Traffic energy efficiency control system based on 3D terrain mapping and vehicle-road collaboration

Abstract

The present invention relates to the technical field of traffic control systems, specifically to a traffic energy efficiency control system that integrates three-dimensional terrain mapping and vehicle-road collaboration. The system includes: a three-dimensional road network terrain perception module that receives vehicle position, speed information and original point cloud data of the surrounding environment through a roadside unit and an on-board unit, assigns slope and curvature coordinates to each road section in the road network, and generates a vehicle-terrain correlation topology map. In the present invention, by integrating the spatial geometric description established by the vehicle position, speed information and point cloud, quantitative assignment of road slope and curvature coordinates is achieved, and a mapping relationship is established between terrain elements and vehicle operating status, so that the vehicle path and terrain attributes are synchronously linked. In the emergency response, the slope angle and driving direction angle of the affected vehicle are used to construct an energy consumption priority sequence. Starting from the motion relationship between the vehicle and the road, the evacuation path selection under the energy efficiency dimension is completed.

Description

Technical Field

[0001] The present invention relates to the technical field of traffic control systems, and in particular to a traffic energy efficiency control system integrating three-dimensional terrain mapping with vehicle-road collaboration. Background Art

[0002] The technical field of traffic control systems involves the monitoring, analysis, prediction and management of road traffic flows, covering key technologies such as traffic signal control, vehicle detection, road state recognition, scheduling optimization, collaborative perception and intelligent decision-making.

[0003] Existing technologies typically focus on analyzing macro-indicators such as traffic flow density, signal timing, and vehicle volume, while ignoring the micro-influence of road terrain on vehicle operation. This makes it difficult to make detailed judgments about a vehicle's acceleration capability and energy consumption characteristics on different slopes, resulting in evacuation routes that lack energy-saving advantages in complex terrain scenarios. In emergency response, dispatch routes are often generated based on fixed priorities or distance weights, lacking energy-efficiency evaluation based on the linkage between vehicle motion and terrain. This can easily lead to biased route selection and prolonged evacuation times. Therefore, improvements are needed. Summary of the Invention

[0004] The purpose of the present invention is to solve the shortcomings of the existing technology and propose a traffic energy efficiency control system that integrates three-dimensional terrain mapping and vehicle-road collaboration.

[0005] To achieve the above objectives, the present invention adopts the following technical solutions: A traffic energy efficiency control system integrating three-dimensional terrain mapping and vehicle-road collaboration includes:

[0006] The 3D road network terrain perception module receives vehicle position, speed information, and raw point cloud data of the surrounding environment through the roadside unit and the vehicle-mounted unit, assigns slope and curvature coordinates to each road segment in the road network, and generates a vehicle-terrain correlation topology map;

[0007] The emergency evacuation priority decision module, upon receiving the emergency event signal, extracts the slope and direction attributes of the affected vehicles around the incident point based on the vehicle-terrain association topology map, obtains a vehicle evacuation energy efficiency potential sequence, and obtains a hierarchical terrain evacuation instruction set based on the vehicle evacuation energy efficiency potential sequence;

[0008] The traffic flow tidal restoration module, after the event is handled, retrieves the highest priority vehicle queue information in the hierarchical terrain evacuation instruction set, establishes a first-order green wave signal timing table, uses the first-order green wave signal timing table as the scheduling basis, sequentially sets the passage windows of subsequent vehicle flows and the opening and closing order of traffic signal groups, and generates a tidal traffic signal restoration plan;

[0009] The ground-driving matching collaborative warning module calls the tidal restoration traffic signal scheme and the vehicle-terrain association topology map to calculate the instantaneous ground-driving matching score, compares the instantaneous ground-driving matching score with a preset threshold, and selects the corresponding prompt or alarm category to generate energy efficiency and safety collaborative warning information.

[0010] Preferably, the steps of obtaining the vehicle-terrain association topology map are:

[0011] The current position coordinates, real-time driving speed value, and original point cloud data of the surrounding environment of the vehicle are collected through the roadside unit and the on-board unit. The coordinates of each point in the three-dimensional space of the road section are parsed based on the original point cloud data. The ratio of the height difference between the coordinates of adjacent points on each road section to the horizontal projection length is calculated to obtain the slope value of each road section. At the same time, the curvature radius of the three-dimensional coordinate point set of each road section is calculated to obtain the slope and curvature coordinates of each road section in the road network;

[0012] Based on the slope and curvature coordinates of each road section in the road network, the current vehicle position coordinate information is extracted to determine the road section where each vehicle position coordinate is located. The vehicle's real-time driving speed value is associated with the slope and curvature coordinates of the road section where it is located, and the corresponding relationship between road sections and vehicles is established one by one, forming a set of corresponding relationships between vehicles and road sections;

[0013] Based on the correspondence set between the vehicles and road sections, and taking the spatial connection relationship between road sections as the topological basis, all vehicle information on each road section is traversed and aggregated, and an association relationship structure between road sections and between vehicles on road sections is established to form a vehicle-terrain association topology map.

[0014] Preferably, the steps for obtaining the vehicle evacuation energy efficiency potential sequence are:

[0015] Based on the vehicle-terrain association topology map, the coordinate information of the incident point is extracted, a spatial influence radius centered on the incident point is set, all vehicle numbers within the influence radius are screened, the driving direction angle, the slope angle value of the road section where the corresponding vehicle is located, and the slope direction angle are retrieved one by one, and the extracted results are recorded with the vehicle number as the index to generate a set of slope angle values, driving direction angles, and slope direction angles of the affected vehicles;

[0016] According to the slope angle value, driving direction angle and slope orientation angle of the affected vehicles, the evacuation energy efficiency potential value of each affected vehicle is calculated to obtain the vehicle evacuation energy efficiency potential sequence.

[0017] Preferably, the steps for obtaining the hierarchical terrain evacuation instruction set are:

[0018] Based on the evacuation energy efficiency potential value in the vehicle evacuation energy efficiency potential sequence, a comparison is performed with a preset starting energy consumption baseline value. If the evacuation energy efficiency potential value is greater than the baseline value, the vehicle is marked as the highest priority; if it is equal to the baseline value, the vehicle is marked as waiting in place; if it is lower than the baseline value, the vehicle is marked as being directed to a secondary road, and a hierarchical terrain evacuation instruction set is generated.

[0019] Preferably, the steps of obtaining the first-order green wave signal timing table are:

[0020] After the incident is handled, the hierarchical terrain evacuation instruction set is called to extract the vehicle queue information marked as the highest priority, retrieve the real-time position coordinates and real-time speed values ​​of each vehicle in the vehicle queue one by one, calculate the travel time values ​​required for each vehicle to continuously pass through each intersection upstream and downstream of the preset path, and arrange the vehicles in the order in which they actually pass, forming a sequence of travel time values ​​required for the highest priority vehicle queue to pass through the intersection;

[0021] Based on the numerical sequence of travel time required for the highest priority vehicle queue to pass through the intersection, the travel time values ​​of each vehicle passing through multiple consecutive downstream intersections are accumulated one by one, and the continuous green light duration required for all highest priority vehicles to completely pass through all intersections is determined. The continuous green light duration is allocated to the corresponding intersection traffic signal light group, and the spatial distance between each intersection and the real-time speed value of the vehicle are combined to calculate and adjust the time when the green light at each intersection is turned on and off to generate the first-order green wave signal timing table.

[0022] Preferably, the steps for obtaining the tidal traffic signal restoration solution are:

[0023] Based on the first-order green wave signal timing table, the passage time window of the subsequent vehicle queue is set, and the green light opening and closing time of the traffic light group determined by the first-order green wave signal timing table is used to calculate the passage interval and release order of the subsequent vehicle queue at each intersection, and the opening and closing order of the traffic light group is adjusted according to the estimated time of the vehicle queue arriving at each intersection to form a tidal traffic signal restoration plan.

[0024] Preferably, the step of obtaining the instantaneous ground-driving matching score is:

[0025] The tidal traffic signal restoration scheme and the vehicle-terrain association topology map are called to extract the current speed value, current acceleration value, and current gear mode of a single vehicle, retrieve the economic speed interval mean, economic speed interval standard deviation, optimal acceleration value, and acceleration tolerance standard deviation of the road section where the vehicle is located, and read the slope angle value and curvature radius value of the road section to form an economic benchmark parameter set of the vehicle driving state and road terrain;

[0026] Based on the vehicle driving state and the road terrain economy benchmark parameter set, the instantaneous road driving matching score of the vehicle is calculated.

[0027] Preferably, the steps for obtaining the energy efficiency and safety collaborative warning information are:

[0028] Based on the vehicle's instantaneous ground-driving matching score, a double judgment is performed according to the set alarm threshold and prompt threshold. If the instantaneous ground-driving matching score is less than the alarm threshold, a red alarm prompt message is generated. If the instantaneous ground-driving matching score is between the alarm threshold and the prompt threshold, an orange prompt reminder is generated. If the instantaneous ground-driving matching score is higher than the prompt threshold, no warning prompt is output, forming an energy efficiency and safety collaborative warning information.

[0029] Compared with the prior art, the advantages and positive effects of the present invention are:

[0030] In the present invention, by integrating the spatial geometric description of vehicle position and speed information with the point cloud, the quantitative assignment of road slope and curvature coordinates is achieved, and a mapping relationship is established between terrain elements and vehicle operating status, so that the vehicle path and terrain attributes are synchronized. In emergency response, the slope angle and driving direction angle of the affected vehicle are used to construct an energy consumption priority sequence. Starting from the motion relationship between the vehicle and the road, the evacuation path selection under the energy efficiency dimension is completed. In the traffic recovery phase, the traffic window is bound to the traffic light timing, and a continuous green light coverage structure is established with the highest priority vehicle queue as a reference, achieving dynamic scheduling with high traffic efficiency and low waiting energy consumption. In the vehicle operating status identification, by comparing the speed, acceleration with the target economic speed range and the optimal acceleration and deceleration curve in real time, a single-vehicle matching scoring mechanism is formed, which improves the accuracy of the judgment of terrain-driving behavior consistency. By coupling terrain constraints with vehicle-road behavior, a continuous link from identification, evacuation, dispatch to early warning is established, achieving improved accuracy of evacuation priority, enhanced continuity of traffic restoration rhythm, and energy-efficient collaborative monitoring between driving behavior and road characteristics. This improves the energy efficiency of emergency response, the continuity of traffic restoration, and the adaptability of single-vehicle driving behavior. BRIEF DESCRIPTION OF THE DRAWINGS

[0031] Figure 1 It is a system flow chart of the present invention. DETAILED DESCRIPTION

[0032] In order to make the purpose, technical solutions and advantages of the present invention more clearly understood, the present invention will be further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not intended to limit the present invention.

[0033] See also Figure 1 The present invention provides a technical solution: a traffic energy efficiency control system integrating three-dimensional terrain mapping and vehicle-road collaboration, including:

[0034] The 3D road network terrain perception module receives vehicle position, speed information, and raw point cloud data of the surrounding environment through the roadside unit and the vehicle-mounted unit, assigns slope and curvature coordinates to each road segment in the road network, and generates a vehicle-terrain correlation topology map;

[0035] The emergency evacuation priority decision module, upon receiving an emergency signal, extracts the slope and direction attributes of the affected vehicles around the incident point based on the vehicle-terrain association topology map, obtains the vehicle evacuation energy efficiency potential sequence, and obtains the hierarchical terrain evacuation instruction set based on the vehicle evacuation energy efficiency potential sequence;

[0036] The traffic flow tidal recovery module, after the incident is handled, retrieves the highest-priority vehicle queue information from the hierarchical terrain evacuation instruction set, establishes a first-order green wave signal timing table, uses the first-order green wave signal timing table as the scheduling basis, sequentially sets the passage windows for subsequent traffic flows and the opening and closing order of traffic signal groups, and generates a tidal traffic signal recovery plan;

[0037] The ground-driving matching collaborative warning module calls the tidal restoration traffic signal scheme and the vehicle-terrain correlation topology map to calculate the instantaneous ground-driving matching score, compares the instantaneous ground-driving matching score with the preset threshold, and selects the corresponding prompt or alarm category to generate energy efficiency and safety collaborative warning information.

[0038] The steps for obtaining the vehicle terrain association topology map are as follows:

[0039] The roadside unit and the onboard unit collect the vehicle's current position coordinates, real-time driving speed value, and raw point cloud data of the surrounding environment. Based on the raw point cloud data, the coordinates of each point in the three-dimensional space of the road section are parsed. The ratio of the height difference between the coordinates of adjacent points on each section to the horizontal projection length is calculated to obtain the slope value of each section. At the same time, the curvature radius of the three-dimensional coordinate point set of each section is calculated to obtain the slope and curvature coordinates of each section in the road network.

[0040] Based on the slope and curvature coordinates of each road section in the road network, the current vehicle position coordinate information is extracted to determine the road section where each vehicle position coordinate is located. The vehicle's real-time driving speed value is associated with the slope and curvature coordinates of the road section where it is located, and the corresponding relationship between road sections and vehicles is established one by one, forming a set of corresponding relationships between vehicles and road sections;

[0041] Based on the corresponding relationship set between vehicles and road sections, and taking the spatial connection relationship between road sections as the topological basis, all vehicle information on each road section is traversed and aggregated, and the association relationship structure between road sections and between vehicles on the road sections is established to form a vehicle-terrain association topology map.

[0042] Specifically, based on the original point cloud data, the point cloud preprocessing process is first started to filter out irrelevant data points. This process uses a voxel-based downsampling method to divide the original point cloud data into a three-dimensional grid of a preset size, such as a cube with a side length of 0.2 meters. The center of mass of all points in each cube voxel is calculated and the original point group is replaced by the center of mass. Then, a statistical outlier removal algorithm is applied to calculate the average distance of each point to a specified number of neighboring points, and the number of neighboring points is set to 50. If the average distance of a point is greater than 1.5 times the sum of the average distance of all data points and the standard deviation, the point is identified as an outlier and removed. Then, in order to accurately separate the road surface from the preprocessed point cloud, a random sampling consensus algorithm (R ANSAC) is used for plane segmentation. The algorithm iteratively randomly selects 3 points to form a candidate plane and calculates the vertical distance from all other points to the plane. Points with distances less than a preset distance threshold are defined as inliers. The distance threshold here is set according to the measurement accuracy of the roadside lidar sensor. For example, for a sensor with an accuracy of 2 cm, the threshold is set to 5 cm. After completing the preset number of iterations, for example 1000 times, the plane containing the largest number of inliers is identified as the road surface, and the coordinates of all 3D points constituting the road surface are extracted. Then, in order to calculate the slope and curvature, the extracted road surface point set is divided into continuous road segments with a length of 10 meters along the centerline of the road. For each segment, its starting point coordinates are selected. and the end point coordinates , by calculating the elevation difference between two points And horizontal projection distance , get the slope value of the road section, that is, the slope ratio is At the same time, in order to calculate the curvature radius of each road section, on the horizontal projection of each road section, three non-collinear two-dimensional coordinate points of the starting point, midpoint and end point are selected from its point set, and the radius of the circumscribed circle is calculated based on these three points as the curvature radius of the road section. The calculation process is to first calculate the three side lengths using the coordinates of the three points, and then calculate the area of ​​the triangle formed by the three points using the Heron formula. Finally, according to the circumscribed circle radius formula The radius of curvature is calculated as follows: 、 、 are the lengths of the three sides of the triangle, The area of ​​the triangle is obtained by storing the identifier of each road section and its corresponding slope value and curvature radius value to obtain the slope and curvature coordinates of each road section in the road network.

[0043] Based on the slope and curvature coordinates of each road section in the road network, the system retrieves the GPS position coordinate information uploaded by each vehicle through the on-board unit. In order to accurately match the vehicle position coordinates to the corresponding road section, a circular search area with a radius of 50 meters is first set with the GPS coordinates of the vehicle as the center, and all candidate road sections whose geometric centers fall within this area are screened. Subsequently, the matching score of each candidate road section is calculated to determine the best matching road section. The matching score calculation combines the two dimensions of distance and direction. The specific calculation formula is: ,in, is the final matching score, is the distance points, For direction, and They are distance weight and direction weight respectively. The setting of weight value is based on experience calibration, giving priority to the accuracy of distance. For example, setting distance weight is 0.7, direction weight is 0.3, and the sum of the two is 1, the distance is The direction is obtained by calculating the minimum vertical distance from the vehicle GPS point to the center line of the candidate road section and normalizing it. The cosine value of the angle between the instantaneous driving direction vector of the vehicle and the direction vector of the candidate road segment is obtained by calculating the vehicle's driving direction from its two consecutive GPS positioning points. The candidate road section is determined to be the road section where the vehicle is currently located. After determining the road section to which the vehicle belongs, the corresponding slope value and curvature radius value are immediately retrieved from the slope and curvature coordinate set generated in the previous step based on the unique identifier of the road section. The vehicle's real-time driving speed value is structurally associated with the retrieved slope value and curvature radius value to form a data record containing the vehicle's unique number, the unique number of the road section it is located in, the real-time speed, the road section slope, and the road section curvature radius. This process is performed synchronously for all vehicles in the network, and all generated real-time data records are collected and processed to form a set of corresponding relationships between vehicles and road sections.

[0044] Based on the correspondence set between vehicles and road sections, the system first loads the pre-built digital road network map, which is stored in the form of a directed graph, where the nodes of the graph represent intersections, and the directed edges represent road sections with unique identifiers. The attributes of the edges record their starting and ending intersections, thereby clearly defining the spatial connection relationship between the road sections. Next, the system initializes a data structure for carrying the final topological graph. This structure is also a graph, but its nodes directly represent road sections, not intersections. The system traverses each edge (i.e., each road section) in the loaded digital road network map and adds these road sections as nodes of the new graph. At the same time, the static geographic attributes of the road section, i.e., the slope value and curvature radius value calculated in the previous step, are stored as the basic attributes of the node. Subsequently, the system traverses the digital road network map again and establishes directed edges between the corresponding road section nodes in the new map based on the connection information at its intersections. If in the digital road network map, the road section A If the ending intersection is the same as the starting intersection of section B, a directed edge is created in the new graph from the node representing section A to the node representing section B. After completing the topological relationship between sections, the system begins to aggregate vehicle information. Specifically, the system traverses each record in the corresponding relationship set between vehicles and sections, and according to the unique section number in the record, adds the vehicle information represented by the record (including vehicle number, real-time speed, etc.) to the dynamic attribute list of the corresponding section node in the new graph. When more than one vehicle information is aggregated on a section node, the system will sort each vehicle according to its longitudinal position along the section, and establish the physical arrangement order of the vehicles on the section, that is, the relationship between the leading vehicle and the following vehicle. This sorting relationship is recorded in the dynamic attributes of the node as the association relationship between vehicles. Through the above traversal and aggregation, a comprehensive data structure is finally formed, that is, the vehicle terrain association topology map.

[0045] The steps for obtaining the vehicle evacuation energy efficiency potential sequence are as follows:

[0046] Based on the vehicle-terrain correlation topology map, the coordinate information of the incident point is extracted. A spatial influence radius centered on the incident point is set. All vehicle numbers within the influence radius are screened. The driving direction angle, road section slope angle value, and slope orientation angle of the corresponding vehicle are retrieved one by one. The extracted results are recorded with the vehicle number as the index to generate a set of slope angle values, driving direction angles, and slope orientation angles of the affected vehicles.

[0047] According to the slope angle value, driving direction angle and slope orientation angle of the affected vehicles, the evacuation energy efficiency potential value of each affected vehicle is calculated to obtain the vehicle evacuation energy efficiency potential sequence. The calculation formula is:

[0048] ;

[0049] in, After the modification The evacuation energy efficiency potential of the affected vehicles, is the terrain sensitivity coefficient, For the The slope angle value of the road section where the affected vehicles are located, For the The driving direction angles of the affected vehicles, For the The slope angle of the road section where the affected vehicles are located, and These are the mapping factors that represent the effects of slope direction and driving direction on efficiency in three-dimensional space.

[0050] Specifically, based on the vehicle terrain association topology map, the emergency event signal transmitted through the standardized event data exchange protocol is first received from the emergency response center. The signal contains the precise geographic coordinates of the incident point, such as longitude 116.397128 degrees and latitude 39.916527 degrees, and the event type code. For example, code "01" represents a traffic accident, and code "02" represents road construction. The system selects the corresponding spatial impact radius setting strategy from the preset rule library according to the event type code. The strategy library is constructed based on the statistical analysis of the impact range of similar historical events. For example, for traffic accidents during peak hours on urban main roads, the radius is set to 500 meters, and for vehicle failures on non-main roads at night, the radius is set to 200 meters. After setting the spatial impact radius, the system uses the coordinates of the incident point as the center of the circle and performs a spatial neighborhood query on the vehicle terrain association topology map with a determined radius length, traversing all vehicle nodes in the topology map, and calculating the real-time position coordinates of each vehicle node and The spherical distance between the coordinates of the incident point is used to identify all vehicles within a set radius as affected vehicles and extract their unique vehicle numbers. Next, for each filtered vehicle number, the system re-queries the vehicle-terrain topology map, accessing the road segment node associated with the vehicle and its dynamic vehicle attributes. Three key parameters are extracted from these data: the vehicle's heading angle, which is calculated using the vehicle's electronic compass or from continuous GPS positioning points, measured clockwise with north as 0 degrees. The second is the slope angle of the road segment on which the vehicle is located. This is a static attribute of the road segment node generated during the initial 3D road network modeling. The third is the slope angle of the road segment, also a static attribute of the road segment node, representing the angle between the steepest slope and true north. The system binds these three extracted parameters to the vehicle number for each affected vehicle to form a data record. All records are then aggregated to generate a set of slope angle values, heading angles, and slope angles for each affected vehicle.

[0051] formula: The benefit of the formula is that it not only quantifies the instantaneous impact of terrain on vehicle evacuation efficiency in the form of an exponential function, but also amplifies the difference in this impact, which can more clearly identify the vehicle with the highest evacuation efficiency. Specifically, by introducing the terrain sensitivity coefficient , which allows the model to be fine-tuned for different types of vehicles (such as heavy trucks that are more sensitive to slopes), and The term directly introduces the steepness of the slope into the calculation. The greater the slope, the more significant its effect. The term describes the relative relationship between the vehicle's direction of travel and the slope. When the vehicle is traveling downhill, the term is positive and the energy efficiency potential value is greater than 1, indicating that the vehicle can utilize gravitational potential energy and has high evacuation efficiency. When the vehicle is traveling uphill, the term is negative and the energy efficiency potential value is less than 1, indicating that the vehicle needs to overcome gravity and has low evacuation efficiency. This design upgrades the assessment of evacuation potential from a two-dimensional plane to a three-dimensional space, improving the scientific nature and energy efficiency of evacuation priority decisions.

[0052] Terrain sensitivity coefficient The steps for obtaining are as follows: this coefficient is used to adjust the degree of influence of terrain factors on the energy efficiency potential evaluation of different vehicles. Its value is not fixed, but is set differently according to the type of vehicle. The acquisition process first classifies the vehicles, for example, they are divided into three categories: small passenger cars, medium-sized buses, and heavy trucks. For each type of vehicle, a calibration experiment is carried out at a dedicated vehicle test site, which contains multiple test sections with precisely measured slopes (for example, from 2% to 10%) and known slope orientation angles. During the experiment, the test vehicle is controlled to pass through these sections at different initial speeds, and its acceleration or deceleration without additional driving force or braking force, as well as the instantaneous fuel consumption data of the engine are recorded. By collecting a large amount of such experimental data, a relationship model between acceleration, fuel consumption, slope, and direction angle is established, and the least squares method is used to fit and solve the best match for the experimental data. For example, after performing regression analysis on 100 test data of heavy trucks on different slopes, the corresponding The value should be set to 2.8. For small passenger cars, the slope is relatively less affected. The value can be set to 1.2.

[0053] No. The slope angle value of the road section where the affected vehicles are located The acquisition step is to obtain the parameter directly from the vehicle terrain association topology map constructed in the previous step. In the initial stage of building the topology map, the original point cloud data collected by roadside laser radar and other equipment is used to generate a high-precision three-dimensional digital road network model. For each standard road segment in the road network (for example, the length is 10 meters), the system will extract the three-dimensional coordinates of its starting point and end point, which are recorded as and , the slope angle value of the road section It is calculated by inverse trigonometric function, and the calculation formula is: The calculation result is stored in degrees as the static attribute of the node of the road section. When the energy efficiency potential of a vehicle is measured, the system reads the pre-stored slope angle value directly from the corresponding road section node in the topology map according to the road section number matched to the real-time position of the vehicle. For example, if the system determines that vehicle j is located on the road section numbered RS-087, it directly retrieves the attributes of the road section and obtains that its slope angle value is 3.5 degrees.

[0054] No. Driving direction angles of affected vehicles The acquisition steps are as follows: this parameter is collected and uploaded in real time by the onboard unit (OBU). There are two main ways to obtain it. The first is to directly read the output value of the built-in high-precision electronic compass on the vehicle. The compass is calibrated to provide the angle between the vehicle's current direction and the geographic north direction. The second way is to calculate through continuous GPS positioning information when there is no electronic compass or its data is unavailable. The system obtains the GPS coordinates of the vehicle at a frequency of 1 Hz. For any two consecutive positioning points and , its driving direction angle It can be calculated by spherical trigonometry, and the calculation formula is: The calculation result is converted into an angle value between 0 and 360 degrees. For example, the current driving direction angle of vehicle j reported by the on-board unit is 60 degrees (northeast by east).

[0055] No. The slope angle of the road section where the affected vehicles are located The steps to obtain the parameter are as follows: This parameter is the geographical angle describing the steepest direction of the slope, which is different from the slope angle value. Similarly, it is the static data of the node attributes of the road section calculated and stored when constructing the vehicle terrain association topology map. Its calculation is based on the plane fitting of the original point cloud data of the road section. After determining the best fitting plane representing the road section, the normal vector of the plane is Also calculated, the slope angle This is the projection vector of the normal vector on the horizontal plane The angle between the axis and the true north direction (usually defined as the positive direction of the Y axis) is calculated as follows: ,in It is a two-parameter inverse tangent function that can correctly handle angles in all quadrants. The calculation result is also converted into an angle value between 0 and 360 degrees. For example, the system reads the pre-stored slope heading angle of 30 degrees (30 degrees east of north) from the topological map node of the road section where vehicle j is located.

[0056] Calculation process:

[0057] Taking the j-th heavy truck in the affected fleet as an example, according to the above parameter acquisition steps, a set of specific parameter values ​​are obtained:

[0058] Obtain the terrain sensitivity coefficient of this type of vehicle from the calibration experiment is 2.8.

[0059] The slope angle value of the road section where vehicle j is located is obtained from the vehicle terrain association topology map. It is 3.5 degrees.

[0060] Obtain the driving direction angle of vehicle j from the on-board unit in real time It is 60 degrees.

[0061] From the vehicle terrain association topology map, we can find out the slope direction angle of the road section where vehicle j is located. It is 30 degrees.

[0062] Substitute the above values ​​into the calculation formula of evacuation energy efficiency potential:

[0063] ;

[0064] The calculation process is as follows:

[0065] First calculate the value of the trigonometric function:

[0066] ;

[0067] ;

[0068] Next, calculate the exponential value:

[0069] ;

[0070] Finally, evaluate the exponential function:

[0071] ;

[0072] The results show that the evacuation energy efficiency potential value of the j-th vehicle is 1.1593. Since this value is greater than 1, it means that the current terrain and driving direction combination of the vehicle is favorable for its evacuation. The vehicle can use part of the gravitational potential energy to accelerate or maintain speed. Compared with driving on flat ground, its energy consumption is lower and it is easier to start and move. After the evacuation energy efficiency potential values ​​of all affected vehicles are calculated, they will be arranged in order of vehicle numbers to form a vehicle evacuation energy efficiency potential sequence.

[0073] The steps for obtaining the hierarchical terrain evacuation instruction set are:

[0074] Based on the evacuation energy efficiency potential value in the vehicle evacuation energy efficiency potential sequence, it is compared with the preset startup energy consumption baseline value. If the evacuation energy efficiency potential value is greater than the baseline value, the vehicle is marked as the highest priority; if it is equal to the baseline value, it is marked as waiting in place; if it is lower than the baseline value, it is marked as being directed to the auxiliary road, and a hierarchical terrain evacuation instruction set is generated.

[0075] Specifically, based on the evacuation energy efficiency potential value in the vehicle evacuation energy efficiency potential sequence, the system initiates a hierarchical decision-making process. The core of this process is to compare it with a dynamically set starting energy consumption benchmark value. This benchmark value is not a fixed constant, but is dynamically adjusted according to the severity of the emergency event and the current road network congestion situation. Its benchmark core value is 1.0, representing the energy consumption level of driving on a flat and flat road surface. The system first obtains the severity rating of the event from the emergency response center (for example, level 1 is the highest and level 3 is the lowest) and the surrounding road network congestion index obtained from the traffic data center (a value between 0 and 1, 1 indicates extreme congestion). The calculation formula for the starting energy consumption benchmark value is: benchmark value = 1.0 + *(1-severity rating / 3) + * Congestion Index, where and are weight coefficients. For example, they are set to 0.1 and 0.2 respectively. The weights are calibrated by traffic engineering experts based on a large number of urban emergency evacuation simulation results. They aim to balance the urgency of the event and the feasibility of evacuation. For example, for a level 1 serious event (rated 1) with a congestion index of 0.8, the benchmark value is 1.0 + 0.1 * (1-1 / 3) + 0.2 * 0.8 ≈ After determining the starting energy consumption baseline for this event, the system traverses each value in the vehicle evacuation energy efficiency potential sequence and performs a three-category judgment on each vehicle. If the vehicle's evacuation energy efficiency potential value is greater than the calculated baseline value (for example, if a vehicle's potential value is 1.3, which is greater than 1.227), the vehicle is marked as the highest priority, indicating that it can start and depart with extremely high efficiency. If the vehicle's evacuation energy efficiency potential value is significantly lower than the neutral value of 1.0 (for example, lower than a preset low-efficiency threshold of 0.85 (this threshold indicates that the vehicle needs to overcome significant gravity resistance and has difficulty starting), it is marked as directed to a secondary road, and the system will plan a temporary route with lower energy consumption for it. Vehicles with evacuation energy efficiency potential values ​​between the low-efficiency threshold of 0.85 and the starting energy consumption baseline of 1.227 are marked as waiting in place. The system summarizes the numbers of all vehicles and their corresponding priority tags (highest priority, waiting in place, directed to secondary road) to generate a hierarchical terrain evacuation instruction set.

[0076] The steps for obtaining the first sequence green wave signal timing table are:

[0077] After the incident is handled, the hierarchical terrain evacuation instruction set is called to extract the vehicle queue information marked as the highest priority. The real-time position coordinates and real-time speed values ​​of each vehicle in the vehicle queue are retrieved one by one. The travel time required for each vehicle to continuously pass through the intersections upstream and downstream of the preset path is calculated. The vehicles are arranged in the order in which they actually pass, forming a sequence of travel time values ​​required for the highest priority vehicle queue to pass through the intersection.

[0078] Based on the numerical sequence of travel time required for the highest priority vehicle queue to pass through the intersection, the travel time values ​​of each vehicle passing through multiple consecutive downstream intersections are accumulated one by one to determine the continuous green light duration required for all highest priority vehicles to completely pass through all intersections. This continuous green light duration is allocated to the corresponding intersection traffic signal light group. Combined with the spatial distance between each intersection and the real-time speed value of the vehicle, the time when the green light is turned on and off at each intersection is calculated and adjusted to generate the first-order green wave signal timing table.

[0079] Specifically, after the incident is handled, the hierarchical terrain evacuation instruction set is called. First, all vehicle numbers marked as "highest priority" are filtered out to form an initial queue. Then, a unified preset path is determined for this queue. The path is calculated using the A* pathfinding algorithm on the vehicle terrain association topology map. The algorithm's cost function comprehensively considers three factors: shortest distance, highest road grade, and least number of turns, with weights set to 0.5, 0.3, and 0.2, respectively. The starting point is the geometric center of the positions of all vehicles in the queue, and the end point is the nearest safe exit from the affected area. After determining the preset path, the system determines the actual order of passage in the queue based on the projected position of each vehicle on the path, with the vehicle closest to the starting point of the path being at the front. Subsequently, the system retrieves the real-time position coordinates and real-time speed values ​​of each vehicle in the queue one by one at a frequency of 1 Hz, and combines the geometric information of each intersection on the path extracted from the digital road network map (such as stop line coordinates, intersection width) to calculate the preset path for each vehicle. The time required to travel through each intersection is composed of two parts. The first part is the time required to travel from the current position of the vehicle to the stop line of the next intersection, which is obtained by dividing the path distance between the two points by the current real-time speed of the vehicle. The second part is the time taken to pass through the intersection itself, which is set to a standard value, namely (intersection width + standard vehicle length) / intersection speed limit, where the standard vehicle length is 5 meters. For example, for the first vehicle in the queue, it is 500 meters away from the next 30-meter-wide intersection and its current speed is If the speed limit is 15 m / s and the speed limit at the intersection is 10 m / s, then the travel time is 500 / 15 + (30 + 5) / 10 = 33.3 + 3.5 = 36.8 seconds. This calculation is performed for each vehicle in the queue and each intersection on the path, and the calculation results are associated with the vehicle number and the intersection number. Finally, the travel time values ​​of all vehicles at all intersections are arranged according to the determined vehicle travel order, forming a sequence of travel time values ​​required for the highest priority vehicle queue to pass through the intersection.

[0080] Based on the numerical sequence of travel time required for the highest priority vehicle queue to pass through the intersection, the system begins to plan green light plans for each intersection along the route. First, for the first intersection on the preset route, the system extracts the travel time value of the first vehicle in the queue arriving at the intersection, as well as the travel time value of the last vehicle arriving at the intersection. The first time point is used as the recommended turning on time of the green light at the intersection, and the arrival time of the last vehicle plus a fixed vehicle clearing time (for example, the queue length is calculated based on the standard of 2 seconds per vehicle and multiplied by this time) is used as the recommended turning off time of the green light. In this way, the continuous green light duration required for the intersection is determined. For example, if the first vehicle arrives at the 10th second and the last vehicle (a total of 10 vehicles in the queue) arrives at the 25th second, the clearing time is 20 seconds, the recommended turning off time of the green light is the 45th second, and the continuous green light duration is 35 seconds. Then, the system assigns this calculated continuous green light duration to the corresponding traffic light group at the intersection, and then, the system The system processes consecutive intersections downstream and determines the phase difference for green wave coordination by calculating the spatial distance between intersections and the average speed of the queue (taking the arithmetic mean of the real-time speeds of all vehicles in the queue). This is the calculation formula: Phase difference = distance between intersections / average speed of the queue. For example, if the first and second intersections are 450 meters apart and the average speed of the queue is 15 meters per second, the phase difference is 30 seconds. Therefore, the green light on time of the second intersection should be set to 30 seconds after the green light on time of the first intersection. The green light duration is recalculated based on the time distribution of vehicles in the queue passing through the intersection, but it must not be shorter than the green light duration of the upstream intersection. In this way, the system calculates and adjusts the green light on and off times of all intersections on the preset path one by one, organizing each intersection's identifier, green light on absolute time, and green light off absolute time into a structured list to generate a first-order green wave signal timing table.

[0081] The steps to obtain the tidal restoration traffic signal solution are:

[0082] Based on the first-order green wave signal timing table, the passage time window for subsequent vehicle queues is set. According to the green light opening and closing times of the traffic light group determined by the first-order green wave signal timing table, the passage interval and release order of the subsequent vehicle queues are calculated for each intersection, and the opening and closing order of the traffic light group is adjusted according to the estimated time of arrival of the vehicle queue at each intersection, forming a tidal traffic signal restoration plan.

[0083] Specifically, based on the first-order green wave signal timing table, the system begins to dispatch the remaining affected vehicles, that is, the vehicles marked as "waiting in place" and "guided to the auxiliary road" in the graded terrain evacuation instruction set. First, the system sets a passage time window for these subsequent vehicle queues. The starting time of the window is defined as the time point when the last vehicle in the highest priority queue in the first-order green wave signal timing table completely passes through a certain intersection, plus a safety interval. The safety interval is dynamically set according to the size of the intersection and the complexity of the traffic. For example, it is set to 5 seconds for standard intersections and 10 seconds for complex multi-directional intersections. Within the passage time window, the system starts calculating the passage intervals and release order of the subsequent vehicle queues according to the green light on and off time determined for the first intersection in the first-order green wave signal timing table. The principle for determining the release order is to give priority to releasing vehicles in the "waiting in place" queue on the main road, followed by those in the The system then estimates the estimated time it will take for the leading vehicle in the subsequent queue to arrive at each downstream intersection based on its real-time position, and dynamically adjusts the order of traffic lights at the downstream intersections. The specific adjustment method is to scale the green light duration set for the highest priority queue in the original first-order green wave signal timing table according to the length of the subsequent queue. For example, if the subsequent queue has only 5 vehicles and the highest priority queue has 10 vehicles, the green light duration can be shortened by about half, while retaining the phase difference determined by the intersection spacing and speed, thus forming a continuous, smaller-scale "secondary green wave". By sequentially creating such windows and secondary green waves for subsequent traffic flows of different directions and priorities, a tidal traffic signal restoration solution is formed.

[0084] The steps to obtain the instantaneous ground-driving matching score are as follows:

[0085] The tidal traffic signal restoration scheme and the vehicle-terrain correlation topology are called to extract the current speed value, current acceleration value, and current gear mode of a single vehicle. The mean value of the economic speed interval, the standard deviation of the economic speed interval, the optimal acceleration value, and the standard deviation of the acceleration tolerance of the road section where the vehicle is located are retrieved. The slope angle value and curvature radius value of the road section are also read to form an economic benchmark parameter set of the vehicle driving status and road terrain.

[0086] Based on the vehicle's driving state and the road terrain economic benchmark parameter set, the vehicle's instantaneous ground-driving matching score is calculated using the following formula:

[0087] ;

[0088] in, For the The instantaneous ground-driving matching score of each vehicle, For the The current speed value of the vehicle, For the The average economic speed range of the road section where the vehicle is located, For the The standard deviation of the economic speed range of the road section where the vehicle is located, For the The current acceleration value of the vehicle, For the The optimal acceleration value of the road section where each vehicle is located, For the The standard deviation of the acceleration tolerance of the road section where each vehicle is located, is the dynamic speed deviation weight based on the mean of the economic speed range, is the dynamic acceleration deviation weight of the remaining items, The medium speed threshold for scene switching, is the switching slope parameter.

[0089] Specifically, the tidal restoration traffic signal scheme and the vehicle-terrain correlation topology map are called, and the system starts a real-time parameter extraction process for each vehicle in the road network. First, it communicates with the vehicle's controller area network (CAN) bus through the on-board unit (OBU) to read and parse the vehicle's current speed value and current acceleration value at a frequency of 10 Hz. At the same time, the current gear mode information is decoded from the CAN bus message. For example, for automatic transmission vehicles, it is parsed into modes such as P, R, N, and D. For manual or manual-automatic vehicles, it is parsed into specific gear numbers. Then, the system uses the vehicle's real-time GPS coordinates to quickly locate it in the vehicle-terrain correlation topology map, and accurately matches it to the unique road section number where the vehicle is currently located. Once the match is successful, the pre-calculated and stored slope angle value and curvature radius value are immediately read from the static attributes of the section node. At the same time, The system retrieves a pre-built road terrain economic benchmark parameter library, which is established based on the statistical analysis of long-term driving data of a large number of different types of vehicles (such as cars, SUVs, and trucks) on various typical roads (divided by slope, curvature, and speed limit). For each "road section type-vehicle type" combination in the library, a set of economic benchmark parameters is stored. The system uses the current vehicle type (obtained from the vehicle registration information) and the type of the road section (defined by the slope angle value and the curvature radius value) to query this library to obtain the corresponding economic speed range mean, economic speed range standard deviation, optimal acceleration value, and acceleration tolerance standard deviation. For example, for a medium-sized SUV traveling on an urban expressway with a slope of 2 degrees and a curvature radius of 500 meters, the system retrieves its economic speed range mean of 75 kilometers per hour, the standard deviation of 8 kilometers per hour, and the optimal acceleration value of 1.2 meters per second. 2, the standard deviation of acceleration tolerance is 0.5 m / s 2 ,Finally, the current speed value, current acceleration value, and current gear mode of the ,vehicle extracted in real time, are integrated into a structured data record with the retrieved ,economic speed interval mean, economic speed interval standard deviation, optimal ,acceleration tolerance standard deviation, and the slope angle value and ,curvature radius value read from the topological map, forming a ,vehicle driving status and road terrain economic benchmark ,parameter set.

[0090] formula: The benefit of the formula is that it maps the deviation of driving behavior from the ideal state to a normalized score between 0 and 1 through an exponential function, which is intuitive and easy to compare, and introduces a dynamic weight based on economic speed. and , so that the focus of the evaluation can be adaptively adjusted according to different driving scenarios (highways, city streets). In scenarios with higher economic speeds, more attention is paid to speed stability, while in scenarios with lower economic speeds, more attention is paid to acceleration smoothness. This dynamic adjustment mechanism is far superior to the static model with fixed weights and can more accurately reflect the key factors of energy efficiency in different scenarios. Using standard deviation and Normalizing speed and acceleration deviations eliminates the dimensionality effects of differences in physical properties across road sections, making matching scores comparable across the entire road network. Ultimately, a comprehensive, instantaneous ground-to-driving matching score provides drivers with accurate, real-time energy efficiency feedback and early warnings.

[0091] No. The current speed value of the vehicle and the current acceleration value The acquisition steps are as follows: these two parameters are a direct reflection of the vehicle's instantaneous dynamics. They are captured and analyzed in real time from the vehicle's CAN bus by the onboard unit (OBU) at a high frequency (for example, 10 Hz). The current speed value is directly read from the wheel speed sensor or the transmission output shaft speed sensor signal, and the standard speed value is processed by the vehicle control unit. The current acceleration value is obtained by differential calculation of continuous speed readings. The calculation formula is: ,in is the sampling time interval (for example, 0.1 seconds). To smooth the noise, a sliding average filter is usually applied to the calculated acceleration sequence. For example, at time t, the onboard unit reads the current speed as 18.0 m / s, and the speed read 0.1 seconds ago was 17.95 m / s. The calculated current acceleration value is m / s 2 , and finally get is 18.0 m / s, 0.5 m / s2 .

[0092] No. The average economic speed range of the road section where the vehicle is located , standard deviation of economic speed range , optimal acceleration value and acceleration tolerance standard deviation The acquisition steps are as follows: these parameters together constitute the energy-efficient driving benchmark for a specific road section. They are stored in a pre-established database, which is generated by mining and analyzing large-scale actual driving data. The database construction process includes: collecting millions of kilometers of driving data covering different vehicle models and different road types (divided by slope, curvature, and speed limit). The data dimensions include speed, acceleration, fuel consumption / electricity consumption, geographical location, etc. For each "road section type-vehicle model" combination, all corresponding data points are screened out, and a curve of the relationship between energy consumption per unit distance and speed is drawn. The speed range corresponding to the lowest point of the curve is the economic speed range, and the mean and standard deviation of this range are and Similarly, by analyzing the instantaneous energy consumption changes under different accelerations, find the acceleration value with the slowest energy consumption increase rate as the optimal acceleration value. , and statistically analyze the energy consumption data distribution near it, and calculate the standard deviation of acceleration tolerance For example, for a vehicle traveling on a road with a slope of 2 degrees and a speed limit of 80 km / h , the system obtains its is 16.0 m / s, is 2.0 m / s, 0.8 m / s 2 , 0.4 m / s 2 .

[0093] Scene switching medium speed threshold and switching slope parameters The steps to obtain are, these two parameters together define the behavior of the dynamic weight function, It is the boundary that distinguishes low-speed and high-speed driving scenarios. Its setting is based on the statistical analysis of urban traffic flow data. By analyzing the average driving speed distribution under different road grades, a critical speed is selected to effectively divide urban congested road conditions from urban expressways or highways. For example, by clustering the floating vehicle data collected in a certain city for one month, it was found that the speed distribution had an obvious trough at 40 kilometers per hour (about 11.1 meters per second), so the critical speed is set to 40 kilometers per hour (about 11.1 meters per second). Set to 11.1 m / s and switch the slope parameter Determines the smoothness of the transition of weights from one scene to another. Its value is calibrated in the simulation environment through the optimization algorithm. The goal is to make the weight function In a speed range nearby (for example, ±10 km / h), it can smoothly transition from 0.1 to 0.9 to avoid weight mutation. After repeated testing and tuning, a suitable slope value is determined. For example, Set to 0.5.

[0094] Calculation process:

[0095] First Taking a vehicle as an example, according to the above parameter acquisition steps, the following set of specific values ​​are obtained:

[0096] = 18.0 m / s;

[0097] = 0.5 m / s 2 ;

[0098] = 16.0 m / s;

[0099] = 2.0 m / s;

[0100] = 0.8 m / s 2 ;

[0101] = 0.4 m / s 2 ;

[0102] = 11.1 m / s;

[0103] = 0.5;

[0104] First, calculate the dynamic weight and :

[0105] ;

[0106] ;

[0107] Then, substitute all the parameters into the instantaneous ground-to-ground matching score formula:

[0108] ;

[0109] The calculation process is as follows:

[0110] Calculate the square term within the brackets:

[0111] ;

[0112] ;

[0113] Calculate the value of the exponential part:

[0114] ;

[0115] Finally, evaluate the exponential function:

[0116] ;

[0117] The results show that the vehicle The instantaneous ground-driving matching score at this moment is 0.3809, which is a value between 0 and 1. The closer the score is to 1, the closer the driving behavior is to the most energy-saving mode under the current road conditions. Conversely, the closer it is to 0, the further the driving behavior deviates from the energy-saving mode. The current score of 0.3809 is low, indicating that there is a large room for energy-saving optimization in the driving operation of this vehicle.

[0118] The steps for obtaining energy efficiency and safety collaborative early warning information are as follows:

[0119] Based on the vehicle's instantaneous ground-driving matching score, a double judgment is performed according to the set alarm threshold and prompt threshold. If the instantaneous ground-driving matching score is less than the alarm threshold, a red alarm prompt message is generated. If the instantaneous ground-driving matching score is between the alarm threshold and the prompt threshold, an orange prompt reminder is generated. If the instantaneous ground-driving matching score is higher than the prompt threshold, no warning prompt is output, forming an energy efficiency and safety collaborative warning information.

[0120] Specifically, based on the vehicle's instantaneous road-to-driving match score, the system executes a warning information generation program. The core of this program is to judge the score based on two preset thresholds: the warning threshold and the prompt threshold. These thresholds are determined based on statistical analysis of historical instantaneous road-to-driving match score data for a large number of drivers under various road conditions. The specific setting process involves sorting millions of collected score samples and combining them with driving behavior data labeled as "excellent," "average," and "poor" by traffic safety and energy conservation experts to determine the threshold cutoff points. For example, the cutoff point in the top 20% of all scores (for example, 0.85) is set as the prompt threshold. Driving behavior above this value is considered excellent and does not require a prompt. The cutoff point in the bottom 30% of all scores (for example, 0.50) is set as the warning threshold. Driving behavior below this value is considered potentially risky or extremely uneconomical and requires a warning. These two thresholds are fixed in the system configuration. The system uses the real-time calculated instantaneous road-to-driving match score, for example, the 0. 3809. Since 0.3809 is less than the warning threshold of 0.50, the system determines that a red warning message is needed. The specific content of the warning message is not a general "dangerous driving" message. Instead, by retrospectively analyzing the score calculation process, it analyzes whether the speed or acceleration deviation contributes more to the low score, generating targeted recommendations. In this example, the squared weighted deviation of the speed item (0.9205) is much greater than that of the acceleration item (0.0447). Therefore, the system determines that the main problem lies in speed control and generates a red warning message: "Red Warning: Current speed deviates from the economic range. It is recommended to adjust the speed to around 75 km / h." If the calculated score is 0.70, since it is between the warning threshold of 0.50 and the prompt threshold of 0.85, the system will generate an orange warning message, such as "Orange Warning: Please maintain steady driving and avoid sudden acceleration." If the score is higher than 0.85, no output is triggered. Through this hierarchical, specific judgment and information generation mechanism, energy efficiency and safety coordinated early warning information is ultimately formed.

Claims

1. A traffic energy efficiency control system integrating three-dimensional terrain mapping and vehicle-road collaboration, characterized by: The system comprises: The 3D road network terrain perception module receives vehicle position, speed information, and raw point cloud data of the surrounding environment through the roadside unit and the vehicle-mounted unit, assigns slope and curvature coordinates to each road segment in the road network, and generates a vehicle-terrain correlation topology map; The emergency evacuation priority decision module, upon receiving the emergency event signal, extracts the slope and direction attributes of the affected vehicles around the incident point based on the vehicle-terrain association topology map, obtains a vehicle evacuation energy efficiency potential sequence, and obtains a hierarchical terrain evacuation instruction set based on the vehicle evacuation energy efficiency potential sequence; The traffic flow tidal restoration module, after the event is handled, retrieves the highest priority vehicle queue information in the hierarchical terrain evacuation instruction set, establishes a first-order green wave signal timing table, uses the first-order green wave signal timing table as the scheduling basis, sequentially sets the passage windows of subsequent vehicle flows and the opening and closing order of traffic signal groups, and generates a tidal traffic signal restoration plan; The ground-driving matching collaborative warning module calls the tidal restoration traffic signal scheme and the vehicle-terrain association topology map to calculate the instantaneous ground-driving matching score, compares the instantaneous ground-driving matching score with a preset threshold, and selects the corresponding prompt or alarm category to generate energy efficiency and safety collaborative warning information.

2. The traffic energy efficiency control system integrating three-dimensional terrain mapping and vehicle-road collaboration according to claim 1 is characterized in that: The steps for obtaining the vehicle terrain association topology map are: The current position coordinates, real-time driving speed value, and original point cloud data of the surrounding environment of the vehicle are collected through the roadside unit and the on-board unit. The coordinates of each point in the three-dimensional space of the road section are parsed based on the original point cloud data. The ratio of the height difference between the coordinates of adjacent points on each road section to the horizontal projection length is calculated to obtain the slope value of each road section. At the same time, the curvature radius of the three-dimensional coordinate point set of each road section is calculated to obtain the slope and curvature coordinates of each road section in the road network; Based on the slope and curvature coordinates of each road section in the road network, the current vehicle position coordinate information is extracted to determine the road section where each vehicle position coordinate is located. The vehicle's real-time driving speed value is associated with the slope and curvature coordinates of the road section where it is located, and the corresponding relationship between road sections and vehicles is established one by one, forming a set of corresponding relationships between vehicles and road sections; Based on the correspondence set between the vehicles and road sections, and taking the spatial connection relationship between road sections as the topological basis, all vehicle information on each road section is traversed and aggregated, and an association relationship structure between road sections and between vehicles on road sections is established to form a vehicle-terrain association topology map.

3. The traffic energy efficiency control system integrating three-dimensional terrain mapping and vehicle-road collaboration according to claim 1 is characterized in that: The steps for obtaining the vehicle evacuation energy efficiency potential sequence are: Based on the vehicle-terrain association topology map, the coordinate information of the incident point is extracted, a spatial influence radius centered on the incident point is set, all vehicle numbers within the influence radius are screened, the driving direction angle, the slope angle value of the road section where the corresponding vehicle is located, and the slope direction angle are retrieved one by one, and the extracted results are recorded with the vehicle number as the index to generate a set of slope angle values, driving direction angles, and slope direction angles of the affected vehicles; According to the slope angle value, driving direction angle and slope orientation angle of the affected vehicles, the evacuation energy efficiency potential value of each affected vehicle is calculated to obtain the vehicle evacuation energy efficiency potential sequence.

4. The traffic energy efficiency control system integrating three-dimensional terrain mapping and vehicle-road collaboration according to claim 1 is characterized in that: The steps for obtaining the hierarchical terrain evacuation instruction set are: Based on the evacuation energy efficiency potential value in the vehicle evacuation energy efficiency potential sequence, a comparison is performed with a preset starting energy consumption baseline value. If the evacuation energy efficiency potential value is greater than the baseline value, the vehicle is marked as the highest priority; if it is equal to the baseline value, the vehicle is marked as waiting in place; if it is lower than the baseline value, the vehicle is marked as being directed to a secondary road, and a hierarchical terrain evacuation instruction set is generated.

5. The traffic energy efficiency control system integrating three-dimensional terrain mapping and vehicle-road collaboration according to claim 1 is characterized in that: The steps for obtaining the first sequence green wave signal timing table are: After the incident is handled, the hierarchical terrain evacuation instruction set is called to extract the vehicle queue information marked as the highest priority, retrieve the real-time position coordinates and real-time speed values ​​of each vehicle in the vehicle queue one by one, calculate the travel time values ​​required for each vehicle to continuously pass through each intersection upstream and downstream of the preset path, and arrange the vehicles in the order in which they actually pass, forming a sequence of travel time values ​​required for the highest priority vehicle queue to pass through the intersection; Based on the numerical sequence of travel time required for the highest priority vehicle queue to pass through the intersection, the travel time values ​​of each vehicle passing through multiple consecutive downstream intersections are accumulated one by one, and the continuous green light duration required for all highest priority vehicles to completely pass through all intersections is determined. The continuous green light duration is allocated to the corresponding intersection traffic signal light group, and the spatial distance between each intersection and the real-time speed value of the vehicle are combined to calculate and adjust the time when the green light at each intersection is turned on and off to generate the first-order green wave signal timing table.

6. The traffic energy efficiency control system integrating three-dimensional terrain mapping and vehicle-road collaboration according to claim 1 is characterized in that: The steps for obtaining the tidal traffic signal restoration solution are as follows: Based on the first-order green wave signal timing table, the passage time window of the subsequent vehicle queue is set, and the green light opening and closing time of the traffic light group determined by the first-order green wave signal timing table is used to calculate the passage interval and release order of the subsequent vehicle queue at each intersection, and the opening and closing order of the traffic light group is adjusted according to the estimated time of the vehicle queue arriving at each intersection to form a tidal traffic signal restoration plan.

7. The traffic energy efficiency control system integrating three-dimensional terrain mapping and vehicle-road collaboration according to claim 1 is characterized in that: The steps for obtaining the instantaneous ground-to-driving matching score are as follows: The tidal traffic signal restoration scheme and the vehicle-terrain association topology map are called to extract the current speed value, current acceleration value, and current gear mode of a single vehicle, retrieve the economic speed interval mean, economic speed interval standard deviation, optimal acceleration value, and acceleration tolerance standard deviation of the road section where the vehicle is located, and read the slope angle value and curvature radius value of the road section to form an economic benchmark parameter set of the vehicle driving state and road terrain; Based on the vehicle driving state and the road terrain economy benchmark parameter set, the instantaneous road driving matching score of the vehicle is calculated.

8. The traffic energy efficiency control system integrating three-dimensional terrain mapping and vehicle-road collaboration according to claim 1 is characterized in that: The steps for obtaining the energy efficiency and safety collaborative early warning information are as follows: Based on the vehicle's instantaneous ground-driving matching score, a double judgment is performed according to the set alarm threshold and prompt threshold. If the instantaneous ground-driving matching score is less than the alarm threshold, a red alarm prompt message is generated. If the instantaneous ground-driving matching score is between the alarm threshold and the prompt threshold, an orange prompt reminder is generated. If the instantaneous ground-driving matching score is higher than the prompt threshold, no warning prompt is output, forming an energy efficiency and safety collaborative warning information.

Images