CN109828571A - Automatic driving vehicle, method and apparatus based on V2X - Google Patents
Automatic driving vehicle, method and apparatus based on V2X Download PDFInfo
- Publication number
- CN109828571A CN109828571A CN201910123370.7A CN201910123370A CN109828571A CN 109828571 A CN109828571 A CN 109828571A CN 201910123370 A CN201910123370 A CN 201910123370A CN 109828571 A CN109828571 A CN 109828571A
- Authority
- CN
- China
- Prior art keywords
- vehicle
- information
- obstacle
- current vehicle
- radar
- 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
- 238000000034 method Methods 0.000 title claims abstract description 43
- 230000010354 integration Effects 0.000 claims description 10
- 230000004888 barrier function Effects 0.000 abstract 3
- 238000011897 real-time detection Methods 0.000 abstract 1
- 230000015654 memory Effects 0.000 description 11
- 238000001514 detection method Methods 0.000 description 7
- 230000008569 process Effects 0.000 description 7
- 230000006870 function Effects 0.000 description 5
- 238000010586 diagram Methods 0.000 description 4
- 241001465754 Metazoa Species 0.000 description 2
- 230000005540 biological transmission Effects 0.000 description 2
- 230000003993 interaction Effects 0.000 description 2
- 230000003287 optical effect Effects 0.000 description 2
- 238000013473 artificial intelligence Methods 0.000 description 1
- 230000009286 beneficial effect Effects 0.000 description 1
- 238000004891 communication Methods 0.000 description 1
- 238000013500 data storage Methods 0.000 description 1
- 230000007123 defense Effects 0.000 description 1
- 238000003708 edge detection Methods 0.000 description 1
- 230000007613 environmental effect Effects 0.000 description 1
- 238000010191 image analysis Methods 0.000 description 1
- 238000012986 modification Methods 0.000 description 1
- 230000004048 modification Effects 0.000 description 1
- 238000003672 processing method Methods 0.000 description 1
Landscapes
- Traffic Control Systems (AREA)
Abstract
The invention discloses a kind of automatic driving vehicles based on V2X, method and apparatus, belong to intelligent vehicle field.Vehicle includes: vehicle body, vehicle to extraneous Information Exchange System V2X system, multiple first radars, the second radar, third radar, multiple 4th radars, global positioning module GPS module, the first camera and multiple second cameras.Using the present invention, the actual barrier and lane line around multiple sensors real-time detection current vehicle can be passed through, and the information of vehicles of other vehicles is obtained with look-ahead barrier and interference of other vehicles to current vehicle by V2X system, traffic route is adjusted, influence of the weather conditions to barrier judgment is effectively reduced.
Description
Technical Field
The invention relates to the field of intelligent vehicles, in particular to an automatic driving vehicle, a method and a device based on V2X.
Background
In order to relieve the fatigue of drivers and even liberate drivers, automatic driving vehicles are being developed; an automatic vehicle (Self-steering automatic vehicle) is also called as an unmanned vehicle, a computer-driven vehicle or a wheeled mobile robot, and is an intelligent vehicle for realizing unmanned driving through a computer system. In addition, the automatic driving vehicle depends on the cooperative cooperation of artificial intelligence, a camera, a radar and a global positioning system, so that the vehicle can be driven automatically without any active human intervention.
At present, cameras are mainly arranged at the front end and the rear end of a vehicle respectively, and radars are arranged on two sides of the vehicle respectively. The method comprises the steps that a vehicle obtains first image data of the front and the back of the vehicle through a camera, and lane line information of a lane where the vehicle is located is obtained through a radar; and controlling the vehicle to run according to the first image data and the lane line information.
In the process of implementing the invention, the inventor finds that the prior art has at least the following problems:
when the weather condition can only be applied to present automatic driving vehicle when better, present on-vehicle radar and camera receive the environmental impact great, and the great unable effective obstacle of discernment error under rainy or foggy environment causes very big potential safety hazard.
Disclosure of Invention
In order to solve the problems of the prior art, embodiments of the present invention provide a method and an apparatus of (a).
The technical scheme is as follows:
in a first aspect, there is provided a V2X-based autonomous vehicle, the vehicle comprising:
the system comprises a vehicle body, a vehicle-to-outside information exchange system V2X system, a plurality of first radars, a second radar, a third radar, a plurality of fourth radars, a global positioning module GPS module, a first camera and a plurality of second cameras;
the first radars are respectively arranged at the outer sides of four corners of the vehicle body and are used for detecting first radar information of a first range on two sides of the vehicle and behind the vehicle;
the second radar is arranged right in front of the vehicle body and used for detecting second radar information of a second range in front of the vehicle;
the third radar is arranged on the top of the vehicle body and used for detecting third radar information within a third range of 360 degrees around the vehicle;
the plurality of fourth radars are uniformly arranged around the outer side of the vehicle body and used for detecting fourth radar information in a fourth range around the vehicle;
the first camera is arranged at the position of a rearview mirror in the vehicle and used for acquiring a first image in a fifth range in front of the vehicle;
the plurality of second cameras are arranged at the positions of the rearview mirrors at the front and rear parts of the vehicle body and at the two sides of the vehicle body and are used for acquiring second images in a sixth range around the vehicle;
the V2X system is used to obtain road conditions provided by the server and vehicle information distributed by other vehicles.
In a second aspect, a method for automatic driving based on V2X is provided, the method comprising:
acquiring an obstacle identification model from a server, wherein the obstacle identification model is used for determining the type of an obstacle;
obtaining first radar information from a plurality of first radars, second radar information from a second radar, third radar information from a third radar, fourth radar information from a plurality of fourth radars, a first image from a first camera, and a second image from a plurality of second cameras;
identifying an obstacle and a lane line from the first radar information, the second radar information, the third radar information, the fourth radar information, the first image and the second image based on the obstacle identification model, and acquiring obstacle information of the obstacle and lane line information of the lane line;
acquiring the vehicle information of other vehicles within the preset range received by the V2X, identifying an obstacle vehicle from the vehicle information, and acquiring the vehicle information of the obstacle vehicle, wherein the other vehicles are vehicles outside the current vehicle within the preset range;
performing obstacle integration based on the obstacle information and the vehicle information of the obstacle vehicle to obtain a target obstacle around the current vehicle and acquire the obstacle information of the target obstacle;
the method comprises the steps that a current vehicle area is located periodically based on a GPS module, and the position of the current vehicle in the area is determined based on lane line information and obstacle information of a target obstacle;
and formulating a traveling route of the current vehicle based on the obstacle information of the target obstacle and the position of the current vehicle, and controlling the current vehicle to travel according to the traveling route.
Optionally, identifying the obstacle vehicle from the vehicle information includes:
predicting the traveling range of each other vehicle within preset time based on the speed and the traveling direction of the other vehicles;
acquiring the speed and the driving direction of the current vehicle, and predicting the traveling range of the current vehicle within preset time;
the obstacle vehicle is determined based on the travel range of the current vehicle and the travel ranges of the other vehicles.
Optionally, the integrating the obstacle based on the obstacle information and the vehicle information of the obstacle vehicle to obtain the target obstacle around the current vehicle includes:
acquiring the type of an obstacle, the position information of the obstacle and the position information of an obstacle vehicle;
and merging the obstacles or obstacle vehicles of the same type at the same position to obtain the target obstacles around the current vehicle.
Optionally, the obstacle information includes a road sign and a landmark building, the periodically determining the area where the current vehicle is located based on the GPS module, and determining the position of the current vehicle in the area based on the lane line and the obstacle information, including:
acquiring map information from a server, and determining the area range of a road in the map information of the current vehicle based on a GPS module;
the position of the current vehicle within the area is determined based on the landmarks and landmark buildings.
Optionally, the step of formulating a travel route of the current vehicle based on the target obstacle and the position of the current vehicle, and controlling the current vehicle to travel according to the travel route includes:
acquiring position information of a target obstacle and combining the map information and the position information of the target obstacle;
and formulating a traveling route of the current vehicle based on the position of the current vehicle and the position information of the target obstacle, and controlling the vehicle to travel according to the traveling route.
In a third aspect, there is provided a V2X-based autopilot apparatus, the apparatus comprising:
the acquisition module is used for acquiring an obstacle identification model from the server, and the obstacle identification model is used for determining the type of an obstacle;
an acquisition module, configured to acquire first radar information from the plurality of first radars, to acquire second radar information from the second radar, to acquire third radar information from the third radar, to acquire fourth radar information from the plurality of fourth radars, to acquire a first image from the first camera, and to acquire a second image from the plurality of second cameras;
the identification module is used for identifying obstacles and lane lines from the first radar information, the second radar information, the third radar information, the fourth radar information, the first image and the second image based on the obstacle identification model and acquiring obstacle information of the obstacles and lane line information of the lane lines;
the identification module is used for acquiring the vehicle information of other vehicles in the preset range received by the V2X, identifying an obstacle vehicle from the vehicle information, and acquiring the vehicle information of the obstacle vehicle, wherein the other vehicles are vehicles outside the current vehicle in the preset range;
the integration module is used for integrating obstacles based on the obstacle information and the vehicle information of the obstacle vehicle to obtain target obstacles around the current vehicle and obtain the obstacle information of the target obstacles;
the positioning module is used for periodically positioning the area where the current vehicle is located based on the GPS module and determining the position of the current vehicle in the area based on the lane line information and the obstacle information of the target obstacle;
and the control module is used for making a traveling route of the current vehicle based on the obstacle information of the target obstacle and the position of the current vehicle and controlling the current vehicle to travel according to the traveling route.
Optionally, the identification module is configured to:
predicting the traveling range of each other vehicle within preset time based on the speed and the traveling direction of the other vehicles;
acquiring the speed and the driving direction of the current vehicle, and predicting the traveling range of the current vehicle within preset time;
the obstacle vehicle is determined based on the travel range of the current vehicle and the travel ranges of the other vehicles.
Optionally, the integration module is configured to:
acquiring the type of an obstacle, the position information of the obstacle and the position information of an obstacle vehicle;
and merging the obstacles or obstacle vehicles of the same type at the same position to obtain the target obstacles around the current vehicle.
Optionally, the positioning module is configured to:
acquiring map information from a server, and determining the area range of a road in the map information of the current vehicle based on a GPS module;
the position of the current vehicle within the area is determined based on the landmarks and landmark buildings.
In a fourth aspect, a vehicle-mounted terminal is provided, the terminal comprises a processor and a memory, and the memory stores at least one instruction, and the at least one instruction is loaded and executed by the processor to realize the automatic driving method based on V2X according to the first aspect.
In a fifth aspect, a computer-readable storage medium is provided, having stored therein at least one instruction, which is loaded and executed by the processor, to implement the method for V2X-based autopilot as described in the first aspect above.
The technical scheme provided by the embodiment of the invention has the beneficial effects that at least:
in the embodiment of the invention, the radar information and the image are acquired through the various radars and the cameras which are arranged on the current vehicle and used for acquiring the information around the current vehicle, the obstacles around the vehicle are identified based on the obstacle identification model acquired by the server, the vehicle information of other vehicles in the preset range of the current vehicle is acquired through the V2X system, the driving route of the current vehicle is planned based on the obstacles and the vehicle information of the other vehicles, the influence of the environment on the detection of the sensor is effectively reduced, and the safety of automatic driving is improved.
Drawings
In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the drawings needed to be used in the description of the embodiments will be briefly introduced below, and it is obvious that the drawings in the following description are only some embodiments of the present invention, and it is obvious for those skilled in the art to obtain other drawings based on these drawings without creative efforts.
FIG. 1 is a schematic structural diagram of an automatic driving vehicle based on V2X according to an embodiment of the invention;
FIG. 2 is a flow chart of an automatic driving method based on V2X according to an embodiment of the present invention;
FIG. 3 is a schematic structural diagram of an automatic driving device based on V2X according to an embodiment of the present invention;
fig. 4 is a schematic structural diagram of a vehicle-mounted terminal of an autonomous vehicle based on V2X according to an embodiment of the present invention.
Detailed Description
In order to make the objects, technical solutions and advantages of the present invention more apparent, embodiments of the present invention will be described in detail with reference to the accompanying drawings.
The embodiment of the invention provides an automatic driving vehicle based on V2X, and the specific vehicle type can be a car, a jeep, an SUV and the like, and can also be a truck, a van and the like without specific limitation. The V2X system (vehicle to X, meaning vehicle to outside information exchange system) may enable communication between vehicles, vehicles and base stations, and base stations. Therefore, a series of traffic information such as real-time road conditions, road information, pedestrian information and the like is obtained.
As shown in fig. 1, the present embodiment is described by taking a common car model for family use as an example, and the vehicle may include:
the system comprises a vehicle body, a vehicle-to-outside information exchange system V2X system, a plurality of first radars, a second radar, a third radar, a plurality of fourth radars, a global positioning module GPS module, a first camera and a plurality of second cameras; the first radars are respectively arranged at the outer sides of four corners of the vehicle body and are used for detecting first radar information of a first range on two sides of the vehicle and behind the vehicle; the second radar is arranged right in front of the vehicle body and used for detecting second radar information of a second range in front of the vehicle; the third radar is arranged on the top of the vehicle body and used for detecting third radar information within a third range of 360 degrees around the vehicle; the plurality of fourth radars are uniformly arranged around the outer side of the vehicle body and used for detecting fourth radar information in a fourth range around the vehicle; the first camera is arranged at the position of a rearview mirror in the vehicle and used for acquiring a first image in a fifth range in front of the vehicle; the plurality of second cameras are arranged at the positions of the rearview mirrors at the front and rear parts of the vehicle body and at the two sides of the vehicle body and are used for acquiring second images in a sixth range around the vehicle; the V2X system is used to obtain road conditions provided by the server and vehicle information distributed by other vehicles.
Specifically, the V2X system can realize information interaction with external vehicles and base stations through the V2X system, that is, the V2X system is used to obtain vehicle information of surrounding vehicles and information such as road conditions and maps provided by the base stations. The vehicle is further provided with a third radar arranged on the roof, the third radar can be a laser radar, obstacle information in a range of 360 degrees around the vehicle can be detected, specific laser radars can be arranged on the roof in the middle of front row seats, the outer side positions of four corners of the vehicle body can be respectively provided with a first radar, the first radar can be a medium-range radar, specific four medium-range radar sensors can be arranged at positions, close to tires, of four corners of the vehicle, the detection angle of each medium-range radar sensor can be set to be 150 degrees, the detection distance is 80 meters, and obstacles in corresponding ranges of two sides and the rear of the vehicle can be detected. A second radar may be located directly in front of the vehicle, and the second radar may be a long-range radar having a detection angle range of 20 degrees at a distance, 175 meters at a distance, 90 degrees at a distance, and 60 meters at a distance. The long-range radar can detect an obstacle in front of the vehicle and the distance of the obstacle from the vehicle. Ten fourth radars, which may be ultrasonic radars, may be further disposed around the vehicle, and may be uniformly disposed along the entire vehicle body to detect the distance of obstacles in a close distance around the vehicle body.
The vehicle can also be provided with a GPS module which can be arranged at a corresponding position in the middle of a rear seat in the vehicle, so that the vehicle can be positioned. In addition, the vehicle can also include a forward-looking camera and a look-around camera, specifically, the first camera can be a forward-looking camera, and the forward-looking camera can be arranged at the position of a rearview mirror in the vehicle and also can be arranged at other positions to collect image information in front of the vehicle. The second camera can be the look around camera, can just the both sides of vehicle and set up a look around camera respectively around with gather the image information around the vehicle, and the look around camera of specific vehicle both sides can set up the position department at the vehicle rearview mirror, and the look around camera around the vehicle can set up the position department directly in the place ahead of vehicle and directly behind the vehicle. The obstacle information around the vehicle can be obtained by looking around the image information acquired by the camera and analyzing the image information.
The information such as the type and the distance of the obstacle information appearing in front of the vehicle can be analyzed through the image information acquired by the stereo camera. The vehicle also comprises an interconnection control unit, namely a central processing unit, wherein the central processing unit can receive data collected by the sensors, the laser radar and the two cameras, and can also receive V2X information.
The embodiment of the invention provides an automatic driving method based on V2X for the vehicle, which can be realized by a vehicle-mounted terminal and a server. Wherein,
the server may include a processor, memory, transceiver, etc. The processor, which may be a CPU (central processing Unit), may be configured to determine target audio data corresponding to a target area range to which the terminal location belongs, and perform other processing. The Memory may be a RAM (Random Access Memory), a Flash Memory, or the like, and may be configured to store received data, data required by the processing procedure, data generated in the processing procedure, or the like, such as an acquisition request sent by the vehicle-mounted terminal. The transceiver may be used for data transmission with a vehicle-mounted terminal or other server (e.g., a positioning server), for example, to transmit map information and an obstacle recognition model to the vehicle-mounted terminal, and may include an antenna, a matching circuit, a modem, and the like.
The in-vehicle terminal may include a processor, memory, screen, etc. The processor, which may be a Central Processing Unit (CPU), may be configured to determine whether the touch signal satisfies a preset trigger condition, receive an instruction, control the display to display, and perform other processing. The Memory may be a RAM (Random Access Memory), a Flash (Flash Memory), and the like, and may be configured to store received data, data required by the processing procedure, data generated in the processing procedure, and the like, such as the first radar information, the first image, and the like. The screen may be a touch screen, may be used to display device lists, control pages, and may also be used to detect touch signals, etc. The in-vehicle terminal may further include a transceiver, an image detection part, an audio output part, an audio input part, and the like. The transceiver may be used for data transmission with other devices, for example, to receive the map information and the obstacle identification model transmitted by the server, and may include an antenna, a matching circuit, a modem, and the like. The image detection means may be a camera or the like. The audio output component may be a speaker, headphones, or the like. The audio input means may be a microphone or the like.
As shown in fig. 2, the processing flow of the method may include the following steps:
in step 101, an obstacle recognition model is obtained from a server, and the obstacle recognition model is used for determining the type of an obstacle.
In implementation, the in-vehicle terminal may acquire the obstacle recognition model from the server, wherein the obstacle recognition model may include an image recognition model and a sensor recognition model. And identifying buildings, road signs and the like in the acquired environment around the vehicle based on the image identification model and the sensor model to obtain obstacles influencing the driving of the vehicle. The image recognition model can be an image recognition model trained by technicians based on image information acquired by different vehicles and acquired by a server, and corresponding feature points are respectively set for different information such as vehicles, buildings, road signs, lane lines, people and animals, so that the vehicles, the buildings, the road signs, the lane lines, the people and the animals can be recognized in the images based on the image recognition model when the vehicles acquire the images. Similarly, the sensor identification model is a model which is established based on the obtained outline of the obstacle and is used for setting corresponding characteristic information so that the obstacle is identified based on the characteristic information when the sensor or the radar collects the obstacle information.
Step 102, obtain first radar information from a plurality of first radars, second radar information from a second radar, third radar information from a third radar, fourth radar information from a plurality of fourth radars, a first image from a first camera, and a second image from a plurality of second cameras.
Specifically, the first radar may be a medium-distance radar in the vehicle embodiment, the second radar may be a long-distance radar in the vehicle embodiment, the third radar may be a laser radar in the vehicle embodiment, the fourth radar may be an ultrasonic radar in the vehicle embodiment, the first camera may be a forward-looking camera in the vehicle embodiment, and the second camera may be a looking-around camera in the vehicle embodiment. The method comprises the steps of respectively obtaining radar information of a medium-distance radar, radar information of a long-distance radar, radar information of a laser radar, radar information of an ultrasonic radar, an image collected by a forward-looking camera and an image collected by a look-around camera.
And 103, recognizing the obstacle and the lane line from the first radar information, the second radar information, the third radar information, the fourth radar information, the first image and the second image based on the obstacle recognition model, and acquiring the obstacle information of the obstacle and the lane line information of the lane line.
Respectively analyzing radar information of a medium-distance radar, radar information of a long-distance radar, radar information of a laser radar and radar information of an ultrasonic radar, establishing a coordinate system to obtain corresponding point cloud data, obtaining contour information to be recognized from the point cloud data, extracting feature points based on the contour information, matching the feature points with features in a sensor recognition model in an obstacle recognition model, and determining a contour with a matching degree reaching a preset threshold value as an obstacle type or a lane line in the sensor recognition model, namely recognizing the obstacle and the lane line. After the obstacle and the lane line are identified, corresponding obstacle information and lane line information can be acquired from the radar which collects the obstacle and the lane line.
The first image and the second image are subjected to image analysis, and the acquired images are processed by image processing methods such as an edge detection method, a binarization method and the like, so that the obstacles and the lane lines are identified based on the image identification model. After the obstacle and the lane line are identified, corresponding obstacle information and lane line information can be acquired from the acquired images of the obstacle and the lane line.
The obstacle information may include information such as a direction, a distance, and the like of the obstacle with respect to the current vehicle, and the lane line information may include information such as a direction and a distance of the lane line from the current vehicle.
And step 104, acquiring the vehicle information of other vehicles in the preset range received by the V2X, identifying an obstacle vehicle from the vehicle information, and acquiring the vehicle information of the obstacle vehicle, wherein the other vehicles are vehicles out of the current vehicle in the preset range.
The central processing unit may obtain vehicle information of other vehicles, which is received by the V2X system, in a preset range, where the preset range may be a range preset by a technician, for example, the preset range may be within 300m around the current vehicle, or within 500m, and is not limited herein specifically according to the requirement. The vehicle information of the other vehicle may include a vehicle speed of the other vehicle, vehicle position information of the other vehicle, a brake anti-lock system state, a vehicle body stability system state, a traction control system state, a lane departure warning system state, and the like. The vehicle position information of other vehicles is the actual positions of the other vehicles on the map, and the states of the anti-lock braking system, the vehicle body stability system and the traction control system can be used for judging whether the other vehicles are in an out-of-control state or not. The lane departure warning system can be used for judging whether other vehicles turn at the next intersection in the traveling direction of other vehicles. Further, vehicle information such as the traveling state, traveling direction, traveling speed, and the like of another vehicle can be obtained.
Optionally, the travel range of each other vehicle within the preset time may be predicted based on the vehicle speed and the driving direction of the other vehicles; acquiring the speed and the driving direction of the current vehicle, and predicting the traveling range of the current vehicle within preset time; the obstacle vehicle is determined based on the travel range of the current vehicle and the travel ranges of the other vehicles.
The process of identifying the obstacle vehicle may be to acquire the vehicle speed and the driving direction of the other vehicle from the vehicle information of the other vehicle, the driving defense line may include steering information of the other vehicle, predict a position where the other vehicle may travel within a preset time based on a time parameter, i.e., a preset time, preset by a technician, obtain a travel range of each other vehicle, then acquire the vehicle speed and the driving direction of the current vehicle, predict the travel range of the current vehicle within the preset time, thereby determine the other vehicle corresponding to the travel range of the other vehicle that intersects the travel range of the current vehicle, and take the other vehicle as the obstacle vehicle.
And 105, integrating obstacles based on the obstacle information and the vehicle information of the obstacle vehicle to obtain target obstacles around the current vehicle and acquire the obstacle information of the target obstacles.
The method comprises the steps of obtaining position information of an obstacle and an obstacle vehicle corresponding to each moment from obstacle information and vehicle information of the obstacle vehicle, determining the obstacle or the obstacle vehicle which is located at the same position at the same moment as the same obstacle or the same obstacle vehicle, further obtaining a target obstacle in a preset range around the current vehicle, wherein the target obstacle can comprise the obstacle vehicle, and then obtaining corresponding obstacle information, namely the obstacle information of the target obstacle, from a corresponding radar, a camera or a V2X system.
Optionally, the process of integrating the obstacles based on the obstacle information and the vehicle information of the obstacle vehicle to obtain the target obstacles around the current vehicle may be as follows: acquiring the type of an obstacle, the position information of the obstacle and the position information of an obstacle vehicle; and merging the obstacles or obstacle vehicles of the same type at the same position to obtain the target obstacles around the current vehicle.
And step 106, periodically positioning the area where the current vehicle is located based on the GPS module, and determining the position of the current vehicle in the area based on the lane line information and the obstacle information of the target obstacle.
The positioning information of the current vehicle in the GPS module can be periodically acquired, the position of the current vehicle in the current period is determined, the actually determined accurate position is an area due to the precision problem of the GPS module, and the actual position of the current vehicle can be determined through the lane line information and the target obstacle information acquired by the vehicle.
Optionally, the obstacle information may include road signs and landmark buildings, the current vehicle is determined to be in the area based on the GPS module periodically, and the current vehicle position is determined based on the lane line and the obstacle information, and the process may be as follows: map information can be acquired from a server, and the regional range of a road in the map information where the current vehicle is located is determined based on a GPS module; the position of the current vehicle within the area is determined based on the landmarks and landmark buildings.
The central processing unit may acquire map information from the server through the V2X system, determine the position of the current vehicle in the map based on the positioning information determined by the GPS module, i.e., the area where the current vehicle is located, and then determine the position of the current vehicle in the area, i.e., the actual position, based on the positions of the identified landmarks and landmark buildings and the corresponding landmarks and map buildings in the map.
And step 107, making a traveling route of the current vehicle based on the obstacle information of the target obstacle and the position of the current vehicle, and controlling the current vehicle to travel according to the traveling route.
After the target obstacle is determined, one or more feasible routes of the current vehicle passing through the target obstacle can be determined based on the information of the size, the position, the speed, the moving direction and the like of the obstacle in the obstacle information and the position of the current vehicle, and then an optimal route can be determined from the feasible routes, wherein the optimal route can be a route with the shortest form distance. The central processing unit may then control the vehicle to travel along the optimal route, i.e., along the planned travel route.
Optionally, a travel route of the current vehicle is formulated based on the target obstacle and the position of the current vehicle, and the current vehicle is controlled to travel according to the travel route, and the specific process may be as follows: acquiring position information of a target obstacle and combining the map information and the position information of the target obstacle; and formulating a traveling route of the current vehicle based on the position of the current vehicle and the position information of the target obstacle, and controlling the vehicle to travel according to the traveling route.
After the specific position of the current vehicle on the map is determined, the obstacle information of the determined target obstacle can be obtained, then the position information of the target obstacle is obtained, the position information is relative to the position information of the current vehicle, the position information of the target obstacle and the map information can be integrated based on the specific position of the current vehicle on the map, the corresponding positions are combined, namely the target obstacle is marked in the map, then the traveling route of the current vehicle is made based on the position of the target obstacle in the map, and the vehicle is controlled to travel according to the traveling route.
Optionally, the vehicle information of the current vehicle, including the vehicle speed of the other vehicle, the vehicle position information of the other vehicle, the state of an anti-lock braking system, the state of a vehicle body stability system, the state of a traction control system, the state of a lane departure warning system and the like, can also be broadcast to the other vehicles by the V2X system during the process that the current vehicle travels along the travel route.
In the embodiment of the invention, the radar information and the image are acquired by the various radars and the cameras which are arranged on the current vehicle and used for acquiring the information around the current vehicle, the obstacles around the vehicle are identified based on the obstacle identification model acquired by the server, the vehicle information of other vehicles in the preset range of the current vehicle is acquired by the V2X system, the driving route of the current vehicle is planned based on the obstacles and the vehicle information of other vehicles, the influence of the environment on radar detection is effectively reduced by the cooperation of the various radars, the vehicle information such as the state, the position and the like of the surrounding vehicles can be more accurately mastered by the interaction between the vehicles through the V2X system, and the safety of automatic driving is improved.
Based on the same technical concept, the embodiment of the present invention further provides an automatic driving device based on V2X, which may be an in-vehicle terminal in the above embodiment, as shown in fig. 3, and the device includes:
an obtaining module 310, configured to obtain an obstacle identification model from a server, where the obstacle identification model is used to determine an obstacle type;
an obtaining module 310, configured to obtain first radar information from the plurality of first radars, obtain second radar information from the second radars, obtain third radar information from the third radars, obtain fourth radar information from the plurality of fourth radars, obtain a first image from the first camera, and obtain a second image from the plurality of second cameras;
the identification module 320 is configured to identify an obstacle and a lane line from the first radar information, the second radar information, the third radar information, the fourth radar information, the first image and the second image based on the obstacle identification model, and acquire obstacle information of the obstacle and lane line information of the lane line;
the identification module 320 is used for acquiring the vehicle information of other vehicles in the preset range received by the V2X, identifying an obstacle vehicle from the vehicle information, and acquiring the vehicle information of the obstacle vehicle, wherein the other vehicles are vehicles out of the current vehicle in the preset range;
the integration module 330 is configured to perform obstacle integration based on the obstacle information and the vehicle information of the obstacle vehicle, obtain a target obstacle around the current vehicle, and obtain obstacle information of the target obstacle;
the positioning module 340 is used for periodically positioning the area where the current vehicle is located based on the GPS module and determining the position of the current vehicle in the area based on the lane line information and the obstacle information of the target obstacle;
and a control module 350 for making a traveling route of the current vehicle based on the obstacle information of the target obstacle and the position of the current vehicle, and controlling the current vehicle to travel according to the traveling route.
Optionally, the identifying module 320 is configured to:
predicting the traveling range of each other vehicle within preset time based on the speed and the traveling direction of the other vehicles;
acquiring the speed and the driving direction of the current vehicle, and predicting the traveling range of the current vehicle within preset time;
the obstacle vehicle is determined based on the travel range of the current vehicle and the travel ranges of the other vehicles.
Optionally, the integrating module 330 is configured to:
acquiring the type of an obstacle, the position information of the obstacle and the position information of an obstacle vehicle;
and merging the obstacles or obstacle vehicles of the same type at the same position to obtain the target obstacles around the current vehicle.
Optionally, the positioning module 340 is configured to:
acquiring map information from a server, and determining the area range of a road in the map information of the current vehicle based on a GPS module;
the position of the current vehicle within the area is determined based on the landmarks and landmark buildings.
Optionally, the control module 350 is configured to:
acquiring position information of a target obstacle and combining the map information and the position information of the target obstacle;
and formulating a traveling route of the current vehicle based on the position of the current vehicle and the position information of the target obstacle, and controlling the vehicle to travel according to the traveling route.
With regard to the apparatus in the above-described embodiment, the specific manner in which each module performs the operation has been described in detail in the embodiment related to the method, and will not be elaborated here.
It should be noted that: the automatic driving device based on V2X provided in the above embodiment is only illustrated by the division of the above function modules when automatically driving a vehicle, and in practical applications, the above function allocation may be completed by different function modules according to needs, that is, the internal structure of the device may be divided into different function modules to complete all or part of the above described functions. In addition, the automatic driving device based on V2X provided in the above embodiment and the automatic driving method based on V2X are the same concept, and the specific implementation process thereof is described in the method embodiment and is not described herein again.
In an exemplary embodiment, a computer readable storage medium is also provided, in which at least one instruction is stored, the at least one instruction being loaded and executed by a processor to implement the automatic driving method based on V2X in the above embodiments. For example, the computer readable storage medium may be a ROM, a Random Access Memory (RAM), a CD-ROM, a magnetic tape, a floppy disk, an optical data storage device, and the like.
Fig. 4 is a schematic structural diagram of a computer device according to an embodiment of the present invention, where the computer device may be an in-vehicle terminal in the foregoing embodiment. The computer apparatus 1000 may have a relatively large difference due to different configurations or performances, and may include one or more processors (CPUs) 1001 and one or more memories 1002, wherein the memory 1002 stores at least one instruction, and the at least one instruction is loaded and executed by the processors 1001 to implement the following steps of the automatic driving method based on V2X:
acquiring an obstacle identification model from a server, wherein the obstacle identification model is used for determining the type of an obstacle;
obtaining first radar information from a plurality of first radars, second radar information from a second radar, third radar information from a third radar, fourth radar information from a plurality of fourth radars, a first image from a first camera, and a second image from a plurality of second cameras;
identifying an obstacle and a lane line from the first radar information, the second radar information, the third radar information, the fourth radar information, the first image and the second image based on the obstacle identification model, and acquiring obstacle information of the obstacle and lane line information of the lane line;
acquiring the vehicle information of other vehicles within the preset range received by the V2X, identifying an obstacle vehicle from the vehicle information, and acquiring the vehicle information of the obstacle vehicle, wherein the other vehicles are vehicles outside the current vehicle within the preset range;
performing obstacle integration based on the obstacle information and the vehicle information of the obstacle vehicle to obtain a target obstacle around the current vehicle and acquire the obstacle information of the target obstacle;
the method comprises the steps that a current vehicle area is located periodically based on a GPS module, and the position of the current vehicle in the area is determined based on lane line information and obstacle information of a target obstacle;
and formulating a traveling route of the current vehicle based on the obstacle information of the target obstacle and the position of the current vehicle, and controlling the current vehicle to travel according to the traveling route.
Optionally, identifying the obstacle vehicle from the vehicle information includes:
predicting the traveling range of each other vehicle within preset time based on the speed and the traveling direction of the other vehicles;
acquiring the speed and the driving direction of the current vehicle, and predicting the traveling range of the current vehicle within preset time;
the obstacle vehicle is determined based on the travel range of the current vehicle and the travel ranges of the other vehicles.
Optionally, the integrating the obstacle based on the obstacle information and the vehicle information of the obstacle vehicle to obtain the target obstacle around the current vehicle includes:
acquiring the type of an obstacle, the position information of the obstacle and the position information of an obstacle vehicle;
and merging the obstacles or obstacle vehicles of the same type at the same position to obtain the target obstacles around the current vehicle.
Optionally, the obstacle information includes a road sign and a landmark building, the periodically determining the area where the current vehicle is located based on the GPS module, and determining the position of the current vehicle in the area based on the lane line and the obstacle information, including:
acquiring map information from a server, and determining the area range of a road in the map information of the current vehicle based on a GPS module;
the position of the current vehicle within the area is determined based on the landmarks and landmark buildings.
Optionally, the step of formulating a travel route of the current vehicle based on the target obstacle and the position of the current vehicle, and controlling the current vehicle to travel according to the travel route includes:
acquiring position information of a target obstacle and combining the map information and the position information of the target obstacle;
and formulating a traveling route of the current vehicle based on the position of the current vehicle and the position information of the target obstacle, and controlling the vehicle to travel according to the traveling route.
It will be understood by those skilled in the art that all or part of the steps for implementing the above embodiments may be implemented by hardware, or may be implemented by a program instructing relevant hardware, where the program may be stored in a computer-readable storage medium, and the above-mentioned storage medium may be a read-only memory, a magnetic disk or an optical disk, etc.
The above description is only for the purpose of illustrating the preferred embodiments of the present invention and is not to be construed as limiting the invention, and any modifications, equivalents, improvements and the like that fall within the spirit and principle of the present invention are intended to be included therein.
Claims (10)
1. An autonomous vehicle based on V2X, characterized in that the vehicle comprises
The system comprises a vehicle body, a vehicle-to-outside information exchange system V2X system, a plurality of first radars, a second radar, a third radar, a plurality of fourth radars, a global positioning module GPS module, a first camera and a plurality of second cameras;
the plurality of first radars are respectively arranged at the outer sides of four corners of the vehicle body and are used for detecting first radar information of first ranges at two sides of the vehicle and behind the vehicle;
the second radar is arranged right in front of the vehicle body and used for detecting second radar information of a second range in front of the vehicle;
the third radar is arranged on the top of the vehicle body and used for detecting third radar information within a third range of 360 degrees around the vehicle;
the plurality of fourth radars are uniformly arranged around the outer side of the vehicle body and used for detecting fourth radar information in a fourth range around the vehicle;
the first camera is arranged at the position of a rearview mirror in the vehicle and used for collecting a first image in a fifth range in front of the vehicle;
the plurality of second cameras are arranged at positions right in front of and right behind the vehicle body and at positions of rearview mirrors on two sides of the vehicle body and are used for collecting second images in a sixth range around the vehicle;
the V2X system is used for acquiring road conditions provided by a server and vehicle information issued by other vehicles.
2. A V2X-based automatic driving method based on the vehicle of claim 1, wherein the method comprises:
acquiring an obstacle identification model from a server, wherein the obstacle identification model is used for determining the type of an obstacle;
obtaining the first radar information from the plurality of first radars, the second radar information from the second radars, the third radar information from the third radars, the fourth radar information from the plurality of fourth radars, the first image from the first camera, and the second image from the plurality of second cameras;
identifying an obstacle and a lane line from the first radar information, the second radar information, the third radar information, the fourth radar information, the first image and the second image based on the obstacle identification model, and acquiring obstacle information of the obstacle and lane line information of the lane line;
acquiring vehicle information of other vehicles within a preset range received by the V2X, identifying an obstacle vehicle from the vehicle information, and acquiring vehicle information of the obstacle vehicle, wherein the other vehicles are vehicles outside the current vehicle within the preset range;
performing obstacle integration based on the obstacle information and the vehicle information of the obstacle vehicle to obtain a target obstacle around the current vehicle and acquire the obstacle information of the target obstacle;
periodically positioning the area where the current vehicle is located based on a GPS module, and determining the position of the current vehicle in the area based on the lane line information and the obstacle information of the target obstacle;
and formulating a traveling route of the current vehicle based on the obstacle information of the target obstacle and the position of the current vehicle, and controlling the current vehicle to travel according to the traveling route.
3. The method of claim 2, wherein said identifying an obstacle vehicle from the vehicle information comprises:
predicting the traveling range of each other vehicle within preset time based on the speed and the traveling direction of the other vehicles;
acquiring the speed and the driving direction of the current vehicle, and predicting the traveling range of the current vehicle within the preset time;
determining an obstacle vehicle based on the travel range of the current vehicle and the travel ranges of the other vehicles.
4. The method of claim 2, wherein the performing obstacle integration based on the obstacle information and vehicle information of the obstacle vehicle to obtain a target obstacle around the current vehicle comprises:
acquiring the type of the obstacle, the position information of the obstacle and the position information of the obstacle vehicle;
and merging the obstacles of the same type or the obstacle vehicles at the same position to obtain the target obstacles around the current vehicle.
5. The method of claim 2, wherein the obstacle information includes a landmark and a landmark building, and wherein the periodically determining an area in which a current vehicle is located based on the GPS module and determining a location of the current vehicle in the area based on the lane line and the obstacle information comprises:
acquiring map information from the server, and determining the area range of the road in the map information of the current vehicle based on a GPS module;
determining a location of the current vehicle within the area based on the landmark and the landmark building.
6. The method of claim 5, wherein said formulating a travel route for the current vehicle based on the target obstacle and the location of the current vehicle and controlling the current vehicle to travel along the travel route comprises:
acquiring the position information of the target obstacle and combining the map information and the position information of the target obstacle;
and formulating a traveling route of the current vehicle based on the position of the current vehicle and the position information of the target obstacle, and controlling the vehicle to travel according to the traveling route.
7. A V2X-based automatic driving device based on the vehicle of claim 1, wherein the device comprises:
the system comprises an acquisition module, a storage module and a processing module, wherein the acquisition module is used for acquiring an obstacle identification model from a server, and the obstacle identification model is used for determining the type of an obstacle;
the acquisition module is configured to acquire the first radar information from the plurality of first radars, the second radar information from the second radars, the third radar information from the third radars, the fourth radar information from the plurality of fourth radars, the first image from the first camera, and the second image from the plurality of second cameras;
the identification module is used for identifying obstacles and lane lines from the first radar information, the second radar information, the third radar information, the fourth radar information, the first image and the second image based on the obstacle identification model, and acquiring obstacle information of the obstacles and lane line information of the lane lines;
the identification module is used for acquiring vehicle information of other vehicles in a preset range received by the V2X, identifying an obstacle vehicle from the vehicle information, and acquiring vehicle information of the obstacle vehicle, wherein the other vehicles are vehicles out of the current vehicle in the preset range;
the integration module is used for integrating obstacles based on the obstacle information and the vehicle information of the obstacle vehicle to obtain target obstacles around the current vehicle and obtain the obstacle information of the target obstacles;
the positioning module is used for periodically positioning the area where the current vehicle is located based on the GPS module and determining the position of the current vehicle in the area based on the lane line information and the obstacle information of the target obstacle;
and the control module is used for making a traveling route of the current vehicle based on the obstacle information of the target obstacle and the position of the current vehicle and controlling the current vehicle to travel according to the traveling route.
8. The apparatus of claim 7, wherein the identification module is configured to:
predicting the traveling range of each other vehicle within preset time based on the speed and the traveling direction of the other vehicles;
acquiring the speed and the driving direction of the current vehicle, and predicting the traveling range of the current vehicle within the preset time;
determining an obstacle vehicle based on the travel range of the current vehicle and the travel ranges of the other vehicles.
9. The method of claim 7, wherein the integration module is configured to:
acquiring the type of the obstacle, the position information of the obstacle and the position information of the obstacle vehicle;
and merging the obstacles of the same type or the obstacle vehicles at the same position to obtain the target obstacles around the current vehicle.
10. The method of claim 7, wherein the positioning module is configured to:
acquiring map information from the server, and determining the area range of the road in the map information of the current vehicle based on a GPS module;
determining a location of the current vehicle within the area based on the landmark and the landmark building.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201910123370.7A CN109828571A (en) | 2019-02-18 | 2019-02-18 | Automatic driving vehicle, method and apparatus based on V2X |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201910123370.7A CN109828571A (en) | 2019-02-18 | 2019-02-18 | Automatic driving vehicle, method and apparatus based on V2X |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN109828571A true CN109828571A (en) | 2019-05-31 |
Family
ID=66863796
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN201910123370.7A Pending CN109828571A (en) | 2019-02-18 | 2019-02-18 | Automatic driving vehicle, method and apparatus based on V2X |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN109828571A (en) |
Cited By (14)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN110422176A (en) * | 2019-07-04 | 2019-11-08 | 苏州车萝卜汽车电子科技有限公司 | Intelligent transportation system, automobile based on V2X |
| CN110715667A (en) * | 2019-09-29 | 2020-01-21 | 吴中红 | Real-time early warning navigation device, map display method and terminal equipment |
| CN110830956A (en) * | 2019-09-29 | 2020-02-21 | 浙江合众新能源汽车有限公司 | Vehicle external environment detection system and method based on V2X |
| CN110940979A (en) * | 2019-10-28 | 2020-03-31 | 杭州飞步科技有限公司 | Obstacle detection method, apparatus, device, and storage medium |
| CN111301374A (en) * | 2020-03-05 | 2020-06-19 | 河池学院 | Automatic anti-collision system for automobile panoramic detection |
| CN112015178A (en) * | 2020-08-20 | 2020-12-01 | 中国第一汽车股份有限公司 | Control method, device, equipment and storage medium |
| WO2020253764A1 (en) * | 2019-06-18 | 2020-12-24 | 华为技术有限公司 | Method and apparatus for determining running region information |
| CN113408320A (en) * | 2020-03-16 | 2021-09-17 | 上海博泰悦臻网络技术服务有限公司 | Method, electronic device, and computer storage medium for vehicle collision avoidance |
| CN113682322A (en) * | 2021-08-26 | 2021-11-23 | 北京京东乾石科技有限公司 | Method and device for determining vehicle running path |
| CN113777625A (en) * | 2021-09-08 | 2021-12-10 | 上海伯镭智能科技有限公司 | Intelligent unmanned driving obstacle type judgment method and device |
| CN113793532A (en) * | 2021-08-11 | 2021-12-14 | 岚图汽车科技有限公司 | Remote obstacle lane information sharing method and system |
| CN113997953A (en) * | 2021-11-24 | 2022-02-01 | 上海网车科技有限公司 | Auxiliary driving system for automatic ramp vehicle convergence and automatic ramp vehicle convergence method |
| CN114719867A (en) * | 2022-05-24 | 2022-07-08 | 北京捷升通达信息技术有限公司 | Vehicle navigation method and system based on sensor |
| WO2024007570A1 (en) * | 2022-07-04 | 2024-01-11 | 惠州市德赛西威汽车电子股份有限公司 | Obstacle recognition method and apparatus, electronic device, and storage medium |
Citations (10)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN102381252A (en) * | 2010-09-02 | 2012-03-21 | 张士达 | Automobile 360-degree visual-angle monitoring system and automobile using the same |
| CN103832434A (en) * | 2012-11-22 | 2014-06-04 | 中国移动通信集团公司 | Traveling safety control system and method |
| CN105678316A (en) * | 2015-12-29 | 2016-06-15 | 大连楼兰科技股份有限公司 | Active driving method based on multi-information fusion |
| CN105676253A (en) * | 2016-01-15 | 2016-06-15 | 武汉光庭科技有限公司 | Longitudinal positioning system and method based on city road marking map in automatic driving |
| CN106708040A (en) * | 2016-12-09 | 2017-05-24 | 重庆长安汽车股份有限公司 | Sensor module of automatic driving system, automatic driving system and automatic driving method |
| CN108521800A (en) * | 2017-09-29 | 2018-09-11 | 深圳市大疆创新科技有限公司 | Control method, control terminal and the machine readable storage medium of automatic driving vehicle |
| CN108801274A (en) * | 2018-04-16 | 2018-11-13 | 电子科技大学 | A kind of terrestrial reference ground drawing generating method of fusion binocular vision and differential satellite positioning |
| DE112016006519T5 (en) * | 2016-03-29 | 2018-11-22 | Ford Global Technologies, Llc | Real-time communication with mobile infrastructure |
| CN208180895U (en) * | 2018-05-11 | 2018-12-04 | 毕龙基 | Automobile outer rear-view mirror with front camera |
| CN109087503A (en) * | 2018-09-04 | 2018-12-25 | 惠州市德赛西威智能交通技术研究院有限公司 | A kind of collaborative truck system and method based on V2X |
-
2019
- 2019-02-18 CN CN201910123370.7A patent/CN109828571A/en active Pending
Patent Citations (10)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN102381252A (en) * | 2010-09-02 | 2012-03-21 | 张士达 | Automobile 360-degree visual-angle monitoring system and automobile using the same |
| CN103832434A (en) * | 2012-11-22 | 2014-06-04 | 中国移动通信集团公司 | Traveling safety control system and method |
| CN105678316A (en) * | 2015-12-29 | 2016-06-15 | 大连楼兰科技股份有限公司 | Active driving method based on multi-information fusion |
| CN105676253A (en) * | 2016-01-15 | 2016-06-15 | 武汉光庭科技有限公司 | Longitudinal positioning system and method based on city road marking map in automatic driving |
| DE112016006519T5 (en) * | 2016-03-29 | 2018-11-22 | Ford Global Technologies, Llc | Real-time communication with mobile infrastructure |
| CN106708040A (en) * | 2016-12-09 | 2017-05-24 | 重庆长安汽车股份有限公司 | Sensor module of automatic driving system, automatic driving system and automatic driving method |
| CN108521800A (en) * | 2017-09-29 | 2018-09-11 | 深圳市大疆创新科技有限公司 | Control method, control terminal and the machine readable storage medium of automatic driving vehicle |
| CN108801274A (en) * | 2018-04-16 | 2018-11-13 | 电子科技大学 | A kind of terrestrial reference ground drawing generating method of fusion binocular vision and differential satellite positioning |
| CN208180895U (en) * | 2018-05-11 | 2018-12-04 | 毕龙基 | Automobile outer rear-view mirror with front camera |
| CN109087503A (en) * | 2018-09-04 | 2018-12-25 | 惠州市德赛西威智能交通技术研究院有限公司 | A kind of collaborative truck system and method based on V2X |
Non-Patent Citations (1)
| Title |
|---|
| 许光义: "消除车辆行驶中盲区的策略", 《科技创新与应用》 * |
Cited By (18)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US11698459B2 (en) | 2019-06-18 | 2023-07-11 | Huawei Technologies Co., Ltd. | Method and apparatus for determining drivable region information |
| US20220108552A1 (en) | 2019-06-18 | 2022-04-07 | Huawei Technologies Co., Ltd. | Method and Apparatus for Determining Drivable Region Information |
| WO2020253764A1 (en) * | 2019-06-18 | 2020-12-24 | 华为技术有限公司 | Method and apparatus for determining running region information |
| CN110422176A (en) * | 2019-07-04 | 2019-11-08 | 苏州车萝卜汽车电子科技有限公司 | Intelligent transportation system, automobile based on V2X |
| CN110715667A (en) * | 2019-09-29 | 2020-01-21 | 吴中红 | Real-time early warning navigation device, map display method and terminal equipment |
| CN110830956A (en) * | 2019-09-29 | 2020-02-21 | 浙江合众新能源汽车有限公司 | Vehicle external environment detection system and method based on V2X |
| CN110940979A (en) * | 2019-10-28 | 2020-03-31 | 杭州飞步科技有限公司 | Obstacle detection method, apparatus, device, and storage medium |
| CN111301374B (en) * | 2020-03-05 | 2022-03-22 | 河池学院 | Automatic anti-collision system for automobile panoramic detection |
| CN111301374A (en) * | 2020-03-05 | 2020-06-19 | 河池学院 | Automatic anti-collision system for automobile panoramic detection |
| CN113408320A (en) * | 2020-03-16 | 2021-09-17 | 上海博泰悦臻网络技术服务有限公司 | Method, electronic device, and computer storage medium for vehicle collision avoidance |
| CN112015178A (en) * | 2020-08-20 | 2020-12-01 | 中国第一汽车股份有限公司 | Control method, device, equipment and storage medium |
| CN112015178B (en) * | 2020-08-20 | 2022-10-21 | 中国第一汽车股份有限公司 | Control method, device, equipment and storage medium |
| CN113793532A (en) * | 2021-08-11 | 2021-12-14 | 岚图汽车科技有限公司 | Remote obstacle lane information sharing method and system |
| CN113682322A (en) * | 2021-08-26 | 2021-11-23 | 北京京东乾石科技有限公司 | Method and device for determining vehicle running path |
| CN113777625A (en) * | 2021-09-08 | 2021-12-10 | 上海伯镭智能科技有限公司 | Intelligent unmanned driving obstacle type judgment method and device |
| CN113997953A (en) * | 2021-11-24 | 2022-02-01 | 上海网车科技有限公司 | Auxiliary driving system for automatic ramp vehicle convergence and automatic ramp vehicle convergence method |
| CN114719867A (en) * | 2022-05-24 | 2022-07-08 | 北京捷升通达信息技术有限公司 | Vehicle navigation method and system based on sensor |
| WO2024007570A1 (en) * | 2022-07-04 | 2024-01-11 | 惠州市德赛西威汽车电子股份有限公司 | Obstacle recognition method and apparatus, electronic device, and storage medium |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN109828571A (en) | Automatic driving vehicle, method and apparatus based on V2X | |
| CN109634282B (en) | Autonomous vehicle, method and apparatus | |
| US12005904B2 (en) | Autonomous driving system | |
| CN110873568B (en) | High-precision map generation method and device and computer equipment | |
| US12037015B2 (en) | Vehicle control device and vehicle control method | |
| US11011063B2 (en) | Distributed data collection and processing among vehicle convoy members | |
| CN110099831B (en) | Vehicle control system, vehicle control method, and storage medium | |
| US10204516B2 (en) | Automobile and computing system | |
| US20170008531A1 (en) | Driving assistant for vehicles | |
| WO2020116264A1 (en) | Vehicle travel assistance method, vehicle travel assistance device and autonomous driving system | |
| CN112208533A (en) | Vehicle control system, vehicle control method, and storage medium | |
| CN112124311A (en) | Vehicle control device, vehicle control method, and storage medium | |
| CN111231961A (en) | Vehicle control device, vehicle control method, and storage medium | |
| JP2022139009A (en) | Drive support device, drive support method, and program | |
| US20180286222A1 (en) | Vehicle identifying device | |
| CN112305499B (en) | Method and device for positioning according to light source | |
| CN109835343B (en) | Vehicle control device, vehicle control method, and storage medium | |
| CN113884090A (en) | Intelligent platform vehicle environment sensing system and data fusion method thereof | |
| CN113525378A (en) | Vehicle control device, vehicle control method, and storage medium | |
| CN110194153B (en) | Vehicle control device, vehicle control method, and storage medium | |
| US11548521B2 (en) | Systems, methods and vehicles for determining wrong direction driving | |
| JP2019095875A (en) | Vehicle control device, vehicle control method, and program | |
| KR20220111749A (en) | Tracking system for self driving cars | |
| JP2021047609A (en) | Periphery recognition device, periphery recognition method, and program | |
| CN111862654A (en) | Intelligent navigation method, application, intelligent navigation system and vehicle |
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 | ||
| RJ01 | Rejection of invention patent application after publication | ||
| RJ01 | Rejection of invention patent application after publication |
Application publication date: 20190531 |