CN112918472A - Vehicle driving assistance system, vehicle using the same, and corresponding method and medium - Google Patents
Vehicle driving assistance system, vehicle using the same, and corresponding method and medium Download PDFInfo
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- 238000004590 computer program Methods 0.000 claims description 11
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- 230000003993 interaction Effects 0.000 claims description 5
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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
- B60W30/0953—Predicting travel path or likelihood of collision the prediction being responsive to vehicle dynamic parameters
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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
- B60W40/00—Estimation or calculation of non-directly measurable driving parameters for road vehicle drive control systems not related to the control of a particular sub unit, e.g. by using mathematical models
- B60W40/08—Estimation or calculation of non-directly measurable driving parameters for road vehicle drive control systems not related to the control of a particular sub unit, e.g. by using mathematical models related to drivers or passengers
- B60W40/09—Driving style or behaviour
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Abstract
A vehicle driving assistance system, a vehicle using the same, and a corresponding vehicle driving assistance method and computer-readable storage medium are provided. The vehicle driving assistance system includes an identification information recognition unit, a driving habit data acquisition unit, a travel track prediction unit, and a control unit. The identity information identification unit is configured to identify identity information of drivers of other vehicles in a preset area around the current vehicle under a predetermined scene. The driving habit data acquisition unit is configured to acquire driving habit data of a driver in a predetermined scene based on the identity information. The travel track prediction unit is configured to predict a travel track of the other vehicle based on the driving habit data. The control unit is configured to control the current vehicle driving based on the predicted travel locus to avoid a collision with another vehicle. According to the invention, the collision between the current vehicle and other vehicles can be avoided, and the driving safety is improved.
Description
Technical Field
The present invention relates to the field of vehicle technologies, and more particularly, to a vehicle driving assistance system, a vehicle using the same, and a corresponding vehicle driving assistance method and computer-readable storage medium.
Background
The current vehicle typically encounters other vehicles during its travel. For example, when the current vehicle is traveling straight at an intersection, it may encounter a turning vehicle turning toward it. If the driver of another vehicle (e.g., a turning vehicle) is driving without avoiding other traffic participants, but the current vehicle (e.g., a straight-going vehicle traveling straight at an intersection) is still traveling straight at the established speed and path, there may be a risk of collision.
Accordingly, there is a need for a vehicle driving assistance system, a vehicle using the same, and a corresponding vehicle driving assistance method and computer readable storage medium to at least partially solve the problems in the prior art.
Disclosure of Invention
In order to solve the technical problems, the invention provides a scheme for predicting the driving track of other vehicles based on the driving habit data of drivers of other vehicles and controlling the driving of the current vehicle based on the predicted driving track, and aims to reduce the collision risk of the current vehicle and other vehicles and improve the driving safety.
In a first aspect, a vehicle driving assistance system is provided. The vehicle driving assist system includes:
the identification information recognition unit is configured to recognize the identification information of drivers of other vehicles in a preset area around the current vehicle under a preset scene;
a driving habit data acquisition unit configured to acquire driving habit data of the driver in the predetermined scene based on the identity information;
a travel track prediction unit configured to predict a travel track of the other vehicle based on the driving habit data; and
a control unit configured to control the current vehicle driving to avoid a collision with the other vehicle based on the predicted travel locus.
According to the vehicle driving assistance system, the driving habit data of the driver in the preset scene can be acquired according to the identity information of the driver in the other vehicle in the preset scene, and the driving track of the other vehicle is predicted based on the driving habit data, so that the current vehicle driving (such as deceleration, stop, emergency braking, lane change and the like) can be controlled, the current vehicle is prevented from colliding with the other vehicle, and the driving safety is improved.
Optionally, the control unit is configured to control the current vehicle to lane change to avoid the travel track if it is predicted that the other vehicle will not avoid the current vehicle.
Optionally, the vehicle driving assistance system further includes a machine learning unit configured to train a driving habit data model provided to the driving habit data obtaining unit by performing a training process in a machine learning manner; and the driving habit data obtaining unit is further configured to obtain the driving habit data from the trained driving habit data model.
Optionally, the driving habit data comprises at least one of speed, acceleration, steering wheel angle, interaction with other traffic participants.
Optionally, the predetermined scene is an intersection, the other vehicles are turning vehicles turning at the intersection, and the current vehicle is a straight-going vehicle going straight at the intersection.
Optionally, the identity information identifying unit is configured to detect facial information of the driver and identify the identity information of the driver based on the facial information.
In a second aspect of the present invention, a vehicle is provided. The vehicle may utilize the vehicle driving assist system of any one of the above.
In a third aspect of the invention, a vehicle driving assistance method is provided. The vehicle driving assistance method includes: identifying identity information of drivers of other vehicles in a preset area around the current vehicle in a preset scene; acquiring driving habit data of the driver in the preset scene based on the identity information; predicting a travel track of the other vehicle based on the driving habit data; and controlling the current vehicle driving based on the predicted travel locus to avoid a collision with the other vehicle.
According to the vehicle driving assisting method, the driving habit data of the driver in the preset scene can be acquired according to the identity information of the driver of the other vehicle in the preset scene, and the driving track of the other vehicle is predicted based on the driving habit data, so that the current vehicle can be controlled to drive (such as deceleration, stop, emergency braking, lane change and the like), the current vehicle is prevented from colliding with the other vehicle, and the driving safety is improved.
Optionally, the step of controlling the current vehicle to drive based on the predicted travel track includes controlling the current vehicle to lane change to avoid the travel track if it is predicted that the other vehicle will not avoid the current vehicle.
Optionally, the driving habit data is obtained from a driving habit data model, wherein the driving habit data model is trained by performing a training process in a machine learning manner.
Optionally, the driving habit data comprises at least one of speed, acceleration, steering wheel angle, interaction with other traffic participants.
Optionally, the predetermined scene is an intersection, the other vehicles are turning vehicles turning at the intersection, and the current vehicle is a straight-going vehicle going straight at the intersection.
Optionally, the step of identifying the identity information of the drivers of the other vehicles within a preset area around the current vehicle under the predetermined scene includes detecting face information of the drivers and identifying the identity information of the drivers based on the face information.
In a fourth aspect of the present invention, a computer-readable storage medium having a computer program stored thereon is provided. The computer program, when executed by a processor, implements any of the vehicle driving assistance methods described above.
Drawings
Non-limiting and non-exhaustive embodiments of the present invention are described by way of example with reference to the following drawings, in which:
FIG. 1 shows a schematic diagram of a vehicle driving assistance system according to one embodiment of the invention;
FIG. 2 shows a schematic diagram of a vehicle driving assistance system according to another embodiment of the invention;
FIG. 3 is a schematic diagram illustrating an application scenario of a driving assistance system for vehicle according to an embodiment of the present invention; and
fig. 4 shows a flowchart of a driving assistance method for vehicle according to an embodiment of the invention.
Detailed Description
In order to make the objects, technical solutions and advantages of the present invention more apparent, the present invention is described in further detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative of the invention and are not intended to limit the invention.
In a first aspect of the invention, a vehicle driving assistance system is provided. Fig. 1 shows a schematic view of a driving assistance system 100 for vehicle according to an embodiment of the invention. Fig. 2 shows a schematic view of a driving assistance system 200 for vehicle according to another embodiment of the invention. Fig. 3 is a schematic diagram of an application scenario of the driving assistance system 100/200 according to an embodiment of the present invention. The driving assistance system for vehicle provided by the present invention will be described in detail below with reference to fig. 1 to 3.
As shown in fig. 1, a driving assistance system 100 for vehicle according to an embodiment of the present invention includes an identification information recognition unit 110, a driving habit data acquisition unit 120, a travel track prediction unit 130, and a control unit 140.
In order to better describe the driving assistance system 100 of the vehicle provided by the present invention, an application scenario of the driving assistance system 100 of the vehicle according to an embodiment of the present invention is first described in detail with reference to fig. 3. The driving assistance system 100 for vehicle according to an embodiment of the present invention may be applied to the present vehicle 310 in the application scenario shown in fig. 3. It should be noted that the application scenario of the driving assistance system 100 for vehicle of the present invention is not limited to the embodiment shown in fig. 3.
As shown in fig. 3, the current vehicle 310 is a straight-traveling vehicle that travels straight at an intersection. There are also turning vehicles, i.e., other vehicles 320, at the intersection that turn at the intersection. The "straight-ahead vehicle" referred to herein may be a straight-ahead vehicle that is traveling straight through an intersection, or may be a straight-ahead vehicle that is about to travel straight through an intersection (for example, a straight-ahead vehicle that is traveling at a red light such as an intersection). Similarly, the "turning vehicle" may be a turning vehicle that is turning at an intersection, or may be a turning vehicle that is about to turn at an intersection.
The other vehicle 320 may be a turning vehicle that turns at an intersection toward the straight-ahead current vehicle 310. Turning toward the current vehicle 310 may be turning from a lane parallel to the travel path of the current vehicle 310 (e.g., left turning) to a lane perpendicular to the travel path of the current vehicle 310 as shown in fig. 3, or turning from a lane perpendicular to the travel path of the current vehicle 310 (e.g., left turning) to a lane parallel to the travel path of the current vehicle 310. The other vehicle 320 may also be a turning vehicle that is located at an intersection and turns away from the straight-ahead current vehicle 310. For example, a turning vehicle that turns away from the straight-ahead current vehicle 310 may be a lane that turns (e.g., turns right) from a lane that is perpendicular to the travel path of the current vehicle 310 to a lane that is parallel to the travel path of the current vehicle 310.
The current vehicle 310 may have an autonomous driving function. For example, the current vehicle 310 may be a partially autonomous vehicle, a conditionally autonomous vehicle, or a fully autonomous vehicle. The current vehicle 310 may be mounted with the identification information recognition unit 110, the driving habit data acquisition unit 120, the travel track prediction unit 130, and the control unit 140 shown in fig. 1. Alternatively, at least one of the identification information recognition unit 110, the driving habit data acquisition unit 120, the travel track prediction unit 130 and the control unit 140 may be provided on a roadside infrastructure or an online server that communicates with the current vehicle 310 through a mobile network or Wi-Fi, and the current vehicle 310 acquires the relevant information from at least one of the identification information recognition unit 110, the driving habit data acquisition unit 120, the travel track prediction unit 130 and the control unit 140 by communicating with the roadside infrastructure or the online server.
As shown in fig. 1 to 3, the identification information recognizing unit 110 is configured to recognize the identification information of the driver of the other vehicle 320 within a preset area around the current vehicle under a predetermined scene. The predetermined scene may be an intersection as shown in fig. 3, or other scenes such as lane change, parking, vehicle meeting (especially narrow-road vehicle meeting), and the like. The "preset area" refers to an area within a predetermined distance range (for example, within 1 meter to 100 meters, such as within 1 meter, within 4 meters, within 20 meters, within 50 meters, within 85 meters, within 100 meters, and the like) from the current vehicle around the current vehicle.
In one embodiment of the present invention, the identity information recognition unit 110 is configured to detect facial information of the driver of the other vehicle 320 and recognize the identity information of the driver based on the detected facial information. More specifically, the identification information recognition unit 110 may include a camera mounted on the current vehicle 310 (e.g., a front windshield of the current vehicle 310), roadside infrastructure, or the like. The camera may be a high resolution camera to make the identified identity information more accurate. The identity information recognition unit 110 may then upload the facial information of the driver of the other vehicle 320 collected by the camera to the online server and perform similarity matching with the facial information in the crowd information base on the online server. The crowd information base comprises face information and identity information corresponding to the face information one by one. If the similarity between the facial information of the driver of the other vehicle 320 collected by the camera and one of the facial information in the crowd information base is greater than or equal to a predetermined threshold (e.g., 95%), the identity information corresponding to the facial information in the crowd information base is identified as the identity information of the driver of the other vehicle 320.
The driving habit data obtaining unit 120 is configured to obtain driving habit data of the driver of the other vehicle 320 in a predetermined scene based on the identified identity information of the driver of the other vehicle 320. The driving habit data may be defined based on parameters or may be defined based on behaviors. For example, when the driving habit data is defined based on the parameters, the driving habit data may include at least one of a speed, an acceleration, a steering wheel angle, and a wheel angle. When the other vehicle is a turning vehicle, the driving habit data of the turning size can be obtained based on the steering wheel angle or the wheel angle. For another example, when the driving habit data is defined based on behaviors, the driving habit data may include an interactive relationship with other traffic participants, such as whether to avoid a straight-ahead vehicle, whether to avoid a lane-changed vehicle, whether to avoid an oncoming vehicle meeting the lane-changed vehicle, whether to avoid a parked vehicle, and the like.
The driving habit data obtaining unit 120 may send a request to the online server to obtain driving habit data corresponding to the recognized identity information from the driving habit data model trained by the online server. The driving habit data model may be trained in a variety of ways.
For example, in one embodiment of the present invention, the predetermined scene is an intersection. If it is detected at any one time that there is a straight-going vehicle at the intersection and a turning vehicle within a preset area around the straight-going vehicle, the identity information of the driver in the turning vehicle is recognized, and at least one of the speed, acceleration, steering wheel angle, wheel angle of the turning vehicle driven by the driver is uploaded to the online server. The online server determines an average value of a plurality of speeds, accelerations, steering wheel angles, and the like as driving habit data of the driver in a predetermined scene of the intersection. The identification of the identification information of the driver of the turning vehicle may refer to the above-described identification manner of the identification information of the driver of the other vehicle 320. The speed, acceleration, steering wheel angle, wheel angle of the turning vehicle may be detected by sensors (e.g., cameras, millimeter wave radar, lidar, ultrasonic sensors, or any other suitable sensor, or combinations thereof) disposed on the turning vehicle, the vehicle around the turning vehicle (e.g., the current vehicle), or the roadside infrastructure.
For another example, in another embodiment of the present invention, the predetermined scenario is lane change. If a lane-change vehicle desiring lane change (e.g., flashing of a right turn light) and a running vehicle within a preset area around the lane-change vehicle (e.g., behind a right lane) are detected at any one time, identification information of a driver in the running vehicle is recognized, and at least one of a speed and an acceleration of the running vehicle driven by the driver is uploaded to the online server. The online server determines an average value of a plurality of speeds, accelerations, and the like as driving habit data of the driver in a predetermined scene of lane change. The identification of the identity information of the driver of the traveling vehicle may refer to the above-described identification manner of the identity information of the driver of the other vehicle 320. The speed, acceleration of the traveling vehicle may be detected by sensors (e.g., cameras, millimeter wave radar, lidar, ultrasonic sensors, or any other suitable sensor, or combinations thereof) disposed on the traveling vehicle, a vehicle surrounding the traveling vehicle (e.g., a lane-change vehicle), or roadside infrastructure.
The driving habit data model may also be trained by performing a training process in a machine learning manner. Specifically, as shown in fig. 2, the vehicle driving assistance system 200 further includes a machine learning unit 250. The machine learning unit 250 is configured to train the driving habit data model provided to the driving habit data obtaining unit 120 by performing a training process in a machine learning manner. The driving habit data obtaining unit 120 is further configured to obtain driving habit data from the trained driving habit data model. The machine learning unit 250 may be located on an online server, and the driving habit data obtaining unit 120 of the current vehicle 310 may be connected to the online server through a mobile network or Wi-Fi, etc.
For example, in one embodiment of the present invention, the predetermined scene is an intersection. The machine learning unit 250 is configured to train the driving habit data model by: identifying identity information of a driver of a turning vehicle at an intersection; acquiring a result value of whether the turning vehicle avoids a straight-going vehicle or a result value of the speed, the acceleration, the steering wheel angle and the wheel angle of the turning vehicle; and the machine learning unit 250 trains a driving habit data model by using the identity information and the result value. The identification of the identification information of the driver of the turning vehicle may refer to the above-described identification manner of the identification information of the driver of the other vehicle 320. Whether the turning vehicle dodges the straight-ahead vehicle or the resultant values of the speed, acceleration, steering wheel angle, wheel angle of the turning vehicle may be obtained by sensors (e.g., cameras, millimeter-wave radar, laser radar, ultrasonic sensors, or any other suitable sensor, or a combination thereof) disposed on the turning vehicle, the vehicle around the turning vehicle (e.g., the straight-ahead vehicle), or the roadside infrastructure.
Specifically, if it is detected at any one time that there is a turning vehicle and a straight-ahead vehicle at the intersection, the identity information of the driver in the turning vehicle is identified; and acquiring a result value of whether the turning vehicle avoids the straight-going vehicle or a result value of the speed, acceleration, steering wheel angle, wheel angle of the turning vehicle. The online server may train the driving habit data model using the identity information/result values. For example, model training is performed by a machine learning method such as a probabilistic model/support vector machine/neural network.
The travel locus prediction unit 130 is configured to predict the travel locus of the other vehicle 320 based on the driving habit data in the predetermined scene acquired by the driving habit data acquisition unit 120. For example, when the driving habit data obtaining unit 120 obtains driving habit data such as speed, acceleration, steering wheel angle, and the like at an intersection that matches the driver of the other vehicle 320 from the driving habit data model, the travel locus prediction unit 130 may predict the travel locus of the other vehicle 320 based on the driving habit data. When the driving habit data obtaining unit 120 obtains driving habit data of whether or not to avoid a straight-ahead vehicle (for example, not to avoid a straight-ahead vehicle) at the intersection, which matches the driver of the other vehicle 320, from the driving habit data model, the travel locus prediction unit 130 predicts the travel locus of the other vehicle 320 based on the driving habit data of whether or not to avoid a straight-ahead vehicle. For another example, when the driving habit data acquiring unit 120 acquires driving habit data such as speed, acceleration, steering wheel angle, and wheel angle at the time of meeting on a narrow road, which is matched with the driver of the other vehicle 320, from the driving habit data model, the travel locus predicting unit 130 may predict the travel locus of the other vehicle 320 based on the driving habit data. When the driving habit data obtaining unit 120 obtains driving habit data of whether or not the oncoming vehicle is avoided (e.g., does not avoid the oncoming vehicle) at the narrow road meeting vehicle, which matches the driver of the other vehicle 320, from the driving habit data model, the travel locus prediction unit 130 predicts the travel locus of the other vehicle 320 based on the above-described driving habit data of whether or not the oncoming vehicle is avoided.
The control unit 140 is configured to control the current vehicle 310 to drive based on the predicted travel trajectory of the other vehicle 320 so as to avoid a collision with the other vehicle 320. The control unit 140 may control a steering system, a braking system, an acceleration system, etc. of the present vehicle 310. For example, the control unit 140 may control the current vehicle 310 to decelerate, stop completely, or brake urgently to avoid a collision with the other vehicle 320. Preferably, in a case where it is predicted that the other vehicle 320 will not avoid the current vehicle, the control unit 140 may control the current vehicle 310 to change the lane so as to avoid the predicted travel trajectory of the other vehicle 320. For example, in a case where the predetermined scene is an intersection, when there is another vehicle 320 that is located right ahead of the current vehicle 310 and turns right on a lane perpendicular to the travel path of the current vehicle 310 and the driving habit data of the driver of the other vehicle 320 is not to avoid a straight-ahead vehicle, the travel locus of the other vehicle predicted by the travel locus prediction unit based on the driving habit data of the driver of the other vehicle 320 may overlap a part of the travel locus of the current vehicle 310. In this case, the control unit 140 may control the current vehicle 310 to change the lane to the left so as to avoid the predicted travel trajectory of the other vehicle 320.
In summary, the driving assistance system for vehicle according to the present invention may acquire driving habit data of the driver in a predetermined scene according to the identity information of the driver of the other vehicle 320 in the predetermined scene, and predict the driving trajectory of the other vehicle 320 based on the driving habit data, so as to control the current vehicle 310 to drive (e.g., decelerate, stop, emergency brake, lane change, etc.) to avoid the current vehicle 310 colliding with the other vehicle 320.
Of course, the identification information of the driver of the other vehicle and the driving habit data in the predetermined scene may also be recognized and stored by the other vehicle in advance, and then acquired by the current vehicle through communication between the other vehicle and the current vehicle.
In a second aspect of the present invention, a vehicle is provided. The vehicle may have an automatic driving function. The vehicle is configured to be able to utilize the above-described vehicle driving assist system. For example, in one embodiment of the present invention, at least one of the above-described identification information recognition unit 110, driving habit data acquisition unit 120, travel track prediction unit 130 and control unit 140 is provided on the vehicle. For another example, in another embodiment of the present invention, the vehicle may communicate with the above-described control unit 140 provided on an on-line server or roadside infrastructure through a mobile network or Wi-Fi to receive an instruction for driving.
In a third aspect of the invention, a vehicle driving assistance method is provided. Fig. 4 shows a flowchart of a driving assistance method for vehicle according to an embodiment of the invention.
As shown in fig. 4, the vehicle driving assist method includes:
step S410: identifying identity information of drivers of other vehicles in a preset area around the current vehicle in a preset scene;
step S420: acquiring driving habit data of the driver in the preset scene based on the identity information;
step S430: predicting a travel track of the other vehicle based on the driving habit data; and
step S440: controlling the current vehicle driving based on the predicted travel track to avoid a collision with the other vehicle.
Optionally, the step of controlling the current vehicle driving based on the predicted travel track includes controlling the current vehicle to change lanes to avoid the travel track.
Optionally, the driving habit data is obtained from the driving habit data model, wherein the driving habit data model is trained by performing a training process in a machine learning manner.
Optionally, the driving habit data comprises at least one of speed, acceleration, steering wheel angle, interaction with other traffic participants.
Optionally, the predetermined scene is an intersection, the other vehicles are turning vehicles turning at the intersection, and the current vehicle is a straight-going vehicle going straight at the intersection.
Optionally, the step of identifying the identity information of the drivers of the other vehicles within a preset area around the current vehicle under the predetermined scene includes detecting face information of the drivers and identifying the identity information of the drivers based on the face information.
The specific details of the vehicle driving assistance method provided by the present invention may refer to the description of the vehicle driving assistance system, and are not described herein again.
In a fourth aspect of the invention, there is provided a computer readable storage medium having stored thereon a computer program which, when executed by a processor, implements the method of any of the above. For example, the computer program, when executed by a processor, is capable of instructing the processor and/or the respective component to carry out the steps of: identifying identity information of drivers of other vehicles in a preset area around the current vehicle in a preset scene; acquiring driving habit data of the driver in the preset scene based on the identity information; predicting a travel track of the other vehicle based on the driving habit data; and controlling the current vehicle driving based on the predicted travel locus to avoid a collision with the other vehicle.
Further, it should be understood that the various elements of the vehicle driving assistance system 100/200 described above may be implemented in whole or in part by software, hardware, and combinations thereof. The units can be embedded in a hardware form or independent from a processor in the computer device, and can also be stored in a memory in the computer device in a software form, so that the processor can call and execute operations corresponding to the units.
In one embodiment, a computer device is provided, which includes a memory and a processor, the memory stores a computer program operable on the processor, and the processor implements the steps of the vehicle driving assistance method in any one of the above embodiments when executing the computer program. The computer device may be a server or a vehicle-mounted terminal. The computer device includes a processor, a memory, a network interface, and a database connected by a system bus. Wherein the processor of the computer device is configured to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system, a computer program, and a database. The internal memory provides an environment for the operation of an operating system and computer programs in the non-volatile storage medium. The network interface of the computer device is used for communicating with an external terminal through a network connection. The computer program is executed by a processor to implement the vehicle driving assist method of the invention.
It will be appreciated by those skilled in the art that the schematic diagrams of the vehicle driving assistance system 100/200 shown in fig. 1 and 2 are merely block diagrams of portions of structures associated with aspects of the present invention and do not constitute limitations on the computing devices to which aspects of the present invention may be applied, as particular computing devices may include more or fewer components than shown, or may combine certain components, or have a different arrangement of components.
It will be understood by those skilled in the art that all or part of the steps in the method according to the above embodiments of the present invention may be implemented by indicating the relevant hardware to do so via a computer program, which may be stored in a non-volatile computer-readable storage medium, and which, when executed, may implement the steps of the above embodiments of the method. Any reference to memory, storage, database, or other medium used in the embodiments provided herein may include non-volatile and/or volatile memory, among others. Non-volatile memory can include read-only memory (ROM), Programmable ROM (PROM), Electrically Programmable ROM (EPROM), Electrically Erasable Programmable ROM (EEPROM), or flash memory. Volatile memory can include Random Access Memory (RAM) or external cache memory.
The technical features of the above embodiments can be arbitrarily combined, and for the sake of brevity, all possible combinations of the technical features in the above embodiments are not described, but should be considered as the scope of the present specification as long as there is no contradiction between the combinations of the technical features.
While the present invention has been described in connection with the embodiments, it is to be understood by those skilled in the art that the foregoing description and drawings are merely illustrative and not restrictive of the broad invention, and that this invention not be limited to the disclosed embodiments. Various modifications and variations are possible without departing from the spirit of the invention.
Claims (14)
1. A vehicle driving assist system characterized by comprising:
the identification information recognition unit is configured to recognize the identification information of drivers of other vehicles in a preset area around the current vehicle under a preset scene;
a driving habit data acquisition unit configured to acquire driving habit data of the driver in the predetermined scene based on the identity information;
a travel track prediction unit configured to predict a travel track of the other vehicle based on the driving habit data; and
a control unit configured to control the current vehicle driving to avoid a collision with the other vehicle based on the predicted travel locus.
2. The vehicular drive assist system according to claim 1, characterized in that the control unit is configured to control the current vehicle to lane change to avoid the running locus if it is predicted that the other vehicle will not avoid the current vehicle.
3. The vehicular drive assist system according to claim 1 or 2, characterized in that the vehicular drive assist system further comprises a machine learning unit configured to train a driving habit data model provided to the driving habit data obtaining unit by performing a training process in a machine learning manner; and
the driving habit data obtaining unit is further configured to obtain the driving habit data from the trained driving habit data model.
4. The vehicle driving assistance system according to claim 3, wherein the driving habit data includes at least one of speed, acceleration, steering wheel angle, and interaction with other traffic participants.
5. The vehicle driving assist system recited in claim 4, wherein the predetermined scene is an intersection, the other vehicle is a turning vehicle that turns at the intersection, and the current vehicle is a straight-going vehicle that goes straight at the intersection.
6. The vehicular drive assist system according to claim 1 or 2, characterized in that the identification information identifying unit is configured to detect face information of the driver and identify the identification information of the driver based on the face information.
7. A vehicle characterized in that the vehicle can utilize the vehicular drive assist system according to any one of claims 1 to 6.
8. A vehicle driving assist method characterized by comprising:
identifying identity information of drivers of other vehicles in a preset area around the current vehicle in a preset scene;
acquiring driving habit data of the driver in the preset scene based on the identity information;
predicting a travel track of the other vehicle based on the driving habit data; and
controlling the current vehicle driving based on the predicted travel track to avoid a collision with the other vehicle.
9. The vehicular drive assist method according to claim 8, characterized in that the step of controlling the current vehicle drive based on the predicted travel locus includes controlling the current vehicle to lane change to avoid the travel locus in a case where it is predicted that the other vehicle will not avoid the current vehicle.
10. The vehicular drive assist method according to claim 8 or 9, characterized in that the driving habit data is acquired from a driving habit data model, wherein the driving habit data model is trained by performing a training process in a machine learning manner.
11. The vehicle driving assist method recited in claim 10, wherein the driving habit data includes at least one of speed, acceleration, steering wheel angle, and interaction with other traffic participants.
12. The vehicle driving assist method recited in claim 11, wherein the predetermined scene is an intersection, the other vehicle is a turning vehicle that turns at the intersection, and the current vehicle is a straight-going vehicle that goes straight at the intersection.
13. The vehicular drive assist method according to claim 8 or 9, characterized in that the step of identifying the driver's identification information of other vehicles within a preset area around a current vehicle under a predetermined scene includes detecting face information of the driver and identifying the driver's identification information based on the face information.
14. A computer-readable storage medium, on which a computer program is stored, which, when being executed by a processor, carries out a vehicle driving assistance method according to any one of claims 8 to 13.
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