CN109878535B - Driving assistance system and method - Google Patents
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- CN109878535B CN109878535B CN201711249749.XA CN201711249749A CN109878535B CN 109878535 B CN109878535 B CN 109878535B CN 201711249749 A CN201711249749 A CN 201711249749A CN 109878535 B CN109878535 B CN 109878535B
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Abstract
The invention relates to a driving assistance system and method. The driving assistance system includes a detection device configured to detect a running parameter of another vehicle around the host vehicle; a control device configured to calculate a time to collision between the other vehicle and the host vehicle based on the running parameters of the other vehicle and the running parameters of the host vehicle, and determine whether a driving behavior of a driver of the host vehicle is abnormal based on the time to collision; and an output device configured to output a control signal when the control device determines that the driving behavior of the driver is abnormal. According to the embodiment of the application, the abnormal driving behavior of the driver can be detected, so that the driver is reminded of driving safely and traffic accidents are avoided.
Description
Technical Field
The present invention relates to the field of assisted driving of vehicles. In particular, the present invention relates to a driving assistance system and method capable of detecting abnormal driving behavior of a driver to remind the driver of safe driving.
Background
Today, urban traffic is increasingly crowded as the holding capacity of automobiles continues to grow. In the face of complex road conditions, drivers may have a state of enthusiasm such as frequently pressing a horn, hurry following or changing lanes and the like, and may also have a state of lackluster such as dozing, watching a mobile phone and the like. However, either impatient driving behavior or sluggish driving behavior is very dangerous to the driver and surrounding vehicles as well as pedestrians. Therefore, there is a need for timely reminding or warning of dangerous driving behavior of the driver so that the driver can drive safely to avoid traffic accidents.
In addition, each driver has own unique driving behavior habit, so it is necessary to record and learn the daily driving habit of the driver, and establish a driving behavior model associated with the driver, so as to determine whether the current driving behavior of the driver is safe according to the driving behavior model.
Disclosure of Invention
It is an object of the present invention to provide a driving assistance system and method capable of automatically detecting abnormal driving behavior of a driver. It is another object of the present invention to provide a driving assistance system and method of a driving behavior model associated with the driver's daily driving behavior.
One aspect of the present invention provides a driving assistance system for a vehicle, including: a detection device configured to detect a running parameter of another vehicle around the host vehicle; a control device configured to calculate a time to collision between the other vehicle and the host vehicle based on the running parameters of the other vehicle and the running parameters of the host vehicle, and determine whether or not the driving behavior of the driver of the host vehicle is abnormal based on the time to collision; and an output device configured to output a control signal when the control device determines that the driving behavior of the driver is abnormal.
According to an embodiment of the present invention, the control device is configured to calculate a difference between the collision time and a collision time mean value stored in advance, and determine that the driving behavior of the driver is abnormal when the difference between the collision time and the collision time mean value is smaller than a threshold value set in advance.
According to an embodiment of the invention, the control device is further configured to identify the driver, wherein the control device is configured to obtain the time-to-collision mean value from the identification result.
According to an embodiment of the present invention, the driving assistance system further includes: and a learning device configured to record all collision times of the driver within a previously specified driving unit according to the recognition result, and calculate the collision time average value according to the all collision times to establish a driving behavior model corresponding to the driver, wherein the control device is configured to acquire the collision time average value from the driving behavior model.
According to an embodiment of the present invention, the learning device is further configured to recalculate the time-to-collision mean value in accordance with the time-to-collision, thereby modifying the driving behavior model.
Another aspect of the invention provides a vehicle including the above-described driving assistance system according to the invention.
Another aspect of the present invention provides a driving assistance method for a vehicle, including: a detection device configured to detect a running parameter of another vehicle around the host vehicle; calculating a collision time between the other vehicle and the host vehicle according to the running parameters of the other vehicle and the running parameters of the host vehicle, and determining whether or not the driving behavior of the driver of the host vehicle is abnormal according to the collision time; and outputting a control signal when the driving behavior of the driver is judged to be abnormal.
According to an embodiment of the present invention, the driving assistance method further includes: and calculating the difference between the collision time and a pre-stored collision time average value, and judging that the driving behavior of the driver is abnormal when the difference between the collision time and the collision time average value is smaller than a pre-set threshold value.
According to an embodiment of the present invention, the driving assistance method further includes: identifying the driver; and acquiring the collision time mean value according to the recognition result.
According to an embodiment of the present invention, the driving assistance method further includes: recording all collision time of the driver in a previously specified driving unit according to the recognition result, and calculating the mean value of the collision time according to all the collision time so as to establish a driving behavior model corresponding to the driver; and obtaining the collision time mean value from the driving behavior model.
According to an embodiment of the present invention, the driving assistance method further includes recalculating the collision time average value according to the collision time, thereby modifying the driving behavior model.
Therefore, the driving assist system according to the embodiment of the invention can detect the abnormal driving behavior of the driver to remind the driver of safe driving. Further, the driving assist system according to the embodiment of the invention establishes a driving behavior model associated with each driver, thereby determining whether the current driving behavior of the driver is safe or not from the driving behavior model.
Drawings
The present invention may be better understood from the following description of specific embodiments thereof taken in conjunction with the accompanying drawings, in which like reference numerals identify identical or functionally similar elements.
Fig. 1 is a simplified schematic illustration of a motor vehicle comprising a driving assistance system according to the invention.
Fig. 2 is a simplified schematic diagram of a driving assistance system according to an embodiment of the invention.
FIG. 3 is a simplified schematic diagram of a driving behavior model according to an embodiment of the invention.
Fig 4 is a flowchart of a driving assistance method according to an embodiment of the invention,
Detailed Description
Features and exemplary embodiments of various aspects of the present invention will be described in detail below. In the following detailed description, numerous specific details are set forth in order to provide a thorough understanding of the present invention. It will be apparent, however, to one skilled in the art that the present invention may be practiced without some of these specific details. The following description of the embodiments is merely intended to provide a better understanding of the present invention by illustrating examples of the present invention. The present invention is in no way limited to any specific configuration and algorithm set forth below, but rather covers any modification, replacement or improvement of elements, components or algorithms without departing from the spirit of the invention. In the drawings and the following description, well-known structures and techniques are not shown in order to avoid unnecessarily obscuring the present invention.
Fig. 1 is a simplified schematic diagram of a car 100 comprising a driving assistance system according to the invention. Although an automobile is taken as an example, the present application is not limited to application to automobiles, but may be applied to various motor vehicles, such as cars, trucks, motorcycles, sport utility vehicles, tractors, and the like, which may use one or more power sources, such as an internal combustion engine, an electric motor, and the like, as power mechanisms.
As shown in FIG. 1, the automobile 100 includes an electronic control system 110, an in-vehicle sensor 120, a driving assistance system 130, and an operation execution system 140, which may be connected to each other, for example, to a Controller Area Network (CAN) bus 160 or to an operation execution system 140 of the automobile 100
A network. Well-known power and steering devices, drive trains, and the like in the vehicle 100 are not shown in FIG. 1 for the sake of clarity. Optionally, the car 100 may further include a communication device 150, an entertainment device (not shown), and the like, which may also be connected to the electronic control system 110, the driving assistance system 130, the operation execution system 140, and the like of the car 100 through respective interfaces.
The electronic control system 110 may include, for example, an Electronic Control Unit (ECU). The ECU may be implemented with a processor (e.g., a microprocessor), a controller (e.g., a microcontroller), programmable logic circuitry (e.g., a Field Programmable Gate Array (FPGA)), an Application Specific Integrated Circuit (ASIC), and so forth. The ECU may include one or more memories, such as Random Access Memory (RAM), Read Only Memory (ROM), erasable programmable memory (EPROM), electrically erasable programmable memory (EEPROM), and the like. The memory may be used to store data, instructions, software, code, etc. that are executed to perform the actions described herein.
The onboard sensors 120 may include one or more of the following various sensors: an image pickup apparatus, an ultrasonic sensor, a radar device, a laser device, and the like. The image pickup apparatus may be installed in front, rear, side, top, inside, or the like of the vehicle, and may include a visible light camera, an infrared camera, or the like. The visible light camera can, for example, capture images of the interior and/or exterior of the vehicle (e.g., operating at an angle of around 60 °) in real time and present to the driver and/or passenger. Further, by analyzing the images captured by the cameras, information such as driver parameters, traffic light indications, intersection conditions, running states of other vehicles, and the like can be detected. The infrared camera may capture images under night vision conditions. The ultrasonic sensors can be arranged around the vehicle to accurately measure the distance between other vehicles and the main vehicle. Ultrasonic sensors typically provide more range finding accuracy for near objects than for distant objects. The radar apparatus may be mounted in front of, behind, or otherwise in the vehicle. The radar apparatus can accurately measure the distance of other vehicles from the host vehicle using the characteristics of electromagnetic waves, and is generally more sensitive to metal objects. Radar devices can also use the doppler effect to measure the change in velocity of a vehicle relative to an object. A laser device (e.g., a LIDAR) may be mounted in front of, behind, or otherwise on the vehicle. The laser device can detect accurate object edge and shape information, so that accurate object identification and tracking can be carried out. The in-vehicle sensor 120 may further include a device that senses a self state of the vehicle (e.g., a current load capacity and a distribution thereof, a maintenance condition of the vehicle, a running state), a surrounding environment of the vehicle (e.g., temperature, humidity, brightness, air pressure, etc.), and the like.
The driving assistance system 130 is connected to the electronic control system 110 and a not-shown transmission system. The driving assistance system 130 may have, for example, one or more of the following functions: controlling the vehicle lamp; controlling a loudspeaker; actuation controls such as shifting, braking, acceleration, steering, etc.
The operation performing system 140 controls operations of various components of the vehicle, such as a power system, a steering system, a transmission system, a brake system, a safety system, and the like of the vehicle.
The communication device 150 may include a wireless communication device that allows the automobile 100 to communicate with other information sources. For example, the automobile 100 may communicate with other vehicles in its vicinity (referred to as "Car to Car (Car-2-Car)" or "Vehicle to Vehicle (V-2-V)" communication). More generally, the automobile 100 may communicate with nearby vehicles, pedestrians, facilities, etc. (referred to as "Car to X (Car-2-X)" or "Vehicle to X (V-2-X)" communications). For example, the automobile 100 may also transmit its own information, such as model number, traveling direction, traveling speed, and the like, to a nearby vehicle or facility through the communication device 150 or detect outside information from a nearby vehicle or facility. The communication device 150 may include a communication device based on any type of electromagnetic wave (e.g., infrared, microwave, millimeter wave, etc.), and may perform Car-2-Car or Car-2-X communication based on any preset communication protocol.
Fig. 2 is a schematic structural diagram of the driving assistance system 130 according to the embodiment of the present application. As shown in fig. 2, the driving assistance system 130 includes a detection device 131, a control device 132, and an output device 133. These means may be implemented by hardware circuits, by software modules, or by a combination of hardware and software. The operation of these devices will be described in detail below.
The detection means 131 may detect the running parameters of other vehicles around the automobile 100. The other vehicles may be vehicles located in front of, behind, or to the side of the automobile 100. The driving parameters of the other vehicle may include the position, profile, direction of movement, speed and acceleration of movement, etc. of the other vehicle. The detection device 131 may detect the contours of other vehicles around the automobile 100 and the position relative to the automobile 100, for example, by receiving and analyzing images outside the automobile 100 captured by the image sensor in the in-vehicle sensor 120. The detection device 131 may detect the movement speed and the movement acceleration of the other vehicle, for example, by the distance and the speed change of the other vehicle with respect to the automobile 100 measured by the ultrasonic sensor or the radar device in the in-vehicle sensor 120. The detection device 131 may also detect the travel parameters of other vehicles via the communication device 150 using Car-2-Car or Car-2-X communication.
The control device 132 may be in wired or wireless communication with the detection device 131. The control device 132 may calculate the time of collision between the automobile 100 and the other vehicle from the running parameters of the other vehicle detected by the detection device 131, and may determine whether the driving behavior of the driver is abnormal from the time of collision. For example, assume that the other vehicle is the front vehicle of the automobile 100. The control device 132 may calculate the collision time between the automobile 100 and the preceding vehicle based on the following expression (1):
TTC＝D/VR(1)。
where D is the distance of the front vehicle relative to the automobile 100, VRIs the speed of the vehicle in front of the vehicle relative to the vehicle 100. D may be directly measured by an ultrasonic sensor or a radar device in the in-vehicle sensor 120, or may be calculated by the control device 132 based on the position parameter of the preceding vehicle detected by the detection device 131 and the position parameter of the automobile 100, wherein the position parameter of the automobile 100 may be obtained from a navigation device (not shown) of the automobile 100. VRIt may be directly measured by an ultrasonic sensor or a radar device in the vehicle-mounted sensor 120, or may be calculated by the control device 132 according to the speed parameter of the preceding vehicle detected by the detection device 131 and the speed parameter of the automobile 100, wherein the speed parameter of the automobile 100 may also be obtained from a navigation device (not shown) of the automobile 100.
The control device 132 may calculate a difference between the collision time and a collision time mean value stored in advance, and determine that the driving behavior of the driver is abnormal when the difference between the collision time and the collision time mean value is smaller than a threshold value set in advance. The time-to-collision mean refers to all Times To Collision (TTC) within a specified unit of travel1，TTC2，……，TTCi，……，TTCn) Average value of (a). The predetermined travel unit is a unit of a predetermined travel time or travel distance, for example, a one-month travel time or a ten-kilometer travel distance. The average collision time may be set in advance by the control device 132 based on the driving experience or daily driving habits, or may be acquired by the control device 132 from a learning device 135 described later.
According to an embodiment of the present application, the control device 132 may calculate a difference between the time to collision TTC and the mean value of the time to collision, and determine whether the driving behavior of the driver is abnormal by comparing the difference with a preset threshold value, wherein the magnitude of the threshold value may be determined according to the driving experience or daily driving habits. If the difference between the time to collision TTC and the time to collision mean is smaller than the threshold value, which means that the distance of the automobile 100 from the preceding vehicle is small or the speed of the automobile 100 from the preceding vehicle is large, the control device 132 determines that the driving behavior of the driver is abnormal. For example, the driver may be anxious, irritated, etc. and the following distance is short or the following speed is fast. Since such driving behavior is dangerous, the driver should react in time and notify surrounding vehicles or pedestrians that they should avoid or brake in time. On the other hand, if the difference between the time to collision TTC and the mean time to collision is greater than the threshold, the control device 132 determines that the driving behavior of the driver is normal.
The output device 133 may be in wired or wireless communication with the detection device 131 and/or the control device 132. The output device 133 may also be in wired or wireless communication with the operation execution system 140. The output device 133 may output a control signal when the control device 132 determines that the driving behavior of the driver is abnormal. The control signal may be realized by communication over CAN or FlexRay.
The control signal may be a signal providing a driver with a sensible information. Such perceptible information may include one or more of visual information, auditory information, tactile information, and the like. For example, the control signal may be used to cause a component of the automobile 100, such as an instrument panel, a navigation device, a video device, and/or a warning light, to display warning information in the form of text, graphics, images, video, and the like. The control signals may also be used to cause components of the automobile 100 such as navigation devices, audio devices, buzzers, and/or alarms to emit voice or other audio warning messages. The control signals may also be used to generate tactile warning information in the form of vibrations, deformations, etc. of components of the vehicle 100 such as the steering wheel, foot pedals, seat, steering column, and/or door handles.
The control signal may also be a signal that sounds a horn of the automobile 100. For example, the output device 133 may drive a horn to emit a sound to alert a vehicle or pedestrian outside the automobile. The control signal may also be a signal that causes a display lamp outside the automobile 100 to blink or change color. For example, the output device 133 may make the display lamps on the front side, the rear side, or the roof of the automobile 100 continuously flash or change colors, so that the vehicles or pedestrians around the automobile 100 can timely know the driving state of the driver and timely avoid to prevent traffic accidents.
In addition, different drivers are known to have different driving habits and correspond to different average values of collision time. Therefore, in order to accurately judge whether or not the driver driving the automobile 100 has abnormal driving behavior, it is necessary to determine the corresponding collision time average value for different drivers. Therefore, according to the embodiment of the present application, the driving assistance system 130 needs to identify the driver and determine the collision time average value from the identification result.
According to other embodiments of the present application, the driving assistance system 130 further comprises a recognition means 134 and a learning means 135. The identification means 134 may identify the driver from driver data acquired by the in-vehicle sensors 120 of the car 100. For example, the recognition device 134 may recognize the driver from an image of the driver, such as an upper body image, a face image, an eye image, or the like, acquired by an image sensor in the in-vehicle sensor 120. The image sensor may be provided at a place where image capturing of the driver, particularly the upper body of the driver, is facilitated. The image sensor may be arranged to be adjustable so that its field of view covers the upper body of the driver. For example, the image sensor may be arranged to be able to rotate or translate relative to the driver's face to obtain a suitable viewing angle and/or field of view.
The recognition device 134 may perform identification based on the face recognition processing pair. According to an embodiment of the present invention, the recognition device 134 may compare the driver's facial image acquired by the image sensor in the in-vehicle sensor 120 with one or more early facial images to recognize the driver. The recognition means 134 may acquire one or more face images in advance as early-stage face images and store them in an internal memory of the automobile 100 (for example, a memory included in the electronic control apparatus 110), an external memory that can be connected (for example, a removable memory that can be electrically connected as a peripheral device of the automobile 100, or a remote database that can be accessed wirelessly, or the like), or a learning means 135 described later. These algorithms are well known in the art of image processing and will not be described in detail.
The learning device 135 may be in wired or wireless communication with the control device 132. The learning means 135 may record or store all the collision times of the automobile 100 in the previously specified running unit calculated by the control means 132, and calculate the average value of the collision times of the automobile 100 in the previously specified running unit based on all the collision times.
The learning means 135 may also communicate with the recognition means 134 according to an embodiment of the invention. The learning means 135 may record the time of collision calculated by the control means 132 for each recognition after the driver is recognized by the recognition means 134. In other words, the learning means 134 may record all the collision times of the identified driver within the previously specified driving unit based on the recognition result of the recognition means 134, thereby storing the history of the collision times corresponding to the driver.
The learning device 135 may also calculate the mean value of the collision times of the identified driver within the previously prescribed driving unit from the historical collision times corresponding to the identified driver. In an exemplary embodiment, the learning device 135 may calculate the collision time average value according to the following expression (2):
where N represents the total number of all the collision times recorded by the learning device 135 in the expression (2).
Thus, the learning device 135 can build a driving behavior model based on the recognition result of the recognition device 134, the recorded historical collision time, and the calculated mean value of the collision time. Fig. 3 shows an embodiment of the driving behavior model created by the learning device 135. As shown in fig. 3, the driving behavior model includes drivers identified by the identifying means 134, and all Times To Collision (TTC) corresponding to the respective drivers1，TTC2，……，TTCi，……，TTCn) And a time-to-collision mean value, each driver having a respective historical time-to-collision and time-to-collision mean value. Examples of only three drivers (e.g., driver a, driver B, driver C) are listed in fig. 3, however, it should be understood that the driving behavior model is not limited to these three drivers.
Thus, according to the embodiment of the present invention, the control device 132 may acquire the mean value of the collision time corresponding to the identified driver from the learning device 135 according to the recognition result of the recognition device 134, and further determine whether the driving behavior of the identified driver is abnormal according to the mean value of the collision time and the collision time TTC calculated by the control device 132.
According to an embodiment of the present invention, the learning device 135 may also modify the driving behavior model according to the recorded time of collision. For example, when the learning device 135 recordsTime to collision TTC associated with driver An+1Then, the time to collision TTC can be determinedn+1And comparing with a preset safe average value. If time to collision TTCn+1Falling within a safe mean range, i.e. TTCmin＜TTCn+1＜TTCmaxThen the learning device 135 may Time To Collision (TTC)1，TTC2，……，TTCi，……，TTCn，TTCn+1) On the basis, the average value of the collision time corresponding to the driver a is recalculated, thereby correcting the driving behavior model of the driver a.
Therefore, the driving assist system according to the embodiment of the invention can detect the abnormal driving behavior of the driver, thereby prompting the driver to drive safely. Further, the driving assist system according to the embodiment of the invention can establish a driving behavior model for each driver, thereby enabling to judge the abnormal driving behavior of the driver more accurately according to the driving behavior habit specific to each driver.
The control device 132 is described above as identifying the identity of the driver based on a facial recognition process. However, the present invention is not limited thereto. The control device may identify the driver based on one or more of facial recognition, voice recognition, and fingerprint recognition. For example, the control device may select to perform recognition with voice recognition processing and/or fingerprint recognition processing according to the acquired driver data. If the detection means has only acquired the fingerprint information of the driver, the control means identifies the driver on the basis of a fingerprint identification process, i.e. by comparing the acquired fingerprint information with a pre-stored library of early fingerprints.
The control device 132 determines whether the driver is in the safe driving state based on the collision time between the vehicle and the other vehicle as described above. However, the present invention is not limited thereto. The control means may determine whether the driving behavior of the driver is abnormal, based on the distance between the host vehicle and the other vehicle. Specifically, the control device may determine whether the current distance between the vehicle and the other vehicle falls within a preset safe distance range. If the current distance between the vehicle and the other vehicle does not fall within the safe distance range, the control device determines that the driver is not in the safe driving state, that is, the driving behavior of the driver is abnormal.
A driving assist method according to an embodiment of the invention will be described below with reference to the drawings. Fig. 4 shows a flow chart of a driving assistance method 200 according to an embodiment of the invention. The method 200 may be performed by the driving assistance system 130 of the automobile 100.
As shown in fig. 4, in step 210, the running parameters of other vehicles around the host vehicle are detected. The driving parameters may include position, profile, direction of movement, speed and acceleration of movement, etc. In an exemplary embodiment, the position and speed of a leading vehicle of the host vehicle relative to the host vehicle may be detected. The method 200 then proceeds to step 215.
In step 215, a collision time between the host vehicle and the other vehicle is calculated from the running parameters of the other vehicle detected in step 210. In an exemplary embodiment, the other vehicle is a preceding vehicle, and the time of collision between the preceding vehicle of the host vehicle and the host vehicle may be calculated from a relative position and a relative velocity between the preceding vehicle of the host vehicle and the host vehicle. The method 200 then proceeds to step 220.
In step 220, the difference between the time-to-collision and the pre-set mean time-to-collision is calculated. The average collision time may be preset according to the driving experience or daily driving habits, or may be obtained from the driving behavior model established in step 205. The determination of the time-to-collision mean value has been described in detail above, and therefore will not be described in detail here. The method 200 then proceeds to step 225.
In step 225, the difference between the time to collision calculated in step 220 and the mean value of the time to collision is compared with a preset threshold. If the difference between the time of collision and the mean of the time of collision is less than a preset threshold, meaning that the host vehicle is at a smaller distance or velocity relative to the other vehicles, then the method 200 proceeds to step 230. If the difference between the time-to-collision and the mean time-to-collision is greater than the threshold, which means that the driver's driving behavior is normal, the method 200 returns to step 210.
At step 230, a control signal is output to alert a driver of the host vehicle or other vehicles and/or pedestrians around the host vehicle. The control signals include vibration, sound, light, graphics, text, and the like.
Alternatively, the driving assistance method according to the invention may further include step 205 of establishing a driving behavior model by learning. According to an embodiment of the present invention, the time-to-collision calculated in step 220 may be recorded, and a mean time-to-collision may be calculated and corrected based thereon. The construction of the driving behavior model has already been described in detail above, and therefore will not be described herein again.
While the invention has been described with reference to exemplary embodiments, it is to be understood that the invention is not limited to the construction and methods of the embodiments described above. On the contrary, the invention is intended to cover various modifications and equivalent arrangements. In addition, while the various elements and method steps of the disclosed invention are shown in various example combinations and configurations, other combinations, including more, less or all, of the elements or methods are also within the scope of the invention.
Claims (5)
1. A driving assistance system comprising:
a detection device configured to detect a running parameter of another vehicle around the host vehicle;
a control device configured to calculate a time to collision between the other vehicle and the host vehicle based on the running parameters of the other vehicle and the running parameters of the host vehicle, and determine whether or not the driving behavior of the driver of the host vehicle is abnormal based on the time to collision; and
an output device configured to output a control signal when the control device determines that the driving behavior of the driver is abnormal,
wherein the driving assistance system further includes:
an identification device configured to identify the driver; and
a learning device configured to record all collision times of the driver within a previously specified unit of travel based on the recognition result and calculate the collision time average based on the all collision times to establish a driving behavior model corresponding to the driver,
wherein the control device is configured to acquire the time-to-collision mean value from the driving behavior model, calculate a difference between the time-to-collision and the time-to-collision mean value, and determine that the driving behavior of the driver is abnormal when the difference between the time-to-collision and the time-to-collision mean value is smaller than a preset threshold value.
2. The driving assistance system according to claim 1,
the learning device is further configured to recalculate the collision time mean value in accordance with the collision time, thereby modifying the driving behavior model.
3. A vehicle comprising the driving assistance system according to claim 1 or 2.
4. A driving assistance method comprising:
detecting a driving parameter of other vehicles around the host vehicle;
calculating a collision time between the other vehicle and the host vehicle according to the running parameters of the other vehicle and the running parameters of the host vehicle, and determining whether or not the driving behavior of the driver of the host vehicle is abnormal according to the collision time; and
outputting a control signal when it is judged that the driving behavior of the driver is abnormal,
wherein the driving assistance method further includes:
identifying the driver;
recording all collision time of the driver in a previously specified driving unit according to the recognition result, and calculating the collision time mean value according to all the collision time so as to establish a driving behavior model corresponding to the driver;
and acquiring the average value of the collision time from the driving behavior model, calculating the difference between the collision time and the average value of the collision time, and judging that the driving behavior of the driver is abnormal when the difference between the collision time and the average value of the collision time is smaller than a preset threshold value.
5. The driving assistance method according to claim 4, further comprising:
recalculating the collision time mean value according to the collision time, thereby modifying the driving behavior model.
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| CN113370986A (en) * | 2020-02-25 | 2021-09-10 | 华为技术有限公司 | Method and device for controlling vehicle |
| CN113291311B (en) * | 2021-05-28 | 2022-06-17 | 同济大学 | Method for detecting abnormal behavior of driver under emergency collision avoidance working condition and storage medium |
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| CN102320280A (en) * | 2011-06-30 | 2012-01-18 | 清华大学 | Automatic alarm method for preventing front crash of vehicles at turning |
| EP2674343A2 (en) * | 2012-06-16 | 2013-12-18 | WABCO GmbH | Method and device for determining the driving behaviour of vehicle drivers |
| US20140148988A1 (en) * | 2012-11-29 | 2014-05-29 | Volkswagen Ag | Method and system for controlling a vehicle |
| CN103350670A (en) * | 2013-07-16 | 2013-10-16 | 厦门金龙联合汽车工业有限公司 | Vehicle forward collision warning method based on vehicle networking technology |
| CN103531042A (en) * | 2013-10-25 | 2014-01-22 | 吉林大学 | Rear-end collision pre-warning method based on driver types |
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