Driving behavior analysis and display method

Abstract

The invention relates to the technical field of automobiles, and discloses a driving behavior analysis and display method, which is suitable for a vehicle with an image sensor, is used for motorcade dispatching management, and can be used for transmitting vehicle actions and behavior data of a driver on the vehicle to a server for driving behavior analysis. The method comprises the following steps of: acquiring actual driving data of a vehicle corresponding to a specific road section, wherein the actual driving data comprises a driving (human) behavior; obtaining a driving standard corresponding to the specific road section, wherein the driving standard is variable; comparing the difference between the actual driving data and the driving specifications; and displaying differently according to the difference degree in a graphical mode.

Description

Technical Field

[0001] The present invention relates to the technical field of automobiles, and particularly to a method for analyzing and displaying driving behaviors. Background Art

[0002] In order to improve driving safety, there have been a large number of studies and implementations on analyzing whether driving behaviors are abnormal in the past, especially for management platforms for fleet management. Since there are many vehicles that need to be controlled, how to design an appropriate display interface is a problem.

[0003] The conventional design aims at whether a driver is speeding. It calculates the driving sections of each driver, the difference between the vehicle speed and the speed limit on each of the sections, and designs a statistical chart based on the degree of difference. Different colors are set for the speeding situations of specific drivers to achieve the effect of graphically displaying the speeding situations of drivers.

[0004] The conventional design is based on the "fixed" speed limit of individual sections. However, whether a driving behavior is abnormal and may pose a danger depends on the environmental conditions of the vehicle's travel. For example, if the speed limit of a specific section is 50 km / h, in case of rain or when neighboring vehicles are not following the rules, it may be necessary to reduce the speed to increase one's response time. Therefore, the conventional design does not meet the requirements.

[0005] Furthermore, the conventional design takes vehicle behaviors as the management object, or takes the vehicle behaviors demonstrated by a driver's operation of the vehicle as the management object. For example, when we say that a certain driver likes to speed, we are observing the speeding situation of the vehicle, not the driver's behavior. That is, the danger of driving behaviors is ultimately demonstrated by the vehicle, and the management focus is limited to the vehicle itself, ignoring the driver factor. Therefore, the need to improve driving behaviors through management cannot be satisfied. Summary of the Invention

[0006] An object of the present invention is to provide a method for analyzing and displaying driving behaviors, which can change driving norms according to drivers and driving environmental conditions, and one of the purposes of the present invention is to accurately reflect whether driving behaviors are safe.

[0007] To achieve the above object, the present invention provides the following technical solutions:

[0008] A method for analyzing and displaying driving behaviors, applicable to a vehicle including at least an image sensor, capable of transmitting vehicle data to a server for driving behavior analysis, which includes: (a) obtaining actual driving data of the vehicle corresponding to a specific section, and the actual driving data includes driver behaviors; (b) obtaining the driving norms corresponding to the specific section, and the driving norms are variable; (c) comparing the difference between the actual driving data and the driving norms; (d) presenting the analysis results in a graphical manner according to the severity of the difference in step (c).

[0009] Preferably, the driving norms mentioned in step (b) are adjusted according to the data obtained by the image sensor, and the data includes the situations inside and outside the vehicle.

[0010] Preferably, if the situations inside and outside the vehicle both violate the driving norms, the severity level in process (d) will be increased and the presentation result will be changed.

[0011] Preferably, the image sensor obtains the frequency of being inserted before and after the vehicle to define the nearby traffic situation, and adjusts the driving norms accordingly.

[0012] Preferably, the driving norms are changed according to the real-time traffic information obtained by the central control center.

[0013] Preferably, it is characterized in that step (d) presents the analysis results before and after the change of the driving norms in a two-stage manner.

[0014] Preferably, the driving norms are adjusted according to the road curvature data in the map data.

[0015] Preferably, the driving norms are adjusted according to the vehicle type data.

[0016] Preferably, it further includes step (e) of feeding back the analysis result of step (d) and the decision-making factors for the next task dispatch to individual drivers.

[0017] Compared with the prior art, the beneficial effect of the present invention is: to provide a design close to the driving safety goal, thus putting forward the view that the driving norms can be changed, and the changes of the driving norms vary with some factors, and how these factors are measured is a technical problem. BRIEF DESCRIPTION OF THE DRAWINGS

[0018] Figure 1 It is a schematic diagram of the system architecture of this case. DETAILED DESCRIPTION OF THE INVENTION

[0019] The present invention is a method for analyzing and presenting driving behaviors, which can be implemented in a computer software program. This computer software program can be installed in electronic products such as servers, laptops or mobile phones, and must have the function of remote data transmission, such as having LTE or Wi-Fi or BT, etc. Product reference:

[0020] https: / / www.mio.com / tw / products / car-camera / all-series / cdr / misentry- 12t。

[0021] It is connected to a device for obtaining and collecting image data, such as a driving recorder. The driving recorder has front and rear cameras and can obtain the driving images in front of and behind the vehicle. Product reference:

[0022] https: / / www.mio.com / tw / products / car-camera / all-series / dual / mivue-783dual. It also has an interior camera that can capture the driver's upper body posture or movements, etc. The product is called MiSentry TM 12T, 4G LTE connected dual-lens front and interior camera main unit for driving recorders. Reference:

[0023] https: / / www.mio.com / tw / products / car-camera / all-series / cdr / misentry- 12t 。

[0024] Please refer to the first figure. The system architecture of this case includes a driving recorder 1, a rear camera 2, and vehicle-mounted sensors 3. The driving recorder 1 can be connected to the rear camera 2 through wired or wireless means. The wireless means can be through ethernet, which can simplify the in-vehicle wiring. Cable connections need to consider the in-vehicle environment, but the communication stability is high. The vehicle-mounted sensors 3 include various sensors such as temperature, pressure, and position, used to obtain the current vehicle state. The vehicle-mounted sensors 3 can be connected to the driving recorder 1 through the controller area network 31.

[0025] The driving recorder 1 is installed on the (this) vehicle's windshield, close to the driver's seat, usually selected to be installed next to the rearview mirror inside the vehicle. It includes a front camera 11, an interior camera 12, a positioning chip 13, a wireless communication chip 14, and a processor 15. The front camera 11 and the interior camera 12 respectively include image sensors 111, 121. The image sensors 111, 121 receive the image data obtained by the front camera 11, interior camera 12. The front camera 11 points to the front of the vehicle, and the interior camera 12 points to the inside of the vehicle, and can obtain the driver's facial image.

[0026] The rear camera 2 is installed inside the vehicle and on the rear windshield. The rear camera 2 points to the rear of the vehicle. The rear camera 2 includes an image sensor 21, which can collect the traffic images behind the vehicle, such as the images of the vehicles behind (the following vehicles), especially the license plate images of the following vehicles. The rear camera 2 is connected to the driving recorder 1 through wired or wireless means. The traffic images obtained by the rear camera 2 are transmitted to the processor 15 of the driving recorder 1 through the image sensor 21.

[0027] The front camera 11, the interior camera 12, the rear camera 2, the vehicle-mounted sensors 3, and the positioning chip 13 are all connected to the processor 15. The processor 15 transmits the processed data to the server 4 through the wireless communication chip 14.

[0028] The server 4 includes a wireless communication chip 41, a processor 42, and a database 43. The wireless communication chip 41 is connected to the wireless communication chip 14 of the driving recorder 1, and then integrates information such as the situation of the vehicle itself, the traffic conditions before and after the vehicle, and the driving behavior of the driver (such as facial expressions) in the database 43 of the server 4. The database 43 is connected to the processor 42, and the data in the database 43 can be calculated by the processor 42 and displayed on the display 5.

[0029] The database 43 includes map data 431, vehicle operation data 432, driver behavior data 433, and driving specification data 434. The map data 431 may include geographical coordinates such as longitude and latitude, road speed limits, speed limit periods, road widths, bridge height limits, vehicle type restrictions, road types, etc., which are built with pre-collected data and can be updated with subsequent data through driver feedback information. The vehicle operation data 432 is built with data obtained by the vehicle-mounted sensor 3. The driver behavior data 433 mainly obtains the driver's expression through the in-vehicle camera 12, such as whether the eyes become smaller, the blink frequency becomes smaller, the head angle changes, etc. Physiological sensors (not shown in the figure) can also be added to understand the driver's physiological condition and comprehensively judge the driver's behavior. Obtaining the vehicle operation situation through the vehicle-mounted sensor 3, and matching the time, longitude and latitude coordinates, and road sections (map data) to establish the basic data for analyzing vehicle behavior is a well-known technology and will not be elaborated here. The driving specification data 434 includes road sections (such as between No. 3 and No. 21 of the first section of Wenhua Road, or road sections separated by two intersections), the speed limit of 90 km / h for the said road section, restrictions on large trucks not being allowed to enter, the restricted range of not being allowed to change lanes, and general specifications for drivers, such as: both hands cannot leave the steering wheel, and the driver's perspective cannot deviate more than 30 degrees to the left or right from the center of the lane directly ahead, etc. Although the driving specification data 434 is designed in the server 4, it can also be arranged in the driving recorder 1. The driving specification adjustment of the present invention is executed by the processor 15 of the driving recorder 1 or the processor 42 of the server 4.

[0030] The driving recorder 1 is equipped with a positioning chip 13 (i.e., GPS) that can obtain the coordinates of the vehicle. Based on the journey of the driving recorder 1 (from the starting point to the ending point), playback software can be installed in the memory of the server 4 (not shown in the figure) to integrate Google Maps and play the videos corresponding to the locations side by side synchronously. The map is provided with the speed limits for each road section. Therefore, it can be shown on Google Maps whether the actual driving situation of the vehicle is below the speed limit (marked in green). If the driving speed is 10% higher than the speed limit, it is marked in yellow, and if it is 20% higher than the speed limit, it is marked in red. Thus, whether the driving speed is complied with and the level of compliance can be simply presented through color markings and can be classified and statistically analyzed according to the time length. For example, 30 minutes in yellow, 15 minutes in red, and so on. Taking the speed limit as an example, the level of compliance in this embodiment is divided into three levels, but it is not limited thereto.

[0031] If there are events worthy of attention during the journey, then mark these events. For example: there is a risk event of a vehicle collision in front on the left side of the journey, and a risk event of lane departure on the right side, etc. Hyperlinks can be set for each of the risk event categories, and the user can click to enter and view. For example, there are 2 risk events of vehicle collision in front during the journey, and the videos of each individual event can also be clicked to watch.

[0032] In addition to complying with the speed limit, there are other behaviors of the driver that may change the level of compliance. For example: (1) The driver complies with the speed limit, but the driver is distracted, and the distraction time is very short. Image recognition can be used to judge the changes in the driver's line of sight, facial direction, etc. At this time, the level does not change; (2) The driver complies with the speed limit, but the distraction situation is serious, and the driver's head is down, resulting in the driver's face not being recognized. Then it changes from green to yellow; (3) The driver exceeds the speed limit by 10% higher than the speed limit and is originally marked in yellow, but because the distraction situation of the driver is serious, the marking changes from yellow to red. This is illustrated by this example.

[0033] The driver's score can be set with a full score of 100 and only starts to be calculated when the driver's driving distance exceeds, for example, 1000 kilometers. The driver's score can be based on the relevant data obtained by the sensors. Points will be deducted if there are events of sudden acceleration or sudden deceleration. The point deduction method can be based on the average value of the overall driving. The farther away from the average value, the more points will be deducted. The events can cover whether various types of driving behaviors are compliant, (ADAS / LDWS / Stop&Go…), following too closely, serpentine behavior, sudden braking, sharp turning and other vehicle behaviors, or the driver yawning, drinking water, talking on the phone with the mobile phone (at least one hand leaving the steering wheel), being distracted and not looking at the front of the vehicle, but it is not limited thereto. The changes in the driver's score as described above can be feedback to the driver. Furthermore, the items of score changes can be marked, or only the items with relatively large changes can be given prompts.

[0034] It is specifically mentioned that driving norms are centered around the perspective of driving safety. For example: (1) When the vehicle speed is relatively high, the reaction time for driving is shortened. Driving norms need to adjust the vehicle distance according to the speed. (2) For the behavior of changing lanes while driving, different driving norm requirements apply depending on the vehicle distances and speeds of the vehicles in front and behind. To confirm this, it is necessary to record the vehicle distances between the two vehicles in front and behind before changing lanes and the speeds of each vehicle, as well as the reactions of the vehicles in front and behind during the lane-changing process, such as whether a certain safety vehicle distance is gradually given to the vehicle changing lanes, and thereby define a "friendly traffic environment". (3) When driving on a highway, the key is to maintain a safe vehicle distance. If the vehicle speed is lower than the average speed of the surrounding traffic, unnecessary risks may arise due to the overtaking behavior of the surrounding vehicles. The driving norms on the highway should state that one should drive according to the speed limit range and try to stay within the average speed of the surrounding traffic. Therefore, it is necessary to detect the speeds of the surrounding traffic flow and calculate the average speed, and thereby define the driving norms. For example, the speed limit on the highway is 60 - 90 km / h, but the average speed of the surrounding vehicles is 85 km / h. The driving norm should be close to 85 km / h, rather than the standard of not getting a ticket at 60 km / h. The average speed of the surrounding traffic can be calculated on the server based on the speed data transmitted back by mobile phones for each section of the road. If the speed of this vehicle is lower than the average speed, resulting in frequent rear-end collisions or being overtaken by the following vehicle, then the driving norms are adjusted. (4) For the weaving behavior (weave in) of adjacent vehicles, the number of times, frequency, distance from this vehicle, and speed relationship between the vehicles at that time when a single vehicle encounters the above weaving behavior in different sections or during a journey can be used to judge the driving risk, and the same definition can be used to compare the occurrence of the weaving behavior for a specific section or journey relative to the overall journey. There are various statistical methods that can be used. In short, it is to judge the degree of difference between the specific and the average, and thereby change the driving norms. (5) Driving norms are adjusted according to the vehicle type. For example, the weight of a large vehicle is greater than that of a small vehicle. In the event of a collision, because the momentum of the large vehicle is greater than that of the small vehicle, the damage to other vehicles may be greater. Therefore, for certain sections, large vehicles may be restricted from entering or at least required to reduce their driving speed limits. The vehicle type is input by the vehicle driver, and the driving norms corresponding to the vehicle type can be replaced by external statistical data.

[0035] Example of adjusting driving norms (3) Supplementary explanation: This vehicle detects whether the following vehicle is tailgating through the rear camera 2, and then judges whether the preceding vehicle is moving away from this vehicle through the front camera 11, that is, the situation where the preceding vehicle is moving away from this vehicle while the following vehicle is approaching this vehicle. The judgment method is to obtain the license plates of the preceding vehicle or the following vehicle. Since the license plate has a standard size and specific shape, size, and text content, the error rate of image recognition is low. By the change in the size of the license plate within a certain period of time, the change in the speed difference between the two vehicles is judged. For example, when the distance between the preceding vehicle and this vehicle is 20 meters, the number of pixels occupied by the license plate on the screen is 20 in length * 12 in width. When the distance between the preceding vehicle and this vehicle is 10 meters, the number of pixels occupied by the license plate in the screen is 40 in length * 20 in width. According to the relationship between the number of pixels in the screen and the distance in the real world, the distance and speed changes between this vehicle and the preceding vehicle are estimated. If the speed of this vehicle obtained from the vehicle-mounted sensor 3 is 80 km / h, but the license plate of the following vehicle becomes larger while the license plate of the preceding vehicle becomes smaller, and the speed limit of the section is 90 km / h, the driving norms require this vehicle to increase the speed to 90 km / h.

[0036] When the driving norms change, such as increasing the speed from 80 km / h to 90 km / h as described above, the changes in the license plates of the preceding vehicle and the following vehicle are obtained again. For example, the license plate of the preceding vehicle first becomes smaller by 5% and then by 2%. It can be considered that the distance and speed between the preceding vehicle and this vehicle are getting closer. The license plate of the following vehicle first becomes larger by 8% and then by 2%. Or, according to the vehicle speed, the reasonable vehicle distance is judged, and based on this, the expected size of the license plate on the screen under this condition and whether this size should be maintained are calculated, so as to judge whether the change in the driving norms is effective. If this vehicle does not fully comply with the adjustment of the driving norms, for example, only increases the speed to 85 km / h, it can be recorded as an act of not complying with the adjustment of the driving norms once, or recorded in an amount of 50% according to the proportion of the increased speed for subsequent compliance degree statistics. The vehicle speed can be detected by the speedometer of the vehicle-mounted sensor 3, or estimated by the positioning chip 13 of the driving recorder 1 and transmitted to the database 43 of the server 4 through the wireless communication chip 14 for recording.

[0037] Example of implementation of driving norm adjustment (4) Supplementary description: Regarding the judgment of weaving in, please refer to Patent Certificate No. I757964 proposed by the applicant. It is mainly judged according to the frequency of the vehicle in front or behind entering the control area of the vehicle. In this case, the situation of the vehicle encountering the weaving behavior of the vehicles in front and behind is further recorded. Matching the time and road section, the average value of the total number of weavings in a certain road section divided by the time can be calculated. Then, the total number of weavings is accumulated over a certain period. If it is found that the accumulated total exceeds the average value, the running road section is defined as a dangerous road section. It is judged whether the vehicle speed of the vehicle is lower than the speed limit of the road section and the proportion of being lower than the speed limit. If it is lower than the speed limit by more than 10%, and the situation of the vehicle behind approaching and the vehicle in front moving away does not exceed the threshold, the driving norm is recommended to be adjusted to reduce the vehicle distance and increase the degree of driver intervention. For example, cancel the cruise control function. This is calculated by the processor 41 of the server 4 in cooperation with the database 43. The calculation result and the recommendation are transmitted to the driving recorder 1 through the wireless communication chip 41. The vehicle-mounted sensor 3 records the vehicle behavior, and whether the driver follows the driving norm is observed accordingly. For example, the reduction of the vehicle distance can be estimated through the image data obtained by the front lens 11. Each of the above recommendations can be regarded as a driving norm, and the compliance degree of the driver is judged by counting them.

[0038] The driving norm may change due to environmental changes. For example, the normal speed limit of a specific road section is 80 km / h, and the special speed limit drops to 50 km / h in case of heavy rain or snow. Or in case of a car accident or road debris, the driving recorder obtains TMC accident information or obtains it indirectly through the central control center {server / TMC}, which may change the driving norm of a specific road section. For such temporary environmental changes, the driver's compliance and cooperation measures have an impact on driving safety. Therefore, it is also within the scope of driving behavior analysis, but it does not deal with real-time danger prevention. Therefore, issues such as the data operation period and processing burden do not need to be considered.

[0039] It is particularly mentioned that the inner lens is used to record the actions of the driver. Currently, both the common inner lens and outer lens are installed on the driving recorder, and the driving recorder is installed on the front windshield of the vehicle or the rearview mirror inside the vehicle. Therefore, the shooting range of the driver is mainly around the driver's face. For the judgment of whether at least one hand of the driver leaves the steering wheel, it is judged based on whether a hand is seen around the driver's face by the inner lens. However, the inner lens can also be replaced with a 360-degree panoramic lens, which is installed in the center of the carriage, and can also shoot the steering wheel.

[0040] Regarding the application of driver scoring, an incentive mechanism can be adopted. For example, for drivers with high scores, more tasks can be assigned and bonus payments can be given, etc.; for routes or tasks with relatively large expected changes in driving norms, drivers with a "higher degree of compliance with driving norm changes" can be assigned to handle them.

[0041] The present invention takes into account the qualities of drivers and external environmental factors other than drivers, comprehensively defines driving norms based on both, analyzes driving conditions according to these driving norms, and then presents the analysis results. The driving norms in this case can change according to specific situations, so they can reasonably and appropriately evaluate whether a driving behavior is good or bad.

[0042] Furthermore, the driving norms of the present invention include the definition of quantitative data. For example: for a road type with a sharp turn, at what position of the road curvature, the driver needs to decelerate by how much at a distance before that position. If there is a change in weather, the above driving norms will change accordingly. For example, at the same position, further deceleration is required, or deceleration actions need to be taken at a certain distance before that position. The above quantitative data can be obtained through various software and hardware such as GPS, G sensor, Gyro, and map data, and the obtained quantitative data is used as a reference to judge good and bad drivers.

Claims

1. A driving behavior analysis and display method, applicable to a vehicle including at least an imaging sensor, capable of transmitting vehicle data back to a server for driving behavior analysis. Characterized in that, It includes: (a) Obtaining the actual driving data of the vehicle corresponding to a specific section, and the actual driving data includes the driver's behavior; (b) Obtaining the driving norms corresponding to the specific section, and the driving norms are variable; (c) Comparing the differences between the actual driving data and the driving norms; (d) Presenting the analysis results separately in a graphical manner according to the severity of the differences in step (c).

2. A driving behavior analysis and display method according to claim 1, Characterized in that, The driving norms mentioned in step (b) are adjusted according to the data obtained by the imaging sensor, and the data includes the situations inside and outside the vehicle.

3. A driving behavior analysis and display method according to claim 2, Characterized in that, Situations where both inside and outside the vehicle violate the driving norms will increase the severity in process (d) and change the presentation results.

4. A driving behavior analysis and display method according to claim 2, Characterized in that, The imaging sensor defines the nearby traffic conditions according to the frequency of being cut in front of and behind the vehicle, and adjusts the driving norms accordingly.

5. A driving behavior analysis and display method according to claim 1, Characterized in that, The driving norms are changed according to the real-time traffic information obtained by the central control center.

6. A driving behavior analysis and display method according to claim 2 or 5, Characterized in that, Step (d) presents the analysis results before and after the change of the driving norms in a two-stage manner.

7. A driving behavior analysis and display method according to claim 1, Characterized in that, The driving norms are adjusted according to the road curvature data in the map data.

8. A driving behavior analysis and display method according to claim 1, Characterized in that, The driving norms are adjusted according to the vehicle type data.

9. A driving behavior analysis and display method according to claim 6, Characterized in that, It further includes step (e) feeding back the analysis results in step (d) and the decision-making factors for the next task dispatch to individual drivers.

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