CN117022260A - Safe driving assistance method, device, electronic equipment and storage medium - Google Patents
Safe driving assistance method, device, electronic equipment and storage medium Download PDFInfo
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- CN117022260A CN117022260A CN202311100103.0A CN202311100103A CN117022260A CN 117022260 A CN117022260 A CN 117022260A CN 202311100103 A CN202311100103 A CN 202311100103A CN 117022260 A CN117022260 A CN 117022260A
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- B—PERFORMING OPERATIONS; TRANSPORTING
- B60—VEHICLES IN GENERAL
- B60W—CONJOINT CONTROL OF VEHICLE SUB-UNITS OF DIFFERENT TYPE OR DIFFERENT FUNCTION; CONTROL SYSTEMS SPECIALLY ADAPTED FOR HYBRID VEHICLES; ROAD VEHICLE DRIVE CONTROL SYSTEMS FOR PURPOSES NOT RELATED TO THE CONTROL OF A PARTICULAR SUB-UNIT
- B60W30/00—Purposes of road vehicle drive control systems not related to the control of a particular sub-unit, e.g. of systems using conjoint control of vehicle sub-units
- B60W30/08—Active safety systems predicting or avoiding probable or impending collision or attempting to minimise its consequences
- B60W30/09—Taking automatic action to avoid collision, e.g. braking and steering
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- B—PERFORMING OPERATIONS; TRANSPORTING
- B60—VEHICLES IN GENERAL
- B60W—CONJOINT CONTROL OF VEHICLE SUB-UNITS OF DIFFERENT TYPE OR DIFFERENT FUNCTION; CONTROL SYSTEMS SPECIALLY ADAPTED FOR HYBRID VEHICLES; ROAD VEHICLE DRIVE CONTROL SYSTEMS FOR PURPOSES NOT RELATED TO THE CONTROL OF A PARTICULAR SUB-UNIT
- B60W30/00—Purposes of road vehicle drive control systems not related to the control of a particular sub-unit, e.g. of systems using conjoint control of vehicle sub-units
- B60W30/08—Active safety systems predicting or avoiding probable or impending collision or attempting to minimise its consequences
- B60W30/095—Predicting travel path or likelihood of collision
- B60W30/0956—Predicting travel path or likelihood of collision the prediction being responsive to traffic or environmental parameters
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Abstract
The embodiment of the application discloses a safe driving assistance method, a safe driving assistance device, electronic equipment and a storage medium. Acquiring vehicle control information sent by a vehicle control device and vehicle environment information acquired by each sensor; each sensor comprises at least one of a camera, a looking-around camera and a radar; determining the risk of the vehicle environment information corresponding to each sensor according to the vehicle control information and the vehicle environment information acquired by each sensor; and performing obstacle avoidance control on the vehicle according to the risk of the vehicle environment information corresponding to each sensor. The embodiment of the application realizes the purpose of assisting the vehicle in avoiding danger when the danger exists, and improves the driving safety.
Description
Technical Field
The embodiment of the application relates to an intelligent driving technology, in particular to a safe driving assistance method, a safe driving assistance device, electronic equipment and a storage medium.
Background
Safe driving is an important technology in intelligent driving. When a driver may not be able to more comprehensively evaluate the driving environment, the driving safety is improved through a safe driving function.
In the prior art, safe driving can only realize reversing collision reminding, overspeed reminding and the like. When danger exists, only danger reminding can be carried out, vehicles cannot be assisted in danger avoidance, and safety accidents cannot be effectively avoided.
Disclosure of Invention
The application provides a safe driving assisting method, a safe driving assisting device, electronic equipment and a storage medium, which are used for assisting a vehicle to avoid danger when danger exists, so that the driving safety is improved.
In a first aspect, an embodiment of the present application provides a safe driving assistance method, including:
acquiring vehicle control information sent by a vehicle control device and vehicle environment information acquired by each sensor; each sensor comprises at least one of a camera, a looking-around camera and a radar;
determining the risk of the vehicle environment information corresponding to each sensor according to the vehicle control information and the vehicle environment information acquired by each sensor;
and performing obstacle avoidance control on the vehicle according to the risk of the vehicle environment information corresponding to each sensor.
In a second aspect, an embodiment of the present application further provides a safe driving assistance device, including:
the information acquisition module is used for acquiring vehicle control information sent by the vehicle control equipment and vehicle environment information acquired by each sensor; each sensor comprises at least one of a camera, a looking-around camera and a radar;
the risk determination module is used for determining the risk of the vehicle environment information corresponding to each sensor according to the vehicle control information and the vehicle environment information acquired by each sensor;
the obstacle avoidance control module is used for carrying out obstacle avoidance control on the vehicle according to the dangerousness of the vehicle environment information corresponding to each sensor.
In a third aspect, an embodiment of the present application further provides an electronic device, including:
one or more processors;
a storage means for storing one or more programs;
the one or more programs, when executed by the one or more processors, cause the one or more processors to implement any one of the safe driving assistance methods as provided by the embodiments of the present application.
In a fourth aspect, embodiments of the present application also provide a storage medium comprising computer executable instructions which, when executed by a computer processor, are adapted to carry out any one of the safe driving assistance methods as provided by the embodiments of the present application.
The application acquires the vehicle control information sent by the vehicle control equipment and the vehicle environment information acquired by each sensor; each sensor comprises at least one of a camera, a looking-around camera and a radar; the comprehensive environment information of the vehicle can be acquired to accurately determine the running environment of the vehicle, so that the accuracy and the comprehensiveness of the subsequent risk determination are improved; according to the vehicle control information and the vehicle environment information acquired by each sensor, determining the risk of the vehicle environment information corresponding to each sensor, and accurately determining the risk of the vehicle environment information; according to the dangers of the vehicle environment information corresponding to the sensors, obstacle avoidance control is carried out on the vehicle, specific obstacle avoidance measures are determined according to the dangers, the vehicle is assisted to avoid the obstacle, and driving safety is improved. Therefore, through the technical scheme of the application, the problems that only danger reminding can be carried out, the vehicle can not be assisted in danger avoiding and the occurrence of safety accidents can not be avoided are solved, and the effects of assisting the vehicle in danger avoiding and improving the driving safety are achieved.
Drawings
FIG. 1 is a flow chart of a safe driving assistance method in accordance with a first embodiment of the present application;
fig. 2 is a flowchart of a safe driving assistance method in the second embodiment of the application;
FIG. 3 is a flow chart of a safe driving assistance method in a third embodiment of the application;
fig. 4 is a schematic structural view of a safe driving assistance device in a fourth embodiment of the present application;
fig. 5 is a schematic structural diagram of an electronic device in a fifth embodiment of the present application.
Detailed Description
In order that those skilled in the art will better understand the present application, a technical solution in the embodiments of the present application will be clearly and completely described below with reference to the accompanying drawings in which it is apparent that the described embodiments are only some embodiments of the present application, not all embodiments. All other embodiments, which can be made by those skilled in the art based on the embodiments of the present application without making any inventive effort, shall fall within the scope of the present application.
It should be noted that the terms "first" and "second" and the like in the description and the claims of the present application and the above drawings are used for distinguishing between similar objects and not necessarily for describing a particular sequential or chronological order. It is to be understood that the data so used may be interchanged where appropriate such that the embodiments of the application described herein may be implemented in sequences other than those illustrated or otherwise described herein. Furthermore, the terms "comprises," "comprising," and "having," and any variations thereof, are intended to cover a non-exclusive inclusion, such that a process, method, system, article, or apparatus that comprises a list of steps or elements is not necessarily limited to those steps or elements expressly listed but may include other steps or elements not expressly listed or inherent to such process, method, article, or apparatus.
Example 1
Fig. 1 is a flowchart of a safe driving assistance method according to an embodiment of the present application, where the method may be performed by a safe driving assistance device, and the device may be implemented by software and/or hardware and is specifically configured at a vehicle end when it is detected that there is a danger in a driving process of a vehicle.
Referring to the safe driving assistance method shown in fig. 1, the method specifically includes the following steps:
s110, acquiring vehicle control information sent by a vehicle control device and vehicle environment information acquired by each sensor; each sensor includes at least one of a camera, a look-around camera, and a radar.
The vehicle control information may transmit various control instructions issued to the vehicle control apparatus for determining the running state of the vehicle. For example, the vehicle control information may include vehicle speed information, acceleration information, steering information, and the like. By way of example, the vehicle control device may be an accelerator pedal, a brake pedal, a steering wheel, and the like. Specifically, the vehicle control devices are configured with sensors, and vehicle control information generated by each vehicle control device is acquired through the sensors and sent to the whole vehicle controller. Therefore, the vehicle control information may be obtained directly from the sensor corresponding to each vehicle control device, or may also be obtained from the vehicle control unit, which is not particularly limited in the present application.
The vehicle environment information may be information for measuring the driving environment of the vehicle, and is used for determining the safety of the current driving environment. Specifically, the vehicle environment information may be acquired by various sensors installed around the vehicle. By way of example, various sensors mounted around the vehicle may include cameras, looking around cameras, radar, and the like. The camera can be an on-vehicle front view camera and can be used for collecting image information of the right front angle of the vehicle within the range of 60-120 degrees. The vehicle-mounted looking-around fisheye camera can be used for collecting image information around a vehicle body. The radar may include a through-ultrasonic radar and a millimeter wave radar, and may be used to collect radar obstacle information around the vehicle.
S120, determining the risk of the vehicle environment information corresponding to each sensor according to the vehicle control information and the vehicle environment information acquired by each sensor.
The risk may be that the vehicle is at risk of collision when the vehicle is controlled to travel according to the current vehicle control information, and the risk is used for performing obstacle avoidance control on the vehicle when the collision is at risk. By way of example, hazards may include both hazardous and non-hazardous. Determining whether an obstacle exists in the vehicle environment information acquired by the sensor according to the vehicle environment information acquired by the sensor, and predicting whether the vehicle is in collision risk with the obstacle according to the vehicle control information when the obstacle exists, if so, determining that the risk of the vehicle environment information corresponding to the sensor is dangerous, otherwise, no risk exists.
Specifically, road information such as lane lines, roadblocks, road edges and the like can be determined according to image information acquired by a camera and a looking-around camera, and the pose of a vehicle in a road is determined by combining vehicle control information; and determining whether an obstacle exists or not based on the vehicle environment information acquired by each sensor, and if so, predicting the risk of collision between the vehicle and the fixed obstacle according to the pose of the vehicle in the road and the vehicle control signal. For example, pedestrian information in a front sidewalk of a vehicle is collected according to a camera, if a pedestrian exists, the risk of collision between the vehicle and the pedestrian is determined according to vehicle control information, and when the risk of collision exists, the risk of vehicle environment information corresponding to the camera is determined to be dangerous.
S130, performing obstacle avoidance control on the vehicle according to the risk of the vehicle environment information corresponding to each sensor.
The obstacle avoidance control can be used for avoiding the collision risk of the vehicle and adopting control measures for the vehicle so as to ensure the running safety of the vehicle. For example, the obstacle avoidance control may be lane change to the left, lane change to the right, left turn, right turn, deceleration or braking, etc., which is not particularly limited in the present application. When the danger of the vehicle environment information corresponding to the sensor is dangerous, the vehicle can be controlled to avoid the obstacle according to the specific collision danger. For example, if a pedestrian is detected in front of the vehicle according to the camera, and it is determined that the vehicle has a collision risk with the pedestrian according to the speed information in the vehicle control information, the vehicle is subjected to deceleration or braking control.
According to the technical scheme, vehicle control information sent by the vehicle control equipment and vehicle environment information acquired by each sensor are acquired; each sensor comprises at least one of a camera, a looking-around camera and a radar; the comprehensive environment information of the vehicle can be acquired to accurately determine the running environment of the vehicle, so that the accuracy and the comprehensiveness of the subsequent risk determination are improved; according to the vehicle control information and the vehicle environment information acquired by each sensor, determining the risk of the vehicle environment information corresponding to each sensor, and accurately determining the risk of the vehicle environment information; according to the dangers of the vehicle environment information corresponding to the sensors, obstacle avoidance control is carried out on the vehicle, specific obstacle avoidance measures are determined according to the dangers, the vehicle is assisted to avoid the obstacle, and driving safety is improved. Therefore, through the technical scheme of the application, the problems that only danger reminding can be carried out, the vehicle can not be assisted in danger avoiding and the occurrence of safety accidents can not be avoided are solved, and the effects of assisting the vehicle in danger avoiding and improving the driving safety are achieved.
Example two
Fig. 2 is a flowchart of a flowchart method of a safe driving assistance method according to a second embodiment of the present application, and the technical solution of the present embodiment is further refined based on the technical solution.
Further, "obstacle avoidance control for a vehicle according to the risk of the vehicle environment information corresponding to each sensor" is refined as follows: determining the corresponding dangerous weight of each sensor according to weather information; and carrying out obstacle avoidance control on the vehicle according to the corresponding dangerous weight of each sensor and the dangerousness of the vehicle environment information so as to assist the vehicle to avoid the obstacle.
Referring to fig. 2, a safe driving assistance method includes:
s210, acquiring vehicle control information sent by a vehicle control device and vehicle environment information acquired by each sensor; each sensor includes at least one of a camera, a look-around camera, and a radar.
S220, determining the risk of the vehicle environment information corresponding to each sensor according to the vehicle control information and the vehicle environment information acquired by each sensor.
S230, determining the corresponding dangerous weight of each sensor according to weather information.
Weather information is weather information, visibility, temperature and other information and is used for determining the corresponding dangerous weight of each sensor. The collection of vehicle environmental information by each sensor is affected by weather information. The accuracy of information acquired by the same sensor in different weather is different. The dangerous weight can be a weight determined according to the accuracy of data acquired by each sensor and is used for determining obstacle avoidance control on the vehicle. The risk weight may be determined from a trial or experience value, which is not particularly limited by the present application. The influence of vehicle environment information with low accuracy on obstacle avoidance control of the vehicle can be reduced through the dangerous weight, the accuracy of obstacle avoidance control of the vehicle is improved, and the running safety of the vehicle is improved. For example, in the case where visibility is low at night or in the case of heavy fog, the reliability of camera data is lowered, and the risk weight of vehicle environment information corresponding to the camera is also required to be lowered. For another example, in a rainy day, the millimeter wave radar may have a significant attenuation, so that the dangerous weight of the vehicle environment information corresponding to the millimeter wave radar needs to be reduced.
S240, performing obstacle avoidance control on the vehicle according to the danger weights corresponding to the sensors and the danger of the vehicle environment information.
And weighting the dangers of the vehicle environment information according to the corresponding dangerous weights of the sensors, and determining obstacle avoidance control to be performed on the vehicle through a mathematical model or algorithm. By assigning different dangerous weights for the dangers of the vehicle environment information of each sensor to weight, the accuracy of obstacle avoidance control of the vehicle is improved.
In an alternative embodiment, the obstacle avoidance control is performed on the vehicle according to the risk weights corresponding to the sensors and the risk of the vehicle environment information, and the method comprises the following steps: determining a target driving scene according to the vehicle control information and the vehicle environment information; determining a target decision tree according to the target driving scene; based on the target decision tree, obstacle avoidance control is carried out on the vehicle according to the corresponding dangerous weight of each sensor and the dangerousness of the vehicle environment information.
The target driving scene may be a driving scene where the vehicle is currently driving, and is used for determining a target decision tree. For example, the target driving scenario may include: straight running, turning, overtaking, lane changing, turning around, etc., the application is not particularly limited thereto. The driving state of the vehicle is determined according to the vehicle control information, and the target driving scene can be determined in combination with the driving environment information. For example, when it is detected that the right turn lamp is turned on by the vehicle control information and it is detected that the front road sign is a right turn lane by the vehicle environment information, it may be determined that the target driving scene of the vehicle is a right turn.
The target decision tree may be a decision tree determined according to a target driving scenario, for determining obstacle avoidance control. Decision trees are predictive models that can be used to analyze data to aid in decision making. The judging process and influencing factors for determining obstacle avoidance control in different driving scenes are different, so that the target decision trees corresponding to different target driving scenes are different. For example, the target decision tree may be dynamically generated based on the target driving scenario.
In an alternative embodiment, determining a target decision tree based on a target driving scenario includes: and determining the structure of each node in the target decision tree and the vehicle environment information corresponding to each node according to the target driving scene so as to determine the target decision tree.
Nodes may be structural elements in a decision tree for determining a target decision tree. Illustratively, the nodes may include leaf nodes, child nodes, and root nodes. The structure of each node can be the parent-child relationship of each node and is used for forming a decision tree. Each node corresponds to a type of vehicle environment information. According to the target driving scene, the structure of each node and the vehicle environment information corresponding to each node in the target decision tree are dynamically determined, and according to the structure of each node and the vehicle environment information corresponding to each node, the target decision tree is determined.
The structure of each node in the target decision tree and the vehicle environment information corresponding to each node are determined according to the target driving scene to determine the target decision tree, and the target decision tree is dynamically determined according to the target driving scene, so that the structure of the target decision tree accords with the judging process of the vehicle control in the target table driving scene, and the accuracy of obstacle avoidance control of the vehicle in different target driving scenes is improved.
And according to the corresponding dangerous weight of each sensor and the dangerousness of the vehicle environment information, inputting the dangerous weight into the nodes in the target decision tree, and outputting obstacle avoidance control by the root node. And integrating and calculating the data of each layer of nodes, summarizing the final calculation result to a root node, and outputting obstacle avoidance control on the vehicle.
Determining a target driving scene according to the vehicle control information and the vehicle environment information; determining a target decision tree according to the target driving scene, so that the target decision tree accords with the target driving scene, and improving the output accuracy of the target decision tree; based on the target decision tree, the vehicle is subjected to obstacle avoidance control according to the dangerous weight corresponding to each sensor and the dangerous nature of the vehicle environment information, the accuracy of the output of the target decision tree is improved through the dangerous weight corresponding to each sensor, the accuracy of the obstacle avoidance control on the vehicle is improved, and the running safety of the vehicle is improved.
Taking the target driving scene as an intersection right turn example, when the vehicle turns right, and when a pedestrian passes through a blind area in the right rear view of the vehicle, if the camera can not accurately capture barrier information at night or in foggy days, the radar signal has better accuracy. According to the current target driving scene, the dangerous weight of the vehicle environment information collected by the camera is dynamically reduced, the dangerous weight of the vehicle environment information collected by the radar is increased, and a current target decision tree is generated according to the right-turn driving scene. The object decision tree has the vehicle environment information input collected by the camera and the vehicle environment information input collected by the radar, at the moment, the danger of the camera input is no danger, and the danger of the radar input is dangerous. If there is no risk weight management, there may be false detection or missed detection. The dangerous weight of the camera is dynamically reduced by the target decision tree, and the dangerous weight of the radar is increased, so that the dangerous situation under the scene is accurately determined. And through multi-layer data decision fusion, finally, obstacle avoidance control signals (such as deceleration or left turning) are output to ensure the distance between the vehicle and the obstacle, and the driving safety of the vehicle is ensured.
According to the technical scheme, the corresponding dangerous weights of the sensors are determined according to weather information, the influence of different weather on the accuracy of information acquired by the sensors is considered, the corresponding dangerous weights are dynamically determined, and the stability of a subsequent result is guaranteed; according to the danger weight corresponding to each sensor and the danger of the vehicle environment information, the vehicle is subjected to obstacle avoidance control, the accuracy of the obstacle avoidance control on the vehicle is improved through different danger weights, and the running safety of the vehicle is improved.
Example III
Fig. 3 is a flowchart of a flowchart method of a safe driving assistance method according to a third embodiment of the present application, and the technical solution of this embodiment is further refined based on the above technical solution.
Further, "determining the risk of the vehicle environment information corresponding to each sensor according to the vehicle control information and the vehicle environment information acquired by each sensor" is refined as follows: "predicting a travel locus of the vehicle based on the vehicle control information; determining whether obstacles exist in the vehicle environment information corresponding to each sensor according to the running track and the vehicle environment information acquired by each sensor; if so, determining that the risk of the vehicle environment information having the obstacle is dangerous, so as to determine the risk of the vehicle environment information.
Referring to fig. 3, a safe driving assistance method includes:
s310, acquiring vehicle control information sent by a vehicle control device and vehicle environment information acquired by each sensor; each sensor includes at least one of a camera, a look-around camera, and a radar.
S320, predicting the running track of the vehicle according to the vehicle control information.
The travel track may be a movement track of the vehicle in the road for determining whether an obstacle exists. The running track of the vehicle running continuously according to the current vehicle control information can be predicted according to the vehicle control information. Specifically, according to the acceleration, angular velocity, wheel pulse, vehicle speed, steering wheel rotation angle and other information of the X/Y/Z axis in the vehicle control information, vehicle pose and state information are determined, and the running track of the vehicle is predicted by fusion processing of the change trend of data in the multi-frame vehicle control information and signals of the speed, direction, acceleration and the like of the vehicle within a period of time.
S330, determining whether an obstacle exists in the vehicle environment information corresponding to each sensor according to the running track and the vehicle environment information acquired by each sensor.
The obstacle may be an object that may collide with the vehicle for determining the risk of vehicle environmental information. By way of example, the obstacle may be a pedestrian, a vehicle adjacent to a lane, a road barrier, a road edge, etc., as the application is not particularly limited.
Specifically, after each sensor collects the vehicle environment information, the vehicle environment information is preprocessed. For example, the collected image information is subjected to operations such as coordinate transformation, de-distortion and the like, the image information is inferred by using a corresponding inference model, and the inferred image information is subjected to unified formatting. Illustratively, the collected radar information is subjected to filtering and false alarm filtering, and then the radar information is formatted.
And analyzing whether an object which is possibly collided with the vehicle exists in the vehicle environment information acquired by each sensor, and if so, determining the object as an obstacle.
In an alternative embodiment, determining whether an obstacle exists in the vehicle environment information corresponding to each sensor according to the driving track and the vehicle environment information acquired by each sensor includes: determining a visual field blind area of the vehicle in the running track according to the running track; predicting whether an obstacle exists in a visual field blind area according to vehicle environment information corresponding to each sensor; and determining whether the obstacle exists in the vehicle environment information corresponding to each sensor according to the prediction result of whether the obstacle exists in the visual field blind area.
The blind spot may be an area of the vehicle that cannot be viewed by the driver during travel, where there is a higher risk of collision relative to other areas. For example, the blind spot of view may be rearward right of the vehicle when the vehicle turns right. And determining whether an object positioned in a blind area of a visual field or an object to be moved to the blind area of the visual field exists in the vehicle environment information corresponding to each sensor according to the vehicle environment information corresponding to each sensor, and if so, determining the object as an obstacle. Accordingly, if the prediction result is that an obstacle exists in the blind area of the visual field, the vehicle environment information corresponding to the sensor for detecting the obstacle is determined to be the obstacle. By way of example, if the looking-around camera determines that a pedestrian exists in the blind area of the vehicle field of view, then it is determined that an obstacle exists in the vehicle environment information corresponding to the looking-around camera. And if the prediction result is that no obstacle exists in the visual field blind area, determining that no obstacle exists in the vehicle environment information corresponding to the sensor.
Determining a visual field blind area of the vehicle in the driving track according to the driving track; predicting whether an obstacle exists in a visual field blind area according to vehicle environment information corresponding to each sensor; according to the prediction result of whether the obstacle exists in the blind area of the visual field, whether the obstacle exists in the vehicle environment information corresponding to each sensor is determined, and the obstacle in the blind area of the visual field can be effectively determined by predicting the obstacle in the blind area of the visual field, so that the running safety of the vehicle is improved.
In an alternative embodiment, predicting whether an obstacle exists in the blind area of the field of view according to the vehicle environment information corresponding to each sensor includes: judging whether a candidate obstacle moving to a visual field blind area exists or not according to vehicle environment information corresponding to each sensor; if so, predicting whether the candidate obstacle and the vehicle have collision risks according to the speed of the candidate obstacle; and predicting whether an obstacle exists in the visual field blind area according to a prediction result of collision risk of the candidate obstacle and the vehicle.
Based on the vehicle environment information corresponding to each sensor, whether a candidate obstacle moving to the blind area of the visual field exists or not is judged according to continuous multi-frame image data or continuous multiple radar data. If the candidate obstacle moves to the blind area of the visual field, predicting whether the candidate obstacle and the vehicle have collision risks according to the speed of the candidate obstacle and the running track of the vehicle. If the predicted result is that the candidate obstacle and the vehicle have collision risk, predicting that the obstacle exists in the visual field blind area; otherwise, the predicted result is that the candidate obstacle and the vehicle have no collision risk, and no obstacle exists in the visual field blind area.
Judging whether a candidate obstacle moving to a visual field blind area exists or not according to vehicle environment information corresponding to each sensor; if so, predicting whether the candidate obstacle and the vehicle have collision risks according to the speed of the candidate obstacle; predicting whether an obstacle exists in a visual field blind area according to a prediction result of collision risk of the candidate obstacle and the vehicle, predicting whether the moving candidate obstacle exists in the visual field blind area according to the moving speed, improving the comprehensiveness of obstacle detection, predicting potential obstacles, reducing the risk of collision of the vehicle during running, and improving the driving safety.
And S340, if yes, determining that the risk of the vehicle environment information with the obstacle is dangerous.
If so, that is, if an obstacle exists in the vehicle environment information corresponding to the sensor, the risk of the vehicle environment information with the obstacle is determined to be dangerous. If no obstacle exists in the vehicle environment information corresponding to the sensor, it is determined that there is no risk of the vehicle environment information having no obstacle.
S350, performing obstacle avoidance control on the vehicle according to the risk of the vehicle environment information corresponding to each sensor.
According to the technical scheme of the embodiment, the running track of the vehicle is predicted according to the vehicle control information; determining whether an obstacle exists in the vehicle environment information corresponding to each sensor according to the running track and the vehicle environment information acquired by each sensor, and determining whether the obstacle exists according to the predicted running track of the vehicle in time; if so, determining that the risk of the vehicle environment information with the obstacle is dangerous, and accurately determining the risk corresponding to the vehicle environment information so as to avoid the collision between the vehicle and the obstacle in the running process, thereby providing an accurate data base for obstacle avoidance control of the vehicle.
Example IV
Fig. 4 is a schematic structural diagram of a safe driving assistance device according to a fourth embodiment of the present application, where the embodiment is applicable to assisting a vehicle in avoiding danger when detecting that there is danger in a driving process of the vehicle, and the safe driving assistance device has the following specific structure:
an information acquisition module 410, configured to acquire vehicle control information sent by a vehicle control device, and vehicle environment information acquired by each sensor; each sensor comprises at least one of a camera, a looking-around camera and a radar;
a risk determination module 420, configured to determine a risk of the vehicle environment information corresponding to each sensor according to the vehicle control information and the vehicle environment information collected by each sensor;
the obstacle avoidance control module 430 is configured to perform obstacle avoidance control on the vehicle according to the risk of the vehicle environment information corresponding to each sensor.
According to the technical scheme, vehicle control information sent by the vehicle control equipment and vehicle environment information acquired by each sensor are acquired; each sensor comprises at least one of a camera, a looking-around camera and a radar; the comprehensive environment information of the vehicle can be acquired to accurately determine the running environment of the vehicle, so that the accuracy and the comprehensiveness of the subsequent risk determination are improved; according to the vehicle control information and the vehicle environment information acquired by each sensor, determining the risk of the vehicle environment information corresponding to each sensor, and accurately determining the risk of the vehicle environment information; according to the dangers of the vehicle environment information corresponding to the sensors, obstacle avoidance control is carried out on the vehicle, specific obstacle avoidance measures are determined according to the dangers, the vehicle is assisted to avoid the obstacle, and driving safety is improved. Therefore, through the technical scheme of the application, the problems that only danger reminding can be carried out, the vehicle can not be assisted in danger avoiding and the occurrence of safety accidents can not be avoided are solved, and the effects of assisting the vehicle in danger avoiding and improving the driving safety are achieved.
Optionally, the obstacle avoidance control module 430 includes:
the dangerous weight determining unit is used for determining the dangerous weight corresponding to each sensor according to weather information;
and the obstacle avoidance control unit is used for carrying out obstacle avoidance control on the vehicle according to the dangerous weight corresponding to each sensor and the dangerousness of the vehicle environment information.
Optionally, the obstacle avoidance control unit includes:
the target driving scene determining subunit is used for determining a target driving scene according to the vehicle control information and the vehicle environment information;
the target decision tree determining subunit is used for determining a target decision tree according to a target driving scene;
the obstacle avoidance control subunit is used for performing obstacle avoidance control on the vehicle based on the target decision tree according to the risk weights corresponding to the sensors and the risk of the vehicle environment information.
Optionally, the target decision tree determining subunit is specifically configured to:
and determining the structure of each node in the target decision tree and the vehicle environment information corresponding to each node according to the target driving scene so as to determine the target decision tree.
Optionally, the risk determination module 420 includes:
a travel track prediction unit for predicting a travel track of the vehicle according to the vehicle control information;
the obstacle judging unit is used for determining whether an obstacle exists in the vehicle environment information corresponding to each sensor according to the running track and the vehicle environment information acquired by each sensor;
and a risk determination unit configured to determine that there is a risk of the vehicle environment information having the obstacle if the risk determination unit is in the affirmative.
Optionally, the obstacle judging unit includes:
a vision blind area determining subunit, configured to determine a vision blind area of the vehicle in the driving track according to the driving track;
an obstacle predicting subunit, configured to predict whether an obstacle exists in the blind area of the field of view according to vehicle environment information corresponding to each sensor;
the obstacle determination is used as a subunit for determining whether the obstacle exists in the vehicle environment information corresponding to each sensor according to the prediction result of whether the obstacle exists in the visual field blind area.
Optionally, the obstacle predicting subunit is specifically configured to:
judging whether a candidate obstacle moving to a visual field blind area exists or not according to vehicle environment information corresponding to each sensor;
if so, predicting whether the candidate obstacle and the vehicle have collision risks according to the speed of the candidate obstacle;
and predicting whether an obstacle exists in the visual field blind area according to a prediction result of collision risk of the candidate obstacle and the vehicle.
The safe driving assistance device provided by the embodiment of the application can execute the safe driving assistance method provided by any embodiment of the application, and has the corresponding functional modules and beneficial effects of executing the safe driving assistance method.
Example five
Fig. 5 is a schematic structural diagram of an electronic device according to a fifth embodiment of the present application, as shown in fig. 5, the electronic device includes a processor 510, a memory 520, an input device 530, and an output device 540; the number of processors 510 in the electronic device may be one or more, one processor 510 being taken as an example in fig. 5; the processor 510, memory 520, input device 530, and output device 540 in the electronic device may be connected by a bus or other means, for example in fig. 5.
The memory 520 is a computer readable storage medium, and may be used to store software programs, computer executable programs, and modules, such as program instructions/modules (e.g., the information acquisition module 410, the risk determination module 420, and the obstacle avoidance control module 430) corresponding to the safe driving assistance method in the embodiment of the present application. The processor 510 executes various functional applications of the electronic device and data processing, i.e., implements the above-described safe driving assistance method, by running software programs, instructions, and modules stored in the memory 520.
Memory 520 may include primarily a program storage area and a data storage area, wherein the program storage area may store an operating system, at least one application program required for functionality; the storage data area may store data created according to the use of the terminal, etc. In addition, memory 520 may include high-speed random access memory, and may also include non-volatile memory, such as at least one magnetic disk storage device, flash memory device, or other non-volatile solid-state storage device. In some examples, memory 520 may further include memory located remotely from processor 510, which may be connected to the electronic device via a network. Examples of such networks include, but are not limited to, the internet, intranets, local area networks, mobile communication networks, and combinations thereof.
The input means 530 may be used to receive input character information and to generate key signal inputs related to user settings and function control of the electronic device. The output 540 may include a display device such as a display screen.
Example six
A sixth embodiment of the present application also provides a storage medium containing computer-executable instructions, which when executed by a computer processor, are for performing a safe driving assistance method, the method comprising: acquiring vehicle control information sent by a vehicle control device and vehicle environment information acquired by each sensor; each sensor comprises at least one of a camera, a looking-around camera and a radar; determining the risk of the vehicle environment information corresponding to each sensor according to the vehicle control information and the vehicle environment information acquired by each sensor; and performing obstacle avoidance control on the vehicle according to the risk of the vehicle environment information corresponding to each sensor.
Of course, the storage medium containing the computer executable instructions provided in the embodiments of the present application is not limited to the method operations described above, and may also perform the related operations in the safe driving assistance method provided in any embodiment of the present application.
From the above description of embodiments, it will be clear to a person skilled in the art that the present application may be implemented by means of software and necessary general purpose hardware, but of course also by means of hardware, although in many cases the former is a preferred embodiment. Based on such understanding, the technical solution of the present application may be embodied essentially or in a part contributing to the prior art in the form of a software product, which may be stored in a computer readable storage medium, such as a floppy disk, a Read-Only Memory (ROM), a random access Memory (Random Access Memory, RAM), a FLASH Memory (FLASH), a hard disk or an optical disk of a computer, etc., and include several instructions for causing an electronic device (which may be a personal computer, a server, or a network device, etc.) to execute the method according to the embodiments of the present application.
It should be noted that, in the above-mentioned embodiments of the search apparatus, each unit and module included are only divided according to the functional logic, but not limited to the above-mentioned division, as long as the corresponding functions can be implemented; in addition, the specific names of the functional units are also only for distinguishing from each other, and are not used to limit the protection scope of the present application.
Note that the above is only a preferred embodiment of the present application and the technical principle applied. It will be understood by those skilled in the art that the present application is not limited to the particular embodiments described herein, but is capable of various obvious changes, rearrangements and substitutions as will now become apparent to those skilled in the art without departing from the scope of the application. Therefore, while the application has been described in connection with the above embodiments, the application is not limited to the embodiments, but may be embodied in many other equivalent forms without departing from the spirit or scope of the application, which is set forth in the following claims.
Claims (10)
1. A safe driving assistance method, characterized by comprising:
acquiring vehicle control information sent by a vehicle control device and vehicle environment information acquired by each sensor; each sensor comprises at least one of a camera, a looking-around camera and a radar;
determining the risk of the vehicle environment information corresponding to each sensor according to the vehicle control information and the vehicle environment information acquired by each sensor;
and carrying out obstacle avoidance control on the vehicle according to the risk of the vehicle environment information corresponding to each sensor.
2. The method according to claim 1, wherein the performing obstacle avoidance control on the vehicle according to the risk of the vehicle environment information corresponding to each sensor includes:
determining the corresponding dangerous weight of each sensor according to weather information;
and carrying out obstacle avoidance control on the vehicle according to the dangerous weight corresponding to each sensor and the danger of the vehicle environment information.
3. The method according to claim 2, wherein the performing obstacle avoidance control on the vehicle according to the risk weight corresponding to each sensor and the risk of the vehicle environment information includes:
determining a target driving scene according to the vehicle control information and the vehicle environment information;
determining the target decision tree according to the target driving scene;
and based on the target decision tree, performing obstacle avoidance control on the vehicle according to the dangerous weight corresponding to each sensor and the danger of the vehicle environment information.
4. A method according to claim 3, wherein said determining said target decision tree from said target driving scenario comprises:
and determining the structure of each node in the target decision tree and the vehicle environment information corresponding to each node according to the target driving scene so as to determine the target decision tree.
5. The method of claim 1, wherein determining the risk of the vehicle environment information corresponding to each sensor based on the vehicle control information and the vehicle environment information collected by each sensor comprises:
predicting the running track of the vehicle according to the vehicle control information;
determining whether obstacles exist in the vehicle environment information corresponding to each sensor according to the running track and the vehicle environment information acquired by each sensor;
if so, it is determined that there is a risk of the vehicle environment information having the obstacle.
6. The method of claim 5, wherein determining whether an obstacle exists in the vehicle environment information corresponding to each sensor according to the travel track and the vehicle environment information acquired by each sensor comprises:
according to the running track, determining a visual field blind area of the vehicle in the running track;
predicting whether an obstacle exists in the visual field blind area according to vehicle environment information corresponding to each sensor;
and determining whether the obstacle exists in the vehicle environment information corresponding to each sensor according to the prediction result of whether the obstacle exists in the visual field blind area.
7. The method of claim 6, wherein predicting whether an obstacle exists in the blind view area according to the vehicle environment information corresponding to each sensor comprises:
judging whether a candidate obstacle moving to the vision blind area exists or not according to vehicle environment information corresponding to each sensor;
if so, predicting whether collision risk exists between the candidate obstacle and the vehicle according to the speed of the candidate obstacle;
and predicting whether an obstacle exists in the visual field blind area according to a prediction result of the collision risk of the candidate obstacle and the vehicle.
8. A safe driving assistance device, characterized by comprising:
the information acquisition module is used for acquiring vehicle control information sent by the vehicle control equipment and vehicle environment information acquired by each sensor; each sensor comprises at least one of a camera, a looking-around camera and a radar;
the risk determination module is used for determining the risk of the vehicle environment information corresponding to each sensor according to the vehicle control information and the vehicle environment information acquired by each sensor;
and the obstacle avoidance control module is used for carrying out obstacle avoidance control on the vehicle according to the risk of the vehicle environment information corresponding to each sensor.
9. An electronic device comprising a memory, a processor and a computer program stored on the memory and executable on the processor, wherein the processor implements the safe driving assistance method of any one of claims 1-7 when the program is executed by the processor.
10. A computer-readable storage medium, on which a computer program is stored, characterized in that the program, when executed by a processor, implements the safe driving assistance method according to any one of claims 1-7.
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Citations (7)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN110103967A (en) * | 2019-05-15 | 2019-08-09 | 广州小鹏汽车科技有限公司 | A kind of automatic lane change method of vehicle and vehicle control system, vehicle |
| CN112572430A (en) * | 2020-12-14 | 2021-03-30 | 深兰人工智能(深圳)有限公司 | Collision risk determination method and device |
| CN113879297A (en) * | 2021-09-29 | 2022-01-04 | 深圳市道通智能汽车有限公司 | Vehicle vision blind area early warning system and method and vehicle |
| US20220206502A1 (en) * | 2020-12-24 | 2022-06-30 | Mitsubishi Electric Corporation | Blind area estimation apparatus, vehicle travel system, and blind area estimation method |
| CN114684120A (en) * | 2020-12-31 | 2022-07-01 | 上海海拉电子有限公司 | Vehicle control method based on obstacle running track prediction |
| CN115257725A (en) * | 2022-07-20 | 2022-11-01 | 湖北秉正讯腾科技有限公司 | Vehicle collision early warning method, device, equipment and storage medium |
| US20230015466A1 (en) * | 2019-12-24 | 2023-01-19 | Hitachi Astemo, Ltd. | Vehicle control system, and vehicle control method |
-
2023
- 2023-08-29 CN CN202311100103.0A patent/CN117022260A/en active Pending
Patent Citations (7)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN110103967A (en) * | 2019-05-15 | 2019-08-09 | 广州小鹏汽车科技有限公司 | A kind of automatic lane change method of vehicle and vehicle control system, vehicle |
| US20230015466A1 (en) * | 2019-12-24 | 2023-01-19 | Hitachi Astemo, Ltd. | Vehicle control system, and vehicle control method |
| CN112572430A (en) * | 2020-12-14 | 2021-03-30 | 深兰人工智能(深圳)有限公司 | Collision risk determination method and device |
| US20220206502A1 (en) * | 2020-12-24 | 2022-06-30 | Mitsubishi Electric Corporation | Blind area estimation apparatus, vehicle travel system, and blind area estimation method |
| CN114684120A (en) * | 2020-12-31 | 2022-07-01 | 上海海拉电子有限公司 | Vehicle control method based on obstacle running track prediction |
| CN113879297A (en) * | 2021-09-29 | 2022-01-04 | 深圳市道通智能汽车有限公司 | Vehicle vision blind area early warning system and method and vehicle |
| CN115257725A (en) * | 2022-07-20 | 2022-11-01 | 湖北秉正讯腾科技有限公司 | Vehicle collision early warning method, device, equipment and storage medium |
Non-Patent Citations (1)
| Title |
|---|
| 武志斐等: "《智能网联汽车技术概论》", 31 July 2022, 北京理工大学出版社, pages: 115 - 116 * |
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