Traffic control scenario construction method for ecological road vehicle networking environment

Abstract

The present invention proposes a method for constructing a traffic control scenario for an ecological road vehicle networking environment. The method completes the construction of a traffic control scenario for animal crossing behavior in an ecological road vehicle networking environment by constructing a vehicle networking technology communication environment, laying out smart transportation infrastructure, collecting and analyzing information on animal crossing behavior, uploading data, and sending data, optimizing the timing of the networked intersection signal control platform, and adjusting the driving status of the autonomous driving vehicle. The present invention can detect animal information in real time, adjust the road status in a timely manner, ensure the safe crossing of animals on the road, avoid unnecessary collisions between vehicles and animals, and improve the traffic efficiency of the road section.

Description

Technical Field

[0001] The present invention belongs to the field of traffic control in a networking environment, and specifically is a method for constructing a traffic control scene for an ecological road vehicle networking environment. Background Art

[0002] Under the existing environment, the development of smart transportation, autonomous driving technology, vehicle-road collaborative technology, wireless communication technology, and digital twin technology is in full swing, and my country's road traffic development model is gradually tending towards intelligence, efficiency, and integration; the existing Internet of Vehicles environment can realize vehicle roadside perception and communication, autonomous driving can reach level 4, the construction of urban brain decision-making hubs in various places, the operation of intelligent parking systems, and the deduction of digital base models. All this shows that the deployment of the future Internet of Vehicles ecological environment, intelligent networking, information interaction, and the realization and implementation of highly autonomous autonomous driving are just around the corner.

[0003] With the implementation of the "dual carbon" policy, more and more cities are paying more attention to the construction and operation of ecological roads. Ecological roads are built in ecological areas where animals live in order to balance ecological protection and vehicle accessibility. They can not only meet the needs of vehicle traffic or sightseeing, but also realize the habitat activities of animals, such as animals crossing roads. In the past traffic control scenes of ecological roads, more people tend to set up warning signs to remind passing vehicles that animals are in front of them and to slow down and observe carefully. However, due to subjective factors of some drivers and objective conditions of the environment, the risk of collision between vehicles and animals is not well avoided, resulting in traffic accidents.

[0004] How to improve traffic control modes to reduce casualties and property losses; traffic control modes based on the Internet of Vehicles environment can solve the above problems. By combining the characteristics of the Internet of Vehicles environment and ecological roads, a traffic control scenario based on animal crossing behavior in the Internet of Vehicles environment on ecological roads can be constructed, which can achieve an organic connection between high-tech and green safety. Summary of the invention

[0005] The purpose of the present invention is to provide a method for constructing a traffic control scene for an ecological road vehicle networking environment, which can detect animal information in real time, adjust the road status in time, ensure the safety of animals crossing the road, avoid unnecessary collision accidents between vehicles and animals, and improve the traffic efficiency of the road section. The technical solutions adopted are as follows:

[0006] A method for constructing a traffic control scenario for an ecological road vehicle networking environment comprises the following steps:

[0007] Step 1: Building a vehicle networking communication system based on the research area; the vehicle networking communication system includes:

[0008] The autonomous driving vehicle is provided with a sensing and recognition unit, which is used to collect information about the external environment around the vehicle; it also has high-frequency and low-latency wireless communication technology, which can realize information exchange between the vehicle and the signal base station, and between vehicles;

[0009] A pair of networked intersection signal control platforms, one networked intersection signal control platform is located at an upstream intersection of the animal and vehicle interweaving area of ​​the ecological road, and the other networked intersection signal control platform is located at a downstream intersection of the animal and vehicle interweaving area of ​​the ecological road;

[0010] The animal-vehicle intersection area refers to the area on the ecological road where vehicles driving normally along the road and animals passing horizontally on the side of the road have conflicting rights of passage;

[0011] And signal base stations, which act as a medium for wireless communication, realize the communication between smart transportation infrastructure, autonomous driving vehicles, and networked intersection signal control platforms. Signal base stations are intermediate transmission tools that play the role of data uploading and data sending;

[0012] Step 2: Deploy smart transportation infrastructure in the study area;

[0013] Step 3: The smart transportation infrastructure collects information 1 and information 2;

[0014] Among them, information 1 is generated after the intelligent transportation infrastructure identifies vehicles and animals within a set area on the road, and includes autonomous driving vehicle information A and animal information A;

[0015] The autonomous driving vehicle information A includes the distance between the autonomous driving vehicle and the area where animals and vehicles intersect, the number of vehicles monitored within the set distance range from the area where animals and vehicles intersect, and the length of the vehicle queue; the animal information A includes the type of animal and the number of animals;

[0016] Information 2: Information B of autonomous driving vehicles at upstream intersections and downstream intersections monitored by the smart transportation infrastructure, including the number of vehicles, vehicle speeds, and length of vehicle queues;

[0017] The intersection refers to: the section of the upstream intersection close to the area where animals and vehicles intersect, and the section away from the area where animals and vehicles intersect.

[0018] Step 4: After receiving the information, the smart transportation infrastructure conducts a specific analysis and outputs the judgment information;

[0019] The determination information is whether the animal information A will interfere with the driving state of the roadside vehicle; if so, executing step 5;

[0020] Step 5: Pack and upload the analysis information, autonomous driving vehicle information A, and information 2 to the signal base station;

[0021] Step 6: The signal base station sends the analysis information and the autonomous driving vehicle information A to the autonomous driving vehicle on the road section, and sends the information 2 to the upstream and downstream networked intersection signal control platforms of the road section;

[0022] Step 7: The networked intersection signal control platform receives the second information and performs timing optimization calculation to achieve adaptive signal timing adjustment, and outputs the best timing solution, which is transmitted by the signal base station to the autonomous driving vehicle on the road section;

[0023] Step 8: The autonomous driving vehicle receives the optimized timing plan, analysis information, autonomous driving vehicle information A and information 2, and changes the driving strategy and adjusts its own driving state based on the autonomous driving control unit.

[0024] Preferably, the smart transportation infrastructure includes: a roadside sensing detection unit, an on-road identification unit, and an intelligent analysis unit;

[0025] The road identification unit is located on the ecological road and is used to identify vehicle information and animal information within a set area on the road, and outputs information one;

[0026] The roadside sensing detection unit arranged along one side of the ecological road is located between a pair of networked intersection signal control platforms, and is used to identify animal information and vehicle information within a set area on the roadside, and output information one;

[0027] The roadside sensing and monitoring units located at the upstream intersection and the downstream intersection are used to monitor vehicle information and output information 2;

[0028] The intelligent analysis unit receives information 1 and outputs analysis and judgment information.

[0029] Preferably, the roadside recognition unit includes a sensor, an infrared camera and a geomagnetic coil; the roadside sensing detection unit includes an infrared camera, a radar and a sensor;

[0030] Animal information A is collected independently by infrared cameras, sensors and geomagnetic coils in the road identification unit.

[0031] The number of vehicles in the autonomous driving vehicle information A is collected independently by the sensors and geomagnetic coils in the road identification unit;

[0032] The length of the vehicle queue in the autonomous driving vehicle information A is collected by an infrared camera;

[0033] The length of the vehicle queue in the autonomous driving vehicle information B is collected by an infrared camera;

[0034] The number of vehicles in the autonomous driving vehicle information B is independently collected by sensors and geomagnetic coils in the roadside sensing detection unit;

[0035] The vehicle speed in the autonomous driving vehicle information B is collected by the radar in the roadside perception detection unit.

[0036] Preferably, the specific process of step 4 includes:

[0037] Step 4A, pre-processing the animal information A in combination with the information 1;

[0038] Step 4B: Based on the size, shape and position of the animal captured by the infrared camera, the number of animals monitored by the sensor in real time, and the number of animals monitored by the geomagnetic coil in real time, use computer vision technology (such as OpenCV, TensorFlow) to analyze the infrared image, identify the size and shape of the animal, and then use a multi-target tracking algorithm (such as Kalman filtering, SORT) to track the animal to form the animal's movement trajectory;

[0039] The moving speed can be determined by the distance difference and time difference between capturing the animal before and after;

[0040] Step 4C: The animal movement trajectory and movement speed obtained in step 4B are combined with intelligent algorithms (such as Kalman filtering, time series model, convolutional neural network) to extract the spatiotemporal characteristics of the animal trajectory data, train the prediction trajectory model, and then combine it with the animal information A monitored in real time to predict the animal trajectory;

[0041] Step 4D: Display the predicted animal trajectory in an image to generate a trajectory map. If the trajectory line overlaps with the pre-set animal and vehicle intertwining area, it means that the animal may enter the area and interfere with normal vehicle driving, and it is necessary to proceed to the next step. Otherwise, there is no overlap.

[0042] Preferably, the width of the simulated road section should be set to 20-30 meters, and the length should be selected along the length direction of the ecological road.

[0043] Preferably, the perception and recognition unit is any one of a vehicle-mounted sensor, a millimeter-wave radar or a camera device.

[0044] Preferably, step 7 specifically includes the following steps:

[0045] Step 7A: Based on the information 2 collected by the roadside equipment of the intelligent transportation infrastructure, combined with the signal timing of the intersection in the current cycle, the intersection signal timing is optimized and solved;

[0046] Step 7B, combining basic signal timing theory with the detected autonomous driving vehicle speed, the number of autonomous driving vehicles, and the length of vehicle queues at the upstream intersection and the downstream intersection, to establish an intersection signal timing optimization model;

[0047] Step 7C: Solve the signal timing optimization model of the intersection through an optimization algorithm (such as a genetic algorithm) to obtain the best timing solution.

[0048] Preferably, step 8 specifically includes the following steps:

[0049] Step 8A: The autonomous driving vehicle receives the judgment information and processes the process of the animal crossing the road ahead as a virtual signal intersection, which is regarded as a conflict of right of way on the road ahead and requires phased passage, and is regarded as a basic virtual signal light control;

[0050] Step 8B: The autonomous driving vehicle receives the optimized timing plan of the upstream networked intersection, coordinates the virtual signal control brought by the animal crossing behavior, and combines the autonomous driving vehicle information A and the number of vehicles and the length of the vehicle queue in the information 2 to perform coordinated optimization control of the green wave trunk line;

[0051] Step 8C: Based on the vehicle intelligent decision-making center, the autonomous driving vehicle solves the green wave coordination control model that satisfies the minimum delay through optimization algorithms (such as particle swarm optimization algorithm and simulated annealing algorithm), and obtains the recommended optimal driving speed, thereby changing the driving strategy and adjusting the driving behavior for safe passage.

[0052] Nearby autonomous driving vehicles conduct efficient deductions and autonomous decisions based on the information sent from the signal base station and the timing plan shared by the nearby networked intersection signal timing platform. They regard the area where animals cross as a prohibited area and adjust the driving status of their own vehicles (driving speed, driving trajectory, driving route).

[0053] Compared with the prior art, the advantages of the present invention are:

[0054] 1. Avoid vehicle-animal collision risks. This method uses the Internet of Vehicles environment to achieve early perception and prediction of animal behavior, and ultimately transmits the information to the autonomous driving vehicle. It can predict animal crossing behavior in advance and adjust the vehicle's driving status, thereby avoiding the possibility of traffic accidents.

[0055] 2. Build a new traffic control method for animal crossing road scenes. Compared with the traditional static traffic control method of setting up warning signs to remind drivers, this method uses roadside facilities and equipment to detect animal information in real time, and through scientific analysis and prevention, based on information communication, optimizes and adjusts nearby networked signal-controlled intersections and autonomous driving vehicles in advance. Through signal timing optimization and vehicle driving status adjustment, the entire closed-loop traffic control scene based on animal crossing behavior is built.

[0056] 3. Improve vehicle traffic efficiency. The traditional traffic control mode for animal crossing behavior is mostly for motor vehicles to stop and wait, which often causes the length of vehicle queues to increase and traffic delays to increase; however, through this method, based on systematic thinking, optimization and adjustment are made from the entire road network. The optimization of the signal timing of the networked intersections ensures that when an animal crosses, the upstream and downstream intersections will place more right of way on the roads in the intersecting direction. From a scientific and systematic perspective, the number of vehicles placed on the road where the animal crosses occurs is determined, thereby improving the vehicle traffic efficiency of the entire road network. BRIEF DESCRIPTION OF THE DRAWINGS

[0057] Figure 1 A flow chart of the method for constructing traffic control scenarios for an eco-road connected vehicle environment;

[0058] Figure 2 This is a simplified schematic diagram of the ecological road traffic scenario;

[0059] Figure 3 Design a schematic diagram for the Internet of Vehicles communication system. DETAILED DESCRIPTION

[0060] The following is a more detailed description of the method for constructing a traffic control scene for an ecological road vehicle networking environment of the present invention in conjunction with a schematic diagram, wherein a preferred embodiment of the present invention is shown, and it should be understood that those skilled in the art can modify the present invention described herein while still achieving the advantageous effects of the present invention. Therefore, the following description should be understood as being widely known to those skilled in the art, and not as a limitation of the present invention.

[0061] A method for constructing a traffic control scenario for an ecological road vehicle networking environment comprises the following steps:

[0062] Step 1: Build a vehicle networking communication system based on the research area; the vehicle networking communication system includes:

[0063] An autonomous driving vehicle is provided with a sensing and recognition unit, which is used to collect information about the external environment around the vehicle and can exchange information with a signal base station and other autonomous driving vehicles;

[0064] A pair of networked intersection signal control platforms, one networked intersection signal control platform is located at an upstream intersection of the animal and vehicle interweaving area of ​​the ecological road, and the other networked intersection signal control platform is located at a downstream intersection of the animal and vehicle interweaving area of ​​the ecological road;

[0065] The animal-vehicle intersection area refers to the area on the ecological road where vehicles driving normally along the road and animals passing horizontally on the side of the road have conflicting rights of passage;

[0066] And signal base stations, which act as a medium for wireless communication, realize the communication between smart transportation infrastructure, autonomous driving vehicles, and networked intersection signal control platforms. Signal base stations are intermediate transmission tools that play the role of data uploading and data sending;

[0067] Step 2: Deploy smart transportation infrastructure in the study area;

[0068] Step 3: The smart transportation infrastructure collects information 1 and information 2;

[0069] Among them, information 1 is generated after the intelligent transportation infrastructure identifies vehicles and animals within a set area on the road, and includes autonomous driving vehicle information A and animal information A;

[0070] The autonomous driving vehicle information A includes the distance between the autonomous driving vehicle and the area where animals and vehicles intersect, the number of vehicles monitored within the set distance range from the area where animals and vehicles intersect, and the length of the vehicle queue; the animal information A includes the type of animal and the number of animals;

[0071] Information 2: Information B of autonomous driving vehicles at upstream intersections and downstream intersections monitored by the smart transportation infrastructure, including the number of vehicles, vehicle speeds, and length of vehicle queues;

[0072] Step 4: After receiving the information, the smart transportation infrastructure conducts a specific analysis and outputs the judgment information;

[0073] Determine whether the animal information A will interfere with the driving state of the roadside vehicle; if so, execute step 5;

[0074] Step 5: Pack and upload the analysis information, autonomous driving vehicle information A, and information 2 to the signal base station;

[0075] Step 6: The signal base station sends the analysis information and the autonomous driving vehicle information A to the autonomous driving vehicle on the road section, and sends the information 2 to the upstream and downstream networked intersection signal control platforms of the road section;

[0076] Step 7: The networked intersection signal control platform receives the second information and performs timing optimization calculation to achieve adaptive signal timing adjustment, and outputs the best timing solution, which is transmitted by the signal base station to the autonomous driving vehicle on the road section;

[0077] Step 8: The autonomous driving vehicle receives the optimized timing plan, analysis information, autonomous driving vehicle information A and information 2, and changes the driving strategy and adjusts its own driving state based on the autonomous driving control unit.

[0078] Among them, the smart transportation infrastructure includes: roadside perception detection unit, road identification unit, and intelligent analysis unit;

[0079] The road identification unit is located on the ecological road and is used to identify vehicle information and animal information within a set area on the road, and its output information is one;

[0080] The roadside sensing detection unit arranged along one side of the ecological road is located between a pair of networked intersection signal control platforms, and is used to identify animal information and vehicle information within a set area on the roadside, and output information one;

[0081] The roadside sensing monitoring units located at the upstream intersection and the downstream intersection are used to monitor vehicle information and output information 2;

[0082] The intelligent analysis unit receives information 1 and outputs analysis and judgment information.

[0083] The roadside recognition unit includes sensors, infrared cameras and geomagnetic coils; the roadside sensing detection unit includes infrared cameras, radars and sensors;

[0084] Animal information A is collected independently by infrared cameras, sensors, and geomagnetic coils in the on-road identification unit;

[0085] The number of vehicles in the autonomous driving vehicle information A is collected independently by the sensors and geomagnetic coils in the road identification unit;

[0086] The length of the vehicle queue in the autonomous driving vehicle information A is collected by an infrared camera;

[0087] The length of the vehicle queue in the autonomous driving vehicle information B is collected by an infrared camera;

[0088] The number of vehicles in the autonomous driving vehicle information B is independently collected by sensors and geomagnetic coils in the roadside sensing detection unit;

[0089] The vehicle speed in the autonomous driving vehicle information B is collected by the radar in the roadside perception detection unit.

[0090] Furthermore, the specific process of step 4 includes:

[0091] Step 4A, pre-processing the animal information A in combination with the information 1;

[0092] Step 4B: Based on the size, shape and position of the animal captured by the infrared camera, the number of animals monitored by the sensor in real time, and the number of animals monitored by the geomagnetic coil in real time, the infrared image is analyzed using computer vision technology to identify the size and shape of the animal, and then a multi-target tracking algorithm is used to track the animal to form the animal's movement trajectory;

[0093] The moving speed can be determined by the distance difference and time difference between capturing the animal before and after;

[0094] Step 4C: The animal movement trajectory and movement speed obtained in step 4B are combined with an intelligent algorithm to extract the spatiotemporal characteristics of the animal trajectory data, train a prediction trajectory model, and then combine it with the animal information A monitored in real time to predict the animal trajectory;

[0095] Step 4D: Display the predicted animal trajectory in an image to generate a trajectory map. If the trajectory line overlaps with the pre-set animal and vehicle intertwining area, it means that the animal may enter the area and interfere with normal vehicle driving, and it is necessary to proceed to the next step. Otherwise, there is no overlap.

[0096] In this embodiment, the width of the simulated road section should be set to 20-30 meters, and the length is selected along the length direction of the ecological road.

[0097] The perception and recognition unit is any one of a vehicle-mounted sensor, a millimeter-wave radar or a camera device.

[0098] Step 7 specifically includes the following steps:

[0099] Step 7A: Based on the information 2 collected by the roadside equipment of the intelligent transportation infrastructure, combined with the signal timing of the intersection in the current cycle, the intersection signal timing is optimized and solved;

[0100] Step 7B, combining basic signal timing theory with the detected speed of the autonomous driving vehicle, the number of autonomous driving vehicles and the length of the vehicle queue, to establish an intersection signal timing optimization model;

[0101] Step 7C: Solve the signal timing optimization model of the intersection through the optimization algorithm to obtain the best timing solution.

[0102] Step 8 specifically includes the following steps:

[0103] Step 8A: The autonomous driving vehicle receives the judgment information and processes the process of the animal crossing the road ahead as a virtual signal intersection, which is regarded as a conflict of right of way on the road ahead and requires phased passage, and is regarded as a basic virtual signal light control;

[0104] Step 8B: The autonomous driving vehicle receives the optimized timing plan of the upstream networked intersection, coordinates the virtual signal control brought by the animal crossing behavior, and combines the autonomous driving vehicle information A and the number of vehicles and the length of the vehicle queue in the information 2 to perform coordinated optimization control of the green wave trunk line;

[0105] Step 8C: Based on the vehicle intelligent decision-making center, the autonomous driving vehicle solves the green wave coordination control model that satisfies the minimum delay through an optimization algorithm, and obtains the recommended optimal driving speed, thereby changing the driving strategy and adjusting the driving behavior for safe passage.

[0106] This method is based on the Internet of Vehicles environment, so all vehicles traveling on the ecological road are networked autonomous driving vehicles with information perception, calculation decision-making and autonomous control functions, and the intersection networked signal control platform located in the Internet of Vehicles environment also has perception and recognition, optimized calculation, real-time response and intelligent decision-making functions.

[0107] Suitable animals for living in urban ecological roads are considered to be common birds and small mammals (goats, zebras, etc.).

[0108] According to the animal species in the urban ecological road and the appropriate width of the biological corridor, the width of the animal crossing area should be set to 20-30 meters. In this case, a certain length of road section is selected as the ecological road simulation section where animal crossing behavior occurs. The upstream and downstream of the road section are signal-controlled intersections in the vehicle networking environment. The two-way two-lane road is used as the reference road. For details, see Figure 2 .

[0109] This method senses and identifies animals that have a "need to cross the street" in advance, then reminds passing vehicles and the signal control platform of the upstream and downstream intersections (a pair of networked intersection signal control platforms) to respond appropriately.

[0110] This method builds traffic control scenarios for the Internet of Vehicles environment of ecological roads, including: smart transportation infrastructure, here cameras and videos; self-driving vehicles; networked signal intersections and signal base stations; it is assumed that the entire Internet of Vehicles environment has been configured according to the functional requirements described above.

[0111] The traffic control scenario construction method for the ecological road vehicle networking environment specifically includes:

[0112] 1. Deploy communication base stations at appropriate locations on roads where animals often cross to meet the needs of receiving and transmitting information in wireless networks; Figure 3 . Figure 3 In this article, "surveillance" refers to infrared cameras.

[0113] Sensors, infrared cameras and infrared video cameras are installed on roadside poles; geomagnetic coils are installed and buried in the carriageway.

[0114] The radar is arranged on the roadside at the intersection.

[0115] 2. Animals 1 and 2 appear in the roadside perception detection unit and are identified and detected by the roadside intelligent transportation infrastructure, which records their status characteristics (two animals, speed 0.5 m / s and specific location) and makes analysis and judgment.

[0116] 3. Smart transportation infrastructure packages animal information and vehicle information obtained by the geomagnetic coil and uploads them to the signal base station.

[0117] 4. The signal base station receives the information and sends it to nearby intersections A and B, vehicle 1, vehicle 2, and vehicle 3.

[0118] 5. Intersection A and Intersection B receive the information, and combined with the vehicle information they have collected, they start the signal timing optimization strategy and make adjustments to the timing optimization: in the next signal cycle, the lights at Intersection A and Intersection B will be red in the east-west direction and green in the north-south direction, and they will share this information with nearby autonomous driving vehicles.

[0119] 6. The autonomous vehicle will collect information and timing plans for picking up and dropping off animals, and will consider areas where animals are crossing ahead as prohibited areas. Based on this, the vehicle will optimize the driving status and plan the speed curve.

[0120] Vehicle 1 slows down and stops in front of the no-entry zone, and vehicle 2 slows down and keeps a certain safe distance from vehicle 1; the traffic light at the intersection ahead of vehicle 3 is about to turn red, so it gradually slows down and stops just before the stop line of intersection A.

[0121] 7. The animal crossing behavior has been completed and no animals have been detected. The information is uploaded and then sent by the signal base station to nearby intersections and autonomous driving vehicles, and the signal timing and vehicle driving status are executed according to the normal state.

[0122] 8. So far, the entire traffic control based on animal crossing behavior in the Internet of Vehicles environment has been completed; the information transmission and information usage of each element subject (animals, vehicles, base stations, intersections) in the entire scene in the control method are summarized in Table 1.

[0123] Table 1 Information interaction table of each subject in traffic control scenario

[0124]

[0125]

[0126] In summary, the present invention is simple and reliable. It can detect animal crossing behavior in real time in a vehicle networking environment and optimize and adjust the road operation status to avoid unnecessary collision accidents, protect the safety of people and animals, and improve the traffic efficiency of intersections.

[0127] The above is only a preferred embodiment of the present invention and does not limit the present invention in any way. Any technician in the relevant technical field, without departing from the scope of the technical solution of the present invention, makes any form of equivalent replacement or modification to the technical solution and technical content disclosed in the present invention, which does not depart from the content of the technical solution of the present invention and still falls within the protection scope of the present invention.

Claims

1. A method for constructing a traffic control scenario for an ecological road vehicle networking environment, characterized in that: The following steps are involved: Step 1: Building a vehicle networking communication system based on the research area; the vehicle networking communication system includes: An autonomous driving vehicle is provided with a sensing and identifying unit, which is used to collect information about the external environment around the vehicle and can exchange information with a signal base station and other autonomous driving vehicles; A pair of networked intersection signal control platforms, one networked intersection signal control platform is located at an upstream intersection of the animal and vehicle interweaving area of ​​the ecological road, and the other networked intersection signal control platform is located at a downstream intersection of the animal and vehicle interweaving area of ​​the ecological road; The animal-vehicle intersection area refers to the area on the ecological road where vehicles driving normally along the road and animals passing horizontally on the side of the road have conflicting rights of passage; And signal base stations, which act as a medium for wireless communication, realize the communication between smart transportation infrastructure, autonomous driving vehicles, and networked intersection signal control platforms. Signal base stations are intermediate transmission tools that play the role of data uploading and data sending; Step 2: Deploy smart transportation infrastructure in the study area; Step 3: The smart transportation infrastructure collects information 1 and information 2; Among them, information 1 is generated after the intelligent transportation infrastructure identifies vehicles and animals within a set area on the road, and includes autonomous driving vehicle information A and animal information A; The autonomous driving vehicle information A includes the distance between the autonomous driving vehicle and the area where animals and vehicles intersect, the number of vehicles monitored within the set distance range from the area where animals and vehicles intersect, and the length of the vehicle queue; the animal information A includes the type of animal and the number of animals; Information 2: Information B of autonomous driving vehicles at upstream intersections and downstream intersections monitored by the smart transportation infrastructure, including the number of vehicles, vehicle speeds, and length of vehicle queues; Step 4: After receiving the information, the smart transportation infrastructure conducts a specific analysis and outputs the judgment information; The determination information is whether the animal information A will interfere with the driving state of the roadside vehicle; if so, executing step 5; Step 5: Pack and upload the analysis information, autonomous driving vehicle information A, and information 2 to the signal base station; Step 6: The signal base station sends the analysis information and the autonomous driving vehicle information A to the autonomous driving vehicle on the road section, and sends the information 2 to the upstream and downstream networked intersection signal control platforms of the road section; Step 7: The networked intersection signal control platform receives the second information and performs timing optimization calculation to achieve adaptive signal timing adjustment, and outputs the best timing solution, which is transmitted by the signal base station to the autonomous driving vehicle on the road section; Step 8: The autonomous driving vehicle receives the optimized timing plan, the judgment information, the autonomous driving vehicle information A and the information 2, and changes the driving strategy and adjusts its driving state based on the autonomous driving control unit; Step 8 specifically includes the following steps: Step 8A: The autonomous driving vehicle receives the judgment information and processes the process of the animal crossing the road ahead as a virtual signal intersection, which is regarded as a conflict of right of way on the road ahead and requires phased passage, and is regarded as a basic virtual signal light control; Step 8B: The autonomous driving vehicle receives the optimized timing plan of the upstream networked intersection, coordinates the virtual signal control brought by the animal crossing behavior, and combines the autonomous driving vehicle information A and the number of vehicles and the length of the vehicle queue in the information 2 to perform coordinated optimization control of the green wave trunk line; Step 8C: Based on the vehicle intelligent decision-making center, the autonomous driving vehicle solves the green wave coordination control model that satisfies the minimum delay through an optimization algorithm, and obtains the recommended optimal driving speed, thereby changing the driving strategy and adjusting the driving behavior for safe passage.

2. The method for constructing a traffic control scenario for an ecological road vehicle networking environment according to claim 1 is characterized in that: The smart transportation infrastructure includes: a roadside sensing detection unit, an on-road identification unit, and an intelligent analysis unit; The road identification unit is located on the ecological road and is used to identify vehicle information and animal information within a set area on the road, and outputs information one; The roadside sensing detection unit arranged along one side of the ecological road is located between a pair of networked intersection signal control platforms, and is used to identify animal information and vehicle information within a set area on the roadside, and output information one; The roadside sensing and monitoring units located at the upstream intersection and the downstream intersection are used to monitor vehicle information and output information 2; The intelligent analysis unit receives information 1 and outputs analysis and judgment information.

3. The method for constructing a traffic control scenario for an ecological road vehicle networking environment according to claim 2 is characterized in that: The roadside recognition unit includes a sensor, an infrared camera and a geomagnetic coil; the roadside sensing detection unit includes an infrared camera, a radar and a sensor; Animal information A is collected independently by infrared cameras, sensors, and geomagnetic coils in the on-road identification unit; The number of vehicles in the autonomous driving vehicle information A is collected independently by the sensors and geomagnetic coils in the road identification unit; The length of the vehicle queue in the autonomous driving vehicle information A is collected by an infrared camera; The length of the vehicle queue in the autonomous driving vehicle information B is collected by an infrared camera; The number of vehicles in the autonomous driving vehicle information B is independently collected by sensors and geomagnetic coils in the roadside sensing detection unit; The vehicle speed in the autonomous driving vehicle information B is collected by the radar in the roadside perception detection unit.

4. The method for constructing a traffic control scenario for an ecological road vehicle networking environment according to claim 1 is characterized in that: The specific process of step 4 includes: Step 4A, pre-processing the animal information A in combination with the information 1; Step 4B: Based on the size, shape and position of the animal captured by the infrared camera, the number of animals monitored by the sensor in real time, and the number of animals monitored by the geomagnetic coil in real time, the infrared image is analyzed using computer vision technology to identify the size and shape of the animal, and then a multi-target tracking algorithm is used to track the animal to form the animal's movement trajectory; The moving speed can be determined by the distance difference and time difference between capturing the animal before and after; Step 4C: The animal movement trajectory and movement speed obtained in step 4B are combined with an intelligent algorithm to extract the spatiotemporal characteristics of the animal trajectory data, train a prediction trajectory model, and then combine it with the animal information A monitored in real time to predict the animal trajectory; Step 4D: Display the predicted animal trajectory in an image to generate a trajectory map. If the trajectory line overlaps with the pre-set animal and vehicle intertwining area, it means that the animal may enter the area and interfere with normal vehicle driving, and it is necessary to proceed to the next step. Otherwise, there is no overlap.

5. The method for constructing a traffic control scenario for an ecological road vehicle networking environment according to claim 1 is characterized in that: The width of the simulated road section should be set to 20-30 meters, and the length should be selected along the length direction of the ecological road.

6. The method for constructing a traffic control scenario for an ecological road vehicle networking environment according to claim 1 is characterized in that: The perception and recognition unit is any one of a vehicle-mounted sensor, a millimeter-wave radar or a camera device.

7. The method for constructing a traffic control scenario for an ecological road vehicle networking environment according to claim 1 is characterized in that: Step 7 specifically includes the following steps: Step 7A: Based on the information 2 collected by the roadside equipment of the intelligent transportation infrastructure, combined with the signal timing of the intersection in the current cycle, the intersection signal timing is optimized and solved; Step 7B, combining basic signal timing theory with the detected speed of the autonomous driving vehicle, the number of autonomous driving vehicles and the length of the vehicle queue, to establish an intersection signal timing optimization model; Step 7C: Solve the signal timing optimization model of the intersection through the optimization algorithm to obtain the best timing solution.

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