CN115016453A - Intelligent networking unmanned vehicle driving system - Google Patents
Intelligent networking unmanned vehicle driving system Download PDFInfo
- Publication number
- CN115016453A CN115016453A CN202210162634.1A CN202210162634A CN115016453A CN 115016453 A CN115016453 A CN 115016453A CN 202210162634 A CN202210162634 A CN 202210162634A CN 115016453 A CN115016453 A CN 115016453A
- Authority
- CN
- China
- Prior art keywords
- road
- information
- vehicle
- unit
- module
- 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.)
- Pending
Links
- 230000006855 networking Effects 0.000 title claims abstract description 16
- 230000007613 environmental effect Effects 0.000 claims abstract description 18
- 238000004458 analytical method Methods 0.000 claims abstract description 10
- 230000008447 perception Effects 0.000 claims abstract description 10
- 238000004364 calculation method Methods 0.000 claims abstract description 3
- 238000004088 simulation Methods 0.000 claims description 51
- 230000010365 information processing Effects 0.000 claims description 9
- 230000010354 integration Effects 0.000 claims description 6
- 238000005352 clarification Methods 0.000 claims description 3
- 238000000034 method Methods 0.000 description 7
- 238000004422 calculation algorithm Methods 0.000 description 3
- 230000000694 effects Effects 0.000 description 2
- 230000004927 fusion Effects 0.000 description 2
- 239000003595 mist Substances 0.000 description 2
- 230000008569 process Effects 0.000 description 2
- 230000000007 visual effect Effects 0.000 description 2
- XLYOFNOQVPJJNP-UHFFFAOYSA-N water Substances O XLYOFNOQVPJJNP-UHFFFAOYSA-N 0.000 description 2
- 230000003044 adaptive effect Effects 0.000 description 1
- 230000004075 alteration Effects 0.000 description 1
- 238000010586 diagram Methods 0.000 description 1
- 238000005516 engineering process Methods 0.000 description 1
- 230000002708 enhancing effect Effects 0.000 description 1
- 238000012986 modification Methods 0.000 description 1
- 230000004048 modification Effects 0.000 description 1
- 230000002265 prevention Effects 0.000 description 1
- 239000004576 sand Substances 0.000 description 1
- 230000003068 static effect Effects 0.000 description 1
Images
Classifications
-
- G—PHYSICS
- G05—CONTROLLING; REGULATING
- G05D—SYSTEMS FOR CONTROLLING OR REGULATING NON-ELECTRIC VARIABLES
- G05D1/00—Control of position, course, altitude or attitude of land, water, air or space vehicles, e.g. using automatic pilots
- G05D1/02—Control of position or course in two dimensions
- G05D1/021—Control of position or course in two dimensions specially adapted to land vehicles
- G05D1/0212—Control of position or course in two dimensions specially adapted to land vehicles with means for defining a desired trajectory
- G05D1/0214—Control of position or course in two dimensions specially adapted to land vehicles with means for defining a desired trajectory in accordance with safety or protection criteria, e.g. avoiding hazardous areas
-
- G—PHYSICS
- G05—CONTROLLING; REGULATING
- G05D—SYSTEMS FOR CONTROLLING OR REGULATING NON-ELECTRIC VARIABLES
- G05D1/00—Control of position, course, altitude or attitude of land, water, air or space vehicles, e.g. using automatic pilots
- G05D1/02—Control of position or course in two dimensions
- G05D1/021—Control of position or course in two dimensions specially adapted to land vehicles
- G05D1/0231—Control of position or course in two dimensions specially adapted to land vehicles using optical position detecting means
- G05D1/0246—Control of position or course in two dimensions specially adapted to land vehicles using optical position detecting means using a video camera in combination with image processing means
-
- G—PHYSICS
- G05—CONTROLLING; REGULATING
- G05D—SYSTEMS FOR CONTROLLING OR REGULATING NON-ELECTRIC VARIABLES
- G05D1/00—Control of position, course, altitude or attitude of land, water, air or space vehicles, e.g. using automatic pilots
- G05D1/02—Control of position or course in two dimensions
- G05D1/021—Control of position or course in two dimensions specially adapted to land vehicles
- G05D1/0257—Control of position or course in two dimensions specially adapted to land vehicles using a radar
Landscapes
- Engineering & Computer Science (AREA)
- Radar, Positioning & Navigation (AREA)
- Remote Sensing (AREA)
- Physics & Mathematics (AREA)
- Aviation & Aerospace Engineering (AREA)
- General Physics & Mathematics (AREA)
- Automation & Control Theory (AREA)
- Computer Vision & Pattern Recognition (AREA)
- Multimedia (AREA)
- Electromagnetism (AREA)
- Traffic Control Systems (AREA)
Abstract
The invention relates to the technical field of unmanned vehicle driving, and discloses an intelligent networking unmanned vehicle driving system, which comprises: the system comprises an information perception module, a road condition analysis module, a network connection calculation module, an obstacle avoidance strategy module and an information synchronization module, wherein the information perception module is used for collecting and processing environmental information around the vehicle and state information of the vehicle; the road condition analysis module is used for establishing a road condition model according to the environmental information; the network connection computing module is used for adjusting an early warning threshold value, a driving strategy and an obstacle avoidance strategy of vehicle driving according to the road condition model and the environment information; the obstacle avoidance strategy module is used for analyzing the predicted track of the vehicle according to the road condition model and the state information and matching a corresponding obstacle avoidance strategy; the information synchronization module is used for synchronizing the driving strategy and the obstacle avoidance strategy to the vehicle. The unmanned vehicle can be subjected to information unified management, comprehensive traffic conditions can be comprehensively mastered, a safe driving strategy is provided, and the driving safety and reliability of the unmanned vehicle are improved.
Description
Technical Field
The invention relates to the technical field of unmanned vehicle driving, in particular to an intelligent networking unmanned vehicle driving system.
Background
The existing intelligent automobile is added with advanced sensors such as a radar and a camera, a controller, an actuator and the like on a common automobile, realizes information exchange with the automobile, a road, a person and the like through a vehicle-mounted environment sensing system and an information terminal, enables the automobile to have intelligent environment sensing capability, can automatically analyze the running safety and dangerous states of the automobile, enables the automobile to reach a destination according to the will of the person, and finally achieves the purposes of unmanned driving, driving decision making and operation.
However, the current unmanned vehicles mainly use a single vehicle as a core, and the decision is made by identifying and analyzing the surrounding road conditions and environment through the single vehicle. Therefore, different vehicles have different hardware and systems, and different strategies for driving and obstacle avoidance in the same road condition and environment are different, so that the unmanned vehicles lack uniform information sharing and cooperation, and under the condition that the current unmanned intelligent degree is in a primary stage, the safety and reliability of the whole traffic system in which the unmanned vehicles participate are further to be improved urgently.
Disclosure of Invention
The invention aims to provide an intelligent network connection unmanned vehicle driving system which can carry out information unified management on unmanned vehicles participating in traffic, realize comprehensive control on traffic comprehensive conditions, provide a safe driving strategy and improve the driving safety and reliability of the unmanned vehicles.
The technical scheme provided by the invention is as follows: an intelligent networking unmanned vehicle driving system, comprising: the system comprises an information perception module, a road condition analysis module, a network connection calculation module, an obstacle avoidance strategy module and an information synchronization module, wherein the information perception module is used for collecting and processing environmental information around the vehicle and state information of the vehicle; the road condition analysis module is used for establishing a road condition model according to the environmental information; the network connection computing module is used for adjusting an early warning threshold value, a driving strategy and an obstacle avoidance strategy of vehicle driving according to the road condition model and the environment information; the obstacle avoidance strategy module is used for analyzing the predicted track of the vehicle according to the road condition model and the state information and matching a corresponding obstacle avoidance strategy; the information synchronization module is used for synchronizing the driving strategy and the obstacle avoidance strategy to the vehicle.
The working principle and the advantages of the invention are as follows: the system collects and analyzes the environmental information around each vehicle and the state information of the vehicles, and realizes the unified collection and simple processing of vehicle big data. And a road condition model is established according to the environment information, so that the real road condition can be intuitively reflected. The road condition models established by the vehicles are combined with the current environmental information of the vehicles, the current traffic comprehensive conditions can be comprehensively mastered through a big data cloud computing technology, corresponding vehicle state early warning threshold values, the driving strategies and the obstacle avoidance strategies of the vehicles are formulated and adjusted, and the safety of the current traffic conditions can be guaranteed under the strategies. And in the running process of the vehicle, analyzing the predicted track of the vehicle in real time, collecting the road condition model according to the state information of the current vehicle, matching the corresponding obstacle avoidance strategy, synchronizing to the target vehicle in real time, and executing the corresponding strategy. The system realizes data sharing through intelligent network connection between vehicles and the system, performs information unified management on unmanned vehicles participating in traffic, realizes comprehensive control of traffic conditions, provides a safe driving strategy, and improves the driving safety and reliability of the unmanned vehicles.
Further, the state information and the early warning threshold of the vehicle comprise real-time position, speed and direction.
Identifying whether the real-time location of the vehicle is at a risk road segment, whether the direction is toward the risk road segment, and whether the speed is too fast. And setting corresponding early warning threshold values for the state information of the vehicles by combining the road condition model and the environmental information.
Further, the information perception module comprises an information acquisition unit and an information processing unit, the information acquisition unit is used for acquiring environmental information around the vehicle and state information of the vehicle, and the information processing unit is used for performing clarification processing and feature recognition on the environmental information.
The data information, especially the image information, is processed by algorithm for clarification, and the characteristics of the image are identified and extracted for classification.
Further, the feature recognition of the information processing unit includes road recognition, vehicle recognition, pedestrian recognition, traffic sign recognition, and traffic light recognition.
Various scenes possibly met by the vehicle on the road surface are integrated, the identification of the road, the vehicle, the pedestrian, the traffic sign and the traffic signal lamp is supported, and the characteristics capable of being identified are wide and comprehensive.
Further, the road condition analysis module comprises a structured road identification unit, a road characteristic identification unit and a road model unit, wherein the structured road identification unit is used for identifying whether the road is a structured road; the road characteristic identification unit is used for identifying road characteristics according to the structured road identification result; and the road model unit is used for establishing a road condition model according to the road characteristic identification result.
The road can be divided into a structured road and an unstructured road according to whether the road line is regular or not, the structured road has obvious road characteristics, and the system can accurately identify according to a road model or manually set characteristics, but in an actual environment, a road section without obvious lane lines or subjected to weather interference such as fog and snow exists, and the road section is called as a partially unstructured road and a completely unstructured road, and wrong decisions can be caused by applying an identification method of the structured road on the unstructured road identification. Therefore, it is necessary to determine whether a structured road is present, identify and calculate the percentage of road features on the road that satisfy the structured road, divide the structured road and the unstructured road (partially unstructured road and completely unstructured road) according to the percentage, and then use a corresponding identification method to establish a road condition model.
Further, the internet computing module comprises a data integration unit, a threshold adjustment unit and a strategy adjustment unit, wherein the data integration unit is used for integrating road condition models and environment information of all vehicles; the threshold adjusting unit is used for adjusting the early warning threshold of vehicle running according to the road condition model and the environment information; the strategy adjusting unit is used for adjusting the driving strategy and the obstacle avoidance strategy of the vehicle according to the road condition model and the environment information.
The road condition models and the environmental information data of the vehicles are integrated, the road condition models and the environmental information in the same place are classified, and the repeated data are combined, so that the road condition and the environment of each place can be known. And adjusting the early warning threshold value, the driving strategy and the obstacle avoidance strategy according to the road condition and the environment of the current place.
Further, the obstacle avoidance strategy module comprises a track prediction unit and a strategy matching unit, wherein the track prediction unit is used for analyzing the predicted track of the vehicle according to the road condition model and the state information; and the strategy matching unit is used for matching the corresponding obstacle avoidance strategies according to the predicted track and the state information.
The method comprises the steps of predicting the running track of a vehicle before obstacle avoidance, judging whether collision is possible, and then executing different obstacle avoidance strategies according to different obstacles in front and the current state of the vehicle so as to guarantee safety.
The system further comprises a scene simulation module, wherein the scene simulation module is used for establishing a traffic simulation scene according to the road condition model, the environment information and the state information.
The method also provides the simulation of the current traffic scene, and the real scene corresponds to the simulated scene one by one, so that related workers can visually know the current specific traffic condition, and the unified coordination management of traffic and the related test are facilitated.
Further, the scene simulation module comprises an environment simulation unit, a field simulation unit and a vehicle simulation unit, wherein the environment simulation unit is used for simulating a weather environment according to environment information; the field simulation unit is used for establishing a simulation field by combining the road condition model and the map model; the vehicle simulation unit is used for simulating vehicle running according to the state information.
The simulation of real traffic scenes is realized mainly from three aspects of weather, places and vehicles, the environment, road conditions and vehicle states correspond to the real conditions, certain sense of reality is achieved, and related workers can directly think of various safety prevention measures according to the simulated scenes.
Further, the traffic simulation scene comprises an electronic traffic simulation scene and a physical traffic simulation scene.
Besides the electronic traffic simulation scene displayed on the screen, the real traffic simulation scene is also provided, the real model is manufactured according to the environment and the field condition, and then the trolley model is commanded to move on the field in a wireless control mode and is in one-to-one correspondence with the real scene, so that the sense of reality of the simulation scene is further improved.
Drawings
Fig. 1 is a block diagram of an intelligent network-connected unmanned vehicle driving system according to an embodiment of the present invention.
Detailed Description
Embodiments of the present invention will be described in detail below with reference to the accompanying drawings. The following examples are only for illustrating the technical solutions of the present invention more clearly, and therefore are only examples, and the protection scope of the present invention is not limited thereby.
The embodiment is as follows:
as shown in fig. 1, the present embodiment discloses an intelligent networking unmanned vehicle driving system, which includes an information sensing module, a road condition analysis module, an internet computing module, an obstacle avoidance policy module, an information synchronization module, and a scene simulation module.
The information perception module comprises an information acquisition unit and an information processing unit, the information acquisition unit is used for acquiring environmental information around the vehicle and state information of the vehicle through a sensor and an instrument on the vehicle, the sensor and the instrument comprise an inertial element, an ultrasonic sensor, a laser radar, a millimeter wave radar, an image sensor, a positioning device, a vehicle-mounted instrument, a vehicle-mounted self-organizing network and the like, and the acquired environmental information comprises data such as weather, temperature, humidity, images and the like. In the embodiment, an information processing unit mainly processes image data, firstly carries out sharpening processing on an image, and comprises the steps of analyzing the image frame by frame aiming at the problem that the acquired image is not sharp due to light flicker of a field environment, selecting a plurality of images with the minimum chromatic aberration, generating an image with stable ambient light through an image multi-frame fusion algorithm, and then enhancing the image contrast and the color; aiming at the problem that the collected image is fuzzy due to heavy field water mist, the image is analyzed frame by frame, an image with a water mist removing effect is generated through a multi-sheet fusion algorithm, and then the image contrast and the color are enhanced. After the image subjected to the sharpening processing is obtained, feature recognition is carried out on the image, in the embodiment, the feature recognition of the information processing unit comprises road recognition, vehicle recognition, pedestrian recognition, traffic sign recognition and traffic signal lamp recognition, wherein the road recognition comprises the recognition of a lane line on the road, and a primary recognition is carried out on the current road through the image; vehicle identification and pedestrian identification are carried out on the basis of image identification by combining radar wave data to accurately identify other vehicles and pedestrians near the vehicle; and the meanings of the traffic sign and the traffic signal lamp are matched according to the image data by the traffic sign identification, the traffic model identification and the like. The vehicle status information includes the real-time location, speed, and direction of the vehicle.
The road condition analysis module comprises a structured road identification unit, a road characteristic identification unit and a road model unit, and the structured road identification unit is used for further identifying the road on the basis of the information perception module. The road can be divided into a structured road and an unstructured road according to whether the road line is regular or not, the structured road has obvious road characteristics, and the system can accurately identify according to a road model or manually set characteristics, but in an actual environment, a road section without obvious lane lines or subjected to weather interference such as fog and snow exists, and the road section is called as a partially unstructured road and a completely unstructured road, and wrong decisions can be caused by applying an identification method of the structured road on the unstructured road identification. Therefore, the structured road identifies whether the current road is the structured road according to whether a lane line or a lane line exists on the image or not, in this embodiment, the degree of regularity of the lane line is below 70% or no obvious lane line is determined as the unstructured road, 20% -70% of the unstructured road is the partially unstructured road, and 20% of the unstructured road is the completely unstructured road. If the identification result is an unstructured road, the road feature identification unit adopts an unstructured road identification model to identify road features, and in this embodiment, feature scenes such as a road tree, a handrail, and a road edge are mainly identified on the unstructured road. The road model unit judges the road trend according to the identified road characteristics, and establishes a road condition model according to the identification result, wherein the road condition model comprises a road model and a vehicle and pedestrian model on the road.
The internet computing module comprises a data integration unit, a threshold value adjusting unit and a strategy adjusting unit, wherein the data integration unit is used for integrating road condition models and environment information data of all vehicles, classifying the road condition models and the environment information in the same place, and combining repeated data so as to know the road condition and the environment of each place. The threshold adjusting unit analyzes the integrated road condition model and environment information, and sets an early warning threshold for vehicle driving with reference to corresponding traffic rules according to specific conditions of different locations, for example, it is known that there are many vehicles on a road at a certain location, and the road is foggy, and the road surface is wet and slippery, so that it is necessary to set the speed threshold of the vehicle to be 50km/h according to the above conditions, some severe foggy road sections are directly set as dangerous road sections, and the vehicle at the real-time position of the road section or the vehicle whose driving direction is the road section is brought into the early warning range. The strategy adjusting unit analyzes the integrated road condition model and environment information, adjusts the driving strategy and obstacle avoidance strategy of the vehicle under the road condition environment, for example, the vehicle is more on the road, the vehicle is in foggy weather, the road surface is wet and slippery, the driving strategy of the vehicle is adjusted to be that the speed is controlled to be about 40km/h, the fog lamp is turned on, and the double-flash is directly turned on in a severe foggy road section. The obstacle avoidance strategy is that if the obstacle in front is detected, if the obstacle in front is a pedestrian, the speed is directly reduced until the pedestrian passes through the road surface; if the front obstacle is a static object such as a roadblock and the like, decelerating and turning on a turn light, and bypassing the obstacle at a low speed under the condition that no pedestrians or vehicles are around; if the front obstacle is a vehicle, when the speed of the front vehicle measured by radar waves exceeds 40km/h, keeping 40km/h to follow, and performing adaptive cruise, if the speed of the front vehicle is lower than 40km/h, turning on an overtaking steering lamp, and when no other vehicles or pedestrians around are in the position, keeping the speed of 40km/h after accelerating and overtaking.
The obstacle avoidance strategy module comprises a track prediction unit and a strategy matching unit, wherein the track prediction unit analyzes the predicted track of the vehicle according to the current road condition model and the current state information of the vehicle, and mainly combines the current road surface condition and the real-time speed and direction of the vehicle, for example, the current vehicle speed is 40km/h, pedestrians pass through the road surface at a constant speed about 100 meters in front, and the predicted track is calculated according to the relative speeds of the two parties to possibly collide.
The information synchronization module is used for synchronizing the driving strategy and the obstacle avoidance strategy to a target vehicle in real time through the information synchronization module so that the vehicle can adopt corresponding strategies in time.
The scene simulation module comprises an environment simulation unit, a field simulation unit and a vehicle simulation unit, wherein the environment simulation unit is used for simulating weather environments including rain, snow, fog, sunny and the like according to environment information. The site simulation unit is used for generating and establishing a simulation site of a certain site according to the road condition model and by combining the existing map model, wherein the site comprises real-time vehicle and pedestrian models. The vehicle simulation unit is used for simulating vehicle running on the simulation site according to the state information of the vehicle. Through the combination of the simulation scenes, the electronic traffic simulation scenes including the driving conditions of the vehicles in the current site and the surrounding environment conditions can be watched on the screen, and the electronic traffic simulation scenes are clear at a glance and are more visual compared with the traditional real-time traffic map. Meanwhile, the embodiment also supports displaying the simulation scene in a physical traffic simulation scene mode, a road model which is restored correspondingly to the field is established on a sand table, a trolley model is placed on the road, the trolley model is controlled to run on the road model in a remote control mode according to the real-time running condition of the vehicle simulation unit, and the current traffic condition is reflected in a physical display mode, so that the simulation scene is more visual.
The foregoing are merely exemplary embodiments of the present invention, and no attempt is made to show structural details of the invention in more detail than is necessary for the fundamental understanding of the art, the description taken with the drawings making apparent to those skilled in the art how the several forms of the invention may be embodied in practice with the teachings of the invention. It should be noted that, for those skilled in the art, without departing from the structure of the present invention, several changes and modifications can be made, which should also be regarded as the protection scope of the present invention, and these will not affect the effect of the implementation of the present invention and the practicability of the patent. The scope of the claims of the present application shall be defined by the claims, and the description of the embodiments and the like in the specification shall be used to explain the contents of the claims.
Claims (10)
1. An intelligent networking unmanned vehicle driving system, comprising: the system comprises an information perception module, a road condition analysis module, a network connection calculation module, an obstacle avoidance strategy module and an information synchronization module, wherein the information perception module is used for collecting and processing environmental information around the vehicle and state information of the vehicle; the road condition analysis module is used for establishing a road condition model according to the environmental information; the network connection computing module is used for adjusting an early warning threshold value, a driving strategy and an obstacle avoidance strategy of vehicle driving according to the road condition model and the environment information; the obstacle avoidance strategy module is used for analyzing the predicted track of the vehicle according to the road condition model and the state information and matching a corresponding obstacle avoidance strategy; the information synchronization module is used for synchronizing the driving strategy and the obstacle avoidance strategy to the vehicle.
2. The intelligent networking unmanned vehicle driving system of claim 1, wherein: the state information and the early warning threshold of the vehicle comprise real-time position, speed and direction.
3. The intelligent networking unmanned vehicle driving system of claim 1, wherein: the information perception module comprises an information acquisition unit and an information processing unit, the information acquisition unit is used for acquiring environmental information around the vehicle and state information of the vehicle, and the information processing unit is used for performing clarification processing and feature recognition on the environmental information.
4. The intelligent networking unmanned vehicle driving system of claim 3, wherein: the feature recognition of the information processing unit comprises road recognition, vehicle recognition, pedestrian recognition, traffic sign recognition and traffic signal lamp recognition.
5. The intelligent networking unmanned vehicle driving system of claim 1, wherein: the road condition analysis module comprises a structured road identification unit, a road characteristic identification unit and a road model unit, wherein the structured road identification unit is used for identifying whether the road is a structured road; the road characteristic identification unit is used for identifying road characteristics according to a structured road identification result; and the road model unit is used for establishing a road condition model according to the road characteristic identification result.
6. The intelligent networking unmanned vehicle driving system of claim 1, wherein: the network connection computing module comprises a data integration unit, a threshold value adjusting unit and a strategy adjusting unit, wherein the data integration unit is used for integrating road condition models and environment information of all vehicles; the threshold adjusting unit is used for adjusting the early warning threshold of vehicle running according to the road condition model and the environment information; and the strategy adjusting unit is used for adjusting the driving strategy and the obstacle avoidance strategy of the vehicle according to the road condition model and the environment information.
7. The intelligent networking unmanned vehicle driving system of claim 1, wherein: the obstacle avoidance strategy module comprises a track prediction unit and a strategy matching unit, wherein the track prediction unit is used for analyzing the predicted track of the vehicle according to the road condition model and the state information; and the strategy matching unit is used for matching the corresponding obstacle avoidance strategies according to the predicted track and the state information.
8. The intelligent networking unmanned vehicle driving system of claim 1, wherein: the traffic simulation system further comprises a scene simulation module, wherein the scene simulation module is used for establishing a traffic simulation scene according to the road condition model, the environmental information and the state information.
9. The intelligent networking unmanned vehicle driving system of claim 8, wherein: the scene simulation module comprises an environment simulation unit, a field simulation unit and a vehicle simulation unit, wherein the environment simulation unit is used for simulating a weather environment according to environment information; the field simulation unit is used for establishing a simulation field by combining the road condition model and the map model; the vehicle simulation unit is used for simulating vehicle running according to the state information.
10. The intelligent networking unmanned vehicle driving system of claim 9, wherein: the traffic simulation scene comprises an electronic traffic simulation scene and a physical traffic simulation scene.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202210162634.1A CN115016453A (en) | 2022-02-22 | 2022-02-22 | Intelligent networking unmanned vehicle driving system |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202210162634.1A CN115016453A (en) | 2022-02-22 | 2022-02-22 | Intelligent networking unmanned vehicle driving system |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN115016453A true CN115016453A (en) | 2022-09-06 |
Family
ID=83067037
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202210162634.1A Pending CN115016453A (en) | 2022-02-22 | 2022-02-22 | Intelligent networking unmanned vehicle driving system |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN115016453A (en) |
Cited By (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN115848295A (en) * | 2022-12-23 | 2023-03-28 | 重庆电子工程职业学院 | Vehicle spontaneous combustion early warning method and system |
-
2022
- 2022-02-22 CN CN202210162634.1A patent/CN115016453A/en active Pending
Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN115848295A (en) * | 2022-12-23 | 2023-03-28 | 重庆电子工程职业学院 | Vehicle spontaneous combustion early warning method and system |
| CN115848295B (en) * | 2022-12-23 | 2024-04-12 | 重庆电子工程职业学院 | Vehicle spontaneous combustion early warning method and system |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN111382768B (en) | Multi-sensor data fusion method and device | |
| US11380105B2 (en) | Identification and classification of traffic conflicts | |
| CN112700470B (en) | Target detection and track extraction method based on traffic video stream | |
| US20220230449A1 (en) | Automatically perceiving travel signals | |
| US10650256B2 (en) | Automatically perceiving travel signals | |
| CN113190921B (en) | Automatic evaluation method and system for intelligent automobile driving performance test | |
| CN112215306B (en) | Target detection method based on fusion of monocular vision and millimeter wave radar | |
| CN109941286A (en) | Method and apparatus for assessing vehicle travel surface | |
| CN111094095B (en) | Method and device for automatically sensing driving signal and vehicle | |
| CN110837800A (en) | Port severe weather-oriented target detection and identification method | |
| US20180299893A1 (en) | Automatically perceiving travel signals | |
| DE102019108610A1 (en) | IMPROVING VEHICLE BEHAVIOR USING INFORMATION FROM OTHER VEHICLE LIGHTS | |
| CN109544725B (en) | Event-driven-based automatic driving accident intelligent processing method | |
| CN111907518B (en) | Method for dynamically optimizing AEB braking strategy based on cloud big data analysis | |
| CN111231971A (en) | Automobile safety performance analysis and evaluation method and system based on big data | |
| US20180300566A1 (en) | Automatically perceiving travel signals | |
| CN114537374A (en) | Vehicle front anti-collision system based on travelable area | |
| CN114915646B (en) | Data grading uploading method and device for unmanned mine car | |
| CN115016453A (en) | Intelligent networking unmanned vehicle driving system | |
| CN113432615B (en) | Detection method and system based on multi-sensor fusion drivable area and vehicle | |
| KR20200092462A (en) | Routing change method and system based on driving situation information fusion | |
| Kanjee et al. | Vision-based adaptive cruise control using pattern matching | |
| CN117008574A (en) | Intelligent network allies oneself with car advanced auxiliary driving system and autopilot system test platform | |
| CN116798222A (en) | Method, system and device for detecting visibility of expressway and intelligently inducing visibility of expressway | |
| Beresnev et al. | Automated Driving System based on Roadway and Traffic Conditions Monitoring. |
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 |