CN115240409B - Method for extracting dangerous scene based on driver model and traffic flow model - Google Patents
Method for extracting dangerous scene based on driver model and traffic flow model Download PDFInfo
- Publication number
- CN115240409B CN115240409B CN202210684071.2A CN202210684071A CN115240409B CN 115240409 B CN115240409 B CN 115240409B CN 202210684071 A CN202210684071 A CN 202210684071A CN 115240409 B CN115240409 B CN 115240409B
- Authority
- CN
- China
- Prior art keywords
- traffic flow
- model
- traffic
- vehicle
- extracting
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Active
Links
- 238000000034 method Methods 0.000 title claims abstract description 14
- 230000003068 static effect Effects 0.000 claims abstract description 6
- 238000012544 monitoring process Methods 0.000 claims abstract description 4
- 230000004044 response Effects 0.000 claims description 6
- 230000001133 acceleration Effects 0.000 claims description 5
- 238000005286 illumination Methods 0.000 claims description 5
- 230000008859 change Effects 0.000 claims description 4
- 238000001514 detection method Methods 0.000 claims description 4
- 238000005070 sampling Methods 0.000 claims description 4
- 238000012360 testing method Methods 0.000 abstract description 19
- 238000010998 test method Methods 0.000 description 4
- 238000011161 development Methods 0.000 description 3
- 206010039203 Road traffic accident Diseases 0.000 description 2
- 230000007547 defect Effects 0.000 description 2
- 238000004088 simulation Methods 0.000 description 2
- 230000009286 beneficial effect Effects 0.000 description 1
- 238000010586 diagram Methods 0.000 description 1
- 238000005516 engineering process Methods 0.000 description 1
- 230000003203 everyday effect Effects 0.000 description 1
- 238000000605 extraction Methods 0.000 description 1
- 230000006872 improvement Effects 0.000 description 1
- 238000012986 modification Methods 0.000 description 1
- 230000004048 modification Effects 0.000 description 1
- 230000008569 process Effects 0.000 description 1
Classifications
-
- G—PHYSICS
- G08—SIGNALLING
- G08G—TRAFFIC CONTROL SYSTEMS
- G08G1/00—Traffic control systems for road vehicles
- G08G1/01—Detecting movement of traffic to be counted or controlled
- G08G1/0104—Measuring and analyzing of parameters relative to traffic conditions
- G08G1/0125—Traffic data processing
-
- G—PHYSICS
- G08—SIGNALLING
- G08G—TRAFFIC CONTROL SYSTEMS
- G08G1/00—Traffic control systems for road vehicles
- G08G1/01—Detecting movement of traffic to be counted or controlled
- G08G1/0104—Measuring and analyzing of parameters relative to traffic conditions
Abstract
The invention discloses a method for extracting dangerous scenes based on a driver model and a traffic flow model, which solves the problems of high cost, long period, large safety risk and limited coverage working condition of the existing road test adopted by automatic driving, and the technical scheme is characterized by comprising the following steps: generating random traffic flow in the virtual environment based on the established driver behavior model and traffic flow model; monitoring the whole traffic flow; when judging that the vehicles collide at the moment T or the distance between the vehicles is smaller than the set dangerous distance, extracting relevant information of the corresponding vehicles and other related traffic participants in the time period from T-deltat to the moment T, and extracting static environment information related to the track to form a dangerous scene; the method for extracting the dangerous scene based on the driver model and the traffic flow model can quickly and efficiently reproduce various dangerous working conditions, can greatly provide testing efficiency, improves testing safety and reduces testing cost.
Description
Technical Field
The invention relates to an automatic driving automobile testing technology, in particular to a method for extracting dangerous scenes based on a driver model and a traffic flow model.
Background
At present, the development and test development in the aspect of domestic automatic driving are very rapid, and in the development process of an automatic driving automobile, the functions and performances of an automatic driving system are required to be checked through testing, so that developers are helped to find out the defects or defects of the system functions, and the system functions are perfected and optimized in a targeted manner. These tests need to be able to cover natural driving scenarios during everyday driving and possibly accident conditions scenarios.
At present, an automatic driving manufacturer mainly adopts a road test method to test, and the test method has the advantages of high test cost, long period, high safety risk, limited dangerous working conditions which can be covered and still has room for improvement.
Disclosure of Invention
The invention aims to provide a method for extracting a dangerous scene based on a driver model and a traffic flow model, which can quickly and efficiently reproduce various dangerous working conditions, can greatly provide testing efficiency, improves testing safety and reduces testing cost.
The technical aim of the invention is realized by the following technical scheme:
a method for extracting dangerous scenes based on a driver model and a traffic flow model, comprising the following steps:
generating random traffic flow in the virtual environment based on the established driver behavior model and traffic flow model;
monitoring the whole traffic flow;
judging whether vehicles collide or not or whether the distance between the vehicles is smaller than a set dangerous distance at the moment T; if not, continuing to monitor;
if so, in the time period from T-deltat to T, extracting traffic flow related information and driver behavior related information of the corresponding vehicle with danger and other related traffic participants, and extracting static environment information related to the track to form a dangerous scene.
In summary, the invention has the following beneficial effects:
the traffic flow can reflect real world traffic conditions to the greatest extent through the driver behavior model and the traffic flow model, dangerous scenes such as traffic accidents possibly generated in the real world can be simulated and covered, one dangerous scene can be correspondingly obtained through extraction and collection of data such as parameters when the accidents occur in the virtual environment, even some edge scenes and extreme scenes with extremely low occurrence probability in the real world can be generated, a large number of dangerous scenes, edge scenes and extreme scenes can be rapidly obtained through large-scale arrangement of the random traffic flow model, the obtained scenes have certain authenticity, and the effectiveness of the scenes can be guaranteed. The simulation test method can quickly and efficiently reproduce various dangerous working conditions, can greatly improve the test efficiency, improve the test safety and reduce the test cost.
Drawings
FIG. 1 is a schematic block diagram of a flow of the method
Detailed Description
The present invention will be described in further detail with reference to the accompanying drawings.
According to one or more embodiments, a method for extracting a dangerous scene based on a driver model and a traffic flow model is disclosed, comprising the steps of:
s1, generating random traffic flow in a virtual environment based on the established driver behavior model and traffic flow model.
The driver behavior model includes a plurality of dimensions including, but not limited to:
desired speed fluctuation value-fluctuation range of desired speed response, such as + -10%, the range being adjustable;
desired acceleration/deceleration fluctuation value-fluctuation range for desired acceleration/deceleration response, such as + -10%, the range being adjustable;
disregarding the speed limit degree-disregarding the maximum speed probability given to traffic signs such as speed limit signs, between 0 and 1, 0 indicating complete compliance, 0.5 indicating 50% of possible compliance, 1 indicating complete disregarding, non compliance;
desired vehicle distance fluctuation value-fluctuation range of the workshop time distance desired to be maintained, such as +/-20%, the range is adjustable;
track line/lane keeping offset-cycle time of offset to planned track line or lane line of road and maximum offset;
the fluctuation value of the expected lane change transverse speed-the fluctuation range of the response of the expected lane change transverse speed, such as +/-10%, is adjustable;
willing to overtake: the relation between the ratio of the speed of the front vehicle to the expected speed of the own vehicle and the possibility of overtaking is similar to a two-dimensional curve, the horizontal axis represents the ratio of the speed of the front vehicle to the expected speed of the own vehicle, the vertical axis represents the possibility of overtaking, if the ratio of the speed of the front vehicle to the expected speed of the own vehicle is 0.8, the possibility of overtaking is 80%, the curve can be set according to the situation, if the curve is set to be in a linear relation, and the larger the ratio of the speed of the front vehicle to the expected speed of the own vehicle is, the smaller the possibility of overtaking is;
compliance with traffic lights/signs will: for example, 50% -100%,50% means that there is 50% of possible adherence after encountering a traffic light, 100% means that there is affirmative adherence, and the parameter range is adjustable;
and (5) turning on a steering lamp will: possibility of turning on the steering lamp during steering, such as 50% -100%;
the driver behavior model is obtained through collecting, counting and analyzing the behavior characteristics of a real-world driver, and a certain range of randomness is set for each parameter.
Traffic flow models include common parameters including, but not limited to:
average vehicle flow: the number of vehicles passing through a certain section in a specified road section in the sampling time;
traffic density: the number of vehicles present over a certain time unit road length;
average vehicle speed: an average value of the travel distance of the vehicle in the detection section within a unit time;
average occupancy: the sum of the time of all vehicles entering the detection road section occupying the traffic flow data sensor is compared with the up-sampling time;
distance between heads: the distance between the front and rear vehicle heads;
headway: the time obtained by dividing the vehicle head distance of the front vehicle and the rear vehicle by the speed of the rear vehicle;
the traffic flow model comprises the distribution conditions of the parameters about time and space, wherein the distribution conditions are obtained by collecting, counting and analyzing traffic data of certain roads in the real world, a certain range of randomness is set for each parameter, and each vehicle in the traffic flow model has a certain automatic driving function.
S2, monitoring the whole traffic flow.
S3, judging whether vehicles collide or whether the distance between the vehicles is smaller than a set dangerous distance at the moment T; if not, continuing to monitor.
And S4, if collision occurs or the distance between vehicles is smaller than the set dangerous distance, extracting traffic flow related information and driver behavior related information of corresponding vehicles with danger and other related traffic participants in a time period from T-delta T to T moment, and extracting static environment information related to the track to form a dangerous scene.
For example, the vehicle a and the vehicle B relate to dangerous events, relevant information such as running tracks, speeds, acceleration head orientations and the like of the vehicle a, the vehicle B and other related traffic participants in the time period from T-deltat to T is extracted, static environment information such as road information, traffic sign information, weather information, illumination information and the like which are related to the tracks are extracted, the road information comprises and is not limited to information such as lane types, lane numbers, gradients, road surface conditions and the like, the traffic sign information comprises traffic lights, ground signs and traffic sign information, the weather information comprises sunny days, rainy days, snowy days, fog, haze and the like, the illumination information comprises daytime, evening, night, backlight, dazzle light and the like. The information is combined to form a dangerous scene.
The driver behavior model and the traffic flow model are obtained by statistics and analysis of real driving/traffic conditions in the real world, the model has authenticity, and meanwhile, certain randomness is set, so that the random traffic flow generated by the method can reflect the traffic conditions in the real world to the maximum extent, can cover dangerous scenes in the real world, such as traffic accidents, and the like, and even can generate some edge scenes and extreme scenes with extremely low probability in the real world. By arranging the random traffic flow model in a large scale, a large number of dangerous scenes, edge scenes and extreme scenes can be quickly obtained, the obtained scenes have a certain reality, and the effectiveness of the scenes can be ensured. The simulation test method can quickly and efficiently reproduce various dangerous working conditions, can greatly improve the test efficiency, improve the test safety and reduce the test cost.
The present embodiment is only for explanation of the present invention and is not to be construed as limiting the present invention, and modifications to the present embodiment, which may not creatively contribute to the present invention as required by those skilled in the art after reading the present specification, are all protected by patent laws within the scope of claims of the present invention.
Claims (3)
1. The method for extracting the dangerous scene based on the driver model and the traffic flow model is characterized by comprising the following steps of:
generating random traffic flow in the virtual environment based on the established driver behavior model and traffic flow model;
collecting, counting and analyzing behavior characteristics of a driver in the real world, setting randomness of a certain range for each dimension parameter, and generating a driver behavior model; collecting, counting and analyzing the distribution condition of the common parameter data of the real traffic flow about time and space, setting randomness of a certain range for each parameter, and generating a traffic flow model; randomly generating traffic flow in the virtual environment according to the set parameters;
monitoring the whole traffic flow;
judging whether vehicles collide or not or whether the distance between the vehicles is smaller than a set dangerous distance at the moment T; if not, continuing to monitor;
if so, extracting traffic flow related information and driver behavior related information of a corresponding vehicle with danger and other related traffic participants in a time period from T-deltat to T moment, and extracting static environment information related to the track to form a dangerous scene;
the static environment information includes:
traffic sign information including traffic lights, ground signs, and traffic sign information; weather information including sunny days, rainy days, snowy days, foggy days, and haze days; the illumination information comprises daytime illumination, evening illumination, night, backlight and dazzle light.
2. The method for extracting a dangerous scene based on a driver model and a traffic flow model according to claim 1, wherein the driver behavior model comprises the following multi-dimensional information:
desired speed fluctuation value-fluctuation range of response to desired speed, the fluctuation range being adjusted according to the setting;
desired acceleration/deceleration fluctuation value-fluctuation range for desired acceleration/deceleration response, the fluctuation range being adjusted according to the setting;
the probability of maximum speed of the invisible speed-limiting traffic sign is set to be between 0 and 1, 0 represents complete adherence, 0.5 represents 50% of possible adherence, 1 represents complete invisible and unobserved adherence;
the fluctuation range of the expected vehicle distance fluctuation value-the fluctuation range of the workshop time distance expected to be kept is adjusted according to the setting;
track line/lane keeping offset-cycle time of offset to planned track line or lane line of road and maximum offset;
fluctuation value of the expected lane change transverse speed-fluctuation range of response to the expected lane change transverse speed, and the fluctuation range is adjusted according to the setting;
willing to overtake: a relationship between a ratio of a front vehicle speed to a desired vehicle speed of the host vehicle and a possibility of overtaking;
the traffic light/traffic sign will and the steering light will are complied with.
3. The method for extracting a dangerous scene based on a driver model and a traffic flow model according to claim 1, wherein the traffic flow model setting comprises the following parameter information:
average vehicle flow: the number of vehicles passing through a certain section in a specified road section in the sampling time;
traffic density: the number of vehicles present over a certain time unit road length;
average vehicle speed: an average value of the travel distance of the vehicle in the detection section within a unit time;
average occupancy: the sum of the time of all vehicles entering the detection road section occupying the traffic flow data sensor is compared with the up-sampling time;
distance between heads: the distance between the front and rear vehicle heads;
headway: the time obtained by dividing the vehicle head distance of the front vehicle and the rear vehicle by the speed of the rear vehicle.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202210684071.2A CN115240409B (en) | 2022-06-17 | 2022-06-17 | Method for extracting dangerous scene based on driver model and traffic flow model |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202210684071.2A CN115240409B (en) | 2022-06-17 | 2022-06-17 | Method for extracting dangerous scene based on driver model and traffic flow model |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN115240409A CN115240409A (en) | 2022-10-25 |
| CN115240409B true CN115240409B (en) | 2024-02-06 |
Family
ID=83669395
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202210684071.2A Active CN115240409B (en) | 2022-06-17 | 2022-06-17 | Method for extracting dangerous scene based on driver model and traffic flow model |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN115240409B (en) |
Citations (19)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| EP1569186A1 (en) * | 2004-02-26 | 2005-08-31 | HONDA MOTOR CO., Ltd. | A road traffic simulation apparatus |
| UA102416C2 (en) * | 2011-06-06 | 2013-07-10 | Севастопольский Национальный Технический Университет | Normal distribution emulator |
| CN105808315A (en) * | 2014-12-31 | 2016-07-27 | 中国移动通信集团公司 | High level architecture synergetic traffic safety simulation method and apparatus |
| WO2018002910A1 (en) * | 2016-06-28 | 2018-01-04 | Cognata Ltd. | Realistic 3d virtual world creation and simulation for training automated driving systems |
| CN108765235A (en) * | 2018-05-09 | 2018-11-06 | 公安部交通管理科学研究所 | Automatic driving vehicle test scene construction method and test method based on the destructing of traffic accident case |
| KR20190057731A (en) * | 2017-11-20 | 2019-05-29 | 한국전자통신연구원 | Self-evolving agent-based simulation system and method thereof |
| CN110795818A (en) * | 2019-09-12 | 2020-02-14 | 腾讯科技（深圳）有限公司 | Method and device for determining virtual test scene, electronic equipment and storage medium |
| CN111783229A (en) * | 2020-07-02 | 2020-10-16 | 北京赛目科技有限公司 | Method and device for generating simulated traffic flow |
| CN112036001A (en) * | 2020-07-01 | 2020-12-04 | 长安大学 | Automatic driving test scene construction method, device and equipment and readable storage medium |
| CN112201033A (en) * | 2020-09-08 | 2021-01-08 | 同济大学 | Road traffic operation active control strategy selection method |
| CN112904827A (en) * | 2021-01-18 | 2021-06-04 | 同济大学 | Unmanned virtual simulation test system for multiple ICUs |
| CN113095377A (en) * | 2021-03-26 | 2021-07-09 | 中国科学院电工研究所 | Dangerous driving scene data random generation method and system |
| CN113093569A (en) * | 2021-04-13 | 2021-07-09 | 中国第一汽车股份有限公司 | Automatic driving simulation test method, device, equipment and storage medium |
| CN113490940A (en) * | 2020-01-21 | 2021-10-08 | 深圳元戎启行科技有限公司 | Scene simulator construction method and device based on deep learning and computer equipment |
| CN113609016A (en) * | 2021-08-05 | 2021-11-05 | 北京赛目科技有限公司 | Method, device, equipment and medium for constructing vehicle automatic driving test scene |
| CN113868875A (en) * | 2021-09-30 | 2021-12-31 | 天津大学 | Method, device and equipment for automatically generating test scene and storage medium |
| CN113918615A (en) * | 2021-08-26 | 2022-01-11 | 武汉光庭信息技术股份有限公司 | Simulation-based driving experience data mining model construction method and system |
| CN113946902A (en) * | 2020-07-16 | 2022-01-18 | 陕西汽车集团有限责任公司 | Method, device and equipment for creating dynamic traffic flow scene and storage medium |
| CN114169247A (en) * | 2021-12-13 | 2022-03-11 | 中国第一汽车股份有限公司 | Method, device and equipment for generating simulated traffic flow and computer readable storage medium |
-
2022
- 2022-06-17 CN CN202210684071.2A patent/CN115240409B/en active Active
Patent Citations (19)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| EP1569186A1 (en) * | 2004-02-26 | 2005-08-31 | HONDA MOTOR CO., Ltd. | A road traffic simulation apparatus |
| UA102416C2 (en) * | 2011-06-06 | 2013-07-10 | Севастопольский Национальный Технический Университет | Normal distribution emulator |
| CN105808315A (en) * | 2014-12-31 | 2016-07-27 | 中国移动通信集团公司 | High level architecture synergetic traffic safety simulation method and apparatus |
| WO2018002910A1 (en) * | 2016-06-28 | 2018-01-04 | Cognata Ltd. | Realistic 3d virtual world creation and simulation for training automated driving systems |
| KR20190057731A (en) * | 2017-11-20 | 2019-05-29 | 한국전자통신연구원 | Self-evolving agent-based simulation system and method thereof |
| CN108765235A (en) * | 2018-05-09 | 2018-11-06 | 公安部交通管理科学研究所 | Automatic driving vehicle test scene construction method and test method based on the destructing of traffic accident case |
| CN110795818A (en) * | 2019-09-12 | 2020-02-14 | 腾讯科技（深圳）有限公司 | Method and device for determining virtual test scene, electronic equipment and storage medium |
| CN113490940A (en) * | 2020-01-21 | 2021-10-08 | 深圳元戎启行科技有限公司 | Scene simulator construction method and device based on deep learning and computer equipment |
| CN112036001A (en) * | 2020-07-01 | 2020-12-04 | 长安大学 | Automatic driving test scene construction method, device and equipment and readable storage medium |
| CN111783229A (en) * | 2020-07-02 | 2020-10-16 | 北京赛目科技有限公司 | Method and device for generating simulated traffic flow |
| CN113946902A (en) * | 2020-07-16 | 2022-01-18 | 陕西汽车集团有限责任公司 | Method, device and equipment for creating dynamic traffic flow scene and storage medium |
| CN112201033A (en) * | 2020-09-08 | 2021-01-08 | 同济大学 | Road traffic operation active control strategy selection method |
| CN112904827A (en) * | 2021-01-18 | 2021-06-04 | 同济大学 | Unmanned virtual simulation test system for multiple ICUs |
| CN113095377A (en) * | 2021-03-26 | 2021-07-09 | 中国科学院电工研究所 | Dangerous driving scene data random generation method and system |
| CN113093569A (en) * | 2021-04-13 | 2021-07-09 | 中国第一汽车股份有限公司 | Automatic driving simulation test method, device, equipment and storage medium |
| CN113609016A (en) * | 2021-08-05 | 2021-11-05 | 北京赛目科技有限公司 | Method, device, equipment and medium for constructing vehicle automatic driving test scene |
| CN113918615A (en) * | 2021-08-26 | 2022-01-11 | 武汉光庭信息技术股份有限公司 | Simulation-based driving experience data mining model construction method and system |
| CN113868875A (en) * | 2021-09-30 | 2021-12-31 | 天津大学 | Method, device and equipment for automatically generating test scene and storage medium |
| CN114169247A (en) * | 2021-12-13 | 2022-03-11 | 中国第一汽车股份有限公司 | Method, device and equipment for generating simulated traffic flow and computer readable storage medium |
Non-Patent Citations (2)
| Title |
|---|
| 自动驾驶汽车测试场景构建关键技术综述;徐向阳 等;《汽车工程》;第43卷(第4期);第610-619页 * |
| 超速行为对交通流扰动影响的仿真研究;王永明 等;《系统仿真学报》;第20卷(第12期);第3301-3304页 * |
Also Published As
| Publication number | Publication date |
|---|---|
| CN115240409A (en) | 2022-10-25 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN110689613B (en) | Vehicle road simulation scene construction method, device, medium and equipment | |
| CN113050455A (en) | Digital twin test system for intelligent networked automobile and control method | |
| CN112819968B (en) | Test method and device for automatic driving vehicle based on mixed reality | |
| CN113609016B (en) | Method, device, equipment and medium for constructing automatic driving test scene of vehicle | |
| CN110232335A (en) | Driving Scene classification method and electronic equipment | |
| CN110763483A (en) | Automatic generation method and device of security level test scene library | |
| CN102298845A (en) | Far-beam light video detection method and system | |
| CN112329248A (en) | Road mixed traffic flow simulation system based on multi-agent system | |
| CN115240409B (en) | Method for extracting dangerous scene based on driver model and traffic flow model | |
| CN114722569A (en) | Construction method of simulated dangerous scene based on accident data | |
| CN112612288B (en) | Expected function safety risk assessment method for error/omission identification of automatic driving vehicle | |
| US20170371985A1 (en) | Method and apparatus of obtaining feature informtoin of simulated agents | |
| CN113254336B (en) | Method and system for simulation test of traffic regulation compliance of automatic driving automobile | |
| Suetomi et al. | Driver behavior under a collision warning system-a driving simulator study | |
| CN112373482B (en) | Driving habit modeling method based on driving simulator | |
| CN114564849A (en) | Data-driven vehicle economy simulation test scene generation method | |
| CN115270400A (en) | Test scene construction method for automatic driving simulation and readable storage medium | |
| Ising | Threshold visibility levels required for nighttime pedestrian detection in a modified Adrian/CIE visibility model | |
| Liu et al. | Study on test scenarios of environment perception system under rear-end collision risk | |
| Li et al. | Drivers' right-angle collision avoidance behaviors at non-signalized intersection—A driving simulator based study | |
| Cavallo | Perceptual distortions when driving in fog | |
| Gao et al. | Driving style recognition based on lane change behavior analysis using naturalistic driving data | |
| Zhang et al. | A method for evaluating the complexity of test scenarios for autonomous vehicles | |
| WO2023097519A1 (en) | Scenario acquisition method and apparatus | |
| Shao et al. | The Effect of Road Tunnel Environment on Driving Lane-changing Behavior |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| TA01 | Transfer of patent application right |
Effective date of registration: 20240105 Address after: Building 10, No. 860, Xinyang Road, Lingang New District, China (Shanghai) pilot Free Trade Zone, Pudong New Area, Shanghai Applicant after: Shangzhilian (Shanghai) Intelligent Technology Co.,Ltd. Address before: 200000 building 22, No. 6055, Jinhai highway, Fengxian District, Shanghai Applicant before: Shanghai intelligent network Automobile Technology Center Co.,Ltd. |
|
| TA01 | Transfer of patent application right | ||
| GR01 | Patent grant | ||
| GR01 | Patent grant |