High-speed accident lane warning navigation method

Abstract

The invention discloses a high-speed accident lane warning navigation method, which comprises the following steps that: after a vehicle which opens a navigation terminal enters a congestion area, the navigation terminal automatically sends voice inquiry to a driver according to a real-time road condition to inquire which lane a congestion point is located at the moment and which lanes are higher in traffic efficiency; the information processing terminal continuously receives feedback data of all navigation terminals of inquiry information sent by a congested road section within 3 minutes, analyzes, calculates and verifies the feedback data, analyzes, calculates and verifies the feedback data again when the consistency of the verification data and the result data is less than 80%, and sends the feedback data to the information processing terminal until the data consistency is more than 80%. And issuing the result information to all vehicles which open the navigation terminals at least 5 kilometers away from the rear of the congested road section.

Description

Technical Field

[0001] The invention relates to a high-speed accident lane warning navigation method. Background Art

[0002] At present, car navigation systems can plan the best route and provide basic information on road conditions. When there is road congestion on the highway, it cannot clearly indicate which lane the congestion occurred in. When drivers approach congested sections, they often cannot know the specific information of the accident lane in advance, and cannot make effective response measures such as changing lanes in advance, which aggravates road congestion. Some navigation systems have the function of feedback on road condition information, but this information is mostly based on the monitoring data of the transportation department or other macro data sources, and cannot accurately determine the cause of congestion in a specific lane and the determination of the accident lane, and the information data is delayed. The existing technology cannot provide drivers with detailed information on the accident lane in a timely and accurate manner, which makes drivers lack effective decision-making basis when facing congested sections, and cannot reasonably plan driving lanes in advance, which reduces driving efficiency and road capacity, and increases the risk of traffic accidents. Summary of the invention

[0003] The present invention is to provide a high-speed accident lane warning navigation method in order to solve the problems existing in the above-mentioned prior art. The present invention significantly improves the real-time, accuracy and reliability of information through real-time feedback and update, dynamic trust model, lane congestion probability function and secondary verification mechanism. At the same time, it also enhances driving safety, improves road traffic efficiency, enhances user experience, reduces system cost and complexity, and has strong adaptability and scalability. In addition, the solution also brings significant social and environmental benefits, and has broad application prospects and promotion value.

[0004] The technical solutions adopted in the present invention are:

[0005] A high-speed accident lane warning navigation method, the steps are as follows:

[0006] S1: When a vehicle with a navigation terminal turned on enters a congested area, the navigation terminal automatically sends a voice inquiry to the driver based on the real-time traffic conditions, asking which lane the congestion point is in and which lanes have higher traffic efficiency. The lanes are marked from the fast lane to the right as 1...N;

[0007] S2: The information processing terminal continuously receives feedback data from all navigation terminals that have sent inquiry information on the congested section within 3 minutes, and performs analysis, calculation and verification. When the consistency between the verification data and the result data is still below 80%, analysis, calculation and verification are performed again until the data consistency is 80% or above, and the result information is released to all vehicles that have turned on the navigation terminal at least 5 kilometers behind the congested section.

[0008] Furthermore, after collecting driver feedback in congested areas, the trustworthiness of the driver feedback information is dynamically analyzed and evaluated through the trust model, and the feedback from high-trust drivers is given a higher weight in the data verification stage.

[0009] Furthermore, the trust model is:

[0010]

[0011] Where: T i (t) is the trust of driver i at time t;

[0012] C i (t) is the number of times the historical feedback of driver i is consistent with the final verification result;

[0013] N i (t) is the total number of feedbacks from driver i;

[0014] α is the attenuation factor, which is used to reduce the weight of historical data. The default value is 0.8;

[0015] η is a weight factor, which is used to adjust the impact of vehicle driving status on trust, and the default value is 0.2;

[0016] V i (t) is the vehicle driving state factor.

[0017] Furthermore, the vehicle driving state factor V i The mathematical expression of (t) is:

[0018]

[0019] v i (t) is the speed of vehicle i at time t;

[0020] v0 is the average speed in the congested area;

[0021] β is the sensitivity coefficient, which is used to adjust the impact of speed changes on trust. The default value is 0.1.

[0022] Furthermore, after obtaining the driver feedback information, the congestion probability of each lane is dynamically calculated through the lane congestion probability function, and the function is:

[0023]

[0024] Where: P k (t) is the congestion probability of lane k at time t;

[0025] I i,k Whether driver i reports lane k as congested, 1 indicates congestion, 0 indicates no congestion;

[0026] T i (t) is the trust of driver i at time t;

[0027] γ is the time sensitivity coefficient, which is used to adjust the impact of feedback time difference on congestion probability. The default value is 0.2;

[0028] Δt i,k Feedback the time difference of lane k congestion for driver i;

[0029] n is the number of drivers who participated in the feedback.

[0030] Furthermore, during data verification:

[0031] When P k When (t)≥0.8, it is determined as a congested lane and information is immediately released to remind drivers to plan their routes in advance;

[0032] If P k When (t)<0.8, start secondary verification:

[0033] 1) Extend the feedback time to 5 minutes;

[0034] 2) Introduce external data for cross-validation;

[0035] 3) If the situation is still not satisfied, it will be marked as “low-trust congestion” and only prompt the driver behind to change lanes carefully.

[0036] Furthermore, the external data includes real-time reports from traffic cameras or traffic police departments.

[0037] The present invention has the following beneficial effects:

[0038] The present invention asks the driver about the congestion situation in real time through the navigation terminal, and collects feedback data in a short time (such as 3 minutes) to ensure the real-time nature of the congestion information. The real-time update mechanism can help the driver understand the road conditions in a timely manner and make decisions in advance. The congestion probability and trust are dynamically adjusted according to the real-time feedback to ensure the accuracy and timeliness of the information. Through the dynamic trust model, the trust can be dynamically adjusted according to the driver's historical feedback and the current vehicle status to improve the accuracy of the feedback information. High-trust driver feedback is given a higher weight in the data verification stage, thereby reducing the impact of false alarms and false information. The dynamic lane congestion probability function combines the trust and time sensitivity of the driver's feedback, and can more accurately calculate the congestion probability of each lane. Compared with the traditional static analysis method, this dynamic calculation method can better reflect the real-time changes in congestion.

[0039] When the probability of congestion does not reach the threshold, the system will start secondary verification and cross-verify with external data (such as traffic cameras and traffic police reports) to further improve the accuracy and reliability of information release. Data verification will be performed before releasing congestion information to ensure the reliability of the information. Congestion information will only be released when the data consistency reaches 80% or above to avoid false alarms caused by inconsistent data. For congestion information that does not reach the threshold, the system will start secondary verification, extend the feedback time window and introduce external data for cross-verification to ensure the reliability of the information. This redundancy mechanism can further improve the stability and reliability of the system. For congestion information that cannot be fully verified, it will be marked as "low-trust congestion" and prompt the driver behind to change lanes carefully. This prompt mechanism can remind drivers of potential congestion risks while avoiding blind lane changes due to false alarms.

[0040] The present invention can release congestion information to the vehicles behind in advance, and the driver can plan lane changes or adjust the driving route in advance based on this information, avoiding sudden lane changes or sudden braking in congested areas, thereby reducing the risk of traffic accidents. The present invention can clearly point out the specific lanes that are congested, and the driver can choose a lane with higher traffic efficiency based on this information, reducing the stay time in the congested area, and further reducing the risk of accidents.

[0041] For unverified congestion information, the driver behind will be prompted to change lanes carefully. This prompt mechanism can remind drivers of potential congestion risks and avoid blind lane changes caused by false alarms. By providing the congestion probability of each lane, drivers can be guided to choose lanes with higher traffic efficiency to avoid all vehicles concentrating in the same lane, thereby optimizing the traffic efficiency of the entire road.

[0042] Releasing congestion information to vehicles behind in advance can prevent more vehicles from rushing into the congested area and reduce the further aggravation of congestion. This helps to ease traffic pressure and improve the overall traffic capacity of the road. It can dynamically adjust congestion information based on real-time feedback to reflect changes in road conditions in a timely manner. This dynamic adjustment mechanism can better adapt to complex traffic environments and ensure the continuous optimization of road traffic efficiency. Collecting driver feedback through voice inquiries is simple and convenient, will not distract the driver's attention, and improves the user experience. It can push congestion information to vehicles behind in real time, so that drivers can plan their routes in advance, reduce waiting time in congested areas, and improve driving comfort and satisfaction. Dynamically adjusting information push based on the driver's trust and vehicle status can provide personalized road condition prompts for different drivers to meet the needs of different users.

[0043] The present invention can effectively reduce traffic congestion, reduce vehicle fuel consumption and exhaust emissions by optimizing lane selection and early warning, which is of great significance to environmental protection. Reducing congestion time means that people can reach their destination faster, saving a lot of time and resources, and improving the overall operating efficiency of society. Through early warning and accurate lane information, the incidence of traffic accidents can be reduced, reducing casualties and property losses, which has significant social benefits. BRIEF DESCRIPTION OF THE DRAWINGS

[0044] Figure 1 It is a flow chart of the present invention. DETAILED DESCRIPTION

[0045] The present invention will be further described below in conjunction with the accompanying drawings.

[0046] like Figure 1 As shown, the present invention provides a high-speed accident lane warning navigation method, the steps are as follows:

[0047] S1: When a vehicle with a navigation terminal turned on enters a congested area, the navigation terminal automatically sends a voice inquiry to the driver based on the real-time traffic conditions, asking which lane the congestion point is in and which lanes have higher traffic efficiency. The lanes are marked from the fast lane to the right as 1...N;

[0048] S2: The information processing terminal continuously receives feedback data from all navigation terminals that have sent inquiry information on the congested section within 3 minutes, and performs analysis, calculation and verification. When the consistency between the verification data and the result data is still below 80%, analysis, calculation and verification are performed again until the data consistency is 80% or above, and the result information is released to all vehicles that have turned on the navigation terminal at least 5 kilometers behind the congested section.

[0049] After collecting feedback from drivers in congested areas, the trustworthiness of the driver feedback is dynamically analyzed and evaluated through the trust model. Feedback from drivers with high trustworthiness is given a higher weight in the data verification stage. The trust model is:

[0050]

[0051] Where: T i (t) is the trust of driver i at time t;

[0052] C i (t) is the number of times the historical feedback of driver i is consistent with the final verification result;

[0053] N i (t) is the total number of feedbacks from driver i;

[0054] α is the attenuation factor, which is used to reduce the weight of historical data. The default value is 0.8;

[0055] η is a weight factor, which is used to adjust the impact of vehicle driving status on trust, and the default value is 0.2;

[0056] V i (t) is the vehicle driving state factor, the vehicle driving state factor V i The mathematical expression of (t) is:

[0057]

[0058] v i (t) is the speed of vehicle i at time t;

[0059] v0 is the average speed in the congested area;

[0060] β is the sensitivity coefficient, which is used to adjust the impact of speed changes on trust. The default value is 0.1.

[0061] After obtaining driver feedback information, the congestion probability of each lane is dynamically calculated through the lane congestion probability function, which is:

[0062]

[0063] Where: P k (t) is the congestion probability of lane k at time t;

[0064] I i,k Whether driver i reports lane k as congested, 1 indicates congestion, 0 indicates no congestion;

[0065] T i (t) is the trust of driver i at time t;

[0066] γ is the time sensitivity coefficient, which is used to adjust the impact of feedback time difference on congestion probability. The default value is 0.2;

[0067] Δt i,k Feedback the time difference of lane k congestion for driver i;

[0068] n is the number of drivers who participated in the feedback.

[0069] When validating data:

[0070] When P k When (t)≥0.8, it is determined as a congested lane and information is immediately released to remind drivers to plan their routes in advance;

[0071] If P k When (t)<0.8, start secondary verification:

[0072] 1) Extend the feedback time to 5 minutes;

[0073] 2) Introduce external data for cross-validation (real-time reports from traffic cameras or traffic police departments);

[0074] 3) If the situation is still not satisfied, it will be marked as “low-trust congestion” and only prompt the driver behind to change lanes carefully.

[0075] Assume that a car accident occurs in the leftmost lane 1 on a four-lane highway (marked as lane 1, lane 2, lane 3, and lane 4 from left to right), causing serious congestion in that lane. Other lanes are open to traffic. When a vehicle enters the congested area, the navigation terminal sends a voice query to the driver and collects feedback data.

[0076] Driver feedback data collection: Assume that 10 vehicles enter a congested area, and the drivers provide feedback on lane congestion based on the actual situation.

[0077] Dynamic trust calculation: Calculate each driver’s trust based on their historical feedback and current vehicle status.

[0078] Lane congestion probability calculation: Calculate the congestion probability of each lane based on driver feedback and trust.

[0079] Data verification and release: Verify whether the congestion probability reaches the threshold (0.8) and release the congestion information.

[0080]

[0081] Table 1

[0082] Table 1 shows the driver feedback data. In Table 1, most drivers (1, 2, 4, 5, 6, 8, 10) reported that lane 1 was congested, which is consistent with the actual scenario. Drivers 3, 7, and 9 reported that other lanes were not congested, which is consistent with the actual scenario.

[0083] Driver No. Dynamic trust Ti(t) 1 0.65 2 0.60 3 0.50 4 0.70 5 0.55 6 0.62 7 0.52 8 0.75 9 0.45 10 0.60

[0084] Table 2

[0085] Table 2 shows the dynamic trust calculation results. In Table 2, driver 4 and driver 8 have the highest trust (0.70 and 0.75 respectively), indicating that their feedback is the most reliable. Driver 9 has the lowest trust (0.45), indicating that the reliability of his feedback is relatively low.

[0086] Lane Number Congestion probability Pk(t) 1 0.90 2 0.20 3 0.15 4 0.10

[0087] Table 3

[0088] Table 3 shows the calculation results of lane congestion probability. In Table 3, the congestion probability of lane 1 is 0.90, far exceeding the threshold of 0.8, and it is determined to be a congested lane. The congestion probability of lanes 2, 3, and 4 is relatively low, which prompts the rear driver to change lanes cautiously.

[0089] The present invention collects driver feedback data in real time, dynamically calculates trust and congestion probability, and ensures the real-time and accuracy of information. The dynamic trust model can dynamically adjust the trust according to the driver's historical feedback and the current vehicle status to improve the reliability of feedback information. It can release congestion information to the following vehicles in advance to help drivers plan their routes in advance and reduce the risk of traffic accidents. Accurate lane congestion information can help drivers choose lanes with higher traffic efficiency and reduce the time spent in congested areas. By optimizing lane selection, drivers are guided to choose lanes with higher traffic efficiency, thereby improving the overall traffic efficiency of the road. The dynamic adjustment mechanism can better adapt to complex traffic environments and ensure the continuous optimization of road traffic efficiency. Driver feedback is collected through voice inquiries, which is simple and convenient to operate and improves user experience.

[0090] The above description is only a preferred embodiment of the present invention. It should be pointed out that a person skilled in the art can make several improvements without departing from the principle of the present invention, and these improvements should also be regarded as within the protection scope of the present invention.

Claims

1. A high-speed accident lane warning navigation method, characterized in that: Here are the steps: S1: When a vehicle with a navigation terminal turned on enters a congested area, the navigation terminal automatically sends a voice inquiry to the driver based on the real-time traffic conditions, asking which lane the congestion point is in and which lanes have higher traffic efficiency. The lanes are marked from the fast lane to the right as 1...N; S2: The information processing terminal continuously receives feedback data from all navigation terminals that have sent inquiry information on the congested section within 3 minutes, and performs analysis, calculation and verification. When the consistency between the verification data and the result data is still below 80%, analysis, calculation and verification are performed again until the data consistency is 80% or above, and the result information is released to all vehicles that have turned on the navigation terminal at least 5 kilometers behind the congested section.

2. The high-speed accident lane warning navigation method according to claim 1, characterized in that: After collecting driver feedback in congested areas, the trustworthiness of the driver feedback information is dynamically analyzed and evaluated through a trust model, and feedback from highly trusted drivers is given a higher weight in the data verification stage.

3. The high-speed accident lane warning navigation method as claimed in claim 2, characterized in that: The trust model is: Where: T i (t) is the trust of driver i at time t; C i (t) is the number of times the historical feedback of driver i is consistent with the final verification result; N i (t) is the total number of feedbacks from driver i; α is the attenuation factor, which is used to reduce the weight of historical data. The default value is 0.8; η is a weight factor, which is used to adjust the impact of vehicle driving status on trust, and the default value is 0.2; V i (t) is the vehicle driving state factor.

4. The high-speed accident lane warning navigation method according to claim 1, characterized in that: The vehicle driving state factor V i The mathematical expression of (t) is: v i (t) is the speed of vehicle i at time t; v0 is the average speed in the congested area; β is the sensitivity coefficient, which is used to adjust the impact of speed changes on trust. The default value is 0.

1.

5. The high-speed accident lane warning navigation method according to claim 1, characterized in that: After obtaining driver feedback information, the congestion probability of each lane is dynamically calculated through the lane congestion probability function, which is: Where: P k (t) is the congestion probability of lane k at time t; I i,k Whether driver i reports lane k as congested, 1 indicates congestion, 0 indicates no congestion; Ti(t) is the trust of driver i at time t; γ is the time sensitivity coefficient, which is used to adjust the impact of feedback time difference on congestion probability. The default value is 0.2; Δt i,k Feedback the time difference of lane k congestion for driver i; n is the number of drivers who participated in the feedback.

6. The high-speed accident lane warning navigation method according to claim 5, characterized in that: When validating data: When P k When (t)≥0.8, it is determined as a congested lane and information is immediately released to remind drivers to plan their routes in advance; If P k When (t)<0.8, start secondary verification: 1) Extend the feedback time to 5 minutes; 2) Introduce external data for cross-validation; 3) If the situation is still not satisfied, it will be marked as "low confidence congestion" and only prompt the driver behind to change lanes carefully.

7. The high-speed accident lane warning navigation method according to claim 6, characterized in that: The external data may include real-time reports from traffic cameras or traffic police departments.

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