Vehicle warning method, device and equipment and storage medium

Abstract

The invention discloses a vehicle warning method, device and equipment and a storage medium, and belongs to the technical field of automobiles. The method specifically comprises the following steps: acquiring driving information of a vehicle, obstacle information sensed by the vehicle and behavior information of a driver; based on the driving information and the obstacle information, utilizing a preset collision algorithm to obtain collision time; based on the behavior information, detecting the driving state of the driver to obtain the concentration degree of the driver; based on the collision time and the concentration degree, utilizing a preset risk strategy to perform risk assessment processing on the vehicle so as to obtain warning information of the vehicle; and outputting the alarm information according to at least one output mode. According to the scheme, the problem that the reliability of collision early warning of the vehicle is poor can be solved.

Description

Technical Field

[0001] The present application relates to the field of automobile technology, specifically to technical fields such as vehicle detection technology, and particularly to a vehicle alarm method, device, equipment and storage medium. Background Art

[0002] Usually, in some special driving scenarios such as parking lot ramps and tunnel exits, the driver may be distracted, such as operating a mobile phone or adjusting the air conditioner, or have obstructed vision, such as a steep slope, which may lead to the risk of collision.

[0003] At present, the vehicle collision warning schemes in related technologies mostly use fixed thresholds to trigger a single alarm. The solutions in related technologies have the following problems: First, the false alarm rate is high. Frequent alarms in low-risk scenarios cause drivers to have "alarm fatigue". Second, the adaptability is poor. Dynamic factors such as slope, vehicle speed, and driver status are not considered. Third, the interaction is stiff. The alarm method is single and lacks progressive guidance. As a result, the reliability of vehicle collision warning is poor. Summary of the invention

[0004] The present application provides a vehicle warning method, device, equipment and storage medium, which can solve the problem of poor reliability of vehicle collision warning. The technical solution is as follows: In a first aspect, a vehicle alarm method is provided, the method comprising: Acquiring driving information of the vehicle, obstacle information perceived by the vehicle, and driver behavior information; Based on the driving information and the obstacle information, a collision time is obtained using a preset collision algorithm; Based on the behavior information, detecting and processing the driving state of the driver to obtain the concentration of the driver; Based on the collision time and the concentration, a preset risk strategy is used to perform risk assessment processing on the vehicle to obtain warning information of the vehicle; The warning information is output in at least one output mode.

[0005] In a possible implementation, the driving information includes a vehicle position and a vehicle speed, the obstacle information includes an obstacle position and an obstacle speed, and the obtaining the collision time based on the driving information and the obstacle information by using a preset collision algorithm includes: Based on the vehicle position and the obstacle position, obtaining the distance between the vehicle and the obstacle; Based on the vehicle speed and the obstacle speed, obtaining the relative speed between the vehicle and the obstacle; Based on the distance and the relative speed, the collision time is obtained.

[0006] In a possible implementation manner, obtaining the collision time based on the distance and the relative speed includes: determining a correction factor based on the driving information; Based on the correction coefficient, the relative speed is corrected to obtain a corrected relative speed; The collision time is obtained based on the distance and the corrected relative speed.

[0007] In a possible implementation, the behavior information includes a sight deviation angle and a steering wheel grip, and the detecting and processing of the driving state of the driver based on the behavior information to obtain the concentration of the driver includes: Based on the sight deviation angle and the steering wheel grip force, the driver's driving state is detected and processed using a preset concentration detection algorithm; Based on the result of the detection process, the concentration of the driver is obtained.

[0008] In a possible implementation, the detecting and processing of the driving state of the driver based on the sight deviation angle and the steering wheel grip using a preset concentration detection algorithm includes: Obtaining a first weight coefficient, a second weight coefficient, a preset grip force, and a preset sight deviation angle; Based on the first weight coefficient, the second weight coefficient, the preset grip strength, the preset sight deviation angle, the driver's sight deviation angle and the steering wheel grip strength, the driver's concentration is calculated to obtain the result of the detection processing.

[0009] In a possible implementation, the risk assessment process is performed on the vehicle based on the collision time and the concentration using a preset risk strategy to obtain the warning information of the vehicle, including: In response to the collision time and the concentration satisfying a first risk trigger condition, determining that the vehicle is at a low risk level and obtaining warning information corresponding to the low risk level, or, In response to the collision time and the concentration satisfying a second risk trigger condition, determining that the vehicle is at a medium risk level, and obtaining warning information corresponding to the medium risk level, or, In response to the collision time and the concentration satisfying a third risk triggering condition, determining that the vehicle is at a high risk level, and obtaining warning information corresponding to the high risk level, or, In response to the collision time and the concentration satisfying a fourth risk triggering condition, it is determined that the vehicle is at an emergency risk level, and warning information corresponding to the emergency risk level is obtained.

[0010] In a second aspect, a vehicle alarm device is provided, the device comprising: An acquisition unit, used to acquire driving information of the vehicle, obstacle information perceived by the vehicle, and behavior information of the driver; An obtaining unit, configured to obtain a collision time based on the driving information and the obstacle information using a preset collision algorithm; a detection unit, configured to detect the driving state of the driver based on the behavior information to obtain the concentration of the driver; An evaluation unit, configured to perform risk evaluation processing on the vehicle based on the collision time and the concentration using a preset risk strategy to obtain warning information of the vehicle; An alarm unit is used to output the alarm information in at least one output mode.

[0011] In a third aspect, a computer-readable storage medium is provided, wherein at least one instruction is stored in the storage medium, and the at least one instruction is loaded and executed by a processor to implement the method of the above-mentioned aspect and any possible implementation manner.

[0012] In a fourth aspect, an electronic device is provided, including: at least one processor; and a memory communicatively connected to the at least one processor; wherein, The memory stores instructions that can be executed by the at least one processor, and the instructions are executed by the at least one processor to enable the at least one processor to perform the method of any possible implementation manner and the aspects described above.

[0013] According to a fifth aspect, a computer program product is provided, comprising a computer program, which, when executed by a processor, implements the above-mentioned aspects and any possible implementation method.

[0014] In a sixth aspect, a new energy vehicle is provided, comprising the electronic device as described above.

[0015] The beneficial effects of the technical solution provided by this application include at least: It can be seen from the above technical scheme that the embodiment of the present application can obtain the vehicle's driving information, the obstacle information perceived by the vehicle, and the driver's behavior information, and then based on the driving information and obstacle information, use a preset collision algorithm to obtain the collision time, and based on the behavior information, detect and process the driver's driving state to obtain the driver's concentration, and based on the collision time and concentration, use a preset risk strategy to perform risk assessment processing on the vehicle to obtain the vehicle's warning information, and output the warning information in at least one output mode. Since the vehicle's collision risk can be assessed and processed based on the collision time between the vehicle and the obstacle and the driver's concentration using a risk strategy to obtain warning information corresponding to the assessment processing result, and the warning information can be output in a variety of output modes, the diversity of vehicle anti-collision warnings can be achieved while improving the reliability of vehicle collision protection, thereby ensuring the reliability and safety of vehicle driving.

[0016] It should be understood that the content described in this section is not intended to identify the key or important features of the embodiments of the present application, nor is it intended to limit the scope of the present application. Other features of the present application will become easily understood through the following description. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] In order to more clearly illustrate the technical solutions in the embodiments of the present application, the drawings required for use in the description of the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0018] Figure 1 It is a flowchart of a vehicle warning method provided by an embodiment of the present application; Figure 2 is a schematic diagram of the process of a vehicle warning method provided by another embodiment of the present application; Figure 3 is a structural block diagram of a vehicle alarm device provided by another embodiment of the present application; Figure 4 It is a block diagram of an electronic device used to implement the vehicle alarm method of an embodiment of the present application. DETAILED DESCRIPTION

[0019] The following is a description of exemplary embodiments of the present application in conjunction with the accompanying drawings, including various details of the embodiments of the present application to facilitate understanding, which should be considered as merely exemplary. Therefore, it should be recognized by those of ordinary skill in the art that various changes and modifications can be made to the embodiments described herein without departing from the scope and spirit of the present application. Similarly, for the sake of clarity and conciseness, the description of well-known functions and structures is omitted in the following description.

[0020] Obviously, the described embodiments are only part of the embodiments of the present application, but not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in the field without creative work are within the scope of protection of the present application.

[0021] It should be noted that the terminal devices involved in the embodiments of the present application may include but are not limited to mobile phones, personal digital assistants (PDAs), wireless handheld devices, tablet computers and other smart devices; display devices may include but are not limited to personal computers, televisions and other devices with display functions.

[0022] In addition, the term "and / or" in this article is only a description of the association relationship between the associated objects, indicating that there can be three relationships. For example, A and / or B can represent: A exists alone, A and B exist at the same time, and B exists alone. In addition, the character " / " in this article generally indicates that the associated objects before and after are in an "or" relationship.

[0023] Please refer to Figure 1 , which shows a flow chart of a vehicle alarm method provided by an embodiment of the present application. The vehicle alarm method may specifically include: Step 101: Acquire vehicle driving information, obstacle information sensed by the vehicle, and driver behavior information.

[0024] Step 102: Based on the driving information and obstacle information, a collision time is obtained using a preset collision algorithm.

[0025] Step 103: Based on the behavior information, the driving state of the driver is detected and processed to obtain the concentration of the driver.

[0026] Step 104: Based on the collision time and the concentration, a preset risk strategy is used to perform risk assessment processing on the vehicle to obtain warning information of the vehicle.

[0027] Step 105: output the warning information in at least one output mode.

[0028] It should be noted that the output method may include but is not limited to a visual output method, an auditory output method, and a tactile output method. The visual output method may include a text output method and an image output method. The tactile output method may include a seat vibration output method and a seat belt adjustment output method.

[0029] It should be noted that the warning information may include warnings of multiple risk levels, including but not limited to beeps, brakes immediately, multi-color identification boxes, and safe route guide lines.

[0030] In this way, the collision risk of the vehicle can be evaluated and processed by using risk strategies according to the collision time between the vehicle and the obstacle and the driver's concentration, and warning information corresponding to the evaluation and processing results can be obtained. The warning information can be output in a variety of output modes, which can achieve the diversity of vehicle anti-collision warnings while improving the reliability of vehicle collision protection, thereby ensuring the reliability and safety of vehicle driving.

[0031] Optionally, in a possible implementation of this embodiment, the driving information may include a vehicle position and a vehicle speed, and the obstacle information may include an obstacle position and an obstacle speed. In step 102, first, based on the vehicle position and the obstacle position, the distance between the vehicle and the obstacle is obtained. Secondly, based on the vehicle speed and the obstacle speed, the relative speed between the vehicle and the obstacle is obtained. Thirdly, based on the distance and the relative speed, the collision time is obtained.

[0032] In a specific implementation process of this implementation, first, a correction coefficient is determined based on the driving information. Second, the relative speed is corrected based on the correction coefficient to obtain a corrected relative speed. Third, the collision time is obtained based on the distance and the corrected relative speed.

[0033] In a specific implementation process, the driving information may also include a ramp angle of the vehicle.

[0034] In one case of the specific implementation process, the correction coefficient can be calculated based on the ramp angle of the vehicle using a preset correction algorithm.

[0035] It is understandable that the distance between the vehicle and the obstacle can also be directly obtained by a radar sensor. For specific implementation methods, reference can be made to existing methods, which will not be described in detail here.

[0036] In this way, the accuracy of the collision time can be further improved by correcting the relative speed between the vehicle and the obstacle.

[0037] Optionally, in a possible implementation of this embodiment, the behavior information may further include a sight deviation angle and a steering wheel grip. In step 103, based on the sight deviation angle and the steering wheel grip, a preset concentration detection algorithm may be used to detect the driver's driving state, and then the driver's concentration may be obtained based on the result of the detection process.

[0038] In a specific implementation process of the implementation method, first, a first weight coefficient, a second weight coefficient, a preset grip force, and a preset sight deviation angle can be obtained. Then, based on the first weight coefficient, the second weight coefficient, the preset grip force, the preset sight deviation angle, the sight deviation angle, and the steering wheel grip force, the driver's concentration is calculated to obtain the result of the detection process.

[0039] In this implementation, the preset grip force may be the driver's maximum steering wheel grip force. The preset sight deviation angle may be the driver's maximum sight deviation angle.

[0040] In another specific implementation process of this implementation, the driver's concentration can be compared with a pre-configured concentration level to obtain the concentration level corresponding to the driver's concentration.

[0041] In this way, the driver's concentration can be calculated more accurately based on the line of sight deviation angle and steering wheel grip using a preset concentration detection algorithm, so that the corresponding warning information for the vehicle can be effectively and accurately determined later.

[0042] It should be noted that the specific implementation process provided in this implementation can be combined with the various specific implementation processes provided in the aforementioned implementation to implement the vehicle alarm method of this embodiment. For a detailed description, please refer to the relevant content in the aforementioned implementation, which will not be repeated here.

[0043] Optionally, in a possible implementation of this embodiment, in step 104, in response to the collision time and concentration satisfying a first risk trigger condition, it is determined that the vehicle is at a low risk level, and warning information corresponding to the low risk level is obtained; or, in response to the collision time and concentration satisfying a second risk trigger condition, it is determined that the vehicle is at a medium risk level, and warning information corresponding to the medium risk level is obtained; or, in response to the collision time and concentration satisfying a third risk trigger condition, it is determined that the vehicle is at a high risk level, and warning information corresponding to the high risk level is obtained; or, in response to the collision time and concentration satisfying a fourth risk trigger condition, it is determined that the vehicle is at an emergency risk level, and warning information corresponding to the emergency risk level is obtained.

[0044] In a specific implementation process of this implementation method, the preset risk strategy may include preset risk conditions satisfied by the current collision time and concentration to determine the risk level and warning information corresponding to the preset risk conditions.

[0045] In this implementation, the preset risk conditions may include a first risk trigger condition, a second risk trigger condition, a third risk trigger condition, and a fourth risk trigger condition.

[0046] In this implementation, the first risk trigger condition may be that the collision time is greater than a first time threshold and the concentration is greater than a first concentration threshold.

[0047] In another specific implementation process of this implementation method, in response to the collision time being greater than a first time threshold and the concentration being greater than a first concentration threshold, it is determined that the vehicle is at a low risk level, and warning information corresponding to the low risk level is obtained.

[0048] In a specific implementation process, illustratively, the first time threshold may be 4 seconds (s). The first concentration threshold may be 0.8.

[0049] In this implementation, the second risk trigger condition may be that the collision time is greater than the second time threshold and less than or equal to the first time threshold, and the concentration is greater than the second concentration threshold and less than the first concentration threshold.

[0050] In the specific implementation process, the warning information corresponding to the medium risk level may include a green identification box.

[0051] In another specific implementation process of this implementation method, in response to the collision time being greater than the second time threshold and less than or equal to the first time threshold, and the concentration being greater than the second concentration threshold and less than the first concentration threshold, it is determined that the vehicle is at a medium risk level, and warning information corresponding to the medium risk level is obtained.

[0052] In a specific implementation process, for example, the first time threshold may be 4 seconds (s), the second time threshold may be 3 seconds, the first concentration threshold may be 0.8, and the second concentration threshold may be 0.5.

[0053] In the specific implementation process, the warning information corresponding to the medium risk level may include a yellow identification box + a projection guidance route.

[0054] In this implementation, the third risk trigger condition may be that the collision time is greater than the third time threshold and less than or equal to the second time threshold, and the concentration is less than the second concentration threshold.

[0055] In another specific implementation process of this implementation method, in response to the collision time being greater than a third time threshold and less than or equal to a second time threshold, and the concentration being less than a second concentration threshold, it is determined that the vehicle is at a high risk level, and warning information corresponding to the high risk level is obtained.

[0056] In a specific implementation process, illustratively, the second time threshold may be 3 seconds, the third time threshold may be 2 seconds, and the second concentration threshold may be 0.5.

[0057] During the specific implementation process, the warning information corresponding to the high-risk level may include a red identification box + collision countdown display, a short beep, and a "please brake immediately" prompt voice.

[0058] In this implementation, the fourth risk trigger condition may be that the collision time is less than or equal to the third time threshold, and the time during which the driver's concentration is less than the first concentration threshold is greater than the fourth time threshold.

[0059] In another specific implementation process of this implementation method, in response to the collision time being less than or equal to the third time threshold and the time when the driver's concentration is less than the first concentration threshold being greater than the fourth time threshold, it is determined that the vehicle is at an emergency risk level, and warning information corresponding to the emergency risk level is obtained.

[0060] In a specific implementation process, for example, the first time threshold may be 4 seconds, the fourth time threshold may be 2 seconds, and the first concentration threshold may be 0.8.

[0061] In the specific implementation process, the warning information corresponding to the emergency risk level may include a full-screen red light display on the Head-Up Display (HUD) and a continuous alarm sound.

[0062] In this way, the corresponding warning information can be determined by matching the collision time and concentration with the multi-dimensional level risk conditions, which can improve the accuracy and reliability of the warning information.

[0063] It should be noted that the specific implementation process provided in this implementation can be combined with the various specific implementation processes provided in the aforementioned implementation to implement the vehicle alarm method of this embodiment. For a detailed description, please refer to the relevant content in the aforementioned implementation, which will not be repeated here.

[0064] In order to better understand the method of the embodiment of the present application, the method of the embodiment of the present application is described below with reference to the accompanying drawings and specific application scenarios.

[0065] Figure 2 is a schematic diagram of the process of a vehicle warning method provided by another embodiment of the present application, such as Figure 2The vehicle alarm method in this embodiment can be applied to a vehicle controller of a vehicle, and specifically may include: Step 201: Acquire the vehicle's driving information, the obstacle information perceived by the vehicle, and the driver's line of sight deviation angle and steering wheel grip.

[0066] In this embodiment, the driving information of the vehicle can be obtained based on the vehicle's sensors. The driving information may include but is not limited to the vehicle's speed, position, slope angle, acceleration, steering wheel angle, and road adhesion coefficient.

[0067] In this embodiment, the driver's steering wheel grip force can also be obtained through a steering wheel grip force sensor. The driver's line of sight deviation angle can be determined using images collected by the in-cabin camera.

[0068] Step 202: Based on the driving information and the obstacle information, the collision time is calculated using a preset collision algorithm.

[0069] In this implementation, Time To Collision (TTC) is the core indicator for measuring the risk of collision between a vehicle and a front obstacle. Its calculation depends on the vehicle's sensor data, such as data collected by radar, camera, and lidar, as well as the vehicle dynamics model.

[0070] Specifically, the vehicle's driving information may include the vehicle's position, vehicle speed, and slope angle. The obstacle information may include the obstacle's position and obstacle speed.

[0071] Here, the distance between the vehicle and the obstacle can be calculated based on the vehicle position and the obstacle position. Secondly, the relative speed between the vehicle and the obstacle can be calculated based on the vehicle speed and the obstacle speed.

[0072] Optionally, the collision time TTC may be calculated based on the distance between the vehicle and the obstacle and the relative speed between the vehicle and the obstacle, which may be shown in the following formula (1): TTC=d / Vrel (1) Wherein, d may represent the distance between the current vehicle and the obstacle, and Vrel may represent the relative speed between the vehicle and the obstacle.

[0073] In addition, laser radar can also be used to directly obtain the distance between the vehicle and the obstacle.

[0074] Optionally, a correction coefficient may be determined based on the slope angle in the driving information, and the relative speed may be corrected based on the correction coefficient to calculate the corrected relative speed, which may be shown in the following formula (2): Vrel_adjusted = Vrel-g*sin α* t (2) Among them, Vrel_adjusted can be the corrected relative velocity, g can be the gravitational acceleration, t can be the time, α can be the slope angle, where α is a positive angle when going uphill and a negative angle when going downhill, g*sinα can represent the acceleration component caused by the slope, that is, the slope correction term, and g*sin α* t can represent the correction coefficient.

[0075] It can be understood that when going uphill (α>0), the gravity component opposes the vehicle's forward movement, the acceleration is negative, and Vrel_adjusted decreases. When going downhill (α<0), the gravity component accelerates the vehicle, the acceleration is positive, and Vrel_adjusted increases.

[0076] In this way, by introducing a correction coefficient and adjusting the vehicle speed, the TTC calculation accuracy can be further improved.

[0077] Exemplarily, the acquired vehicle speed v1=10 meters per second (m / s), obstacle speed v2=2m / s, distance between the vehicle and the obstacle d=20m, slope angle α=+10° (uphill), and detection cycle time t=1s.

[0078] First, the relative speed can be calculated as Vrel - V1-V2=10-2=8m / s. Secondly, the slope correction term g*sinθ=9.8.sin(10°)≈1.7 can be calculated, and then the correction coefficient g*sinθ* t=1.7*1=1.7 can be calculated. Thirdly, the corrected speed is calculated as Vrel_adjusted=8-1.7=6.3m / s. Finally, the collision time TTC is calculated as d / Vrel_adjusted=20 / 6.3≈3.17s.

[0079] Step 203: Based on the first weight coefficient, the second weight coefficient, the preset grip strength, the preset sight deviation angle, the driver's sight deviation angle and the steering wheel grip strength, the driver's concentration is calculated using a preset concentration detection algorithm.

[0080] In this embodiment, the driver's concentration level (CL) is calculated using a preset concentration detection algorithm, which can be expressed as the following formula (3): (3) in, can be the first weight coefficient, can be the second weight coefficient, It can be a preset grip strength, i.e. the maximum grip strength, It can be a preset sight deviation angle, that is, the maximum sight deviation angle. It can be the steering wheel grip force of the driver. It can be the deviation angle of the driver's line of sight.

[0081] Preferably, the concentration can be pre - graded. For example, CL≥0.8 can indicate that the driver is in a low - distraction state, 0.5 < CL < 0.8 can indicate that the driver is in a medium - distraction state, and CL < 0.5 indicates that the driver is in a high - distraction state.

[0082] Exemplarily, when , , the steering wheel grip force F = 30 Newtons (N), = 50N, the deviation angle of the driver's line of sight = 20°, = 45°, the calculated concentration is 0.584. This concentration can indicate that the driver is in a medium - distraction state.

[0083] Step 204: Determine the matching relationship between the collision time and the concentration and the preset risk conditions. The preset risk conditions include the first risk - triggering condition, the second risk - triggering condition, the third risk - triggering condition, and the fourth risk - triggering condition.

[0084] Step 205: When the collision time and the concentration meet the first risk - triggering condition, determine that the vehicle is in a low - risk level and obtain the warning information corresponding to the low - risk level.

[0085] Step 206: When the collision time and the concentration meet the second risk - triggering condition, determine that the vehicle is in a medium - risk level and obtain the warning information corresponding to the medium - risk level.

[0086] Step 207: When the collision time and the concentration meet the third risk - triggering condition, determine that the vehicle is in a high - risk level and obtain the warning information corresponding to the high - risk level.

[0087] Step 208: When the collision time and the concentration meet the fourth risk - triggering condition, determine that the vehicle is in an emergency - risk level and obtain the warning information corresponding to the emergency - risk level.

[0088] Step 209: Output the warning information according to various output methods.

[0089] In this embodiment, the output methods can include but are not limited to the output method based on the driver's vision, the output method based on the driver's hearing, and the output method based on the driver's touch.

[0090] Preferably, the output method based on the driver's vision may include HUD dynamically displaying risk level identification, projecting virtual guide lines, countdown information, and risk level text prompts. The output method based on the driver's hearing may include voice prompt text information and beeps. The output method based on the driver's touch may include adjusting the seat vibration frequency and intensity, and adjusting the tightness of the seat belt.

[0091] In this embodiment, the preset risk strategy may include using a correspondence table of preset risk conditions, risk levels and warning information to judge the preset risk conditions satisfied by the current collision time and concentration, so as to determine the risk level and warning information corresponding to the preset risk conditions.

[0092] Table 1: Correspondence between preset risk conditions, risk levels and warning information Risk Level Risk trigger conditions Alarm information in multiple output modes Low risk level Level 1 TTC > 4.0s; CL ≥ 0.8 Visual reminder: HUD displays a green identification frame; Medium risk level 2 3.0s < TTC≤4.0s;0.5 <CL<0.8 Visual reminder: HUD flashes yellow frame + projection guide route; tactile reminder: low-frequency vibration of the seat (2Hz) High risk level 3 2.0s <TTC≤3.0s;CL<0.5 Visual reminder: HUD flashing red logo frame + countdown display; auditory reminder: short beep (1kHz) + please brake immediately prompt voice; tactile reminder: high-frequency vibration of the seat (5Hz) Emergency risk level 4 TTC≤2.0s; driver does not respond, duration t>2s Visual reminder: HUD full-screen red light flashes; Auditory reminder: Continuous alarm sound (85dB); Tactile reminder: Seat belt rapid pre-tightening (completed within 0.2s). In this embodiment, for example, the collision time is 3.17s and the concentration is 0.584, which means that the vehicle is at a medium risk level. The warning information may include a flashing yellow identification box on the HUD + a projected guidance route; and the seat vibrates at a low frequency of 2Hz to give a prompt.

[0093] For example, in the driving scenario of turning uphill in an underground garage, the vehicle enters the underground garage uphill, and the driver may lower his head to operate the mobile phone and fail to notice the sharp turn ahead. First, the vehicle's laser radar detects the wall 2.5 meters ahead. Based on the vehicle's current driving information and obstacle information, that is, the wall information, the collision time TTC = 2.8 seconds (s) can be calculated using the preset collision algorithm. Secondly, the driver's line of sight deviation angle is detected by the in-cabin camera, and the driver's grip is detected by the steering wheel grip sensor. Based on the driver's line of sight deviation angle and grip, the driver's concentration is calculated to be 0.6 using the preset concentration detection algorithm, which means that the driver is moderately distracted. Thirdly, a Level 2 alarm can be triggered: the HUD displays a yellow warning box, the augmented reality (AR) turning guide line is projected, and the seat belt is slightly pre-tightened to remind the driver to pay attention. Furthermore, when it is detected that the driver raises his head, the vehicle controller can cancel the above alarm processing and resume normal driving mode.

[0094] For example, in a driving scene with strong light at the tunnel exit, the vehicle exits the tunnel, and the strong light causes the driver to be dazzled for a short time, and the driver fails to notice the slow-moving vehicle ahead. First, when the vehicle's forward radar detects the vehicle ahead, based on the vehicle's current driving information and obstacle information, that is, the driving information of the vehicle ahead, the collision time TTC = 1.8s can be calculated using the preset collision algorithm. Secondly, the driver's line of sight deviation angle is detected by the in-cabin camera, and the driver's grip is detected by the steering wheel grip sensor. Based on the driver's line of sight deviation angle and grip, the driver's concentration is calculated to be 0.4 using the preset concentration detection algorithm, and the driver can be determined to be highly distracted. Thirdly, the Level 3 alarm can be triggered: the HUD displays a red warning box, the voice prompts "Please brake immediately", the seat belt is automatically tightened, and the vehicle's electronic parking brake system (EPB) performs a point brake process. Further, when the vehicle decelerates to a safe distance, the alarm can be released.

[0095] In this way, by adopting the solution in this embodiment, through refined classification and multimodal interaction, the emergency response efficiency can be improved while reducing the false alarm rate, which is particularly suitable for complex scenarios such as underground parking lots and mountain roads.

[0096] In addition, by adopting the solution in this embodiment, accurate and flexible collision protection is achieved through multi-dimensional risk assessment and graded response strategy, thereby ensuring the reliability and safety of vehicle driving.

[0097] It should be noted that, for the aforementioned method embodiments, for the sake of simplicity, they are all expressed as a series of action combinations, but those skilled in the art should be aware that the present application is not limited by the described order of actions, because according to the present application, certain steps can be performed in other orders or simultaneously. Secondly, those skilled in the art should also be aware that the embodiments described in the specification are all preferred embodiments, and the actions and modules involved are not necessarily required by the present application.

[0098] In the above embodiments, the description of each embodiment has its own emphasis. For parts that are not described in detail in a certain embodiment, reference can be made to the relevant descriptions of other embodiments.

[0099] Figure 3 The structure block diagram of a vehicle alarm device provided by an embodiment of the present application is shown as follows: Figure 3As shown. The vehicle alarm device 300 of this embodiment may include an acquisition unit 301, an acquisition unit 302, a detection unit 303, an evaluation unit 304 and an alarm unit 305. Among them, the acquisition unit 301 is used to acquire the driving information of the vehicle, the obstacle information perceived by the vehicle, and the behavior information of the driver; the acquisition unit 302 is used to obtain the collision time based on the driving information and obstacle information using a preset collision algorithm; the detection unit 303 is used to detect and process the driving state of the driver based on the behavior information to obtain the concentration of the driver; the evaluation unit 304 is used to perform risk assessment processing on the vehicle based on the collision time and concentration using a preset risk strategy to obtain the alarm information of the vehicle; the alarm unit 305 is used to output the alarm information in at least one output mode.

[0100] Optionally, in a possible implementation of this embodiment, the driving information includes a vehicle position and a vehicle speed, and the obstacle information includes an obstacle position and an obstacle speed. The obtaining unit 302 is used to obtain the distance between the vehicle and the obstacle based on the vehicle position and the obstacle position; obtain the relative speed between the vehicle and the obstacle based on the vehicle speed and the obstacle speed; and obtain the collision time based on the distance and the relative speed.

[0101] Optionally, in a possible implementation of this embodiment, the obtaining unit 302 is used to determine a correction coefficient based on the driving information; based on the correction coefficient, correct the relative speed to obtain a corrected relative speed; based on the distance and the corrected relative speed, obtain the collision time.

[0102] Optionally, in a possible implementation of this embodiment, the behavior information includes a line of sight deviation angle and a steering wheel grip, and the detection unit 303 is used to detect and process the driver's driving state based on the line of sight deviation angle and the steering wheel grip using a preset concentration detection algorithm; and obtain the driver's concentration based on the result of the detection and processing.

[0103] Optionally, in a possible implementation of this embodiment, the detection unit 303 is used to obtain a first weight coefficient, a second weight coefficient, a preset grip strength and a preset sight deviation angle; based on the first weight coefficient, the second weight coefficient, the preset grip strength, the preset sight deviation angle, the driver's sight deviation angle and steering wheel grip strength, calculate the driver's concentration to obtain the result of the detection processing.

[0104] Optionally, in a possible implementation of this embodiment, the evaluation unit 304 can be used to determine that the vehicle is at a low risk level in response to the collision time and concentration satisfying a first risk trigger condition, and obtain warning information corresponding to the low risk level; or, in response to the collision time and concentration satisfying a second risk trigger condition, determine that the vehicle is at a medium risk level and obtain warning information corresponding to the medium risk level; or, in response to the collision time and concentration satisfying a third risk trigger condition, determine that the vehicle is at a high risk level and obtain warning information corresponding to the high risk level; or, in response to the collision time and concentration satisfying a fourth risk trigger condition, determine that the vehicle is at an emergency risk level and obtain warning information corresponding to the emergency risk level.

[0105] In this embodiment, the vehicle's driving information, obstacle information perceived by the vehicle, and the driver's behavior information can be obtained by the acquisition unit. The acquisition unit obtains the collision time based on the driving information and obstacle information using a preset collision algorithm. The detection unit detects and processes the driver's driving state based on the behavior information to obtain the driver's concentration. The evaluation unit performs risk evaluation on the vehicle based on the collision time and concentration using a preset risk strategy to obtain the vehicle's warning information, so that the warning unit can output the warning information in at least one output mode. Since the collision risk of the vehicle can be evaluated and processed using a risk strategy based on the collision time between the vehicle and the obstacle and the driver's concentration, warning information corresponding to the evaluation result can be obtained, and the warning information can be output in a variety of output modes, the diversity of vehicle anti-collision warnings can be achieved while improving the reliability of vehicle collision protection, thereby ensuring the reliability and safety of vehicle driving.

[0106] In the technical solution of this application, the user personal information involved, such as the collection, storage, use, processing, transmission, provision and disclosure of user images and attribute data, etc., complies with the provisions of relevant laws and regulations and does not violate public order and good morals.

[0107] According to an embodiment of the present application, the present application also provides an electronic device, a readable storage medium and a computer program product.

[0108] According to an embodiment of the present application, a new energy vehicle including the provided electronic device is further provided. For example, the new energy vehicle may be a new energy household vehicle, a new energy commercial vehicle, a new energy logistics vehicle, a new energy large vehicle, etc.

[0109] Figure 4A schematic block diagram of an example electronic device 400 that can be used to implement an embodiment of the present application is shown. The electronic device is intended to represent various forms of digital computers, such as laptop computers, desktop computers, workbenches, personal digital assistants, servers, blade servers, mainframe computers, and other suitable computers. The electronic device can also represent various forms of mobile devices, such as personal digital processing, cellular phones, smart phones, wearable devices, and other similar computing devices. The components shown herein, their connections and relationships, and their functions are merely examples and are not intended to limit the implementation of the present application described and / or required herein.

[0110] like Figure 4 As shown, the electronic device 400 includes a computing unit 401, which can perform various appropriate actions and processes according to a computer program stored in a read-only memory (ROM) 402 or a computer program loaded from a storage unit 408 into a random access memory (RAM) 403. In the RAM 403, various programs and data required for the operation of the electronic device 400 can also be stored. The computing unit 401, the ROM 402, and the RAM 403 are connected to each other via a bus 404. An input / output (I / O) interface 405 is also connected to the bus 404.

[0111] Multiple components in the electronic device 400 are connected to the I / O interface 405, including: an input unit 406, such as a keyboard, a mouse, etc.; an output unit 407, such as various types of displays, speakers, etc.; a storage unit 408, such as a disk, an optical disk, etc.; and a communication unit 409, such as a network card, a modem, a wireless communication transceiver, etc. The communication unit 409 allows the electronic device 400 to exchange information / data with other devices through a computer network such as the Internet and / or various telecommunication networks.

[0112] The computing unit 401 may be a variety of general and / or special processing components with processing and computing capabilities. Some examples of the computing unit 401 include, but are not limited to, a central processing unit (CPU), a graphics processing unit (GPU), various dedicated artificial intelligence (AI) computing chips, various computing units running machine learning model algorithms, digital signal processors (DSPs), and any appropriate processors, controllers, microcontrollers, etc. The computing unit 401 performs the various methods and processes described above, such as the method of vehicle alarm. For example, in some embodiments, the method of vehicle alarm may be implemented as a computer software program, which is tangibly contained in a machine-readable medium, such as a storage unit 408. In some embodiments, part or all of the computer program may be loaded and / or installed on the electronic device 400 via the ROM 402 and / or the communication unit 409. When the computer program is loaded into the RAM 403 and executed by the computing unit 401, one or more steps of the method of vehicle alarm described above may be performed. Alternatively, in other embodiments, the computing unit 401 may be configured to perform the method of vehicle alarm by any other appropriate means (e.g., by means of firmware).

[0113] Various implementations of the systems and techniques described above herein can be implemented in digital electronic circuit systems, integrated circuit systems, field programmable gate arrays (FPGAs), application specific integrated circuits (ASICs), application specific standard products (ASSPs), systems on chips (SOCs), complex programmable logic devices (CPLDs), computer hardware, firmware, software, and / or combinations thereof. These various implementations may include: being implemented in one or more computer programs that can be executed and / or interpreted on a programmable system including at least one programmable processor, which can be a special purpose or general purpose programmable processor that can receive data and instructions from a storage system, at least one input device, and at least one output device, and transmit data and instructions to the storage system, the at least one input device, and the at least one output device.

[0114] The program code for implementing the method of the present application can be written in any combination of one or more programming languages. These program codes can be provided to a processor or controller of a general-purpose computer, a special-purpose computer, or other programmable data processing device, so that the program code, when executed by the processor or controller, implements the functions / operations specified in the flow chart and / or block diagram. The program code can be executed entirely on the machine, partially on the machine, partially on the machine and partially on a remote machine as a stand-alone software package, or entirely on a remote machine or server.

[0115] In the context of the present application, a machine-readable medium may be a tangible medium that may contain or store a program for use by or in conjunction with an instruction execution system, device, or equipment. A machine-readable medium may be a machine-readable signal medium or a machine-readable storage medium. A machine-readable medium may include, but is not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, device, or device, or any suitable combination of the foregoing. A more specific example of a machine-readable storage medium may include an electrical connection based on one or more lines, a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing.

[0116] To provide interaction with a user, the systems and techniques described herein can be implemented on a computer having: a display device (e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor) for displaying information to the user; and a keyboard and pointing device (e.g., a mouse or trackball) through which the user can provide input to the computer. Other types of devices can also be used to provide interaction with the user; for example, the feedback provided to the user can be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user can be received in any form (including acoustic input, voice input, or tactile input).

[0117] The systems and techniques described herein may be implemented in a computing system that includes back-end components (e.g., as a data server), or a computing system that includes middleware components (e.g., an application server), or a computing system that includes front-end components (e.g., a user computer with a graphical user interface or a web browser through which a user can interact with implementations of the systems and techniques described herein), or a computing system that includes any combination of such back-end components, middleware components, or front-end components. The components of the system may be interconnected by any form or medium of digital data communication (e.g., a communication network). Examples of communication networks include: a local area network (LAN), a wide area network (WAN), and the Internet.

[0118] A computer system may include a client and a server. The client and the server are generally remote from each other and usually interact through a communication network. The relationship of client and server is generated by computer programs running on respective computers and having a client-server relationship with each other. The server may be a cloud server, a server of a distributed system, or a server combined with a blockchain.

[0119] It should be understood that the various forms of processes shown above can be used to reorder, add or delete steps. For example, the steps recorded in this disclosure can be executed in parallel, sequentially or in different orders, as long as the desired results of the technical solution disclosed in this application can be achieved, and this document is not limited here.

[0120] The above specific implementations do not constitute a limitation on the protection scope of this application. It should be understood by those skilled in the art that various modifications, combinations, sub-combinations and substitutions can be made according to design requirements and other factors. Any modifications, equivalent substitutions and improvements made within the spirit and principles of this application should be included in the protection scope of this application.

Claims

1. A vehicle warning method, characterized in that: The method comprises: Acquiring driving information of the vehicle, obstacle information perceived by the vehicle, and driver behavior information; Based on the driving information and the obstacle information, a collision time is obtained using a preset collision algorithm; Based on the behavior information, detecting and processing the driving state of the driver to obtain the concentration of the driver; Based on the collision time and the concentration, a preset risk strategy is used to perform risk assessment processing on the vehicle to obtain warning information of the vehicle; The warning information is output in at least one output mode.

2. The method according to claim 1, characterized in that The driving information includes a vehicle position and a vehicle speed, the obstacle information includes an obstacle position and an obstacle speed, and the method of obtaining a collision time based on the driving information and the obstacle information by using a preset collision algorithm includes: Based on the vehicle position and the obstacle position, obtaining the distance between the vehicle and the obstacle; Based on the vehicle speed and the obstacle speed, obtaining the relative speed between the vehicle and the obstacle; Based on the distance and the relative speed, the collision time is obtained.

3. The method according to claim 2, characterized in that The obtaining the collision time based on the distance and the relative speed comprises: determining a correction factor based on the driving information; Based on the correction coefficient, the relative speed is corrected to obtain a corrected relative speed; The collision time is obtained based on the distance and the corrected relative speed.

4. The method according to claim 1, characterized in that: The behavior information includes a sight deviation angle and a steering wheel grip, and the detecting and processing of the driver's driving state based on the behavior information to obtain the driver's concentration includes: Based on the sight deviation angle and the steering wheel grip force, the driver's driving state is detected and processed using a preset concentration detection algorithm; Based on the result of the detection process, the concentration of the driver is obtained.

5. The method according to claim 4, characterized in that The detecting and processing of the driving state of the driver based on the sight deviation angle and the steering wheel grip using a preset concentration detection algorithm includes: Obtaining a first weight coefficient, a second weight coefficient, a preset grip force, and a preset sight deviation angle; Based on the first weight coefficient, the second weight coefficient, the preset grip strength, the preset sight deviation angle, the driver's sight deviation angle and the steering wheel grip strength, the driver's concentration is calculated to obtain the result of the detection processing.

6. The method according to claim 1, characterized in that The method of performing risk assessment on the vehicle based on the collision time and the concentration using a preset risk strategy to obtain warning information of the vehicle includes: In response to the collision time and the concentration satisfying a first risk trigger condition, determining that the vehicle is at a low risk level and obtaining warning information corresponding to the low risk level, or, In response to the collision time and the concentration satisfying a second risk trigger condition, determining that the vehicle is at a medium risk level, and obtaining warning information corresponding to the medium risk level, or, In response to the collision time and the concentration satisfying a third risk triggering condition, determining that the vehicle is at a high risk level, and obtaining warning information corresponding to the high risk level, or, In response to the collision time and the concentration satisfying a fourth risk triggering condition, it is determined that the vehicle is at an emergency risk level, and warning information corresponding to the emergency risk level is obtained.

7. A vehicle warning device, characterized in that: The device comprises: An acquisition unit, used to acquire driving information of the vehicle, obstacle information perceived by the vehicle, and behavior information of the driver; An obtaining unit, configured to obtain a collision time based on the driving information and the obstacle information using a preset collision algorithm; a detection unit, configured to detect the driving state of the driver based on the behavior information to obtain the concentration of the driver; An evaluation unit, configured to perform risk evaluation processing on the vehicle based on the collision time and the concentration using a preset risk strategy to obtain warning information of the vehicle; An alarm unit is used to output the alarm information in at least one output mode.

8. An electronic device, characterized in that: include: at least one processor; as well as a memory communicatively coupled to the at least one processor; The memory stores instructions executable by the at least one processor, and the instructions are executed by the at least one processor to enable the at least one processor to perform the method according to any one of claims 1 to 6.

9. A non-transitory computer-readable storage medium storing computer instructions, characterized in that: The computer instructions are used to cause the computer to execute the method according to any one of claims 1-6.

10. A new energy vehicle, characterized in that: Comprising an electronic device according to claim 8.

Images