Behavior trajectory correction system based on big data

Abstract

The invention discloses a behavior trajectory correction system based on big data, and relates to the technical field of driving technology, the behavior trajectory correction system comprises a monitoring center, the monitoring center is in communication connection with a data acquisition module, a data processing module, a data analysis module and a trajectory correction module; a simulated advancing route is generated according to driving data of a vehicle in the driving process, whether an obstacle exists on the advancing route or not is detected in the driving process of the vehicle, and when the obstacle exists, the vehicle is automatically decelerated or the advancing route of the vehicle is readjusted, so that the vehicle avoids the obstacle; the congestion time period of each road is judged by performing big data monitoring on each road, meanwhile, the prediction time when the vehicle arrives at the corresponding road is predicted according to the current position of the vehicle, and whether the congestion risk exists in the corresponding road or not when the vehicle arrives at the corresponding road is judged according to the prediction time, so that a driver is helped to avoid the congested road section.

Description

Technical Field

[0001] The present invention relates to the technical field of driving technology, and in particular to a behavior trajectory correction system based on big data. Background Art

[0002] Nowadays, both advanced driver assistance systems and fully autonomous vehicles have aroused extensive research interests among scholars in various fields. There is no doubt that automobile intelligence has become one of the most important trends in the development of the automobile industry. The reason is that intelligent vehicles not only have more efficient, safer and cleaner performance in the transportation system, but also can free humans from controlling the vehicle during the driving process.

[0003] In the existing technology, the driver cannot accurately control the distance between the vehicle and obstacles during driving, which is more obvious among novice drivers. At the same time, when the driver is driving to the destination, the navigation can only monitor whether the driving route is congested at the current moment, so that the driver cannot accurately predict the congestion on the driving route. For this reason, a behavior trajectory correction system based on big data is provided. Summary of the invention

[0004] The purpose of the present invention is to provide a behavior trajectory correction system based on big data.

[0005] The object of the present invention can be achieved by the following technical solutions: A behavior trajectory correction system based on big data, comprising a monitoring center, wherein the monitoring center is communicatively connected with a data acquisition module, a data processing module, a data analysis module and a trajectory correction module; The data acquisition module is used to obtain the driving data of the vehicle after starting and the road information of the location; The data processing module is used to process the driving data of the vehicle after starting acquired by the data acquisition module to obtain a vehicle driving speed change diagram; The data processing module is also used to process the road information to obtain a traffic congestion prediction map of the corresponding road; The data analysis module is used to analyze the obtained driving data and road information of the vehicle, generate corresponding instructions according to the analysis results, and send the generated instructions to the trajectory correction module; The trajectory correction module is used to adjust the driving process of the vehicle according to the received instructions.

[0006] Furthermore, the process of the data acquisition module acquiring the driving data of the vehicle after starting includes: The vehicle's speed and direction are obtained, as well as the initial state of the steering wheel, and the steering wheel's rotation angle based on the initial state during vehicle driving is obtained in real time.

[0007] Furthermore, the process of the data acquisition module acquiring road information includes: Obtain the vehicle's location in real time after it is started, and obtain the road information of the road where the vehicle is located based on the obtained location, the road information includes the number of vehicles, speed limit information and intersection information; Get the traffic volume of each road at different times.

[0008] Furthermore, the data processing module processes the vehicle driving data including: Taking the location of the vehicle as the center, a vehicle model is generated according to the outline of the vehicle, and a simulation scene graph is established, and the vehicle model is displayed in the simulation scene graph; According to the initial state of the vehicle steering wheel obtained when the vehicle is started, the vehicle's travel direction is obtained, and the vehicle's travel direction is mapped into the simulation scene graph; and a travel path is generated in the simulation scene graph in the vehicle's travel direction, and warning points are also set on the travel path; After the vehicle is started, a two-dimensional coordinate system of time related to the vehicle's driving speed is established, and a speed change curve is generated according to the vehicle's driving speed; the speed change curve is mapped into the two-dimensional coordinate system to generate a vehicle driving speed change diagram; and a speed threshold line is set in the vehicle driving speed change diagram.

[0009] Furthermore, the process of the data processing module processing the road information includes: According to the location of the vehicle, the name of the road where the vehicle is located is obtained, and the road information corresponding to the road name is marked; the traffic volume of the road in each time period of the day is obtained, and a traffic volume change curve is generated; a two-dimensional coordinate system of time with respect to traffic volume is established; A traffic flow threshold line is set in the two-dimensional coordinate system, a portion of the traffic flow change curve that exceeds the traffic flow threshold line is marked, and a corresponding time period is marked as a congestion time period; Get whether there are at least two days of overlapping time periods in the marked congestion time periods within the cycle time; A traffic congestion prediction map is established, and the highlighted time periods are mapped to the traffic congestion prediction map.

[0010] Furthermore, the data analysis module analyzes the vehicle's driving data by: The speed value corresponding to the speed threshold line in the vehicle speed variation diagram is marked as V0; According to the vehicle's travel path, determining whether there is an obstacle on the vehicle's travel path; When there is an obstacle, the distance between the obstacle and the vehicle is obtained; the distance between the obstacle and the vehicle is compared with the length of the travel path and the distance between the warning point and the vehicle, and a first-level adjustment instruction or a second-level adjustment instruction is generated according to the comparison result.

[0011] Furthermore, the process of analyzing the road information by the data analysis module includes: Set the destination input port and enter the destination you want to reach into the destination input port; based on the entered destination, a route is generated based on the road where the vehicle is located: Obtain all the intersections corresponding to the vehicle's location that can reach the destination and basic information of the road corresponding to each intersection, and obtain a traffic congestion prediction map corresponding to the road; The predicted arrival time of the vehicle at each intersection is obtained based on the vehicle's current speed, and the predicted arrival time of each intersection is mapped to the corresponding traffic congestion prediction map to determine whether there is a congestion risk on the corresponding road.

[0012] Furthermore, the process of adjusting the vehicle by the trajectory correction module includes: When a first-level adjustment instruction is received, the vehicle is decelerated based on the current speed of the vehicle; When receiving the secondary adjustment instruction, the vehicle position is taken as the reference point, the reference point is connected with the position of the obstacle to generate a reference line, and a number of detection signals with different detection angles from the reference line are generated along both sides of the reference line, and a feedback signal of the detection angle is obtained; A new travel path is generated according to the detection angle of the detection signal.

[0013] Compared with the prior art, the beneficial effects of the present invention are: generating a simulated route by using the driving data of the vehicle during driving, and detecting whether there are obstacles on the route during the driving of the vehicle. When there are obstacles, the vehicle is automatically decelerated or the route of the vehicle is readjusted, so that the vehicle can avoid the obstacles; On the other hand, by conducting big data monitoring on each road, the congested time period of each road can be determined. At the same time, the predicted time when the vehicle arrives at the corresponding road can be predicted based on the current position of the vehicle. Based on the predicted time, it can be determined whether there is a congestion risk on the corresponding road when the vehicle arrives, thereby helping drivers avoid congested sections. BRIEF DESCRIPTION OF THE DRAWINGS

[0014] Figure 1 It is a schematic diagram of the present invention. DETAILED DESCRIPTION

[0015] like Figure 1As shown, a behavior trajectory correction system based on big data includes a monitoring center, and the monitoring center is communicatively connected to a data acquisition module, a data processing module, a data analysis module, and a trajectory correction module; The data acquisition module is used to obtain the driving data of the vehicle after starting and the road information of the location; The specific process of the data acquisition module acquiring the driving data of the vehicle after starting includes: Obtain the vehicle's running speed and mark the vehicle's running speed as V; Obtain the time that the vehicle is in the start state, and mark the time that the vehicle is in the start state as t; The vehicle's driving direction and the initial state of the steering wheel are obtained, and the steering wheel's rotation angle based on the initial state during vehicle driving is obtained in real time.

[0016] The process of the data acquisition module acquiring road information includes: The positioning of the vehicle after starting is obtained in real time, and the road information of the road where the vehicle is located is obtained according to the obtained positioning; it should be further explained that in the specific implementation process, the road information includes the number of vehicles, speed limit information and intersection information; Get the traffic volume of each road at different times; The obtained driving data of the vehicle after starting and the road information of the location are sent to the data processing module.

[0017] The data processing module is used to process the driving data of the vehicle after startup acquired by the data acquisition module, and the specific process includes: Taking the location of the vehicle as the center, a vehicle model is generated according to the outline of the vehicle, and a simulation scene graph is established, and the vehicle model is displayed in the simulation scene graph; According to the initial state of the vehicle steering wheel obtained when the vehicle is started, the vehicle's moving direction is obtained, and the vehicle's moving direction is mapped into the simulation scene graph; According to the rotation angle of the steering wheel based on the initial state, the travel direction in the simulation scene graph is dynamically adjusted, and a travel path is generated in the simulation scene graph in the travel direction of the vehicle. It should be further explained that in the specific implementation process, the length of the travel path generated in the simulation scene graph is a fixed value, and the length of the travel path generated in the simulation scene graph is recorded as S1; a warning point is also set on the travel path, and the distance between the warning point and the position of the vehicle is S2, where S2<S1; After the vehicle is started, a two-dimensional coordinate system of time with respect to the vehicle's driving speed is established, and a speed change curve is generated according to the vehicle's driving speed; Map the speed change curve into a two-dimensional coordinate system to generate a vehicle speed change diagram; Setting a speed threshold line in the vehicle speed variation diagram; The obtained vehicle speed variation diagram is sent to the data analysis module.

[0018] The data processing module is also used to process the road information, and the specific processing process includes: According to the location of the vehicle, the name of the road where the vehicle is located is obtained, and the road information corresponding to the road name is marked; Taking days as units, obtain the traffic volume of the road in each time period of the day and generate a traffic volume change curve; Establish a two-dimensional coordinate system of time with respect to vehicle flow, and map the vehicle flow change curve into the two-dimensional coordinate system; A traffic flow threshold line is set in the two-dimensional coordinate system, and a portion of the traffic flow change curve that exceeds the traffic flow threshold line is marked; Obtain the time period corresponding to the marked traffic flow change curve, and mark the time period as a congestion time period; Taking the month as the unit, the two-dimensional coordinate system of traffic flow is compared in parallel on a daily basis within each month, and the congested time periods are marked; Obtain whether there are at least two days of overlapping time periods in the marked congestion time periods, and highlight the overlapping time periods; it should be further explained that, in the specific implementation process, the more days there are overlapping time periods, the higher the highlight level of the marked time periods; Get the duration of the highlighted time period and the peak traffic flow in the highlighted time period, and mark the peak traffic flow as CF max ; Establish a traffic congestion prediction map, and map the highlighted time periods into the traffic congestion prediction map; The data analysis module is used to analyze the obtained vehicle driving data and road information; The data analysis module analyzes the vehicle's driving data by: The speed value corresponding to the speed threshold line in the vehicle speed variation diagram is marked as V0; When the speed change curve of the vehicle corresponds to V>V0, the time threshold t0 is set, that is, when V>V0, and the duration of the vehicle being in the state of V>V0 is not less than t0, no operation is performed; When V≤V0, or V>V0, and the duration of the vehicle being in the state of V>V0 is less than t0, a travel route is generated; According to the driving speed V of the vehicle during the driving process, the time required for the vehicle to reach the position of the warning point on the driving route is obtained, and the time required for the vehicle to reach the position of the warning point on the driving route is recorded as t1; According to the vehicle's travel path, determining whether there is an obstacle on the vehicle's travel path; When there is an obstacle, the distance between the obstacle and the vehicle is obtained and marked as L; When L≤S2, a first-level adjustment instruction is generated, and the generated first-level adjustment instruction is sent to the trajectory correction module; When S2<L≤S1, a warning message is generated, and a secondary adjustment instruction is generated according to the warning message, and the generated secondary adjustment instruction is sent to the trajectory correction module.

[0019] The process of analyzing the road information by the data analysis module includes: Set the destination input port and input the desired destination into the destination input port; Get the road corresponding to the vehicle's location, and generate a route based on the road at the vehicle's location according to the input destination: Obtain all the intersections corresponding to the vehicle's location that can reach the destination and the basic information of the road corresponding to each intersection; According to the obtained basic information of the road, a traffic congestion prediction map corresponding to the road is obtained; Obtain the predicted arrival time of the vehicle at each intersection based on the vehicle's current speed; Map the predicted arrival time of each intersection to the corresponding traffic congestion prediction map; When the predicted arrival time is within the highlighted time period of the corresponding road, it means that there is a risk of congestion on the road corresponding to the intersection; When the predicted arrival time is not within the highlighted time period of the corresponding road, it means that there is no congestion risk on the road corresponding to the intersection, and so on.

[0020] The trajectory correction module is used to adjust the driving process of the vehicle according to the received instructions, and the specific process includes: When a first-level adjustment instruction is received, the vehicle is decelerated based on the current speed of the vehicle until the speed reaches 0; When receiving the secondary adjustment instruction, the vehicle position is taken as the reference point, the reference point is connected with the position of the obstacle to generate a reference line, and a number of detection signals with different detection angles from the reference line are generated along both sides of the reference line, and a feedback signal of the detection angle is obtained; It should be further explained that, in a specific implementation process, when the obstacle is in the detection direction of the detection signal, the feedback signal is "1", and conversely, when the obstacle is not in the detection direction of the detection signal, the feedback signal is "0"; The detection angle of the detection signal corresponding to the feedback signal "0" is marked, and a new travel path is generated according to the detection angle of the marked detection signal, and the steering wheel angle is automatically adjusted to adjust the vehicle's travel direction to coincide with the new travel path.

[0021] The above embodiments are only used to illustrate the technical method of the present invention rather than to limit it. Although the present invention has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical method of the present invention may be modified or replaced by equivalents without departing from the spirit and scope of the technical method of the present invention.

Claims

1. A behavior trajectory correction system based on big data, including a monitoring center, characterized in that: The monitoring center is communicatively connected to a data acquisition module, a data processing module, a data analysis module and a trajectory correction module; The data acquisition module is used to obtain the driving data of the vehicle after starting and the road information of the location; The data processing module is used to process the driving data of the vehicle after starting acquired by the data acquisition module to obtain a vehicle driving speed change diagram; The data processing module is also used to process the road information to obtain a traffic congestion prediction map of the corresponding road; The data analysis module is used to analyze the obtained driving data and road information of the vehicle, generate corresponding instructions according to the analysis results, and send the generated instructions to the trajectory correction module; The trajectory correction module is used to adjust the driving process of the vehicle according to the received instructions.

2. According to the big data-based behavior trajectory correction system of claim 1, it is characterized in that: The process of the data acquisition module acquiring the driving data of the vehicle after starting includes: The vehicle's speed and direction are obtained, as well as the initial state of the steering wheel, and the steering wheel's rotation angle based on the initial state during vehicle driving is obtained in real time.

3. The behavior trajectory correction system based on big data according to claim 2 is characterized in that: The process of the data acquisition module acquiring road information includes: Obtain the vehicle's location in real time after it is started, and obtain the road information of the road where the vehicle is located based on the obtained location, the road information includes the number of vehicles, speed limit information and intersection information; Get the traffic volume of each road at different times.

4. The behavior trajectory correction system based on big data according to claim 3 is characterized in that: The processing process of the vehicle driving data by the data processing module includes: Taking the location of the vehicle as the center, a vehicle model is generated according to the outline of the vehicle, and a simulation scene graph is established, and the vehicle model is displayed in the simulation scene graph; According to the initial state of the vehicle steering wheel obtained when the vehicle is started, the vehicle's travel direction is obtained, and the vehicle's travel direction is mapped into the simulation scene graph; and a travel path is generated in the simulation scene graph in the vehicle's travel direction, and warning points are also set on the travel path; After the vehicle is started, a two-dimensional coordinate system of time related to the vehicle's driving speed is established, and a speed change curve is generated according to the vehicle's driving speed; the speed change curve is mapped into the two-dimensional coordinate system to generate a vehicle driving speed change diagram; and a speed threshold line is set in the vehicle driving speed change diagram.

5. The behavior trajectory correction system based on big data according to claim 4 is characterized in that: The process of the data processing module processing the road information includes: According to the location of the vehicle, the name of the road where the vehicle is located is obtained, and the road information corresponding to the road name is marked; the traffic volume of the road in each time period of the day is obtained, and a traffic volume change curve is generated; a two-dimensional coordinate system of time with respect to traffic volume is established; A traffic flow threshold line is set in the two-dimensional coordinate system, a portion of the traffic flow change curve that exceeds the traffic flow threshold line is marked, and a corresponding time period is marked as a congestion time period; Get whether there are at least two days of overlapping time periods in the marked congestion time periods within the cycle time; A traffic congestion prediction map is established, and the highlighted time periods are mapped to the traffic congestion prediction map.

6. The behavior trajectory correction system based on big data according to claim 5 is characterized in that: The data analysis module analyzes the vehicle's driving data by: The speed value corresponding to the speed threshold line in the vehicle speed variation diagram is marked as V0; According to the vehicle's travel path, determining whether there is an obstacle on the vehicle's travel path; When there is an obstacle, the distance between the obstacle and the vehicle is obtained; the distance between the obstacle and the vehicle is compared with the length of the travel path and the distance between the warning point and the vehicle, and a first-level adjustment instruction or a second-level adjustment instruction is generated according to the comparison result.

7. The behavior trajectory correction system based on big data according to claim 6 is characterized in that: The process of analyzing the road information by the data analysis module includes: Set the destination input port and enter the destination you want to reach into the destination input port; based on the entered destination, a route is generated based on the road where the vehicle is located: Obtain all the intersections corresponding to the vehicle's location that can reach the destination and basic information of the road corresponding to each intersection, and obtain a traffic congestion prediction map corresponding to the road; The predicted arrival time of the vehicle at each intersection is obtained based on the vehicle's current speed, and the predicted arrival time of each intersection is mapped to the corresponding traffic congestion prediction map to determine whether there is a congestion risk on the corresponding road.

8. The behavior trajectory correction system based on big data according to claim 7 is characterized in that: The process of adjusting the vehicle by the trajectory correction module includes: When a first-level adjustment instruction is received, the vehicle is decelerated based on the current speed of the vehicle; When receiving the secondary adjustment instruction, the vehicle position is taken as the reference point, the reference point is connected with the position of the obstacle to generate a reference line, and a number of detection signals with different detection angles from the reference line are generated along both sides of the reference line, and a feedback signal of the detection angle is obtained; A new travel path is generated according to the detection angle of the detection signal.

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