts; the terms describing positional relationships in the drawings are for illustrative purposes only and are not to be construed as limiting the patent.
Detailed Description
The following detailed description of the preferred embodiments of the present invention, taken in conjunction with the accompanying drawings, will make the advantages and features of the invention easier to understand for those skilled in the art and will therefore make the scope of the invention more clearly defined.
Example 1:
the embodiment provides a traffic condition prediction method based on a road social circle, which is mainly based on a vehicle networking platform and a plurality of vehicle-mounted communication devices arranged on different vehicles on the premise of realizing a high-degree automatic driving technology. The vehicle-mounted communication equipment can be communication equipment fixedly installed on the vehicle, and can also be mobile communication equipment temporarily placed on the vehicle, such as a mobile phone or a platform computer. Each vehicle-mounted communication equipment is respectively in communication connection with the vehicle networking platform to realize information interaction with the vehicle networking platform, and each vehicle-mounted communication equipment can realize information interaction through the vehicle networking platform.
As shown in fig. 1, a traffic condition prediction method based on a road social circle specifically includes the following steps:
101. and sending the social request to the vehicle networking platform.
When the driver needs to know the surrounding traffic conditions through the vehicle networking platform or wants to establish social contact with the surrounding vehicles through the vehicle networking platform, the social contact request can be sent to the vehicle networking platform through the vehicle-mounted communication device so as to express the willingness of the driver to establish the social contact.
102. The Internet of vehicles platform acquires the driving information of the requested vehicle, wherein the driving information comprises the position, the driving direction and the driving speed of the vehicle.
Of course, the acquisition of the travel information of the requested vehicle needs to be completed by the consent of the driver of the requested vehicle. In this embodiment, the vehicle position refers to a GPS position of the vehicle, and the driving direction of the vehicle refers to which direction the vehicle is traveling, such as straight east, straight west, straight north, straight south, and turning east; the driving speed is the mileage of the vehicle in unit time, and can be directly obtained by reading an instrument panel.
103. And taking the vehicle position of the request vehicle as a center, taking the first preset value as a radius to establish a social circle, and regarding each vehicle in the coverage range of the social circle as a clustered vehicle.
The social circle is a circular coverage circle, the social circle can be updated in real time, the vehicle-mounted communication equipment in the coverage area is added into the social circle, and the vehicle-mounted communication equipment in the coverage area of the social circle is deleted. Of course, there are many implementation methods for how to add or delete a social circle in detail, which are not listed here. The clustering vehicles in the coverage area of the social circle not only can realize information interaction with the Internet of vehicles platform, but also can realize information interaction with other clustering vehicles in the coverage area of the social circle. The drivers of the clustered vehicles can share or receive information in real time through the vehicle-mounted communication equipment, the information content can relate to accident information, help seeking information or peripheral service information and the like, and the information types comprise character information, voice information, picture information and the like. And the clustering vehicles can break through the limitation of social circles by paying attention to each other on the premise that the two parties voluntarily, establish long-term connection, and develop into real friends in life.
104. And acquiring the driving information of each clustered vehicle, and mapping each clustered vehicle to a corresponding position of the map.
In this embodiment, the driving information of the clustered vehicles is acquired, the internet of vehicles platform needs to send an acquisition request to the clustered vehicles first, and the acquisition of the driving information can be completed only after the driver of the clustered vehicles agrees, and certainly, the internet of vehicles platform can also take corresponding protection measures for the acquired driving information of the clustered vehicles, so as to avoid loss of users due to information leakage. The map is displayed through the vehicle-mounted hollow screen, and the text information, the voice information, the picture information and the like mentioned in the step 103 can be displayed on the vehicle-mounted hollow screen, so that the driver can operate the map conveniently.
105. And acquiring the vehicle density and the average running speed of each road around the requested vehicle according to the running information of each clustered vehicle.
Specifically, in order to find the vehicle density of each road around the requesting vehicle, first, clustered vehicles in the coverage area of the social circle need to be matched with each road around the requesting vehicle in position, that is, it needs to be determined on which road around the requesting vehicle each clustered vehicle is located. In this embodiment, when the shortest distance between a clustered vehicle and a road is smaller than the third threshold, the clustered vehicle and the road are considered to be matched, that is, the clustered vehicle is driven on the road. The third threshold value is generally set between 0m and 50m, preferably 25 m. It should be noted that, if the shortest distances between a certain clustered vehicle and the roads around the requested vehicle are all greater than the third threshold, the clustered vehicle is ignored, and the clustered vehicle is considered not to run on the roads around the requested vehicle, so that the normal running of the requested vehicle is not affected. After the clustered vehicles are matched with the roads around the request vehicle, the clustered vehicles distributed on the roads are determined, and at the moment, the number of the clustered vehicles on each road is only needed to be counted respectively, namely, the number of the clustered vehicles on each road around the request vehicle is calculated. Then, the number of the clustered vehicles on the road is divided by the calculated length of the road, so that the number of the clustered vehicles in the unit length of each road can be obtained, and the number of the clustered vehicles in the unit length is used as the vehicle density of the road. In this embodiment, the calculated length of the road refers to a length of the road within a coverage area of the social circle, that is, within the coverage area of the social circle, the length of the road is the calculated length of the road, and the calculated length of the road is consistent with the selected range of the clustered vehicles. The unit length is generally 1 ㎞, and of course, the user can adjust and set the unit length according to the needs of different road sections of different roads to satisfy better analysis of traffic conditions.
Preferably, in order to obtain the average traveling speed of the vehicles on each road around the requested vehicle, the traveling speeds of all the clustered vehicles within the calculated length of the road are summed, and then the sum is divided by the total number of the clustered vehicles within the calculated length of the road, and the ratio of the sum and the total number of the clustered vehicles within the calculated length of the road is used as the average traveling speed. Of course, in order to reduce the calculation amount and increase the calculation speed, N clustered vehicles may be randomly extracted on the road, where the value of N is smaller than the total number of clustered vehicles within the road calculation length; and summing the running speeds of the N selected clustered vehicles, and finally dividing the sum of the speeds of the N clustered vehicles by N to obtain the ratio of the sum to the N as the average running speed.
106. And when the vehicle density is greater than the first threshold value and the average running speed is less than the second threshold value, the road is considered to be a congestion area.
Specifically, the first threshold may be 100/㎞, 120/㎞ or other vehicle density values, which may be set according to needs. The second threshold value refers to the average running speed of the vehicle under the condition that the road is clear. The setting of the second threshold is greatly influenced by road sections, weather and other factors, and it can be understood that the second threshold is generally smaller in downtown road sections, and is relatively larger in suburban road sections. In this embodiment, in order to improve the representativeness and accuracy of the second threshold, the second threshold is generally selected from the average driving speed of the vehicle between 21:00 and 23:00 of the road in the evening. That is, to determine the second threshold, the number of vehicles traveling on the road in the evening 21:00-23:00 needs to be counted, the speed of each vehicle is recorded, the sum of the speeds of each vehicle is obtained, the average traveling speed of the road in the evening 21:00-23:00 can be calculated by dividing the sum of the speeds by the number of vehicles, and the average traveling speed of the road in the evening 21:00-23:00 is used as the second threshold and used as the reference standard of the average traveling speed of the clustered vehicles on the road.
Once the vehicle density is greater than the first threshold value and the average running speed is less than the second threshold value, the road is judged to belong to a congestion area, and the congestion area can be automatically mapped onto a map so as to provide reference for a subsequent optimized running path. Of course, the congestion area is determined by simply considering the vehicle density and the average traveling speed, and in order to avoid misleading the requested vehicle due to the deviation, it is generally recommended to confirm the congestion information before optimizing the traveling path of the requested vehicle, that is, an information confirmation step. In the information confirmation step, the requesting vehicle only needs to send the congestion confirmation information to the clustered vehicles in the congestion area, the feedback of the clustered vehicles in the congestion area to the confirmation information is concerned in real time, and when the feedback result of the clustered vehicles in the congestion area to the confirmation information is also congestion, the analysis of the congestion area is accurate and has high referential performance.
107. The travel path of the requested vehicle is optimized.
According to the distribution position of the congested area and the destination of the request vehicle, the vehicle networking platform optimizes the running path of the request vehicle, so that the request vehicle avoids the congested area as far as possible, smooth driving is achieved, and driving experience is improved.
Preferably, under the condition that the requested vehicle is ensured to drive smoothly, in order to further improve the driving safety of the requested vehicle, it is also necessary to predict and evaluate the dangerous driving performance on the road. To evaluate the driving risk, the overspeed, rapid acceleration, rapid deceleration and rapid turning of each clustered vehicle in a preset time period need to be collected, that is, the driving information further includes the overspeed, rapid acceleration, rapid deceleration and rapid turning. Generally, a driver with a mild character can avoid the violent operations of overtaking, rapid acceleration or rapid deceleration and the like as much as possible, the driving behavior is relatively stable and the safety is relatively high, a driver with an impatient and violent anxiety of spleen qi often has intolerance in the driving process, frequently uses overtaking, rapid acceleration or rapid deceleration and the like, and the operation behavior is relatively violent and the risk is relatively high. Therefore, the driving risk of the road can be well reflected by analyzing the overspeed frequency, the rapid acceleration frequency, the rapid deceleration frequency and the rapid turning frequency of the clustered vehicles in the preset time period.
Specifically, in the process of evaluating the driving risk, the overspeed times, the rapid acceleration times, the rapid deceleration times and the rapid turning times of each clustered vehicle within the coverage area of the social circle in the preset time need to be summed, that is, the sum of the overspeed times, the rapid acceleration times, the rapid deceleration times and the rapid turning times of one clustered vehicle within the preset time is obtained. And when the sum of the overspeed times, the rapid acceleration times, the rapid deceleration times and the rapid turning times of one clustered vehicle is greater than a fourth threshold value, the operation of the driver is considered to be relatively violent, namely the personality of the driver is impatient. Marking the clustering vehicles with the impatient characters on the map, and counting the number of drivers with the impatient characters on each road. Once the number of drivers with impatient characters on a certain road exceeds the fifth threshold, it is considered that more impatient drivers exist on the road, which means that over-speed, rapid acceleration, rapid deceleration and rapid turning frequently occur on the road, and the requested vehicle has a certain danger when driving on the road, and the road is considered as a dangerous road to remind the requested vehicle.
Of course, once the congested area and the dangerous road are determined, when the driving path of the requested vehicle is optimized, the vehicle networking platform needs to avoid the congested area and the dangerous road as much as possible so as to ensure smooth driving of the requested vehicle and achieve the purpose of improving driving safety.
It is noted that sometimes congested areas and dangerous roads may be roads that the vehicle must travel to the destination and cannot be avoided. Then, at this time, the vehicle networking platform is required to appropriately remind or suggest the requested vehicle according to the congested area and the dangerous road, if the requested vehicle needs to enter the congested area, the vehicle networking platform can inform the requested vehicle of the current average running speed of the road, and suggest the requested vehicle to adjust the running speed in advance so as to avoid unnecessary accidents such as rear-end collision and the like when entering the congested area; or when the vehicle is requested to enter the dangerous road, the vehicle network platform can advise the driver to slow down and walk in advance, avoid the operations of sudden overspeed, sudden acceleration or sudden deceleration and the like of other clustered vehicles to cause traffic accidents, and reduce the road driving danger.
Preferably, when the personality of the driver is judged to be impatient, the Internet of vehicles platform can also actively push some light music aiming at the impatient driver so as to achieve the purpose of relieving the emotion of the driver. Of course, the car networking platform may push other articles or interview-like programs and the like to the driver that help to smooth the mood, and is not limited herein.
Example 2:
as shown in fig. 2, the present embodiment discloses a traffic condition prediction system, which is based on the traffic condition prediction method based on the road social circle described in embodiment 1, and specifically includes a vehicle networking platform and a plurality of vehicle-mounted communication devices disposed on different vehicles; each vehicle-mounted communication device is respectively in communication connection with the vehicle networking platform so as to realize information interaction with the vehicle networking platform, and each vehicle-mounted communication device can realize information interaction through the vehicle networking platform; the vehicle network platform integrates three major elements of calculation, network and storage, and is used for receiving the driving information sent by the vehicle-mounted communication equipment and analyzing the road traffic condition according to the driving information so as to optimize the driving path.
It should be understood that the above-described embodiments of the present invention are merely examples for clearly illustrating the present invention, and are not intended to limit the embodiments of the present invention. Other variations and modifications will be apparent to persons skilled in the art in light of the above description. And are neither required nor exhaustive of all embodiments. Any modification, equivalent replacement, and improvement made within the spirit and principle of the present invention should be included in the protection scope of the claims of the present invention.
Claims (11)
1. A traffic condition prediction method based on a road social circle is characterized in that the method based on a vehicle networking platform and vehicle-mounted communication equipment comprises the following steps:
sending a social request to the vehicle networking platform;
the Internet of vehicles platform acquires the driving information of the requested vehicle, wherein the driving information comprises the position, the driving direction and the driving speed of the vehicle;
taking the vehicle position of the request vehicle as a center, taking a first preset value as a radius to establish a social circle, and regarding each vehicle in the coverage range of the social circle as a clustered vehicle;
acquiring the driving information of each clustered vehicle, and mapping each clustered vehicle to a corresponding position of a map;
acquiring the vehicle density and the average running speed of each road around the requested vehicle according to the running information of each clustered vehicle;
when the vehicle density is greater than a first threshold value and the average running speed is less than a second threshold value, the road is considered to be a congestion area;
the travel path of the requested vehicle is optimized.
2. The method for predicting traffic conditions based on the social circle of road as claimed in claim 1, wherein the obtaining process of the vehicle density comprises the following sub-steps:
carrying out position matching on the clustered vehicles and all roads around the request vehicle;
respectively counting the number of clustered vehicles on each road;
the number of clustered vehicles per unit length of each road is calculated as the vehicle density of the road.
3. The method as claimed in claim 2, wherein in the step of matching the positions of the clustered vehicles with the positions of the roads around the requesting vehicle, the clustered vehicles are considered to be matched with the road positions when the shortest distance between the clustered vehicles and the roads is less than a third threshold.
4. The method for predicting traffic conditions based on the social circle of road as claimed in claim 2, wherein the obtaining process of the average driving speed comprises the following sub-steps:
randomly extracting N clustered vehicles on a road;
solving the speed sum of N clustered vehicles;
the ratio of the speed sum to N is calculated as the average traveling speed.
5. The method as claimed in claim 4, wherein the second threshold is an average driving speed of the vehicle under a road clear condition.
6. The method for predicting traffic conditions based on the social circle of road according to claim 1, wherein the driving information further comprises the number of speeding, the number of sharp acceleration, the number of sharp deceleration and the number of sharp turning; before the optimization of the driving path of the requested vehicle, the method further comprises a driving risk assessment step, wherein the driving risk assessment step comprises the following steps:
summing the overspeed times, the rapid acceleration times, the rapid deceleration times and the rapid turning times of each clustered vehicle within preset time;
comparing the sum with a fourth threshold, and if the sum is greater than the fourth threshold, determining that the personality of the driver is impatient;
counting the number of drivers with impatient characters on each road;
when the number of drivers with impatience type characters exceeds the fifth threshold, it is considered that there is a certain risk of traveling on the road, and the road is regarded as a dangerous road.
7. The traffic condition prediction method based on the social circle of roads as claimed in claim 6, wherein, in the process of optimizing the driving path of the request vehicle, not only the congested area needs to be avoided, but also the dangerous road needs to be kept away.
8. The method as claimed in claim 6, wherein after the step of comparing the sum with a fourth threshold and if the sum is greater than the fourth threshold, the step of considering the personality of the driver as impatient, the method further comprises:
the vehicle net platform pushes light music to the driver with impatient characters so as to achieve the purpose of relieving the emotion of the driver.
9. The traffic condition prediction method based on the road social circle is characterized in that a vehicle in the coverage area of the social circle can realize information interaction with a vehicle networking platform and other vehicles in the coverage area of the social circle, and the vehicle can break through the limitation of the social circle through mutual attention to establish a long-term connection.
10. The method for predicting traffic conditions based on the social circle of road according to claim 9, further comprising an information confirmation step before the step of optimizing the traveling path of the requesting vehicle:
requesting the vehicles to send confirmation information to the clustered vehicles in the congested area, and acquiring feedback results of the clustered vehicles on the confirmation information in real time;
and when the feedback result of the clustered vehicles is congestion, the analysis of the congestion area is considered to be accurate.
11. A traffic condition prediction system based on the traffic condition prediction method based on the road social circle according to any one of claims 1 to 10, characterized by comprising a vehicle networking platform and a plurality of vehicle-mounted communication devices arranged on different vehicles; each vehicle-mounted communication device is respectively in communication connection with the vehicle networking platform so as to realize information interaction with the vehicle networking platform, and each vehicle-mounted communication device can realize information interaction through the vehicle networking platform; the vehicle network platform is used for receiving the driving information sent by the vehicle-mounted communication equipment and analyzing the road traffic condition according to the driving information so as to optimize the driving path.
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| CN115131958A (en) * | 2021-03-26 | 2022-09-30 | 上海博泰悦臻网络技术服务有限公司 | Jammed road condition pushing method and device, electronic equipment and storage medium |
| CN115438051A (en) * | 2021-11-18 | 2022-12-06 | 北京车和家信息技术有限公司 | Map updating method and device |
| CN116916278A (en) * | 2023-07-17 | 2023-10-20 | 深圳前海壹互联科技投资有限公司 | Driving interaction method and system applied to Internet of vehicles |
| CN116916278B (en) * | 2023-07-17 | 2024-04-26 | 深圳前海壹互联科技投资有限公司 | Driving interaction method and system applied to Internet of vehicles |
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