Path planning method and device, vehicle and storage medium

Abstract

The invention provides a path planning method and device, a vehicle and a storage medium, and relates to the technical field of automatic driving and intelligent traffic, the method comprises the following steps: obtaining first navigation data of a second vehicle, the second vehicle and a first vehicle of which a navigation route is to be determined belong to the same ad hoc network, and the first navigation data are acquired; according to the second navigation data of the first vehicle and the first navigation data of the second vehicle, the first target navigation route of the first vehicle is determined, the most reasonable navigation route of the first vehicle is determined by sharing the navigation data of the vehicles in the same ad hoc network, and the navigation route determination accuracy is improved.

Description

Technical Field

[0001] The present application relates to the field of autonomous driving and intelligent transportation technology, and in particular to a path planning method, device, vehicle and storage medium. Background Art

[0002] With the development of intelligent vehicles, intelligent driving functions are receiving more and more attention from car companies. For intelligent driving functions, rationally planning the vehicle's driving route is an important part of ensuring the safe driving of the vehicle. With the increasing severity of urban traffic congestion, the traditional navigation system has low data update efficiency and certain limitations in responding to real-time road condition changes, which cannot meet the needs of intelligent driving.

[0003] Therefore, how to improve the accuracy of navigation route determination is a technical problem that needs to be solved. Summary of the invention

[0004] The present application aims to solve one of the technical problems in the related art at least to some extent.

[0005] To this end, the present application proposes a path planning method, device, vehicle and storage medium to determine the most reasonable navigation route for the first vehicle by sharing the navigation data of vehicles in the same ad hoc network, thereby improving the accuracy of navigation route determination.

[0006] In one aspect, an embodiment of the present application provides a path planning method, including:

[0007] Acquire first navigation data of a second vehicle; wherein the second vehicle and the first vehicle whose navigation route is to be determined belong to the same ad hoc network;

[0008] A first target navigation route of the first vehicle is determined according to the second navigation data of the first vehicle and the first navigation data of the second vehicle.

[0009] Another aspect of the present application provides a path planning device, including:

[0010] An acquisition module, configured to acquire first navigation data of a second vehicle; wherein the second vehicle and the first vehicle whose navigation route is to be determined belong to the same ad hoc network;

[0011] The determination module is used to determine a first target navigation route of the first vehicle according to the second navigation data of the first vehicle and the first navigation data of the second vehicle.

[0012] Another aspect of the present application provides a vehicle, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein when the processor executes the program, the method described in the above aspect is implemented.

[0013] Another aspect of the present application provides a non-transitory computer-readable storage medium having a computer program stored thereon. When the computer program is executed by a processor, the method described in the aforementioned aspect is implemented.

[0014] Another aspect of the present application provides a computer program product on which a computer program is stored. When the program is executed by a processor, the method described in the above aspect is implemented.

[0015] The path planning method, device, vehicle and storage medium proposed in the present application obtain the first navigation data of the second vehicle, wherein the second vehicle and the first vehicle whose navigation route is to be determined belong to the same self-organizing network, determine the first target navigation route of the first vehicle based on the second navigation data of the first vehicle and the first navigation data of the second vehicle, determine the most reasonable navigation route for the first vehicle by sharing the navigation data of vehicles in the same self-organizing network, and improve the accuracy of navigation route determination.

[0016] Additional aspects and advantages of the present application will be given in part in the description below, and in part will become apparent from the description below, or will be learned through the practice of the present application. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] The above and / or additional aspects and advantages of the present application will become apparent and easily understood from the following description of the embodiments in conjunction with the accompanying drawings, in which:

[0018] Figure 1 A schematic diagram of a flow chart of a path planning method provided in an embodiment of the present application;

[0019] Figure 2 A flowchart of another path planning method provided in an embodiment of the present application;

[0020] Figure 3 A schematic diagram of the architecture of a vehicle provided in an embodiment of the present application;

[0021] Figure 4 A flowchart of another path planning method provided in an embodiment of the present application;

[0022] Figure 5 A flowchart of another path planning method provided in an embodiment of the present application;

[0023] Figure 6 A schematic diagram of the structure of a path planning device provided in an embodiment of the present application;

[0024] Figure 7 A block diagram of a vehicle provided in accordance with an embodiment of the present application. DETAILED DESCRIPTION

[0025] Embodiments of the present application are described in detail below, and examples of the embodiments are shown in the accompanying drawings, wherein the same or similar reference numerals throughout represent the same or similar elements or elements having the same or similar functions. The embodiments described below with reference to the accompanying drawings are exemplary and are intended to be used to explain the present application, and should not be construed as limiting the present application.

[0026] The path planning method, device, vehicle and storage medium of the embodiments of the present application are described below with reference to the accompanying drawings.

[0027] Figure 1 A flowchart of a path planning method provided in an embodiment of the present application.

[0028] The embodiment of the present application takes the path planning method being configured in a path planning device as an example. The path planning device can be applied to an on-board device of any vehicle so that the on-board device can execute a command processing function.

[0029] like Figure 1 As shown, the method may include the following steps:

[0030] Step 101, obtaining first navigation data of a second vehicle.

[0031] The second vehicle and the first vehicle whose navigation route is to be determined belong to the same ad hoc network, wherein the ad hoc network is implemented based on short-range wireless communication technology, such as zigbee, Z-Wave, Bluetooth, and Wi-Fi HaLow.

[0032] The first vehicle is a vehicle in any established ad hoc network, and any vehicle in the ad hoc network can be used as the first vehicle. As for the establishment method of the ad hoc network, as an implementation method, the first vehicle is used as an example for explanation. When the first vehicle is started, the wireless communication module in the first vehicle automatically initializes and starts to monitor the surrounding communication signals. When the networking request sent by vehicle C is detected, identity authentication is performed by exchanging digital certificates and keys. In response to the identity authentication passing, it is determined that vehicle C meets the networking conditions, and then a secure communication link between the first vehicle and vehicle C is established. Thereby, networking is achieved between the first vehicle and the vehicle C, and vehicle C is called the second vehicle, where vehicle C can be one or more. In this scenario, since there is a direct communication link between the first vehicle and the second vehicle, data interaction between the first vehicle and the second vehicle can be achieved based on the communication link.

[0033] As a second implementation, when vehicle C, which is networked with the first vehicle, detects a networking request sent by vehicle B, the above-mentioned method of establishing a connection between the first vehicle and vehicle C is adopted, and vehicle C and vehicle B establish a communication link, thereby realizing the networking of vehicle C and vehicle B, that is, the self-networking includes the first vehicle, vehicle B and vehicle C. It should be noted that during the networking process, the networking range and members can be dynamically adjusted considering the mobility of the vehicle and the change of the signal strength.

[0034] Among them, since vehicle C and the first vehicle are networked, the first vehicle, vehicle C and vehicle B belong to the same ad hoc network, so that vehicle C and vehicle B are both second vehicles. In this scenario, there is a communication link between any two vehicles in the ad hoc network, so that for each vehicle in the ad hoc network, the interaction data of other vehicles in the same ad hoc network can be obtained directly and indirectly. For example, taking the first vehicle as vehicle D as an example, vehicle D, vehicle B and vehicle C belong to the same ad hoc network, vehicle B can communicate with vehicle C, and vehicle B sends its first navigation data to vehicle C. Vehicle C sends its first navigation data and the first navigation data of vehicle B to vehicle D based on the communication link between vehicle D.

[0035] The interactive data includes first navigation data, and the first navigation data includes at least one of a navigation route, a congestion status, a vehicle speed, a vehicle position, and the like.

[0036] In one implementation of an embodiment of the present application, the first navigation data of the second vehicle is the initial navigation data obtained by the second vehicle based on navigation software planning, or is the navigation data corresponding to the navigation route re-planned based on the navigation data shared by other vehicles in the same self-organizing network, that is, the first navigation data includes navigation data from the current position of the second vehicle to the destination.

[0037] In a second implementation of the embodiment of the present application, the first navigation data of the second vehicle is a part of the navigation route in the entire navigation route. As an implementation, the initial navigation data of at least one third vehicle is obtained, and the first navigation route to be detected is determined from the initial navigation route of the first vehicle according to the current position of the first vehicle and the information of the road on which it is traveling. The first navigation route to be detected is a navigation route of a set length in front of the current position of the first vehicle. For example, if the information of the road on which it is traveling indicates that the road is an ordinary road, the first navigation route to be detected is a navigation route of 1-2 kilometers in front of the current position of the vehicle; if the information of the road on which it is traveling indicates that the road is an expressway, the first navigation route to be detected is a navigation route of 3-5 kilometers in front of the current position of the vehicle; if the information of the road on which it is traveling indicates that the road is an expressway, the first navigation route to be detected is a navigation route of 6-10 kilometers in front of the current position of the vehicle. As another implementation, according to the information of the road on which it is traveling, the part of the road section that is prone to congestion is determined, and the corresponding first navigation route to be detected is determined.

[0038] Furthermore, the first navigation route to be detected is matched with the initial navigation route of at least one third vehicle to determine the second vehicle, wherein the initial navigation route of the second vehicle contains a second navigation route to be detected that matches the first navigation route to be detected, wherein route matching means that the degree of matching is greater than a set threshold, such as 85%, which is not limited in this embodiment. Based on the second navigation route to be detected of the second vehicle, the first navigation data corresponding to the second navigation route to be detected is determined from the initial navigation data of the second vehicle. Among them, the second vehicle can be one or more.

[0039] Step 102: determining a first target navigation route of the first vehicle according to the second navigation data of the first vehicle and the first navigation data of the second vehicle.

[0040] In an embodiment of the present application, for vehicles belonging to the same self-organizing network, navigation data sharing is achieved, and the navigation data includes information such as navigation routes, congestion conditions, vehicle speed and position provided by other surrounding vehicles. For the current first vehicle, the road condition information on the planned path of the first vehicle can be determined based on the second navigation data of the first vehicle and the first navigation data of the second vehicle. The road condition information includes the vehicle's load, road conditions, congestion conditions, etc., so that it can be determined whether there is congestion on the planned path of the first vehicle. If there is congestion, re-route planning can be carried out, and the re-planned path can be used as the first target navigation route of the first vehicle; if there is no congestion, the current planned path of the first vehicle can continue to be used as the first target navigation route of the first vehicle.

[0041] In the path planning method of the embodiment of the present application, the first navigation data of the second vehicle is obtained, wherein the second vehicle and the first vehicle belong to the same self-organizing network, and the first target navigation route of the first vehicle is determined based on the second navigation data of the first vehicle and the first navigation data of the second vehicle. By sharing the navigation data of vehicles in the same self-organizing network, the most reasonable navigation route for the first vehicle is determined, thereby improving the accuracy of navigation route determination.

[0042] Based on the above embodiments, Figure 2 A flow chart of another path planning method provided in an embodiment of the present application is shown as follows: Figure 2 As shown, the method comprises the following steps:

[0043] Step 201, obtaining first navigation data of a second vehicle.

[0044] The relevant explanations in the aforementioned embodiments are also applicable to step 201 , and the principles are the same, which will not be described again here.

[0045] As an example, Figure 3 A schematic diagram of a vehicle architecture provided in an embodiment of the present application, such as Figure 3 As shown, the vehicle includes a smart connected technology (Vehicle-to-Vehicle Communication, V2V) communication module, a navigation module, a sensor module, a data processing module and a display module.

[0046] Among them, the V2V communication module: adopts Zigbee or similar short-distance communication protocols to support self-organizing networks between vehicles.

[0047] The navigation module is used to generate initial navigation data based on the navigation map, and to obtain real-time traffic data determined by the navigation software based on big data.

[0048] The sensor module is used to collect data such as the speed and position of the vehicle.

[0049] The display module is used to display information to be displayed, such as a planned navigation route or prompt information.

[0050] The data processing module is used to compress and optimize the data shared between vehicles. As a way to implement data compression, a dictionary-based compression algorithm, such as the LZW algorithm, is used to efficiently compress the recurring data patterns in the large amount of real-time navigation and road condition data frequently exchanged in V2V communication, thereby reducing the amount of data transmission, communication delay and bandwidth consumption. In addition, different compression levels are set according to the importance and real-time nature of the data. For example, for emergency road condition information and data directly related to driving safety, a high compression ratio and lossless method are used to ensure that the information is accurate and timely. Preprocessing is performed during compression to remove redundant and invalid information such as the same position or speed repeatedly sent by vehicles in a short period of time, thereby improving compression efficiency.

[0051] As a data optimization, data verification and error correction can be performed. As an implementation method, a navigation data verification rule base is constructed to cover checks on data range, format, logical relationships, etc. In view of the fact that there may be data anomalies caused by signal interference or transmission errors in V2V communication, these rules can ensure that the received navigation data conforms to the expected format and logic. For continuously transmitted navigation data, a sliding window mechanism is used to correct errors and recover lost or erroneous data segments. Especially when the vehicle moves quickly and the communication environment is unstable, the lost data can be effectively recovered to ensure accurate route planning and traffic avoidance decisions. With the help of data redundancy and correlation, cross-validation and comparison of navigation data shared with surrounding vehicles can also be used to more accurately identify and correct erroneous data.

[0052] As data optimization, data filtering and smoothing can also be performed. As an implementation method, multiple filtering methods such as median filtering and Gaussian filtering are used to select appropriate filtering methods based on the characteristics of data collected by vehicle sensors in V2V communication to remove noise and fluctuation interference. As a second implementation method, an adaptive filtering algorithm is introduced to dynamically adjust the filtering parameters according to real-time road conditions. For example, when traffic is congested, the filtering intensity is increased to smooth speed and position data fluctuations, and when the road is unobstructed, the intensity is appropriately reduced to maintain data sensitivity.

[0053] Then, the filtered and smoothed data is post-processed, such as detecting and removing outliers. Abnormal navigation data that obviously deviates from the normal range, such as excessive speed or unreasonable position data, is processed to prevent misleading route planning and traffic avoidance decisions, thereby improving the accuracy of navigation data.

[0054] Among them, for the navigation data transmitted between vehicles, a security mechanism can also be used for verification, as described below:

[0055] As the first implementation method, identity authentication is performed. Specifically, based on digital certificates and the public key infrastructure (PKI) system, a unique digital certificate is issued to each vehicle participating in V2V communication for identity authentication. When the vehicle joins the congestion avoidance communication network, that is, when it joins the ad hoc network, two-way authentication is performed to ensure the authenticity and legitimacy of the vehicle identity and improve the security of communication.

[0056] As the second implementation method, transmission encryption is set. Specifically, in the scenario where V2V vehicles avoid congestion, the Advanced Encryption Standard (AES) 256-bit encryption algorithm is used to encrypt the navigation data transmitted between vehicles, including key information such as the degree of road congestion and the estimated travel time, to prevent malicious theft and tampering. At the same time, an asymmetric encryption algorithm (such as RSA) is used for key exchange to ensure the security of the encryption key during transmission, providing protection for subsequent symmetric encryption communications. A hardware security module (HSM) is introduced to store and process encryption keys to enhance the security and anti-attack capabilities of the keys.

[0057] As a third implementation method, the integrity of the received navigation data is verified. As an implementation method, the message authentication code (MAC) technology is used to generate a unique verification code for the transmitted navigation data. The receiver verifies the MAC to confirm that the data has not been tampered with during the transmission process. As a second implementation method, a hash function (such as SHA-256) is used to calculate the summary of the data, and the summary value is recalculated and compared at the receiving end to ensure the integrity of the data. As a third implementation method, for the continuously transmitted navigation data, a serial number and a timestamp are set, and the integrity of the data is verified by checking the continuity of the serial number and the rationality of the timestamp.

[0058] As the fourth implementation method, control is carried out from the security protocol. The security extension protocol of the IEEE 802.11p standard is adopted to define the security mechanism and message format of inter-vehicle communication. The encryption and authentication mechanisms in the security protocol are used to ensure the communication security of vehicles when they quickly exchange congestion information. The security protocol is regularly scanned and updated for vulnerabilities to deal with emerging security threats.

[0059] As the fifth implementation method, a firewall and intrusion detection mechanism are set up. Specifically, a state detection-based firewall is deployed in the vehicle's communication system to filter and control the data packets according to the source address, destination address, port number and other information. An intrusion detection system (IDS) is also configured to monitor abnormal behaviors in vehicle communication traffic in real time, such as abnormal packet frequency, abnormal access requests, etc. When an intrusion is detected, the alarm mechanism is automatically triggered, and emergency measures such as blocking communication are taken to prevent congestion information from being maliciously interfered with.

[0060] As the sixth implementation method, privacy protection is set up. Specifically, the vehicle's location information is blurred, and spatial anonymization technology is used to convert the vehicle's precise location into an area representation within a certain range, so as to protect the privacy of the vehicle's specific location without affecting the congestion judgment. Differential privacy technology is used to process sensitive data such as vehicle speed and route preferences to ensure that the privacy of individual vehicles is not leaked during the data analysis process. A privacy policy management mechanism is established to allow users to choose whether to share certain sensitive information, and to clearly inform users of the purpose and scope of use of the information.

[0061] As the seventh implementation method, security audit and logging are set up. Specifically, security audit and logging record the communication interaction process between vehicles in detail, including the congestion-related data packets sent and received, authentication and authorization operations, encryption and decryption processes, etc. Blockchain technology is used to store audit logs to ensure that the logs cannot be tampered with and are traceable. Audit logs are analyzed regularly to discover potential security risks and abnormal behavior patterns, providing a basis for adjusting security policies.

[0062] Through the comprehensive application of the above security mechanisms, the communication security and data privacy of V2V vehicles in the process of avoiding congestion can be effectively guaranteed, and the reliability and safety of the transportation system can be improved.

[0063] Step 202: Determine predicted road condition information of a first navigation route to be detected based on the first navigation data and the second navigation data.

[0064] One implementation method of the embodiment of the present application introduces machine learning algorithms, such as decision trees, neural networks, etc., to conduct in-depth analysis of the collected navigation data shared by other surrounding vehicles to obtain predicted road condition information, which includes road congestion, traffic flow, vehicle load and average vehicle speed.

[0065] Step 203, acquiring attribute data and operating parameters of the second vehicle, and attribute data and operating parameters of the first vehicle.

[0066] Among them, attribute data includes vehicle type, brand, load, size, etc. Vehicle operating parameters include speed, performance parameters, remaining battery capacity, etc.

[0067] Step 204, determining a first target navigation route of the first vehicle based on the predicted road condition information, the attribute data and operating parameters of the second vehicle, and the attribute data and operating parameters of the first vehicle.

[0068] In the embodiment of the present application, according to the predicted road condition information, it can be determined whether there are abnormal road conditions on the first navigation route to be detected. If there are abnormal road conditions, it is necessary to re-plan the route to improve the feasibility of the route, that is, re-plan according to the attribute data and operating parameters of the second vehicle and the attribute data and operating parameters of the first vehicle to obtain the first target navigation route of the first vehicle. When planning specifically, the path planning can be performed according to the attribute data and operating parameters of the first vehicle and the second vehicle traveling on the first navigation route to be detected. For example, the remaining battery capacity of the electric vehicle or the remaining fuel volume of the fuel vehicle in the operating parameters, the vehicle in the attribute data, such as a car, an off-road vehicle SUV or a large truck, the vehicle load, etc., are all factors that need to be considered for path re-planning. Since there is more than one path to reach the same destination, it is necessary to reasonably plan according to the attribute data and operating parameters of the vehicle. For example, if the vehicle has insufficient fuel remaining, it is necessary to give priority to the shortest path to achieve the fastest arrival; if the vehicle is a large truck, there are some sections in some routes that are restricted at set times or some routes have requirements on load, then it is necessary to select a passable route to improve the accuracy of path re-planning.

[0069] In the path planning method of the embodiment of the present application, first navigation data of a second vehicle is obtained, wherein the second vehicle and the first vehicle of the navigation route to be determined belong to the same self-organizing network, predicted road condition information of the first navigation route to be detected is determined based on the first navigation data and the second navigation data, attribute data and operating parameters of the second vehicle, as well as the attribute data and operating parameters of the first vehicle are obtained, and a first target navigation route of the first vehicle is determined based on the predicted road condition information, the attribute data and operating parameters of the second vehicle, and the attribute data and operating parameters of the first vehicle, and the road condition of the navigation route to be detected is predicted to obtain predicted road condition information by sharing the navigation data of vehicles in the same self-organizing network, and a new path planning is performed based on the predicted road condition information, as well as the attribute data and operating parameters of the own vehicle and other shared vehicles, to obtain the most reasonable navigation route for the first vehicle, thereby improving the accuracy of navigation route determination.

[0070] Based on the above embodiments, Figure 4 A flow chart of another path planning method provided in an embodiment of the present application is shown as follows: Figure 4 As shown, the method comprises the following steps:

[0071] Step 401, obtaining first navigation data of a second vehicle.

[0072] Step 402: Determine predicted road condition information of a first navigation route to be detected based on the first navigation data and the second navigation data.

[0073] Step 403, acquiring attribute data and operating parameters of the second vehicle, and attribute data and operating parameters of the first vehicle.

[0074] Among them, steps 401 to 403 can refer to the relevant explanations in the aforementioned embodiments, and the principles are the same, so they will not be repeated here.

[0075] Step 404: Obtain at least one of historical traffic information and real-time traffic information of the first navigation route to be detected.

[0076] The historical traffic condition information is the actual traffic condition information of the first navigation route to be detected in the same historical period as the period to which the predicted traffic condition information is generated.

[0077] In an embodiment of the present application, at least one of historical traffic information and real-time traffic information of the first navigation route to be detected is obtained, wherein the historical traffic information is the actual traffic information of the first navigation route to be detected in the same historical period as the time period to which the predicted traffic information is generated. The historical traffic information can be stored locally or in the cloud. The real-time traffic information is generated by the navigation map software based on the acquired big data, and is the current actual traffic information on the first navigation route to be detected.

[0078] Step 405 , determining whether there is an abnormal road condition on the first navigation route to be detected of the first vehicle based on at least one of the historical road condition information and the real-time road condition information, and the predicted road condition information.

[0079] In the embodiment of the present application, it is determined whether there is an abnormal road condition on the first navigation route to be detected of the first vehicle according to at least one of the historical road condition information and the real-time road condition information, and the predicted road condition information. The following describes different implementation methods:

[0080] In a first implementation of the embodiment of the present application, it is determined whether there is an abnormal road condition on the first navigation route to be detected of the first vehicle based on historical road condition information and predicted road condition information, wherein the historical road condition information includes multiple actual road condition information of the same historical period, thereby, based on multiple historical road condition information, it can be determined whether abnormal road conditions often occur on the first navigation route to be detected in the historical period, thereby, comparing the multiple historical road condition information with the predicted road condition information, and the following is explained for different scenarios:

[0081] In one scenario, if historical road condition information with a proportion greater than a set proportion among multiple historical road condition information all indicate that abnormal road conditions occurred on the first navigation route to be detected in the corresponding historical time period, and the predicted road condition information indicates that there are no abnormal road conditions, then it is considered that abnormal road conditions will occur on the first navigation route to be detected, where the set proportion is 90%. This is because if abnormal road conditions have always occurred in this time period in the historical scenario, then the probability of abnormal road conditions occurring at present is very high, and thus it is considered that the accuracy of the predicted road condition information is insufficient, and it is still considered that abnormal road conditions will occur on the first navigation route to be detected.

[0082] In the second scenario, if the historical road condition information among multiple historical road condition information accounts for less than the set proportion, indicating that abnormal road conditions occurred on the first navigation route to be detected in the corresponding historical period, and the predicted road condition information indicates that there are no abnormal road conditions, then it is determined that there are no abnormal road conditions. If there are not many abnormal road conditions on this section of road in the historical scenario, the actual predicted road condition shall prevail, and it is considered that there are abnormal road conditions.

[0083] In the third scenario, if historical road condition information with a proportion less than the set ratio among multiple historical road condition information all indicate that abnormal road conditions occurred on the first navigation route to be detected in the corresponding historical period, and the predicted road condition information indicates the existence of abnormal road conditions, it is determined that abnormal road conditions exist.

[0084] In a second implementation method of the embodiment of the present application, it is determined whether there are abnormal road conditions on the first navigation route to be detected of the first vehicle based on real-time road condition information and predicted road condition information. If the real-time road condition information indicates the existence of abnormal road conditions and the predicted road condition information also indicates the existence of abnormal road conditions, it is considered that there are abnormal road conditions on the first navigation route to be detected of the first vehicle; if the real-time road condition information indicates the existence of abnormal road conditions and the predicted road condition information indicates the absence of abnormal road conditions, the real-time road conditions shall prevail, and it is considered that there are abnormal road conditions on the first navigation route to be detected of the first vehicle; if the real-time road condition information indicates the absence of abnormal road conditions and the predicted road condition information also indicates the absence of abnormal road conditions, it is considered that there are no abnormal road conditions on the first navigation route to be detected of the first vehicle.

[0085] Among them, as a way to achieve the acquisition of real-time traffic information, a real-time traffic information interface is established with the traffic management department to obtain authoritative real-time traffic information and improve the accuracy of route planning.

[0086] In the third implementation of the embodiment of the present application, based on the historical road condition information, the real-time road condition information and the predicted road condition information, it is determined whether there is an abnormal road condition on the first navigation route to be detected of the first vehicle. As an implementation, if the real-time road condition indicates that there is an abnormal road condition, it is considered that there is an abnormal road condition on the first navigation route to be detected of the first vehicle; as a second implementation, if the real-time road condition indicates that there is no abnormal road condition, it is considered that there is no abnormal road condition on the first navigation route to be detected of the first vehicle. As a third implementation, if any two road condition information indicate that there is an abnormal road condition, it is considered that there is an abnormal road condition on the first navigation route to be detected of the first vehicle.

[0087] Step 406, in response to determining that there is an abnormal road condition, replanning is performed based on the attribute data and operating parameters of the second vehicle and the attribute data and operating parameters of the first vehicle to obtain a first target navigation route of the first vehicle.

[0088] The relevant explanations in the aforementioned embodiments are also applicable to step 406 , and the principles are the same, which will not be described again here.

[0089] In one implementation of the embodiment of the present application, the navigation route of the first vehicle is replanned based on the attribute data and operating parameters of the second vehicle and the attribute data and operating parameters of the first vehicle to obtain a replanned navigation route, and the replanned navigation route is evaluated based on the map data and traffic information, that is, whether the replanned navigation route is feasible and legal, wherein legality refers to compliance with traffic regulations, if it is feasible and legal, the evaluation result is normal, if it does not have either of the feasibility and legality, the evaluation result is considered to be abnormal.

[0090] In the first scenario, in response to an abnormality in the evaluation result, the re-planned navigation route is modified according to the evaluation result so that the revised navigation route is feasible and legal. Then, the modified navigation route is used as the first target navigation route of the first vehicle, thereby improving accuracy.

[0091] In the second scenario, in response to an abnormality in the evaluation result, the re-planned navigation route and the evaluation result are sent to the second vehicle; wherein the evaluation result and the re-planned navigation route are used by the second vehicle to determine whether to re-plan the route. As an implementation method, the second vehicle matches its own re-planned navigation route with the navigation route sent by the first vehicle. If the degree of similarity is greater than a set threshold, such as 80%, the routes are considered to be basically the same, and the evaluation result of the first vehicle is used to correct the navigation route planned by itself to improve feasibility and legitimacy. If the degree of similarity is less than the set threshold, the routes are considered inconsistent, and the evaluation result is not used for correction. In addition, if the vehicle has the ability to evaluate itself, it will re-evaluate itself; if the vehicle does not have the ability to evaluate itself, the evaluation result is discarded and no evaluation is performed.

[0092] In the third scenario, in response to an abnormality in the evaluation result, the re-planned navigation route is modified according to the evaluation result, so that the corrected navigation route is feasible and legal. Then, the modified navigation route is used as the first target navigation route of the first vehicle. At the same time, the re-planned navigation route and the evaluation result are sent to the second vehicle, so as to realize not only the route correction for itself but also the route correction for other vehicles around it, so as to save the computing power of other vehicles.

[0093] It should be noted that the vehicle's attribute data also includes the driver's driving preferences, such as giving priority to highways, avoiding toll roads, etc., in order to generate personalized route planning.

[0094] Step 407 , in response to determining that an abnormal road condition exists, generating a first route change request, and sending the first route change request to the second vehicle based on short-range wireless communication technology.

[0095] In the embodiments of the present application, abnormal road conditions generally include deterioration of road conditions caused by multiple factors such as problems with the road itself, abnormal changes in traffic flow, and improper behavior of traffic participants, and are generally abnormal traffic congestion. Improper behavior of traffic participants, such as pedestrians jaywalking, non-motor vehicles occupying lanes, vehicles changing lanes illegally, speeding, drunk driving, etc. In response to determining that there are abnormal road conditions, a route change request is generated, which is referred to as a first route change request for ease of distinction. The first route change request is sent to the second vehicle. When the second vehicle receives the second change request, it learns that there are abnormal road conditions on the route to be traveled, and then re-plans the route. The method for re-planning the route for the second vehicle is the same as that for the first vehicle, which will not be repeated here.

[0096] Step 408, in response to determining that there is an abnormal road condition and the first vehicle has a target demand, a first route change request and a route change reason are generated, and based on the short-range wireless communication technology, the first route change request and the route change reason are sent to the second vehicle.

[0097] Among them, target demand refers to the need to take prompt action to ensure life safety or prevent serious health damage. For example, the first vehicle needs to be transported to a doctor urgently, such as an impending birth or a sudden illness.

[0098] In an embodiment of the present application, in response to determining that an abnormal road condition exists and that the first vehicle has a target demand, a first route change request and a reason for the route change are generated, and based on short-range wireless communication technology, the first route change request and the reason for the route change are sent to the second vehicle, so that the second vehicle understands the reason why the first vehicle requires it to re-plan the route, thereby improving the success rate of the second vehicle's re-route planning to achieve route change.

[0099] Step 409 , in response to receiving feedback information from a set number of second vehicles, taking the initial navigation route of the first vehicle as a first target navigation route.

[0100] The feedback information indicates that the second vehicle determines to re-plan the path.

[0101] In an embodiment of the present application, if the first vehicle receives feedback information from a set number of second vehicles, it means that a set number of second vehicles have re-planned their routes. Then for the first vehicle, in the event of abnormal road conditions, by notifying other vehicles to re-plan their routes, the congested abnormal road conditions can be alleviated, and the vehicle itself does not need to perform route planning.

[0102] In the path planning method of the embodiment of the present application, the first navigation data of the second vehicle is obtained, wherein the second vehicle and the first vehicle of the navigation route to be determined belong to the same ad hoc network, the predicted road condition information of the first navigation route to be detected is determined according to the first navigation data and the second navigation data, the attribute data and operating parameters of the second vehicle and the attribute data and operating parameters of the first vehicle are obtained, and the first target navigation route of the first vehicle is determined according to the predicted road condition information, the attribute data and operating parameters of the second vehicle and the attribute data and operating parameters of the first vehicle, and the road condition of the navigation route to be detected is predicted by sharing the navigation data of the vehicles in the same ad hoc network to obtain the predicted road condition information, and the path planning is re-performed according to the predicted road condition information and the attribute data and operating parameters of the self-vehicle and other shared vehicles to obtain the most reasonable navigation route of the first vehicle, thereby improving the accuracy of determining the navigation route. At the same time, in the process of path planning, if there is path congestion, and the path is a better path for the self-vehicle, such as the shortest driving distance, the shortest time, etc., that is, when it is inconvenient for the self-vehicle to change the route, other vehicles can also be notified to change the route, thereby reducing congestion and improving the smoothness of traffic.

[0103] Based on the above embodiments, Figure 5 A flow chart of another path planning method provided in an embodiment of the present application is shown as follows: Figure 5 As shown, the method comprises the following steps:

[0104] Step 501, obtaining first navigation data of a second vehicle.

[0105] Step 502: Determine a first target navigation route of the first vehicle based on the second navigation data of the first vehicle and the first navigation data of the second vehicle.

[0106] Among them, step 501 and step 502 can refer to the relevant explanations in the aforementioned embodiments, and the principles are the same, so they will not be repeated here.

[0107] Step 503: Obtain the actual driving speed of the first vehicle.

[0108] In the embodiment of the present application, the actual driving speed of the vehicle is collected according to a sensor in the first vehicle, such as a speed sensor. Specifically, the collection can be performed according to a set collection period, and the size of the collection period is not limited in this embodiment.

[0109] Step 504: compare the actual driving speed of the first vehicle with the planned driving speed of the first vehicle corresponding to the first target navigation route of the first vehicle.

[0110] Step 505 , in response to the actual driving speed being less than the planned driving speed, determining a second target navigation route of the first vehicle according to the currently acquired navigation data of the fourth vehicle and the navigation data of the first vehicle.

[0111] In the embodiment of the present application, after the first vehicle re-plans its route, during the process of driving according to the re-planned route, the actual driving speed of the vehicle is collected, and the collected actual driving speed of the vehicle is compared with the planned driving speed. If the actual driving speed is less than the planned driving speed, it means that the first vehicle is not driving smoothly, and there are abnormal conditions such as road congestion. Then, it is necessary to determine the second target navigation route of the first vehicle based on the navigation data of the fourth vehicle currently acquired by the first vehicle and the navigation data of the first vehicle, wherein the fourth vehicle and the first vehicle belong to the same self-organizing network, so that the planned route is continuously updated during driving, the accuracy of the planned route is ensured, and the smoothness of driving is improved. Among them, the method for determining the second target navigation route of the first vehicle can refer to the method for determining the first target navigation route of the first vehicle, and the principle is the same, which will not be repeated here.

[0112] Step 506 , in response to acquiring the target traffic information, determining a second target navigation route of the first vehicle according to the currently acquired navigation data of the fourth vehicle and the navigation data of the first vehicle.

[0113] In one implementation of the embodiment of the present application, after the first vehicle re-plans its route, while driving according to the re-planned route, if target traffic information is obtained, such as traffic control information, such as prohibition of passage due to an accident, abnormal weather, abnormal road surface, etc., the second target navigation route of the first vehicle is determined according to the navigation data of the fourth vehicle and the navigation data of the first vehicle currently obtained, wherein the fourth vehicle and the first vehicle belong to the same self-organizing network, thereby realizing continuous updating of the planned route during driving, ensuring the accuracy of the planned route, and improving the smoothness of driving.

[0114] Step 507: In response to obtaining the target traffic information, a second route change request is generated, and the second route change request is sent to the second vehicle based on the short-range wireless communication technology. In one implementation of the embodiment of the present application, after the first vehicle re-plans the route, during the process of driving according to the re-planned route, if the target traffic information is obtained, a second route change request is generated, and the second route change request is sent to the second vehicle based on the short-range wireless communication technology to notify the second vehicle to change the route.

[0115] In the process of sending the second route change request to the second vehicle, since the levels of the second vehicles are different, it is necessary to determine the level of each second vehicle. As an implementation method, the attribute information of multiple second vehicles is obtained, wherein the attribute information includes the vehicle model, the region to which it belongs, and the purpose of the vehicle (for example, an emergency rescue vehicle). According to the attribute information of each second vehicle, the authority of each second vehicle is determined, and according to the ranking of the authority of each second vehicle, the second route change request is sent to each second vehicle in turn, wherein the ranking is determined from high to low according to the authority of the multiple second vehicles, that is, the higher the authority of the vehicle, the higher the sending level, that is, the vehicle with higher authority is given priority. In the case of insufficient signal bandwidth or poor network, it can be prioritized to ensure that the vehicle with higher authority receives the second route change request first, for example, it is sent to the rescue vehicle first to ensure that the rescue vehicle receives it first, so as to improve safety.

[0116] Among them, the "second" in the second route change request is used for identification. It is different from the sending scenario of the first route change request, but has the same function, both of which are used to prompt the surrounding vehicles to change routes.

[0117] It should be noted that step 506 and step 507 may be performed either one or both at the same time, that is, the vehicle changes its own route and notifies other vehicles in the same ad hoc network to change their routes.

[0118] It should also be noted that after any route change, the target traffic information may be obtained. Each time the target traffic information is obtained, the route can be changed by the vehicle itself, and / or other vehicles in the same ad hoc network can be notified to change their routes.

[0119] In the path planning method of the embodiment of the present application, the first navigation data of the second vehicle is obtained, wherein the second vehicle and the first vehicle of the navigation route to be determined belong to the same ad hoc network, the predicted road condition information of the first navigation route to be detected is determined according to the first navigation data and the second navigation data, the attribute data and operating parameters of the second vehicle and the attribute data and operating parameters of the first vehicle are obtained, and the first target navigation route of the first vehicle is determined according to the predicted road condition information, the attribute data and operating parameters of the second vehicle and the attribute data and operating parameters of the first vehicle, and the navigation data of the vehicles in the same ad hoc network are shared to predict the road condition of the navigation route to be detected to obtain the predicted road condition information, and the path planning is re-performed according to the predicted road condition information, and the attribute data and operating parameters of the self-vehicle and other shared vehicles, and the most reasonable navigation route of the first vehicle is obtained, thereby improving the accuracy of the navigation route determination. At the same time, in the case of inconvenience for the self-vehicle to change the route, other vehicles can also be notified to change the route to reduce congestion and improve the smoothness of the passage. And, in the case of traffic control, the self-vehicle can change the route, and other vehicles can also be notified to change the route to improve the smoothness of the passage.

[0120] Based on the above embodiment, after the first vehicle determines the first target navigation route, it will send the navigation data of the first target navigation route of the first vehicle to the second vehicle based on the short-range wireless communication link. Similarly, after determining the second target navigation route, the second target navigation route will also be shared with the second vehicle. Similarly, other vehicles will also share their new navigation routes after each navigation route is determined, so that vehicles belonging to the same self-organizing network can share with each other. In the sharing process, time division multiple access (TDMA) or code division multiple access (CDMA) and other technologies are used to ensure that the data of multiple vehicles communicating at the same time does not conflict and is accurately transmitted, thereby realizing the function of automatically helping each vehicle to re-route planning during the driving process of the vehicle to achieve the local fastest navigation route. When dealing with changeable real-time road conditions, the route can be planned more timely and accurately to avoid traffic jams. In order to realize the above embodiment, the embodiment of the present application also proposes a path planning device.

[0121] Figure 6 A schematic diagram of the structure of a path planning device provided in an embodiment of the present application.

[0122] like Figure 6 As shown, the device may include:

[0123] Acquisition module 61, for executing acquisition of first navigation data of a second vehicle; wherein the second vehicle and the first vehicle to be determined navigation route belong to the same ad hoc network.

[0124] The determination module 62 is used to determine a first target navigation route of the first vehicle according to the second navigation data of the first vehicle and the first navigation data of the second vehicle.

[0125] Furthermore, in an implementation of the embodiment of the present application, the acquisition module 61 is further configured to:

[0126] Acquiring initial navigation data of at least one third vehicle; wherein the at least one third vehicle and the first vehicle belong to the same ad hoc network;

[0127] Determining a first navigation route to be detected from an initial navigation route of the first vehicle according to information about the current position of the first vehicle and the road on which the first vehicle is traveling;

[0128] Matching the first navigation route to be detected with the initial navigation route of the at least one third vehicle to determine the second vehicle; wherein the initial navigation route of the second vehicle contains a second navigation route to be detected that matches the first navigation route to be detected;

[0129] According to a second navigation route to be detected of the second vehicle, first navigation data of the second vehicle is determined from initial navigation data of the second vehicle.

[0130] In one implementation of the embodiment of the present application, the determination module 62 is further configured to:

[0131] Determining predicted road condition information of the first navigation route to be detected according to the first navigation data and the second navigation data;

[0132] Acquiring attribute data and operating parameters of the second vehicle and attribute data and operating parameters of the first vehicle;

[0133] A first target navigation route of the first vehicle is determined based on the predicted road condition information, the attribute data and operating parameters of the second vehicle, and the attribute data and operating parameters of the first vehicle.

[0134] In one implementation of the embodiment of the present application, the determination module 62 is further configured to:

[0135] Acquire at least one of historical traffic information and real-time traffic information of the first navigation route to be detected; wherein the historical traffic information is the actual traffic information of the first navigation route to be detected in the same historical period as the period of time when the predicted traffic information is generated;

[0136] Determine whether there is an abnormal road condition on the first to-be-detected navigation route of the first vehicle according to at least one of the historical road condition information and the real-time road condition information, and the predicted road condition information;

[0137] In response to determining that an abnormal road condition exists, replanning is performed based on the attribute data and operating parameters of the second vehicle and the attribute data and operating parameters of the first vehicle to obtain a first target navigation route of the first vehicle.

[0138] In one implementation of the embodiment of the present application, the determination module 62 is further configured to:

[0139] In response to determining that an abnormal road condition exists, generating a first route change request;

[0140] Based on short-range wireless communication technology, sending the first route change request to the second vehicle;

[0141] In response to receiving feedback information from a set number of second vehicles, the initial navigation route of the first vehicle is used as the first target navigation route; wherein the feedback information indicates that the second vehicles determine to re-plan the path.

[0142] In one implementation of the embodiment of the present application, the determination module 62 is further configured to:

[0143] In response to determining that an abnormal road condition exists and that the first vehicle has a target demand, generating a first route change request and a route change reason;

[0144] Based on short-range wireless communication technology, sending the first route change request and the route change reason to the second vehicle;

[0145] In response to receiving feedback information from a set number of second vehicles, the initial navigation route of the first vehicle is used as the first target navigation route; wherein the feedback information indicates that the second vehicles determine to re-plan the path.

[0146] In one implementation of the embodiment of the present application, the determination module 62 is further configured to:

[0147] replanning a navigation route of the first vehicle according to the attribute data and operating parameters of the second vehicle and the attribute data and operating parameters of the first vehicle to obtain a replanned navigation route;

[0148] Evaluate the re-planned navigation route based on map data and traffic information;

[0149] In response to an abnormality in the evaluation result, the re-planned navigation route is modified according to the evaluation result, and the modified navigation route is used as a first target navigation route of the first vehicle.

[0150] In one implementation of the embodiment of the present application, the determination module 62 is further configured to:

[0151] In response to an abnormality in the evaluation result, the re-planned navigation route and the evaluation result are sent to the second vehicle; wherein the evaluation result and the re-planned navigation route are used by the second vehicle to determine whether to re-plan the route. In an implementation of the embodiment of the present application, the device further includes:

[0152] The sending module is used to send the navigation data of the first target navigation route of the first vehicle to the second vehicle based on a short-range wireless communication link.

[0153] In one implementation of the embodiment of the present application, the device further includes:

[0154] a comparison module, configured to obtain an actual driving speed of the first vehicle, and compare the actual driving speed of the first vehicle with a planned driving speed of the first vehicle corresponding to a first target navigation route of the first vehicle;

[0155] A re-planning module is used to determine, in response to the actual driving speed being less than the planned driving speed, a second target navigation route of the first vehicle based on currently acquired navigation data of a fourth vehicle and the navigation data of the first vehicle; wherein the fourth vehicle and the first vehicle belong to the same ad hoc network.

[0156] In one implementation of an embodiment of the present application, the re-planning module is also used to determine the second target navigation route of the first vehicle in response to obtaining target traffic information, based on the currently obtained navigation data of the fourth vehicle and the navigation data of the first vehicle; wherein the fourth vehicle and the first vehicle belong to the same self-organizing network.

[0157] In one implementation of the embodiment of the present application, the sending module is further used to generate a second route change request in response to obtaining the target traffic information; based on the short-range wireless communication technology, the second route change request is sent to the second vehicle. In one implementation of the embodiment of the present application, there are multiple second vehicles, and the sending module is further used to

[0158] obtaining attribute information of a plurality of second vehicles;

[0159] Determining the authority of each second vehicle according to the attribute information of each second vehicle;

[0160] The second route change request is sent to each second vehicle in sequence according to the ranking of the authority of each second vehicle; wherein the ranking is determined from high to low according to the authority of the plurality of second vehicles.

[0161] It should be noted that the above explanation of the method embodiment is also applicable to the device of this embodiment, and will not be repeated here.

[0162] The path planning device proposed in the embodiment of the present application obtains the first navigation data of the second vehicle, wherein the second vehicle and the first vehicle whose navigation route is to be determined belong to the same self-organizing network, and determines the first target navigation route of the first vehicle based on the second navigation data of the first vehicle and the first navigation data of the second vehicle. By sharing the navigation data of vehicles in the same self-organizing network, the most reasonable navigation route for the first vehicle is determined, thereby improving the accuracy of navigation route determination.

[0163] In order to implement the above embodiments, the present application also proposes a vehicle, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein when the processor executes the program, the method described in the above method embodiments is implemented.

[0164] In order to implement the above embodiments, the present application also proposes a non-temporary computer-readable storage medium on which a computer program is stored. When the program is executed by a processor, the method described in the above method embodiments is implemented.

[0165] In order to implement the above embodiments, the present application also proposes a computer program product on which a computer program is stored. When the computer program is executed by a processor, the method described in the above method embodiments is implemented.

[0166] Figure 7 A block diagram of a vehicle provided in an embodiment of the present application. For example, vehicle 600 may be a hybrid vehicle, a non-hybrid vehicle, an electric vehicle, a fuel cell vehicle, or other types of vehicles. Vehicle 600 may be an autonomous vehicle, a semi-autonomous vehicle, or a non-autonomous vehicle.

[0167] Reference Figure 7 , the vehicle 600 may include various subsystems, for example, an infotainment system 610, a perception system 620, a decision control system 630, a drive system 640, and a computing platform 650. The vehicle 600 may also include more or fewer subsystems, and each subsystem may include multiple components. In addition, each subsystem and each component of the vehicle 600 may be interconnected by wire or wireless means.

[0168] In some embodiments, the infotainment system 610 may include a communication system, an entertainment system, and a navigation system, among others.

[0169] The perception system 620 may include several sensors for sensing information about the environment around the vehicle 600. For example, the perception system 620 may include a global positioning system (the global positioning system may be a GPS system, or a Beidou system or other positioning systems), an inertial measurement unit (IMU), a laser radar, a millimeter wave radar, an ultrasonic radar, and a camera.

[0170] The decision control system 630 may include a computing system, a vehicle controller, a steering system, a throttle, and a braking system.

[0171] The drive system 640 may include components that provide powered motion for the vehicle 600. In one embodiment, the drive system 640 may include an engine, an energy source, a transmission system, and wheels. The engine may be one or a combination of an internal combustion engine, an electric motor, and an air compression engine. The engine is capable of converting energy provided by the energy source into mechanical energy.

[0172] Some or all functions of the vehicle 600 are controlled by a computing platform 650. The computing platform 650 may include at least one processor 651 and a memory 652, and the processor 651 may execute instructions 653 stored in the memory 652.

[0173] The processor 651 may be any conventional processor, such as a commercially available CPU. The processor may also include a graphics processor (Graphic Process Unit, GPU), a field programmable gate array (Field Programmable Gate Array, FPGA), a system on chip (System on Chip, SOC), an application specific integrated circuit (Application Specific Integrated Circuit, ASIC) or a combination thereof.

[0174] The memory 652 may be implemented by any type of volatile or nonvolatile storage device or a combination thereof, such as static random access memory (SRAM), electrically erasable programmable read-only memory (EEPROM), erasable programmable read-only memory (EPROM), programmable read-only memory (PROM), read-only memory (ROM), magnetic memory, flash memory, magnetic disk or optical disk.

[0175] In addition to the instructions 653 , the memory 652 may also store data, such as road maps, route information, and data such as the location, direction, and speed of the vehicle. The data stored in the memory 652 may be used by the computing platform 650 .

[0176] In the embodiment of the present disclosure, the processor 651 may execute instruction 653 to complete all or part of the steps of the above method embodiment.

[0177] In the description of this specification, the description with reference to the terms "one embodiment", "some embodiments", "example", "specific example", or "some examples" etc. means that the specific features, structures, materials or characteristics described in conjunction with the embodiment or example are included in at least one embodiment or example of the present application. In this specification, the schematic representations of the above terms do not necessarily refer to the same embodiment or example. Moreover, the specific features, structures, materials or characteristics described may be combined in any one or more embodiments or examples in a suitable manner. In addition, those skilled in the art may combine and combine the different embodiments or examples described in this specification and the features of the different embodiments or examples, without contradiction.

[0178] In addition, the terms "first" and "second" are used for descriptive purposes only and should not be understood as indicating or implying relative importance or implicitly indicating the number of the indicated technical features. Therefore, the features defined as "first" and "second" may explicitly or implicitly include at least one of the features. In the description of this application, the meaning of "plurality" is at least two, such as two, three, etc., unless otherwise clearly and specifically defined.

[0179] Any process or method description in a flowchart or otherwise described herein may be understood to represent a module, fragment or portion of code comprising one or more executable instructions for implementing the steps of a custom logical function or process, and the scope of the preferred embodiments of the present application includes alternative implementations in which functions may not be performed in the order shown or discussed, including performing functions in a substantially simultaneous manner or in the reverse order depending on the functions involved, which should be understood by technicians in the technical field to which the embodiments of the present application belong.

[0180] The logic and / or steps represented in the flowchart or otherwise described herein, for example, can be considered as an ordered list of executable instructions for implementing logical functions, and can be embodied in any computer-readable medium for use by an instruction execution system, device or apparatus (such as a computer-based system, a system including a processor, or other system that can fetch instructions from an instruction execution system, device or apparatus and execute the instructions), or in combination with these instruction execution systems, devices or apparatuses. For the purpose of this specification, "computer-readable medium" can be any device that can contain, store, communicate, propagate or transmit a program for use by an instruction execution system, device or apparatus, or in combination with these instruction execution systems, devices or apparatuses. More specific examples of computer-readable media (a non-exhaustive list) include the following: an electrical connection with one or more wires (electronic device), a portable computer disk box (magnetic device), a random access memory (RAM), a read-only memory (ROM), an erasable and programmable read-only memory (EPROM or flash memory), a fiber optic device, and a portable compact disk read-only memory (CDROM). In addition, the computer-readable medium may even be paper or other suitable medium on which the program is printed, since the program may be obtained electronically, for example, by optically scanning the paper or other medium and then editing, interpreting or processing in other suitable ways if necessary, and then stored in a computer memory.

[0181] It should be understood that the various parts of the present application can be implemented by hardware, software, firmware or a combination thereof. In the above-mentioned embodiments, multiple steps or methods can be implemented by software or firmware stored in a memory and executed by a suitable instruction execution system. For example, if implemented by hardware, as in another embodiment, it can be implemented by any one of the following technologies known in the art or their combination: a discrete logic circuit having a logic gate circuit for implementing a logic function for a data signal, a dedicated integrated circuit having a suitable combination of logic gate circuits, a programmable gate array (PGA), a field programmable gate array (FPGA), etc.

[0182] A person skilled in the art may understand that all or part of the steps in the method for implementing the above-mentioned embodiment may be completed by instructing related hardware through a program, and the program may be stored in a computer-readable storage medium, which, when executed, includes one or a combination of the steps of the method embodiment.

[0183] In addition, each functional unit in each embodiment of the present application may be integrated into a processing module, or each unit may exist physically separately, or two or more units may be integrated into one module. The above-mentioned integrated module may be implemented in the form of hardware or in the form of a software functional module. If the integrated module is implemented in the form of a software functional module and sold or used as an independent product, it may also be stored in a computer-readable storage medium.

[0184] The storage medium mentioned above may be a read-only memory, a magnetic disk or an optical disk, etc. Although the embodiments of the present application have been shown and described above, it can be understood that the above embodiments are exemplary and cannot be understood as limiting the present application. A person of ordinary skill in the art may change, modify, replace and modify the above embodiments within the scope of the present application.

Claims

1. A path planning method, characterized in that: include: Acquire first navigation data of a second vehicle; wherein the second vehicle and the first vehicle whose navigation route is to be determined belong to the same ad hoc network; A first target navigation route of the first vehicle is determined according to the second navigation data of the first vehicle and the first navigation data of the second vehicle.

2. The method according to claim 1, characterized in that The obtaining of the first navigation data of the second vehicle comprises: Acquiring initial navigation data of at least one third vehicle; wherein the at least one third vehicle and the first vehicle belong to the same ad hoc network; Determining a first navigation route to be detected from an initial navigation route of the first vehicle according to information about the current position of the first vehicle and the road on which the first vehicle is traveling; Matching the first navigation route to be detected with the initial navigation route of the at least one third vehicle to determine the second vehicle; wherein the initial navigation route of the second vehicle contains a second navigation route to be detected that matches the first navigation route to be detected; According to a second navigation route to be detected of the second vehicle, first navigation data of the second vehicle is determined from initial navigation data of the second vehicle.

3. The method according to claim 1, characterized in that The determining, according to the second navigation data of the first vehicle and the first navigation data of the second vehicle, a first target navigation route of the first vehicle comprises: Determining predicted road condition information of the first navigation route to be detected according to the first navigation data and the second navigation data; Acquiring attribute data and operating parameters of the second vehicle and attribute data and operating parameters of the first vehicle; A first target navigation route of the first vehicle is determined based on the predicted road condition information, the attribute data and operating parameters of the second vehicle, and the attribute data and operating parameters of the first vehicle.

4. The method according to claim 3, characterized in that Determining a first target navigation route of the first vehicle according to the predicted road condition information, the attribute data and operating parameters of the second vehicle, and the attribute data and operating parameters of the first vehicle includes: Acquire at least one of historical traffic information and real-time traffic information of the first navigation route to be detected; wherein the historical traffic information is the actual traffic information of the first navigation route to be detected in the same historical period as the period of time when the predicted traffic information is generated; Determine whether there is an abnormal road condition on the first to-be-detected navigation route of the first vehicle according to at least one of the historical road condition information and the real-time road condition information, and the predicted road condition information; In response to determining that an abnormal road condition exists, replanning is performed based on the attribute data and operating parameters of the second vehicle and the attribute data and operating parameters of the first vehicle to obtain a first target navigation route of the first vehicle.

5. The method according to claim 4, characterized in that The method further comprises: In response to determining that an abnormal road condition exists, generating a first route change request; Based on short-range wireless communication technology, sending the first route change request to the second vehicle; In response to receiving feedback information from a set number of second vehicles, the initial navigation route of the first vehicle is used as the first target navigation route; wherein the feedback information indicates that the second vehicles determine to re-plan the path.

6. The method according to claim 4, characterized in that The method further comprises: In response to determining that an abnormal road condition exists and that the first vehicle has a target demand, generating a first route change request and a route change reason; Based on short-range wireless communication technology, sending the first route change request and the route change reason to the second vehicle; In response to receiving feedback information from a set number of second vehicles, the initial navigation route of the first vehicle is used as the first target navigation route; wherein the feedback information indicates that the second vehicles determine to re-plan the path.

7. The method according to claim 4, characterized in that Replanning according to the attribute data and operating parameters of the second vehicle and the attribute data and operating parameters of the first vehicle to obtain a first target navigation route of the first vehicle includes: replanning a navigation route of the first vehicle according to the attribute data and operating parameters of the second vehicle and the attribute data and operating parameters of the first vehicle to obtain a replanned navigation route; Evaluate the re-planned navigation route based on map data and traffic information; In response to an abnormality in the evaluation result, the re-planned navigation route is modified according to the evaluation result, and the modified navigation route is used as a first target navigation route of the first vehicle.

8. The method according to claim 7, characterized in that The method further comprises: In response to an abnormality in the evaluation result, the re-planned navigation route and the evaluation result are sent to the second vehicle; wherein the evaluation result and the re-planned navigation route are used by the second vehicle to determine whether to re-plan the path.

9. The method according to any one of claims 1 to 8, characterized in that After determining the first target navigation route of the first vehicle according to the second navigation data of the first vehicle and the first navigation data of the second vehicle, the method further includes: Based on the short-range wireless communication link, the navigation data of the first target navigation route of the first vehicle is sent to the second vehicle.

10. The method according to any one of claims 1 to 8, characterized in that After determining the first target navigation route of the first vehicle according to the second navigation data of the first vehicle and the first navigation data of the second vehicle, the method further includes: Acquiring an actual driving speed of the first vehicle; comparing an actual driving speed of the first vehicle with a planned driving speed of the first vehicle corresponding to a first target navigation route of the first vehicle; In response to the actual driving speed being less than the planned driving speed, a second target navigation route of the first vehicle is determined based on currently acquired navigation data of a fourth vehicle and navigation data of the first vehicle; wherein the fourth vehicle and the first vehicle belong to the same ad hoc network.

11. The method according to any one of claims 1 to 8, characterized in that: After determining the first target navigation route of the first vehicle according to the second navigation data of the first vehicle and the first navigation data of the second vehicle, the method further includes: In response to acquiring the target traffic information, a second target navigation route of the first vehicle is determined based on currently acquired navigation data of a fourth vehicle and the navigation data of the first vehicle; wherein the fourth vehicle and the first vehicle belong to the same ad hoc network.

12. The method according to any one of claims 1 to 8, characterized in that: The method further comprises: generating a second route change request in response to acquiring the target traffic information; Based on short-range wireless communication technology, the second route change request is sent to the second vehicle.

13. The method according to claim 12, characterized in that There are multiple second vehicles, and sending the second route change request to the second vehicles includes: obtaining attribute information of a plurality of second vehicles; Determining the authority of each second vehicle according to the attribute information of each second vehicle; The second route change request is sent to each second vehicle in sequence according to the ranking of the authority of each second vehicle; wherein the ranking is determined from high to low according to the authority of the plurality of second vehicles.

14. A path planning device, characterized in that: include: An acquisition module, configured to acquire first navigation data of a second vehicle; wherein the second vehicle and the first vehicle whose navigation route is to be determined belong to the same ad hoc network; The determination module is used to determine a first target navigation route of the first vehicle according to the second navigation data of the first vehicle and the first navigation data of the second vehicle.

15. A vehicle, characterized in that: include: processor; a memory for storing processor-executable instructions; Wherein, the processor is configured to: Implement the steps of the method according to any one of claims 1 to 13.

16. A non-transitory computer-readable storage medium, when the instructions in the storage medium are executed by a processor of a mobile terminal, the mobile terminal is enabled to perform the steps of the method according to any one of claims 1 to 13.

17. A computer program product, characterized in that The invention comprises a computer program, which, when executed by a processor, implements the method according to any one of claims 1 to 13.

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