Vehicle path planning system and method

Abstract

The application provides a vehicle path planning system and method, belonging to the technical field of automatic driving. The vehicle path planning system is configured in a vehicle and comprises an environment perception module, a data processing module, a path planning module and a vehicle control module. The environment perception module is used to obtain environment perception data. The data processing module is used to determine target environment data based on the environment perception data, wherein the target environment data comprises an object recognition result and a three-dimensional environment model. The path planning module is used to determine path data of a target path from a current position corresponding to the vehicle to a target position based on the target environment data. The vehicle control module is used to determine driving control data based on the path data of the target path, and control the vehicle to perform corresponding driving actions based on the driving control data. The scheme can perceive and understand the dynamic changes of the environment around the vehicle in real time, thereby dynamically adjusting the path and improving the accuracy and adaptability of path planning.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of automatic driving, in particular to a vehicle path planning system and method. BACKGROUND

[0002] With the development of artificial intelligence technology, automatic driving technology is also becoming more and more mature. At present, when an automatic driving vehicle is driving, it mainly relies on a static map and a traditional path planning algorithm for navigation. However, this traditional path planning method based on a static map has certain limitations. In the face of traffic congestion, accidents or construction and the like, since the actual situation of the road deviates from the information provided by the static map, if the automatic driving vehicle continues to drive according to the originally planned path, it may encounter the problem of extremely low traffic efficiency or even unable to pass. Therefore, the traditional path planning method cannot fully consider the dynamically changing traffic environment, resulting in low accuracy and poor adaptability of the planned path. SUMMARY

[0003] The embodiments of the present application provide a vehicle path planning system and method, which can realize real-time perception and understanding of the dynamic changes of the surrounding environment of the vehicle, so as to dynamically adjust the path and improve the accuracy and adaptability of the path planning. The technical solution is as follows:

[0004] In one aspect, a vehicle path planning system is provided, which is configured in a vehicle, and the vehicle path planning system comprises an environment perception module, a data processing module, a path planning module and a vehicle control module.

[0005] The environment perception module is configured to acquire environment perception data, wherein the environment perception data is used to indicate the road structure and object distribution in the surrounding environment of the vehicle; and the environment perception data is sent to the data processing module.

[0006] The data processing module is configured to determine target environment data based on the environment perception data, wherein the target environment data comprises an object recognition result and a three-dimensional environment model, the object recognition result is used to indicate the object type, spatial position and geometric shape of different objects in the surrounding environment of the vehicle, and the three-dimensional environment model is used to indicate the static objects and dynamic objects in the surrounding environment of the vehicle, and the object type comprises roads, traffic signs, vehicles, pedestrians and obstacles; and the target environment data is sent to the path planning module.

[0007] The path planning module is configured to determine path data of a target path from a current position corresponding to the vehicle to a target position based on the target environment data; and the path data of the target path is sent to the vehicle control module.

[0008] The vehicle control module is configured to determine driving control data based on the path data of the target path, the driving control data including at least one of speed control data, gear control data, and steering control data; and control the vehicle to perform corresponding driving actions based on the driving control data.

[0009] In some embodiments, the environment perception module includes a data acquisition module configured to acquire image data, radar data, and sensor data within a preset perception range of the vehicle.

[0010] The environment perception module further includes a data communication module configured to acquire environment data transmitted by other vehicles within a preset communication range of the vehicle, the environment data transmitted by any vehicle being indicative of an environment within the preset perception range of the vehicle.

[0011] In some embodiments, the data processing module is configured to perform target detection based on the environment perception data to obtain the object recognition result; and perform modeling based on the object recognition result to obtain the three-dimensional environment model.

[0012] In some embodiments, the path planning module is configured to simulate vehicle driving conditions in different regions of the surrounding environment of the vehicle based on the target environment data to obtain risk assessment data, the risk assessment data being indicative of risk levels of accidents occurring in different regions of the surrounding environment of the vehicle.

[0013] The path planning module is further configured to determine a plurality of candidate paths from the current location to the target location based on the target environment data; determine a plurality of pre-screening paths from the plurality of candidate paths based on path data of the plurality of candidate paths and the risk assessment data, each pre-screening path including regions within a preset risk level range; select the target path from the plurality of pre-screening paths based on a preset rule, and determine path data of the target path.

[0014] In some embodiments, the path planning module is configured to predict motion conditions of dynamic objects in the surrounding environment of the vehicle based on the target environment data to obtain object prediction data, the object prediction data being indicative of predicted movement trajectories of different dynamic objects in the surrounding environment of the vehicle.

[0015] The path planning module is further configured to determine a reference path from the current location to the target location based on data of the static objects indicated by the three-dimensional environment model; adjust a local path in the reference path based on the object prediction data to obtain the target path, and determine path data of the target path.

[0016] In another aspect, a vehicle path planning method is provided, which is applied to a vehicle path planning system configured in a vehicle, the vehicle path planning system comprising an environment perception module, a data processing module, a path planning module, and a vehicle control module, and the method comprises:

[0017] The environment perception module is configured to acquire environment perception data, the environment perception data being used to indicate road structure and object distribution in a surrounding environment of the vehicle, and send the environment perception data to the data processing module.

[0018] The data processing module is configured to determine target environment data based on the environment perception data, the target environment data comprising object recognition results and a three-dimensional environment model, the object recognition results being used to indicate object types, spatial positions, and geometric shapes of different objects in the surrounding environment of the vehicle, and the three-dimensional environment model being used to indicate static objects and dynamic objects in the surrounding environment of the vehicle, the object types comprising roads, traffic signs, vehicles, pedestrians, and obstacles, and send the target environment data to the path planning module.

[0019] The path planning module is configured to determine path data of a target path from a current position corresponding to the vehicle to a target position based on the target environment data, and send the path data of the target path to the vehicle control module.

[0020] The vehicle control module is configured to determine driving control data based on the path data of the target path, the driving control data comprising at least one of speed control data, gear control data, and steering control data, and control the vehicle to perform a corresponding driving action based on the driving control data.

[0021] In some embodiments, the environment perception module comprises a data acquisition module and a data communication module.

[0022] The environment perception module is configured to acquire environment perception data, which comprises at least one of the following:

[0023] The data acquisition module is configured to acquire image data, radar data, and sensor data within a preset perception range of the vehicle, and the data communication module is configured to acquire environment data sent by other vehicles within a preset communication range of the vehicle, the environment data sent by any vehicle being used to indicate an environment within the preset perception range of the vehicle.

[0024] In some embodiments, the data processing module is configured to determine target environment data based on the environment perception data, which comprises:

[0025] The data processing module performs target detection based on the environment perception data to obtain the object recognition result; and performs modeling based on the object recognition result to obtain the three-dimensional environment model.

[0026] In some embodiments, the path planning module determines, based on the target environment data, path data of a target path from a current position corresponding to the vehicle to a target position.

[0027] The path planning module determines, based on the target environment data, a plurality of candidate paths from the current position to the target position; simulates, based on the target environment data, vehicle driving conditions in different regions in the vehicle surrounding environment to obtain risk assessment data, the risk assessment data being used to indicate risk levels of accidents in different regions in the vehicle surrounding environment; determines, based on path data of the plurality of candidate paths and the risk assessment data, a plurality of preliminary screening paths from the plurality of candidate paths, each preliminary screening path including regions within a preset risk level range; and selects, based on a preset rule, the target path from the plurality of preliminary screening paths and determines path data of the target path.

[0028] In some embodiments, the path planning module determines, based on the target environment data, path data of a target path from a current position corresponding to the vehicle to a target position.

[0029] The path planning module determines, based on data of the static object indicated by the three-dimensional environment model, a reference path from the current position to the target position; predicts, based on the target environment data, motion conditions of dynamic objects in the vehicle surrounding environment to obtain object prediction data, the object prediction data being used to indicate predicted moving tracks of different dynamic objects in the vehicle surrounding environment; adjusts, based on the object prediction data, a local path in the reference path to obtain the target path and determine path data of the target path.

[0030] In another aspect, a vehicle-mounted control system is provided, which includes a master control module including a processor and a memory, the memory being used to store at least one piece of computer program, the at least one piece of computer program being loaded and executed by the processor to implement the vehicle path planning method in the embodiments of the present application.

[0031] In another aspect, a computer readable storage medium is provided, which stores at least one piece of computer program, the at least one piece of computer program being loaded and executed by a processor to implement the vehicle path planning method in the embodiments of the present application.

[0032] In another aspect, a computer program product is provided, including a computer program executed by a processor to implement the vehicle path planning method in the embodiments of the present application.

[0033] The present application provides a vehicle path planning method, which is configured in a vehicle, and the system includes an environment perception module, a data processing module, a path planning module, and a vehicle control module. The environment perception module can obtain environment perception data in real time to perceive the dynamic changes of the surrounding environment of the vehicle in real time. The data processing module can determine target environment data based on the environment perception data, and the target environment data includes object recognition results and a three-dimensional environment model, which realizes real-time understanding of the dynamic changes of the surrounding environment of the vehicle. The path planning module can determine path data of a target path from the current position of the vehicle to a target position based on the target environment data. The vehicle control module can determine driving control data based on the path data of the target path, and control the vehicle to perform corresponding driving actions based on the driving control data. Through the above-mentioned modules, the present application can perceive and understand the dynamic changes of the surrounding environment of the vehicle in real time, so as to dynamically adjust the path, thereby improving the accuracy and adaptability of path planning, and ensuring the safety and efficiency during driving. BRIEF DESCRIPTION OF DRAWINGS

[0034] In order to more clearly illustrate the technical solutions in the embodiments of the present application, the drawings needed in the embodiment description will be briefly introduced. Obviously, the drawings in the following description are only some embodiments of the present application, and other drawings can be obtained by those skilled in the art without creative labor.

[0035] Figure 1 is a schematic diagram of a vehicle path planning system according to an embodiment of the present application;

[0036] Figure 2 is a flowchart of a vehicle path planning method according to an embodiment of the present application;

[0037] Figure 3 is a flowchart of another vehicle path planning method according to an embodiment of the present application;

[0038] Figure 4 is a structural schematic diagram of a vehicle control system according to an embodiment of the present application. DETAILED DESCRIPTION

[0039] In order to make the purpose, technical solutions and advantages of the present application more clear, the embodiments of the present application will be further described in detail below with reference to the drawings.

[0040] The terms "first", "second", and the like are used to distinguish between similar or identical items or items having substantially the same function, and it should be understood that there is no logical or chronological dependency between "first", "second", and "nth", and the number and execution order are not limited.

[0041] The term "at least one" in this application refers to one or more, and the meaning of "multiple" is two or more.

[0042] It should be noted that the information (including but not limited to user device information, user personal information, etc.), data (including but not limited to data for analysis, stored data, displayed data, etc.) and signals involved in this application are authorized by the user or fully authorized by all parties, and the collection, use and processing of related data need to comply with relevant laws, regulations and standards of relevant countries and regions. For example, the environmental perception data involved in this application is obtained under sufficient authorization.

[0043] Figure 1 is a schematic diagram of a vehicle path planning system according to an embodiment of the application. Referring to Figure 1 , the vehicle path planning system includes an environmental perception module 101, a data processing module 102, a path planning module 103, and a vehicle control module 104. The vehicle path planning system is configured in a vehicle 100.

[0044] The environmental perception module 101 is configured to obtain environmental perception data, which is used to indicate the road structure and object distribution in the surrounding environment of the vehicle; and send the environmental perception data to the data processing module 102. The data processing module 102 is configured to determine target environment data based on the environmental perception data, the target environment data including object recognition results and a three-dimensional environment model, the object recognition results being used to indicate the object type, spatial position and geometric shape of different objects in the surrounding environment of the vehicle, and the three-dimensional environment model being used to indicate static objects and dynamic objects in the surrounding environment of the vehicle, the object type including roads, traffic signs, vehicles, pedestrians and obstacles; and send the target environment data to the path planning module 103. The path planning module 103 is configured to determine path data of a target path from a current position corresponding to the vehicle to a target position based on the target environment data; and send the path data of the target path to the vehicle control module 104. The vehicle control module 104 is configured to determine driving control data based on the path data of the target path, the driving control data including at least one of speed control data, gear control data and steering control data; and control the vehicle 100 to perform a corresponding driving action based on the driving control data.

[0045] In some embodiments, the environment perception module 101 comprises a data acquisition module 1011 configured to acquire image data, radar data and sensor data within a preset perception range of the vehicle; the environment perception module 101 further comprises a data communication module 1012 configured to acquire environment data transmitted by other vehicles within a preset communication range of the vehicle, wherein the environment data transmitted by any vehicle is used to indicate the environment within the preset perception range of the vehicle.

[0046] In some embodiments, the data processing module 102 is configured to perform target detection based on the environment perception data to obtain an object recognition result; and perform modeling based on the object recognition result to obtain a three-dimensional environment model.

[0047] In some embodiments, the path planning module 103 is configured to simulate vehicle driving conditions in different regions in the surrounding environment of the vehicle based on the target environment data to obtain risk assessment data, wherein the risk assessment data is used to indicate the risk level of accidents in different regions in the surrounding environment of the vehicle; the path planning module 103 is further configured to determine a plurality of candidate paths from the current location to the target location based on the target environment data; determine a plurality of preliminary screening paths from the plurality of candidate paths based on path data of the plurality of candidate paths and the risk assessment data, wherein each preliminary screening path contains regions within a preset risk level range; select a target path from the plurality of preliminary screening paths based on a preset rule, and determine path data of the target path.

[0048] In some embodiments, the path planning module 103 is configured to predict the motion of dynamic objects in the surrounding environment of the vehicle based on the target environment data to obtain object prediction data, wherein the object prediction data is used to indicate the predicted movement trajectory of different dynamic objects in the surrounding environment of the vehicle; the path planning module 103 is further configured to determine a reference path from the current location to the target location based on data of static objects indicated by the three-dimensional environment model; adjust a local path in the reference path based on the object prediction data to obtain the target path, and determine path data of the target path.

[0049] It should be noted that the vehicle path planning system provided in the above embodiments, when running an application, is only exemplified by the division of the above functional modules, and in actual applications, the above functions can be completed by different functional modules according to needs, i.e., the internal structure of the terminal is divided into different functional modules to complete all or part of the functions described above. In addition, the vehicle path planning system provided in the above embodiments and the following vehicle path planning method embodiments belong to the same concept, and the specific implementation process is shown in the method embodiments.

[0050] Figure 2is a flowchart of a vehicle path planning method provided by an embodiment of the present application, which is executed by a vehicle path planning system configured in a vehicle, the vehicle path planning system comprising an environment perception module, a data processing module, a path planning module, and a vehicle control module, as shown in Figure 2 The method comprises the following steps:

[0051] 201. The system acquires environment perception data through the environment perception module, and sends the environment perception data to the data processing module, the environment perception data being used to indicate the road structure and object distribution around the vehicle.

[0052] In the embodiment of the present application, the environment perception module is mainly used for collecting data for the environment around the vehicle to obtain environment perception data, and performing data transmission and data communication on the environment perception data.

[0053] Optionally, the system acquires the environment perception data through a plurality of devices in communication with the system. The plurality of devices in communication with the system can include laser radar, camera, radar sensor, ultrasonic sensor, and other acquisition devices, and can also include a communication device for transmitting and receiving signals, which can communicate with other vehicles. The embodiment of the present application does not limit the content of the plurality of devices.

[0054] Correspondingly, the environment perception data includes at least one of the radar data collected by the laser radar, the image data collected by the camera, the sensor data collected by the radar sensor, the sensor data collected by the ultrasonic sensor, and the signals sent by other vehicles. The radar data collected by the laser radar is high-resolution point cloud data, which can provide high-precision distance, speed, direction, and obstacle shape information for the vehicle. The image data collected by the camera can indicate color, texture, shape, and other visual information in the environment around the vehicle, which is convenient for identifying road signs, pedestrians, and vehicles. The sensor data collected by the radar sensor can reflect dynamic objects in the far environment. The sensor data collected by the ultrasonic sensor can reflect the information of the near-distance obstacle. The signals sent by other vehicles contain the environment data collected by other vehicles. The environment perception data can reflect the information in the environment around the vehicle, such as roads, traffic signs, pedestrians, other vehicles, and obstacles.

[0055] 202. The system determines target environment data based on the environment perception data through the data processing module, and sends the target environment data to the path planning module, the target environment data including object recognition results and a three-dimensional environment model, the object recognition results being used to indicate the object type, spatial position, and geometric shape of different objects in the environment around the vehicle, and the three-dimensional environment model being used to indicate static objects and dynamic objects in the environment around the vehicle.

[0056] In the embodiments of the present application, after obtaining the environment perception data, the data processing module can perform data processing based on the environment perception data to obtain target environment data, and perform data transmission and data communication on the target environment data. More specifically, by recognizing objects and modeling the environment, object recognition results and a three-dimensional environment model can be obtained, thereby comprehensively understanding the environment around the vehicle. The goal of this stage is to identify safe passing areas and potential hazards.

[0057] The data processing performed by the data processing module can include data preprocessing and data fusion, and the data fusion includes image processing, target detection, semantic segmentation, and data modeling. This stage involves computer vision technology, deep learning algorithms, and data modeling algorithms. To fully understand the environment around the vehicle, objects are divided into different object types, including roads, traffic signs, vehicles, pedestrians, and obstacles. The road structure around the vehicle can be reflected by roads and traffic signs, and the object distribution around the vehicle can be reflected by pedestrians, vehicles, and obstacles.

[0058] The object recognition results can reflect the shape, width, and curvature of the road, the type and position of the traffic sign, the speed, direction, and position of the vehicle and the pedestrian, and the size, shape, and position of the obstacle. The three-dimensional environment model can be used to predict the behavior of the vehicle and the pedestrian.

[0059] 203、The system determines, by the path planning module, path data of a target path from a current position corresponding to the vehicle to a target position based on the target environment data, and sends the path data of the target path to the vehicle control module.

[0060] In the embodiments of the present application, after obtaining the target environment data, the path planning module can determine the path data of the target path based on the target environment data, and perform data transmission and data communication on the path data of the target path. It should be noted that traffic rules, speed limits, obstacle positions, and dynamic environmental changes are considered in this stage to ensure safety and efficiency.

[0061] The path planning module is used to predict traffic conditions, road condition changes, and behaviors of other vehicles and pedestrians based on the target environment data to achieve efficient and safe path generation. After obtaining the position data of the current position and the position data of the target position, the path planning module can directly generate the target path in combination with the target environment data; or it can first generate an initial path, and then correct it to obtain the target path in combination with the target environment data; or it can generate multiple candidate paths, and then filter them to obtain the target path in combination with the target environment data. The specific determination method of the target path is not selected in the embodiments of the present application.

[0062] 204、The system determines driving control data based on the path data of the target path through the vehicle control module, the driving control data including at least one of speed control data, gear control data, and steering control data, and controls the vehicle to perform corresponding driving actions based on the driving control data.

[0063] In the embodiments of the present application, the vehicle control module can calculate specific driving actions based on the path planning data after obtaining the path planning data, and obtain driving control data, i.e., corresponding control instructions. The system controls the vehicle by sending the above control instructions to multiple controllers in communication with the system, so as to make the vehicle travel according to the target path. The multiple controllers in communication with the system include a motor controller, a transmission controller, and a steering controller.

[0064] The embodiments of the present application provide a vehicle path planning method, which can realize vehicle path planning and automatic driving through a vehicle path planning system configured in a vehicle. The environment perception module can obtain environment perception data in real time to realize real-time perception of dynamic changes of the surrounding environment of the vehicle. The data processing module can determine target environment data such as object recognition results and three-dimensional environment models based on the environment perception data. The path planning module can determine path data of the target path, and the vehicle control module can control the vehicle to perform corresponding driving actions, so as to travel according to the target path. The present scheme can realize real-time perception and understanding of dynamic changes of the surrounding environment of the vehicle, so as to dynamically adjust the path, thereby improving the accuracy and adaptability of path planning, and ensuring safety and high efficiency during driving.

[0065] The above Figure 2 The main process of the vehicle path planning method provided by the embodiments of the present application is exemplarily shown, and the following will be a detailed description of the multiple contents in the method. Figure 3 is a flowchart of another vehicle path planning method provided by the embodiments of the present application, which is executed by a vehicle path planning system configured in a vehicle, as shown in Figure 3 The method includes the following steps:

[0066] 301、The system obtains environment data within a preset perception range of the vehicle through the environment perception module, the environment data including at least one of image data, radar data, and sensor data.

[0067] In the embodiment of the present application, the environment perception module includes a data acquisition module, which acquires data through a plurality of connected acquisition devices. The plurality of acquisition devices include a laser radar, a camera, a radar sensor, an ultrasonic sensor, etc. The plurality of acquisition devices are used to collect information of the environment around the vehicle in real time. The system acquires the environment perception data by acquiring the data sent by the plurality of acquisition devices through the data acquisition module.

[0068] The preset perception range refers to the coverage area of the environment perception module for collecting information in real time by integrating a plurality of components. The preset perception range can be dynamically adjusted according to the driving requirements of the vehicle, so as to comprehensively detect the environment around the vehicle. For example, when the road condition is relatively complex, a larger preset perception range is used to obtain more data; when the road condition is relatively simple, a smaller preset perception range is used to save performance.

[0069] 302、The system acquires the environment data sent by other vehicles within the preset communication range of the vehicle through the environment perception module, and the environment data sent by any vehicle is used to indicate the environment within the preset perception range of the vehicle.

[0070] In the embodiment of the present application, the environment perception module includes a data communication module, which acquires data through a communication device connected for signal transmission and reception. The communication device can acquire the wireless broadcast signal sent by other vehicles within the preset communication range of the vehicle. After the communication device acquires the wireless broadcast signal, the system acquires the corresponding data of the wireless broadcast signal sent by the communication device and performs analysis through the data communication module, so as to obtain the environment perception data.

[0071] The wireless broadcast signal includes not only the environment data within the preset perception range of the vehicle sending the signal, but also the coordinates of the vehicle sending the signal and the direction of travel of the vehicle sending the signal, etc. In the case that the current vehicle acquires the wireless broadcast signal, the relative position between the vehicle sending the signal and the current vehicle can be determined, which facilitates subsequent object recognition and modeling according to the environment data carried by the wireless broadcast signal.

[0072] The preset communication range refers to a coverage area of a wireless broadcast signal transmitted by other vehicles and acquired by the environment perception module. The preset communication range can be dynamically adjusted according to the driving requirements of the vehicle, so as to comprehensively detect the surrounding environment of the vehicle. For example, when the road condition is relatively complex, a larger preset communication range is used to obtain environmental data in a larger range around the vehicle, so as to facilitate the planning of a path in advance for the subsequent road condition. When the road condition is relatively simple, there is no need to acquire data in a too large range, and a smaller preset communication range is used, so as to save performance. For example, the preset communication range refers to a range of ten meters in all directions around the current vehicle; or the preset communication range refers to a preset angle range in front of the current vehicle; or the preset communication range refers to a range of five meters in front of and behind the current lane and the adjacent lane.

[0073] It should be noted that steps 301 and 302 are two exemplary ways of acquiring environment perception data by the environment perception module. Alternatively, the system can only perform step 301 or step 302, or can simultaneously perform steps 301 and 302, and the embodiments of the present application do not limit this. The data obtained by the system by performing the above steps are all environment perception data, which are sent to the data processing module by the environment perception module.

[0074] 303. The system determines target environment data based on the environment perception data by using the data processing module, the environment perception data including environment data within a preset perception range of the vehicle and environment data sent by other vehicles, and the target environment data including an object recognition result and a three-dimensional environment model.

[0075] In the embodiments of the present application, the principle of determining the target environment data by the system is the same as that of step 202, and will not be described here.

[0076] In some embodiments, the system sequentially obtains the object recognition result and the three-dimensional environment model based on the environment perception data. Accordingly, after the data processing module acquires the environment perception data, it first performs data preprocessing. Data preprocessing is used to organize the original environment perception data, so as to ensure the effectiveness and accuracy of the subsequent processing steps. Data preprocessing can include at least one of data cleaning, data format conversion, data normalization or standardization, and data dimension reduction. Data cleaning is used to remove noise, fill in missing values, or correct incorrect data; data format conversion is used to adapt to different algorithm requirements; data normalization or standardization is used to eliminate the dimensional difference between different data sources; and data dimension reduction is used to reduce the complexity of data by extracting key features.

[0077] After obtaining the pre-processed environment perception data, the data processing module obtains an object recognition result based on the environment perception data through image processing, target detection, and semantic segmentation. Image processing is used for image data collected by the camera to improve the quality and extract key features, such as denoising, contrast enhancement, edge sharpening, etc. Target detection is used to identify and locate different objects from the image. Semantic segmentation is used to further refine the identification task, based on the pixel points in the image, to classify and distinguish the object types of different objects based on the located different objects. After obtaining the object recognition result, the data processing module obtains a three-dimensional environment model based on the object recognition result through data modeling.

[0078] It should be noted that the multiple data processing processes in step 303 can be implemented by an environment perception model. The environment perception model is a deep learning model suitable for intelligent driving scenarios, which is used to accurately perceive and understand dynamic information such as roads, obstacles, and traffic flow from environment perception data. The environment perception model applies a large amount of driving data and environment perception data, etc. training data in the training stage, which includes sensor data, vehicle state information, and road condition data, etc. During the collection of training data, data preprocessing and labeling are also needed so that the model can accurately identify and understand environmental information during training, which will not be described here.

[0079] 304、The system determines the path data of the target path from the current position of the vehicle to the target position based on the target environment data through the path planning module.

[0080] In the embodiments of the present application, the principle of the system determining the path data of the target path is the same as step 203, which will not be described here.

[0081] In some embodiments, the system determines the target path in combination with risk assessment data. Accordingly, the system determines multiple candidate paths from the current position to the target position based on the target environment data through the path planning module; simulates the driving conditions of vehicles in different regions in the vehicle's surrounding environment based on the target environment data to obtain risk assessment data; determines multiple preliminary screening paths from the multiple candidate paths based on the path data of the multiple candidate paths and the risk assessment data; selects the target path from the multiple preliminary screening paths based on a preset rule, and determines the path data of the target path. The risk assessment data is used to indicate the risk level of accidents in different regions in the vehicle's surrounding environment. Each preliminary screening path contains regions within the preset risk level range. That is, if any candidate path passes through a region outside the preset risk level range, the candidate path is discarded; if any candidate path does not pass through a region outside the preset risk level range, the candidate path is retained and determined as a preliminary screening path.

[0082] In some embodiments, the system determines the target path in combination with object prediction data. Accordingly, the system determines, by the path planning module, a reference path from the current position to the target position based on data of static objects indicated by the three-dimensional environment model; predicts, based on the target environment data, movements of dynamic objects in the surrounding environment of the vehicle to obtain the object prediction data; adjusts a local path in the reference path based on the object prediction data to obtain the target path, and determines path data of the target path. The object prediction data is used to indicate predicted moving trajectories of different dynamic objects in the surrounding environment of the vehicle. For example, the object prediction data is used to indicate predicted moving trajectories of pedestrians or other vehicles. Optionally, the object prediction data can also include predicted moving speeds.

[0083] It should be noted that the data processing process in step 304 can be implemented by a path planning algorithm for dynamic environment. The path planning algorithm for dynamic environment can dynamically adjust the path in real time according to the environmental information and the vehicle state, consider the traffic conditions, road condition changes and behaviors of other vehicles, and realize efficient and safe path generation. The algorithm combines deep learning and traditional path planning algorithms such as A*, Dijkstra and neural RRT*, which enables the system to fully utilize the environmental perception data for path optimization.

[0084] It should be noted that the environmental perception model and the path planning algorithm for dynamic environment are integrated in the system, and the system performance can be continuously improved through online learning and optimization strategies. This includes system architecture design, algorithm implementation, hardware platform adaptation and other aspects to ensure that the system can run stably and perform excellently in different scenarios.

[0085] 305、The system determines, by the vehicle control module, driving control data based on the path data of the target path, and controls the vehicle to perform corresponding driving actions based on the driving control data. The driving control data includes at least one of speed control data, gear control data and steering control data.

[0086] In the embodiments of the present application, the principle and step 204 of the system controlling the vehicle to perform corresponding driving actions are the same, and will not be repeated here. During the operation of the system, feedback data is collected by monitoring the operation of the system, and the performance of the system is continuously improved.

[0087] The embodiment of the present application provides a vehicle path planning method, which can realize vehicle path planning and automatic driving through a vehicle path planning system arranged in a vehicle. The system can collect environment data of a current surrounding of the vehicle in real time or acquire environment data sent by other vehicles to realize real-time sensing of dynamic changes of the surrounding environment of the vehicle. After target environment data are determined based on the environment sensing data, the system can determine path data of a target path in combination with risk evaluation data or object prediction data, so as to control the vehicle to perform corresponding driving actions and thus drive according to the target path.

[0088] The embodiment of the present application can at least realize the following technical effects: (1) Dynamic environment sensing: real-time sensing and understanding of dynamic changes of the surrounding environment of the vehicle, including traffic flow, road conditions and obstacle information. This fully considers dynamic traffic conditions, road conditions and behaviors of other vehicles, so that the system can more accurately understand real-time traffic conditions and thus make more reliable path planning. Even in the face of real-time traffic congestion, accidents or construction, the system can also obtain optimal path planning; (2) Real-time path planning: in combination with an environment sensing model, a path planning algorithm for a dynamic environment is obtained. The algorithm can dynamically adjust the path according to real-time environment information and vehicle state, the system can make timely and safe driving decisions, and the safety and efficiency in the driving process are ensured; (3) Online learning and optimization: by introducing an online learning mechanism, the system can continuously optimize the environment sensing model and the path planning algorithm to adapt to different driving scenarios and user needs, so as to improve the accuracy and adaptability of path planning. This enables the system to continuously improve and adapt to new driving environments and maintain high path planning capability.

[0089] Figure 4 FIG. 1 is a structural schematic diagram of a vehicle control system according to the embodiment of the present application.

[0090] Generally, the vehicle control system 400 includes a main control module 401 and a CAN interface 402. The main control module 401 is connected with the CAN interface 402.

[0091] The main control module 401 generally includes a processor and a memory. The main control module is configured with a vehicle path planning system provided by the system embodiment in the present application. The main control module is used to realize a vehicle path planning method provided by the method embodiment in the present application.

[0092] The processor can include one or more processing cores, such as a 4-core processor, a 4-core processor, etc. The processor can be implemented in at least one of a hardware form of a DSP (Digital Signal Processing), an FPGA (Field-Programmable Gate Array), a PLA (Programmable Logic Array). The processor can also include a main processor and a coprocessor. The main processor is a processor for processing data in an awake state, also known as a CPU (Central Processing Unit). The coprocessor is a low-power processor for processing data in a standby state. In some embodiments, the processor can be integrated with a GPU (Graphics Processing Unit) for rendering and drawing the content required to be displayed on the vehicle display screen. In some embodiments, the processor can also include an AI (Artificial Intelligence) processor for processing machine learning-related computing operations. The memory can include one or more computer-readable storage media, which can be non-transitory. The memory can also include a high-speed random access memory and a non-volatile memory such as one or more disk storage devices, flash memory storage devices. In some embodiments, the non-transitory computer-readable storage medium in the memory is used to store at least one computer program for being executed by the processor to implement the vehicle path planning method provided by the method embodiment in the present application.

[0093] The CAN interface 402 can include a plurality of controller interfaces for communicating with a motor controller, a transmission controller, a steering controller, etc. respectively. The master control module 401 can be connected with the plurality of controllers through the plurality of controller interfaces, and send control instructions to the plurality of controllers to control the vehicle. Among them, the motor controller, the transmission controller and the steering controller are all execution components of the vehicle. The motor controller is used to control the motor speed and torque output of the vehicle to realize speed control and power output. The transmission controller is used to adjust the gear of the vehicle to match the current driving speed and power demand. The steering controller is used to control the steering system of the vehicle to realize steering control.

[0094] The CAN interface 402 can also include a plurality of collection device interfaces for communicating with a laser radar (Light Detection And Ranging, LiDAR), a camera, a radar sensor, and an ultrasonic sensor, etc. The master control module 401 can be connected to the plurality of collection devices through the plurality of collection device interfaces, and obtain data sent by the plurality of collection devices to achieve collection of environmental data around the vehicle. The laser radar can generate high-resolution point cloud data by emitting a laser beam and obtaining a signal reflected back. The camera can collect images of the environment around the vehicle. The radar sensor has long-distance detection capability and is not affected by light conditions, and can collect information of dynamic objects in a long-distance environment. The ultrasonic sensor can collect information of obstacles in a short distance.

[0095] The CAN interface 402 can also include a device interface for communicating with a communication device. The master control module 401 can be connected to the communication device through the device interface, and obtain data corresponding to a wireless broadcast signal sent by the communication device. Since the wireless broadcast signal is a signal containing environmental data sent by other vehicles within a preset communication range of the current vehicle, the master control module 401 can achieve collection of environmental data around the vehicle by analyzing the data corresponding to the wireless broadcast signal.

[0096] Those skilled in the art can understand that Figure 4 The structure shown in the figure does not constitute a limitation on the vehicle control system 400, and can include more or fewer components than shown, or combine certain components, or use different component arrangements.

[0097] The embodiment of the present application also provides a computer readable storage medium, and at least one computer program is stored in the computer readable storage medium. The at least one computer program is loaded and executed by a processor to implement the vehicle path planning method in the above embodiment. For example, the computer readable storage medium can be a read-only memory (Read-Only Memory, ROM), a random access memory (Random Access Memory, RAM), a compact disc read-only memory (Compact Disc Read-Only Memory, CD-ROM), a magnetic tape, a floppy disk, and an optical data storage device, etc.

[0098] The embodiment of the present application also provides a computer program product, which includes a computer program executed by a processor to implement the vehicle path planning method in the embodiment of the present application.

[0099] Those skilled in the art can understand that all or part of the steps of the above-mentioned embodiments can be completed by hardware, or can be instructed by a program to instruct relevant hardware to complete, and the program can be stored in a computer readable storage medium. The storage medium mentioned above can be a read-only memory, a magnetic disk or an optical disk, etc.

[0100] The above only describes optional embodiments of the present application and is not intended to limit the present application. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present application shall be included in the protection scope of the present application.

Claims

1. A vehicle path planning system, characterized by, A vehicle path planning system is configured in a vehicle, and the vehicle path planning system comprises an environment perception module, a data processing module, a path planning module and a vehicle control module. The environment perception module is configured to acquire environment perception data, and the environment perception data is used to indicate road structure and object distribution in a surrounding environment of the vehicle; and the environment perception data is sent to the data processing module. The data processing module is configured to determine target environment data based on the environment perception data, the target environment data comprises object recognition results and a three-dimensional environment model, the object recognition results are used to indicate object types, spatial positions and geometric shapes of different objects in the surrounding environment of the vehicle, and the three-dimensional environment model is used to indicate static objects and dynamic objects in the surrounding environment of the vehicle, and the object types comprise roads, traffic signs, vehicles, pedestrians and obstacles; and the target environment data is sent to the path planning module. The path planning module is configured to determine path data of a target path from a current position corresponding to the vehicle to a target position based on the target environment data. The path data of the target path is sent to the vehicle control module. The vehicle control module is configured to determine driving control data based on the path data of the target path, the driving control data comprises at least one of speed control data, gear control data and steering control data; and the vehicle is controlled to perform corresponding driving actions based on the driving control data. The path planning module is configured to predict motion conditions of the dynamic objects in the surrounding environment of the vehicle based on the target environment data to obtain object prediction data, and the object prediction data is used to indicate predicted moving tracks of different dynamic objects in the surrounding environment of the vehicle. The path planning module is further configured to determine a reference path from the current position to the target position based on data of the static objects indicated by the three-dimensional environment model; adjust a local path in the reference path based on the object prediction data to obtain the target path, and determine the path data of the target path.

2. The vehicle path planning system of claim 1, wherein, The environment perception module comprises a data acquisition module, and the data acquisition module is configured to acquire image data, radar data and sensor data within a preset perception range of the vehicle. The environment perception module further comprises a data communication module, and the data communication module is configured to acquire environment data sent by other vehicles within a preset communication range of the vehicle, and the environment data sent by any vehicle is used to indicate an environment within the preset perception range of the vehicle.

3. The vehicle path planning system of claim 1, wherein, The data processing module is configured to perform target detection based on the environment perception data to obtain the object recognition results; and perform modeling based on the object recognition results to obtain the three-dimensional environment model.

4. The vehicle path planning system of claim 1, wherein, The path planning module is configured to simulate vehicle driving conditions in different regions in the surrounding environment of the vehicle based on the target environment data to obtain risk assessment data, and the risk assessment data is used to indicate risk levels of accidents occurring in different regions in the surrounding environment of the vehicle. The path planning module is further configured to determine a plurality of candidate paths from the current position to the target position based on the target environment data; determine a plurality of pre-screening paths from the plurality of candidate paths based on path data of the plurality of candidate paths and the risk assessment data, each pre-screening path containing a region within a preset risk level range; select the target path from the plurality of pre-screening paths based on a preset rule, and determine path data of the target path.

5. A vehicle path planning method characterized by, The vehicle path planning system configured in a vehicle includes an environment perception module, a data processing module, a path planning module, and a vehicle control module, and the method includes: acquiring environment perception data through the environment perception module, the environment perception data being used to indicate road structures and object distributions in a surrounding environment of the vehicle; and sending the environment perception data to the data processing module; determining target environment data based on the environment perception data through the data processing module, the target environment data including object recognition results and a three-dimensional environment model, the object recognition results being used to indicate object types, spatial positions, and geometric morphologies of different objects in the surrounding environment of the vehicle, and the three-dimensional environment model being used to indicate static objects and dynamic objects in the surrounding environment of the vehicle, the object types including roads, traffic signs, vehicles, pedestrians, and obstacles; and sending the target environment data to the path planning module; determining path data of a target path from a current position corresponding to the vehicle to a target position based on the target environment data through the path planning module; and sending the path data of the target path to the vehicle control module; determining driving control data based on the path data of the target path through the vehicle control module, the driving control data including at least one of speed control data, gear control data, and steering control data; and controlling the vehicle to perform corresponding driving actions based on the driving control data; wherein the determination of the path data of the target path from the current position corresponding to the vehicle to the target position based on the target environment data through the path planning module includes: determining a reference path from the current position to the target position based on data of the static objects indicated by the three-dimensional environment model through the path planning module; predicting motion conditions of the dynamic objects in the surrounding environment of the vehicle based on the target environment data to obtain object prediction data, the object prediction data being used to indicate predicted moving tracks of different dynamic objects in the surrounding environment of the vehicle; adjusting a local path in the reference path based on the object prediction data to obtain the target path, and determining path data of the target path.

6. The vehicle path planning method according to claim 5, characterized by, The environment perception module includes a data acquisition module and a data communication module; the acquisition of the environment perception data through the environment perception module includes at least one of the following: acquiring image data, radar data, and sensor data within a preset perception range of the vehicle through the data acquisition module; and The data communication module is configured to acquire environmental data transmitted by other vehicles within a preset communication range of the vehicle, the environmental data transmitted by any vehicle being used to indicate an environment within a preset perception range of the vehicle.

7. The vehicle path planning method according to claim 5, characterized by, The data processing module is configured to determine target environmental data based on the environmental perception data, including: The data processing module is configured to perform target detection based on the environmental perception data to obtain the object recognition result, and perform modeling based on the object recognition result to obtain the three-dimensional environmental model.

8. The vehicle path planning method according to claim 5, characterized by, The path planning module is configured to determine path data of a target path from a current position corresponding to the vehicle to a target position based on the target environmental data, including: The path planning module is configured to determine a plurality of candidate paths from the current position to the target position based on the target environmental data, simulate vehicle driving conditions in different regions in the surrounding environment of the vehicle based on the target environmental data to obtain risk assessment data, the risk assessment data being used to indicate a risk level of accidents occurring in different regions in the surrounding environment of the vehicle, determine a plurality of preliminary screening paths from the plurality of candidate paths based on path data of the plurality of candidate paths and the risk assessment data, each preliminary screening path including regions within a preset risk level range, and select the target path from the plurality of preliminary screening paths based on a preset rule and determine path data of the target path.

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