An unmanned driving route planning system and method

Abstract

This invention provides an autonomous driving route planning system and method. The system includes: a data acquisition module for collecting real-world traffic data, the current location information of the autonomous vehicle, and current environmental information; a data modeling module for generating a digital twin model and constructing virtual sensor, virtual vehicle, and virtual environment information; a decision-making module for performing initial path planning; subsequent collision risk detection; and path information adjustment based on the detection results; a path optimization module for performing a second path planning; and a driving control module for controlling the autonomous vehicle. This invention enables the virtual vehicle to drive in a virtual environment by establishing a digital twin model, and can adaptively adjust the parameters of the virtual vehicle and the virtual environment.

Description

Technical Field

[0001] This invention relates to the field of autonomous driving technology, and in particular to an autonomous driving route planning system and method. Background Technology

[0002] With the advancement of technology, autonomous vehicles have gradually become a research hotspot in the transportation field. Their core technologies include sensor fusion, computer vision, and control theory. Among these, route planning is one of the key aspects of achieving autonomous driving.

[0003] Existing autonomous vehicle route planning methods are mainly based on rules, machine learning, and other algorithms. However, these methods have certain limitations when dealing with complex traffic scenarios. In addition, existing autonomous vehicle route planning systems usually only consider the vehicle's own driving path and do not fully consider the influence of the surrounding environment, such as traffic flow, road conditions, and weather. Therefore, it is difficult to achieve optimal route planning.

[0004] Therefore, how to solve the above-mentioned technical problems has become a difficult problem for those in the field to solve. Summary of the Invention

[0005] In view of the above-mentioned deficiencies of the prior art, the present invention provides an unmanned driving route planning system and method, which enables virtual vehicles to drive in a virtual environment by establishing a digital twin model, and can adaptively adjust the parameters of the virtual vehicle and the virtual environment.

[0006] To achieve the above and other related objectives, the present invention provides an unmanned driving route planning system, comprising: The data acquisition module is used to collect real-world traffic data, the current location information of autonomous vehicles, and current environmental information; The data modeling module is used to map traffic data acquired by the data acquisition module onto a virtual environment to generate a digital twin model; to construct virtual sensors and virtual vehicles based on the current location information and current environment information acquired by the data acquisition module, and to add the virtual sensors and virtual vehicles to the digital twin model; and to construct virtual environment information in the digital twin model based on the location and attributes of the sensors and vehicles. The decision-making module is used to perform the first path planning based on the traffic data, current location information and current environment information obtained by the data acquisition module; then, it uses a collision detection algorithm to detect the collision risk between the autonomous vehicle and surrounding obstacles, and adjusts the path information in the virtual environment information based on the collision risk detection results; The route optimization module is used to perform a second route planning based on the first route planning, the adjusted route information, and the vehicle's origin and destination information. The driving control module is used to issue commands to the autonomous vehicle for steering, acceleration, and deceleration based on the results of path planning that meets safety conditions, thereby controlling the autonomous vehicle.

[0007] In one embodiment of the present invention, the data acquisition module includes: The data acquisition module is used to collect real-world traffic data; The data perception module is used to collect the current location and environmental information of the autonomous vehicle.

[0008] In one embodiment of the present invention, the digital twin model includes: A map database is used to store traffic data acquired by the data acquisition module; Virtual sensors are used to simulate the virtual environment in which autonomous vehicles operate; Virtual vehicles are used to simulate the movement of driverless vehicles.

[0009] In one embodiment of the present invention, when the collision risk detection result does not match the safety conditions, it is determined that the current path information does not meet the vehicle's safety conditions. The decision module adjusts the path information, executes the path optimization module, and then executes the control form module. When the collision risk detection result matches the safety conditions, the current path information is determined to meet the vehicle's safety conditions, and the control driving module is executed directly.

[0010] In one embodiment of the present invention, the safety conditions include a safe distance between the autonomous vehicle and surrounding vehicles, and a safe distance between the autonomous vehicle and obstacles.

[0011] In one embodiment of the present invention, in the route optimization module, an optimization algorithm is used to perform a second route planning based on the current traffic conditions, vehicle attributes, and origin and destination information to determine the driving route.

[0012] In one embodiment of the present invention, the driving control module, the decision-making module, and the path optimization module are connected and transmit data via an information flow data transmission bus.

[0013] In one embodiment of the present invention, the form of the information flow data transmission bus includes wired transmission technology, wireless transmission technology, and quantum transmission technology.

[0014] This invention also provides a method for planning routes for autonomous driving, the method comprising the following steps: S101. Data acquisition: Real-time acquisition of real-world traffic data, current location information of autonomous vehicles, and current environmental information; S103. The generation of the digital twin model involves mapping the acquired data onto the virtual environment to form a map database, generating a digital twin model, and adding virtual sensors and virtual vehicles to the map database. The virtual environment information is simulated based on the sensors, and the location information is simulated based on the vehicle's position and attributes, thereby simulating the vehicle's movement. S105. Information processing: Based on the data obtained in S101 and the vehicle simulation driving in S103, the first path planning is performed. Then, the collision detection method is used to detect the collision risk between the autonomous vehicle and surrounding obstacles. Based on the collision risk detection results, the path information in the virtual environment information is adjusted. S107. Optimization of the route: Based on the first route planning in S105, the adjusted route information, and the starting and ending point information of the vehicles, the route planning is optimized using an optimization algorithm until the safety conditions are met. S109. Control driving: Based on the results of path planning that meets safety conditions, issue commands to the autonomous vehicle for steering, acceleration, and deceleration, and control the autonomous vehicle.

[0015] In one embodiment of the present invention, if the collision risk detection result does not meet the safety conditions, the process proceeds to step S107; if the collision risk detection result meets the safety conditions, the process proceeds to step S109.

[0016] The beneficial effects of this invention are: (1) It can update traffic data in real time, map the traffic conditions in the real world to the virtual environment, and then plan and optimize the driving path of the vehicle through optimization algorithms, and plan the optimal path according to real-time traffic data, thereby improving the driving efficiency and safety of driverless cars. (2) Simulation and testing can be carried out in a virtual environment, which reduces development costs and development cycle; (3) It can be adaptively adjusted according to different traffic conditions, weather and vehicle parameters, which improves the flexibility and adaptability of the system. Attached Figure Description

[0017] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the accompanying drawings used in the description of the embodiments or the prior art will be briefly introduced below. The accompanying drawings are incorporated in and constitute a part of this specification, illustrating embodiments consistent with this application, and are used together with the description to explain the principles of this application. Obviously, the drawings described below are merely some embodiments of the present invention, and those skilled in the art can obtain other drawings based on these drawings without creative effort.

[0018] Figure 1This is a schematic diagram of an unmanned driving route planning system provided in an embodiment of the present invention.

[0019] Figure 2 This is a block diagram of a data acquisition module provided in an embodiment of the present invention; Figure 3 This is a flowchart of an unmanned driving route planning method provided in an embodiment of the present invention.

[0020] Component designation explanation: 100. Data Acquisition Module; 110. Data Collection Module; 120. Data Sensing Module; 200. Data Modeling Module; 300. Decision-making module; 400. Path optimization module; 500. Driving control module. Detailed Implementation

[0021] To enable those skilled in the art to better understand the present invention, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort should fall within the scope of protection of the present invention.

[0022] The embodiments of the present invention are described in detail below. Examples of these embodiments are shown in the accompanying drawings, wherein the same or similar reference numerals denote the same or similar elements or elements having the same or similar functions throughout. The embodiments described below with reference to the accompanying drawings are exemplary and are only used to explain this application, and should not be construed as limiting this application. It should be noted that although functional modules are divided in the system schematic diagram and a logical order is shown in the flowchart, in some cases, the steps shown or described may be performed in a different order than the module division in the system schematic diagram or the order in the flowchart. The step numbers in the following embodiments are set only for ease of explanation and do not limit the order between steps. The execution order of each step in the embodiments can be adaptively adjusted according to the understanding of those skilled in the art. Without conflict, the embodiments described below or the technical features can be arbitrarily combined to form new embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0023] It should be noted that the use of terms such as "first," "second," etc., in the specification, claims, and drawings of this application is solely for the purpose of distinguishing technical features and should not be construed as indicating or implying relative importance, or implicitly indicating the number of indicated technical features, or implicitly indicating the order of the indicated technical features. Furthermore, unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this application belongs. The terminology used herein is for the purpose of describing embodiments of this application only and is not intended to limit this application. Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention belongs. The terminology used herein in the specification of this invention is for the purpose of describing specific embodiments only and is not intended to limit the invention.

[0024] The vehicles described in the embodiments herein may be autonomous vehicles or self-driving vehicles. (driving automobile). The vehicle can be configured to operate in autonomous driving mode, in which it can navigate its environment with minimal or no input from the driver.

[0025] Please see Figure 1 As shown, this embodiment of the invention provides an unmanned driving route planning system, which may include a data acquisition module 100, a data modeling module 200, a decision-making module 300, a path optimization module 400, and a driving control module 500.

[0026] Please see Figure 1 and Figure 2As shown, the data acquisition module 100 is used to collect real-world traffic data, the current location information of the autonomous vehicle, and the current environmental information. Further, the data acquisition module 100 includes a data collection module 110 and a data perception module 120. The data collection module 110 is used to collect real-world traffic data. The data perception module 120 is used to collect the current location information and environmental information of the autonomous vehicle. Real-world traffic data can include traffic flow, road conditions, weather, etc. The current location information and environmental information of the autonomous vehicle can be collected through a global positioning system. Real-world traffic data can be acquired through sensors installed on the vehicle; for example, mechanical lidar, solid-state lidar, millimeter-wave radar, infrared radar, etc., can be used as sensors to acquire environmental information around the autonomous vehicle. For example, environmental information can include temperature, wind speed, humidity, and light intensity. For example, a camera can be used as a sensor to acquire image information in front of the autonomous vehicle. For example, image information can include road topology, traffic light signals, and traffic signs.

[0027] Please see Figure 1 As shown, the data modeling module 200 is used to map traffic data acquired by the data acquisition module onto a virtual environment to generate a digital twin model; to construct virtual sensors and virtual vehicles based on the current location information and current environment information acquired by the data acquisition module, and to add the virtual sensors and virtual vehicles to the digital twin model; and to construct virtual environment information in the digital twin model based on the location and attributes of the sensors and vehicles. Traffic data can be updated in real time within the digital twin model to reflect real-world traffic conditions. Virtual sensors are used to simulate sensors on autonomous vehicles. For example, virtual sensors can simulate lidar and cameras installed on vehicles. Virtual vehicles are used to simulate the motion state of autonomous vehicles. For example, vehicle attributes can include, but are not limited to, physical data such as geometric dimensions, material properties, circuit structure, and connection relationships. The various parts of the vehicle can be digitally modeled in the virtual environment using CAD drawing methods, CAE approximate numerical analysis methods, or finite element methods, thereby obtaining a virtual vehicle identical to the actual vehicle in the virtual environment. Furthermore, the digital twin model includes a map database, virtual sensors, and virtual vehicles. The map database stores traffic data acquired by the data acquisition module. Virtual sensors and vehicles are added to the map database to simulate virtual environmental information based on the virtual sensors and to simulate the movement of autonomous vehicles based on their location and attributes. By simulating vehicle movement in a digital twin model, traffic conditions can be predicted for a future period, and the map database can be updated in real time to reflect real-world traffic conditions.

[0028] Please see Figure 1 As shown, the decision module 300 performs initial path planning based on traffic data, current location information, and current environmental information acquired by the data acquisition module; then, it uses a collision detection algorithm to detect the collision risk between the autonomous vehicle and surrounding obstacles, and adjusts the path information in the virtual environment information based on the collision risk detection results. For example, the collision detection algorithm can be implemented using OBB (Oriented Bounding Box).

[0029] Please see Figure 1 As shown, specifically, when the detection result does not meet the safety conditions, it is determined that the current path information does not meet the vehicle's safety conditions. The decision module 300 then adjusts the path information, executes the path optimization module 400, and then executes the control module 500. When the detection result meets the safety conditions, it is determined that the current path information meets the vehicle's safety conditions, and the control driving module 500 is directly executed. Specifically, safety conditions can be set. For example, the safety conditions can be set to a distance greater than 1m between the autonomous vehicle and surrounding vehicles, and a distance greater than 30cm between the autonomous vehicle and obstacles.

[0030] Please see Figure 1 As shown, the path optimization module 400 performs a second path planning based on the first path planning, the adjusted path information, and the vehicle's start and end point information. Specifically, the path optimization module 400 can optimize the path planning using optimization algorithms to calculate the optimal driving path. For example, genetic algorithms or particle swarm optimization algorithms can be used to generate an optimal driving path based on current traffic conditions, vehicle attributes, and start and end point information to meet the vehicle's requirements for safe, fast, and comfortable driving.

[0031] Please see Figure 1As shown, the control and driving module 500 issues commands to the autonomous vehicle for steering, acceleration, and deceleration based on the path planning results that meet safety conditions, thereby controlling the autonomous vehicle. This allows for dynamic adjustments based on real-time traffic data, ensuring that the vehicle follows the planned path during actual driving. The control and driving module 500, decision-making module 300, and path optimization module 400 can be connected and transmit data via an information flow data transmission bus. The forms of the information flow data transmission bus include, but are not limited to, wired transmission technology, wireless transmission technology, and quantum transmission technology. For example, wired transmission technologies include, but are not limited to, CAN bus transmission technology, Flexray bus transmission technology, and MOST bus transmission technology. For example, wireless transmission technologies include, but are not limited to, Bluetooth connection transmission, 4G network connection transmission, 5G network connection transmission, and WLAN connection transmission. The use of diverse data transmission methods effectively solves the problem of expected functional safety, meaning that when one data transmission method fails, another data transmission method can be effectively used as a substitute.

[0032] Please see Figure 3 As shown, based on the aforementioned embodiment of the autonomous driving route planning system, this application also provides an autonomous driving route planning method, which includes the following steps: S101. Data acquisition: Real-time acquisition of real-world traffic data, current location information of autonomous vehicles, and current environmental information.

[0033] S103. The generation of the digital twin model involves mapping the acquired data onto a virtual environment to form a map database, generating a digital twin model, and adding virtual sensors and virtual vehicles to the map database. The virtual environment information is simulated based on the sensors, and the location information is simulated based on the vehicle's position and attributes, thereby simulating the vehicle's movement.

[0034] S105. Information processing: Based on the data obtained in S101 and the vehicle simulation driving in S103, the first path planning is performed. Then, the collision detection method is used to detect the collision risk between the autonomous vehicle and surrounding obstacles. The path information in the virtual environment information is adjusted according to the collision risk detection results. If the detection result does not meet the safety conditions, proceed to S107. If the detection result meets the safety conditions, proceed to S109.

[0035] S107. Path optimization: Based on the initial path planning in S105, the adjusted path information, and the vehicle's starting and ending point information, an optimization algorithm is used to optimize the path planning until the safety conditions are met.

[0036] S109. Control driving: Based on the results of path planning that meets safety conditions, issue commands to the autonomous vehicle for steering, acceleration, and deceleration, and control the autonomous vehicle.

[0037] The autonomous driving route planning system and method of this application can update traffic data in real time, map real-world traffic conditions to a virtual environment, plan and optimize the vehicle's driving path, and perform optimal path planning based on real-time traffic data, thereby improving the driving efficiency and safety of autonomous vehicles. At the same time, the system and method can be simulated and tested in a virtual environment, reducing development costs and development cycle. In addition, the system can also adaptively adjust according to different traffic conditions, weather and vehicle parameters, improving the system's flexibility and adaptability.

[0038] The autonomous driving route planning system and method provided in this application can be applied to a terminal, a server, or software running on either a terminal or a server. In some embodiments, the terminal can be a smartphone, tablet, laptop, desktop computer, set-top box, etc.; the server can be configured as an independent physical server, a server cluster or distributed system composed of multiple physical servers, or a cloud server providing basic cloud computing services such as cloud services, cloud databases, cloud computing, cloud functions, cloud storage, network services, cloud communication, middleware services, domain name services, security services, CDN, and big data and artificial intelligence platforms; the software can be an application implementing a visual-touch sensor detection method, etc., but is not limited to the above forms.

[0039] This application can be used in a wide variety of general-purpose or special-purpose computer system environments or configurations. Examples include: personal computers, server computers, handheld or portable devices, tablet devices, multiprocessor systems, microprocessor-based systems, set-top boxes, programmable consumer electronics devices, network PCs, minicomputers, mainframe computers, and distributed computing environments including any of the above systems or devices. This application can be described in the general context of computer-executable instructions executed by a computer, such as program modules. Generally, program modules include routines, programs, objects, components, data structures, etc., that perform specific tasks or implement specific abstract data types. This application can also be practiced in distributed computing environments where tasks are performed by remote processing devices connected via a communication network. In distributed computing environments, program modules can reside in local and remote computer storage media, including storage devices.

[0040] It should be noted that in all specific embodiments of this application, when processing data related to user identity or characteristics, such as user information, user behavior data, user historical data, and user location information, user permission or consent is obtained first. Furthermore, the collection, use, and processing of this data comply with relevant laws, regulations, and standards of the relevant countries and regions. In addition, when embodiments of this application require access to sensitive personal information of users, separate permission or consent from the user is obtained through pop-ups or redirects to confirmation pages. Only after obtaining the user's separate permission or consent is the necessary user-related data for the proper functioning of the embodiments of this application obtained.

Claims

1. An unmanned driving route planning system, characterized in that, include: The data acquisition module is used to collect real-world traffic data, the current location information of autonomous vehicles, and current environmental information; The data modeling module is used to map traffic data acquired by the data acquisition module onto a virtual environment to generate a digital twin model; to construct virtual sensors and virtual vehicles based on the current location information and current environment information acquired by the data acquisition module, and to add the virtual sensors and virtual vehicles to the digital twin model; and to construct virtual environment information in the digital twin model based on the location and attributes of the sensors and vehicles. The decision-making module is used to perform the first path planning based on the traffic data, current location information and current environment information obtained by the data acquisition module; then, it uses a collision detection algorithm to detect the collision risk between the autonomous vehicle and surrounding obstacles, and adjusts the path information in the virtual environment information based on the collision risk detection results; The route optimization module is used to perform a second route planning based on the first route planning, the adjusted route information, and the vehicle's origin and destination information. The driving control module is used to issue commands to the autonomous vehicle for steering, acceleration, and deceleration based on the results of path planning that meets safety conditions, thereby controlling the autonomous vehicle.

2. The unmanned route planning system according to claim 1, characterized in that, The data acquisition module includes: The data acquisition module is used to collect real-world traffic data; The data perception module is used to collect the current location and environmental information of the autonomous vehicle.

3. The unmanned route planning system according to claim 1, characterized in that, The digital twin model includes: A map database is used to store traffic data acquired by the data acquisition module; Virtual sensors are used to simulate the virtual environment in which autonomous vehicles operate; Virtual vehicles are used to simulate the movement of driverless vehicles.

4. The unmanned route planning system according to claim 1, characterized in that, When the collision risk detection results do not match the safety conditions, it is determined that the current path information does not meet the vehicle's safety conditions. The decision module adjusts the path information, executes the path optimization module, and then executes the control form module. When the collision risk detection result matches the safety conditions, the current path information is determined to meet the vehicle's safety conditions, and the control driving module is executed directly.

5. The unmanned route planning system according to claim 4, characterized in that, The safety conditions include the safe distance between the autonomous vehicle and surrounding vehicles, and the safe distance between the autonomous vehicle and obstacles.

6. The unmanned route planning system according to claim 1, characterized in that, In the route optimization module, an optimization algorithm is used to perform a second route planning based on the current traffic conditions, vehicle attributes, and origin and destination information to determine the driving route.

7. The unmanned route planning system according to claim 1, characterized in that, The driving control module, decision-making module, and path optimization module are connected and transmit data via an information flow data transmission bus.

8. The unmanned route planning system according to claim 7, characterized in that, The information flow data transmission bus can take the form of wired transmission technology, wireless transmission technology, or quantum transmission technology.

9. A method for unmanned driving route planning, characterized in that, The method includes the following steps: S101. Data acquisition: Real-time acquisition of real-world traffic data, current location information of autonomous vehicles, and current environmental information; S103. The generation of the digital twin model involves mapping the acquired data onto the virtual environment to form a map database, generating a digital twin model, and adding virtual sensors and virtual vehicles to the map database. The virtual environment information is simulated based on the sensors, and the location information is simulated based on the vehicle's position and attributes, thereby simulating the vehicle's movement. S105. Information processing: Based on the data obtained in S101 and the vehicle simulation driving in S103, the first path planning is performed. Then, the collision detection method is used to detect the collision risk between the autonomous vehicle and surrounding obstacles. Based on the collision risk detection results, the path information in the virtual environment information is adjusted. S107. Optimization of the route: Based on the first route planning in S105, the adjusted route information, and the starting and ending point information of the vehicles, the route planning is optimized using an optimization algorithm until the safety conditions are met. S109. Control driving: Based on the results of path planning that meets safety conditions, issue commands to the autonomous vehicle for steering, acceleration, and deceleration, and control the autonomous vehicle.

10. The unmanned driving route planning method according to claim 9, characterized in that, If the collision risk detection result does not meet the safety conditions, proceed to step S107; if the collision risk detection result meets the safety conditions, proceed to step S109.

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