Vehicle adaptive HUD display method and system in following state

Abstract

A method and system for adaptive HUD display of a vehicle in a following state, which belongs to the field of automobile human-computer interaction. The present invention solves the problem that in a following scenario, the complex HUD display interface and road environment will cause the driver's driving load to exceed its carrying capacity, which is prone to traffic accidents. During the implementation process, the method of the present invention obtains input parameters through the Internet of Vehicles environment, can automatically divide the driving time period and judge the complexity level of the following dynamic road environment in which the driver is located during this time period, and then match the corresponding HUD interface for it. When the complexity of the following dynamic road environment is high, a simple HUD interface is matched to reduce the driver's driving load and avoid driving accidents caused by excessive driving load. The method of the present invention can be applied to the adaptive HUD display of vehicles in the following state.

Description

Technical Field

[0001] The present invention belongs to the field of automobile human-computer interaction, and in particular relates to a vehicle adaptive HUD display method and system in a car-following state. Background Art

[0002] The vehicle-mounted HUD (Head-up Display) can use optical principles to project important driving information such as vehicle speed and navigation onto the front windshield of the car, thereby reducing the frequency of the driver looking down at the instrument or navigation information, thereby improving driving safety. However, with the increasing degree of vehicle electrification and the development of AR-HUD technology, the HUD display interface is becoming more and more complex. During driving, the driver mainly obtains road traffic environment information and information in the HUD display interface through the visual system, and the increasingly complex HUD display interface will occupy the driver's visual resources to varying degrees, increasing the driver's driving load. When the road traffic environment is relatively complex, the driver will not be able to respond to emergencies on the road in a timely manner, which can easily lead to traffic accidents. Summary of the invention

[0003] The purpose of the present invention is to solve the problem that in the following car scenario, due to the unreasonable design of the HUD display interface, the driver's driving load exceeds its carrying capacity, resulting in the driver's inability to respond to sudden situations on the road in time, which is prone to traffic accidents. A vehicle adaptive HUD display method in the following car state is proposed. By establishing a relationship between the quantified complexity of the following car dynamic road environment and the driving load under different numbers of HUD display elements, a vehicle adaptive HUD display system is designed.

[0004] The technical solution adopted by the present invention to solve the above technical problems is:

[0005] According to one aspect of the present invention, a method for adaptive HUD display of a vehicle in a following state comprises the following steps:

[0006] Step 1: respectively obtain the speed and position of the driver's current vehicle and the speed and position of the preceding vehicle, and then calculate the dynamic road environment complexity evaluation index in the following state according to the obtained vehicle speed and position information;

[0007] The dynamic road environment complexity evaluation index in the following state includes: the inverse of the corrected collision margin, the lateral swing coefficient and the speed instability coefficient;

[0008] Step 2: The inverse of the corrected collision margin, the lateral swing coefficient and the speed instability coefficient are used as input data, and the time series segmentation and clustering algorithm TICC is used to segment and cluster the input data, and the input data is divided into 8 categories;

[0009] Step 3, respectively calculating the complexity of the car-following dynamic road environment for each category;

[0010] The complexity of the following dynamic road environment at (0,3] is defined as a low complexity state, the complexity of the following dynamic road environment at (3,7] is defined as a medium complexity state, the complexity of the following dynamic road environment at (7,10] is defined as a heavy complexity state, and the complexity of the following dynamic road environment exceeding 10 is defined as an extremely heavy complexity state;

[0011] Step 4: Design simple, normal and complex HUD display interfaces respectively. The three modes are divided according to the number of display elements in the HUD display interface.

[0012] When the complexity of the following dynamic road environment is in a low-complexity state, the HUD display interface of the complex mode is displayed; when the complexity of the following dynamic road environment is in a moderate-complexity state, the HUD display interface of the normal mode is displayed; when the complexity of the following dynamic road environment is in a heavy-complexity state and an extremely heavy-complexity state, the HUD display interface of the simple mode is displayed.

[0013] Furthermore, in step 1, the calculation method of the inverse of the corrected collision margin is:

[0014]

[0015] Among them, I IMMTC is the inverse of the corrected collision margin, v p is the speed of the preceding vehicle, v f is the current driving vehicle speed, D0 is the distance between the front vehicle and the current driving vehicle, and a is the braking deceleration.

[0016] Furthermore, the braking deceleration a is 8 m / s 2 .

[0017] Furthermore, the lateral swing coefficient is calculated as follows:

[0018]

[0019] Among them, I TOC is the lateral swing coefficient, d(t) is the cumulative longitudinal travel distance within 1 second before time t, W(t) is the cumulative value of lateral offset within 1 second before time t, and F is the data recording frequency.

[0020] Furthermore, the speed instability coefficient is calculated as follows:

[0021]

[0022] Among them, I VICis the speed instability coefficient, V std (t) is the standard deviation of speed within 1 second before time t, V mean (t) is the average speed within 1s before time t.

[0023] Furthermore, the signal dimension of the time series segmentation clustering algorithm TICC is n=3, the window width w=10F, F is the data recording frequency, the cost β=50, and the number of clusters K=8.

[0024] Furthermore, the calculation method of the complexity of the car-following dynamic road environment is:

[0025] C=5.00I IMMTC +3020.83I TOC +23.60I VIC

[0026] Among them, C is the complexity of the dynamic road environment of car-following.

[0027] Furthermore, the number of display elements in the simple mode HUD display interface does not exceed 4, the number of display elements in the normal mode HUD display interface does not exceed 9, and the number of display elements in the complex mode HUD display interface is greater than 9.

[0028] According to another aspect of the present invention, a vehicle adaptive HUD display system in a car-following state is provided, and the system is used to execute a vehicle adaptive HUD display method in a car-following state.

[0029] The beneficial effects of the present invention are:

[0030] The vehicle adaptive HUD display method in the following state of the present invention can adjust the complexity of the HUD display according to the external environment, and solve the problem of overload driving caused by the driver obtaining HUD information in a complex traffic environment, causing the driver's lack of attention, lack of environmental perception, inability to respond to sudden situations on the road in time, and operating errors, thereby causing traffic accidents. During the implementation of the method of the present invention, the input parameters can be obtained through the Internet of Vehicles environment, and the driving time period can be automatically divided and the complexity level of the following dynamic road environment in which the driver is located during this time period can be determined, and then the corresponding HUD interface can be matched for it. When the complexity of the following dynamic road environment is high, a simple HUD interface is matched to reduce the driver's driving load and avoid driving accidents caused by excessive driving load. BRIEF DESCRIPTION OF THE DRAWINGS

[0031] Figure 1 is a flow chart of a method for displaying a vehicle adaptive HUD in a following state according to the present invention;

[0032] Figure 2It is a schematic diagram of the matching of the HUD display mode and the complexity level of the following dynamic road environment in the vehicle adaptive HUD display method in the following state of the present invention. DETAILED DESCRIPTION

[0033] Specific implementation method 1. Combination Figure 1 The method for displaying a vehicle adaptive HUD in a following state described in this embodiment specifically includes the following steps:

[0034] Step 1: respectively obtain the speed and position of the driver's current vehicle and the speed and position of the preceding vehicle (the preceding vehicle of the driver's current vehicle), and then calculate the dynamic road environment complexity evaluation index in the following state according to the obtained vehicle speed and position information;

[0035] The dynamic road environment complexity evaluation index in the following state includes: the inverse of the corrected collision margin, the lateral swing coefficient and the speed instability coefficient;

[0036] Step 2: The inverse of the corrected collision margin, the lateral swing coefficient and the speed instability coefficient are used as input data, and the time series segmentation and clustering algorithm TICC is used to segment and cluster the input data, and the input data is divided into 8 categories;

[0037] Step 3, respectively calculating the complexity of the car-following dynamic road environment for each category;

[0038] The complexity of the following dynamic road environment at (0,3] is defined as a low complexity state, the complexity of the following dynamic road environment at (3,7] is defined as a medium complexity state, the complexity of the following dynamic road environment at (7,10] is defined as a heavy complexity state, and the complexity of the following dynamic road environment exceeding 10 is defined as an extremely heavy complexity state;

[0039] Step 4: Design simple, normal and complex HUD display interfaces respectively. The three modes are divided according to the number of display elements in the HUD display interface.

[0040] When the complexity of the following dynamic road environment is in a low-complexity state, the HUD display interface of the complex mode is displayed; when the complexity of the following dynamic road environment is in a moderate-complexity state, the HUD display interface of the normal mode is displayed; when the complexity of the following dynamic road environment is in a heavy-complexity state and an extremely heavy-complexity state, the HUD display interface of the simple mode is displayed.

[0041] In the Internet of Vehicles environment, the vehicle-mounted HUD system can obtain road traffic environment information in real time. By quantifying the complexity of the dynamic road environment and designing an adaptive HUD display interface that matches the complexity of the road environment, the sum of the complexity of the two can be controlled within the range acceptable to the driver, thereby effectively avoiding the negative impact of the HUD display interface on the driver and reducing driving risks during driving. This provides a new idea for the design of automotive human-computer interaction.

[0042] Specific implementation method 2: This implementation method is different from the specific implementation method 1 in that in step 1, the calculation method of the inverse of the corrected collision margin is:

[0043]

[0044] Among them, I IMMTC is the inverse of the corrected collision margin, v p is the speed of the preceding vehicle, v f is the current driving vehicle speed, D0 is the distance between the front vehicle and the current driving vehicle, and a is the braking deceleration.

[0045] The other steps and parameters are the same as those in the first embodiment.

[0046] Specific implementation method three: This implementation method is different from specific implementation methods one or two in that the braking deceleration a is 8m / s 2 .

[0047] The other steps and parameters are the same as those in the first or second embodiment.

[0048] Specific implementation method 4: This implementation method is different from any one of the specific implementation methods 1 to 3 in that the lateral swing coefficient is calculated as follows:

[0049]

[0050] Among them, I TOC is the lateral swing coefficient, d(t) is the cumulative longitudinal travel distance within 1 second before time t, W(t) is the cumulative value of lateral offset within 1 second before time t, and F is the data recording frequency.

[0051] The other steps and parameters are the same as those in Specific Embodiments 1 to 3.

[0052] Specific implementation method 5: This implementation method is different from the specific implementation methods 1 to 4 in that the speed instability coefficient is calculated as follows:

[0053]

[0054] Among them, I VIC is the speed instability coefficient, V std(t) is the standard deviation of speed within 1 second before time t, V mean (t) is the average speed within 1s before time t.

[0055] The other steps and parameters are the same as those in Specific Embodiments 1 to 4.

[0056] Specific implementation method six: This implementation method is different from any one of specific implementation methods one to five in that the signal dimension of the time series segmentation clustering algorithm TICC is n=3, the window width w=10F, F is the data recording frequency, the cost β=50, and the number of clusters K=8.

[0057] The time series segmentation clustering algorithm TICC can complete the subsequence clustering of multivariate time series. Each cluster in the TICC method is defined by a correlation network or Markov random field, which describes the mutual dependence between different observations in a typical subsequence of the cluster, but the actual scene represented by the clustering result needs to be labeled by itself. The present invention uses the complexity of the dynamic road environment of car-following to label each category.

[0058] The other steps and parameters are the same as those in Specific Implementation Methods 1 to 5.

[0059] Specific implementation method 7: This implementation method is different from one of the specific implementation methods 1 to 6 in that the calculation method of the complexity of the car-following dynamic road environment is:

[0060] C=5.00I IMMTC +3020.83I TOC +23.60I VIC

[0061] Among them, C is the complexity of the dynamic road environment of car-following.

[0062] The other steps and parameters are the same as those in Specific Embodiments 1 to 6.

[0063] Specific implementation eight: This implementation differs from any one of specific implementations one to seven in that the number of display elements in the simple mode HUD display interface does not exceed 4, the number of display elements in the normal mode HUD display interface does not exceed 9, and the number of display elements in the complex mode HUD display interface is greater than 9.

[0064] Display elements refer to the display content in the HUD display interface, such as vehicle speed, navigation, entertainment system, etc.

[0065] The other steps and parameters are the same as those in Specific Embodiments 1 to 7.

[0066] Specific implementation method 9: The vehicle adaptive HUD display system in the car-following state described in this implementation method is used to execute the vehicle adaptive HUD display method in the car-following state.

[0067] The above calculation examples of the present invention are only used to explain the calculation model and calculation process of the present invention in detail, and are not intended to limit the implementation methods of the present invention. For ordinary technicians in the relevant field, other different forms of changes or modifications can be made based on the above description. It is impossible to list all the implementation methods here. All obvious changes or modifications derived from the technical solution of the present invention are still within the scope of protection of the present invention.

Claims

1. A vehicle adaptive HUD display method in a following state, characterized in that: The method specifically comprises the following steps: Step 1: respectively obtain the speed and position of the driver's current vehicle and the speed and position of the preceding vehicle, and then calculate the dynamic road environment complexity evaluation index in the following state according to the obtained vehicle speed and position information; The dynamic road environment complexity evaluation index in the following state includes: the inverse of the corrected collision margin, the lateral swing coefficient and the speed instability coefficient; The calculation method of the inverse of the modified collision margin is: Among them, I IMMTC is the inverse of the corrected collision margin, v p is the speed of the preceding vehicle, v f is the current driving vehicle speed, D0 is the distance between the front vehicle and the current driving vehicle, and a is the braking deceleration; The lateral swing coefficient is calculated as follows: Among them, I TOC is the lateral swing coefficient, d(t) is the cumulative longitudinal travel distance within 1 second before time t, W(t) is the cumulative value of lateral displacement within 1 second before time t, and F is the data recording frequency; The speed instability coefficient is calculated as follows: Among them, I VIC is the speed instability coefficient, V std (t) is the standard deviation of speed within 1 second before time t, V mean (t) is the average speed within 1s before time t; Step 2: The inverse of the corrected collision margin, the lateral swing coefficient and the speed instability coefficient are used as input data, and the time series segmentation and clustering algorithm TICC is used to segment and cluster the input data, and the input data is divided into 8 categories; Step 3, respectively calculating the complexity of the car-following dynamic road environment for each category; The complexity of the following dynamic road environment at (0,3] is defined as a low complexity state, the complexity of the following dynamic road environment at (3,7] is defined as a medium complexity state, the complexity of the following dynamic road environment at (7,10] is defined as a heavy complexity state, and the complexity of the following dynamic road environment exceeding 10 is defined as an extremely heavy complexity state; Step 4: Design simple, normal and complex HUD display interfaces respectively. The three modes are divided according to the number of display elements in the HUD display interface. When the complexity of the following dynamic road environment is in a low-complexity state, the HUD display interface of the complex mode is displayed; when the complexity of the following dynamic road environment is in a moderate-complexity state, the HUD display interface of the normal mode is displayed; when the complexity of the following dynamic road environment is in a heavy-complexity state and an extremely heavy-complexity state, the HUD display interface of the simple mode is displayed.

2. The method for displaying a vehicle adaptive HUD in a following state according to claim 1, characterized in that: The braking deceleration a is 8 m / s 2 .

3. The method for displaying a vehicle adaptive HUD in a following state according to claim 2, characterized in that: The signal dimension of the time series segmentation clustering algorithm TICC is n=3, the window width w=10F, F is the data recording frequency, the cost β=50, and the number of clusters K=8.

4. The method for displaying a vehicle adaptive HUD in a following state according to claim 3, characterized in that: The calculation method of the complexity of the car-following dynamic road environment is: C=5.00I IMMTC +3020.83I TOC +23.60I VIC Among them, C is the complexity of the dynamic road environment of car-following.

5. The method for displaying a vehicle adaptive HUD in a car-following state according to claim 4, characterized in that: The number of display elements in the simple mode HUD display interface does not exceed 4, the number of display elements in the normal mode HUD display interface does not exceed 9, and the number of display elements in the complex mode HUD display interface is greater than 9.

6. The vehicle adaptive HUD display system in the following state is characterized by: The system is used to execute the vehicle adaptive HUD display method in the following state as described in any one of claims 1 to 5.

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