Unmanned driving path planning method and device based on OSM data

Abstract

The invention discloses an unmanned driving path planning method and device based on OSM data, and relates to the technical field of intelligent driving, and the method comprises the steps: obtaining database road information based on an OSM file; obtaining the current road information of the vehicle based on the real-time orientation of the vehicle and the center lines of all roads in the database road information; information of a road where a driving end point corresponding to the set driving end point is located is obtained, and lane changing permission information is set; and setting a shortest planned driving path and corresponding lane changing prompt information based on the real-time azimuth of the vehicle, the current road information of the vehicle, the road information of the driving end point, the lane changing permission information and the driving end point azimuth corresponding to the driving end point. According to the method, the road information and the vehicle information are integrated based on the information of the OSM data, so that the route planning is performed by combining the preset route planning setting conditions, and the method has certain convenience and reliability.

Description

Technical Field

[0001] The present application relates to the field of intelligent driving technology, and in particular to an unmanned driving path planning method and device based on OSM data. Background Art

[0002] Currently, demand for autonomous driving is growing in the intelligent driving sector. Traditional autonomous driving technologies, such as Dijkstra's algorithm, Bellman-Ford's algorithm, Floyd's algorithm, and SPFA, use path length as weights to determine optimal routes at the road or lane level based on the road network. These algorithms generally have little relevance to autonomous driving and are not compatible with the intelligent capabilities of autonomous vehicles.

[0003] In order to meet actual needs, an unmanned driving path planning technology based on OSM data is proposed. Summary of the Invention

[0004] In response to the defects in the existing technology, the purpose of this application is to provide an unmanned driving path planning method and device based on OSM data, which integrates road information and vehicle information based on the information of OSM data, and then combines the preset route planning setting conditions to perform path planning, which has certain convenience and reliability.

[0005] In order to achieve the above objectives, the technical solution adopted by this application is: In a first aspect, the present application provides a method for unmanned driving path planning based on OSM data, the method comprising the following steps: Based on OSM files, obtain database road information; Obtaining information about the road the vehicle is currently on based on the vehicle's real-time position and the center lines of all roads in the road information database; Obtaining the road information corresponding to the set driving destination and setting the lane change permission information; Based on the real-time position of the vehicle, the current road information of the vehicle, the road information of the driving destination, the lane change permission information and the driving destination position corresponding to the driving destination, the shortest planned driving path and the corresponding lane change prompt information are set.

[0006] On the basis of the above technical solution, the method further comprises the following steps: Vehicle control is performed based on the real-time driving path and the lane change prompt information in combination with the real-time vehicle position.

[0007] On the basis of the above technical solution, the method further comprises the following steps: Based on the real-time position of the vehicle and the shortest planned driving path, a vehicle position check is performed, and when the real-time position of the vehicle deviates from the shortest planned driving path, it is determined that the vehicle is yawed.

[0008] Based on the above technical solution, before obtaining the road information corresponding to the driving destination and setting the lane change permission information, the method further includes the following steps: It is determined whether the driving destination is located in the area corresponding to the road information in the database. If so, it is determined that the coordinates are within the range; otherwise, it is determined that the coordinates are out of range.

[0009] On the basis of the above technical solution, after performing vehicle control based on the real-time driving path and the lane change prompt information in combination with the real-time vehicle position, the method further includes the following steps: When the real-time position of the vehicle is consistent with the driving end point, it is determined that the driving is terminated.

[0010] In a second aspect, the present application provides an unmanned driving path planning device based on OSM data, the device comprising: Data acquisition module, which is used to obtain database road information based on OSM files; A road identification module, which is used to obtain the current road information of the vehicle based on the real-time position of the vehicle and the center lines of all roads in the road information database; A planning setting module, which is used to obtain the road information corresponding to the set driving destination and set lane change permission information; A road planning module is used to set the shortest planned driving path and corresponding lane change prompt information based on the real-time position of the vehicle, the current road information of the vehicle, the road information of the driving destination, the lane change permission information, and the driving destination position corresponding to the driving destination.

[0011] On the basis of the above technical solution, the device further includes: A vehicle control module is used to control the vehicle based on the real-time driving path and the lane change prompt information in combination with the real-time vehicle position.

[0012] On the basis of the above technical solution, the device further includes: The yaw determination module is used to perform vehicle position verification based on the real-time position of the vehicle and the shortest planned driving path, and determine that the vehicle is yawed when the real-time position of the vehicle deviates from the shortest planned driving path.

[0013] On the basis of the above technical solution, the device further includes: The end point range determination module is used to determine whether the driving end point is located in the area corresponding to the road information in the database. If so, it is determined that the coordinates are within the range; otherwise, it is determined that the coordinates are out of range.

[0014] On the basis of the above technical solution, the device further includes: The driving termination determination module is used to determine that the driving is terminated when the real-time position of the vehicle is consistent with the driving end point.

[0015] Compared with the prior art, the advantages of this application are: This application integrates road information and vehicle information based on OSM data, and then combines the preset route planning conditions to perform path planning, which has certain convenience and reliability. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] In order to more clearly illustrate the technical solutions in the embodiments of the present application, the following briefly introduces the drawings required for use in the description of the embodiments. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without any creative work.

[0017] Figure 1 This is a flowchart of the steps of the unmanned driving path planning method based on OSM data in an embodiment of the present application; Figure 2 This is a flow chart of the principle of the unmanned driving path planning method based on OSM data in an embodiment of the present application; Figure 3 This is a structural block diagram of an unmanned driving path planning device based on OSM data according to an embodiment of the present application. DETAILED DESCRIPTION

[0018] Explanation of terms: OSM: Original Standard Manufacturing, industry production and manufacturing standard system.

[0019] To make the purpose, technical solutions, and advantages of the embodiments of this application more clear, the technical solutions in the embodiments of this application will be clearly and completely described below in conjunction with the drawings in the embodiments of this application. Obviously, the described embodiments are part of the embodiments of this application, not all of the embodiments. Based on the embodiments in this application, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of this application.

[0020] The embodiments of the present application are further described in detail below with reference to the accompanying drawings.

[0021] The embodiments of the present application provide an unmanned driving path planning method and device based on OSM data, which integrates road information and vehicle information based on the information of OSM data, and thus performs path planning in combination with preset route planning setting conditions, which has certain convenience and reliability.

[0022] To achieve the above technical effects, the overall idea of ​​this application is as follows: A method for unmanned driving path planning based on OSM data, the method comprising the following steps: S1. Obtain database road information based on OSM files; S2. Obtaining the vehicle's current road information based on the vehicle's real-time position and the center lines of all roads in the road information database; S3. Obtaining road information corresponding to the set driving destination and setting lane change permission information; S4. Based on the real-time position of the vehicle, the current road information of the vehicle, the road information of the driving destination, the lane change permission information, and the driving destination position corresponding to the driving destination, set the shortest planned driving path and corresponding lane change prompt information.

[0023] The embodiments of the present application are further described in detail below with reference to the accompanying drawings.

[0024] First, see Figures 1 and 2 As shown, the embodiment of the present application provides an unmanned driving path planning method based on OSM data, which includes the following steps: S1. Obtain database road information based on OSM files; S2. Obtaining the vehicle's current road information based on the vehicle's real-time position and the center lines of all roads in the road information database; S3. Obtaining road information corresponding to the set driving destination and setting lane change permission information; S4. Based on the real-time position of the vehicle, the current road information of the vehicle, the road information of the driving destination, the lane change permission information, and the driving destination position corresponding to the driving destination, set the shortest planned driving path and corresponding lane change prompt information.

[0025] In the embodiment of the present application, road information and vehicle information are integrated based on the information of OSM data, so as to perform path planning in combination with preset route planning setting conditions, which has certain convenience and reliability.

[0026] It should be noted that the existing technology has many objective defects, such as: 1. The road network is of unknown origin, its accuracy is questionable, and it provides no clear guidance for navigation; 2. The road network model includes congestion information, but there is no mechanism to update the road network model in real time; 3. Using congestion as a factor in route planning is static planning. However, static planning only considers the current congestion situation and does not account for changes in congestion after the vehicle actually travels along a certain route. There is no mechanism for dynamic, real-time adjustments.

[0027] 4. The existing technology does not demonstrate interaction with autonomous driving and does not clearly state its connection with autonomous driving. This solution can be used for manned driving, but lacks the interaction link with autonomous driving.

[0028] 5. The existing solution evaluates travel time based solely on road speed limits and lengths, selecting the shortest route as the optimal choice. Given the varying levels of intelligence among autonomous vehicles and the potential for other route selection constraints, such as safety and dynamic adaptability, this solution cannot meet the comprehensive requirements of autonomous driving scenarios.

[0029] 7. It does not take into account that the planned optimal route may not be fully executed due to differences in the capabilities of the autonomous vehicle, as it is different from human driving and does not take into account the actual operating level of the vehicle.

[0030] The technical solution of the embodiment of this application provides an unmanned driving path planning technology based on OSM data to address the limitations of road-level navigation functions in existing unmanned driving navigation systems, especially the problems of insufficiently precise path guidance, insufficient safety, and poor dynamic adaptability in complex traffic scenarios. By generating a lane-level logical map and combining it with an optimized path planning algorithm, the present invention aims to achieve more accurate, safe, and efficient unmanned driving navigation functions, thereby improving the vehicle's intelligence level and user experience during actual driving. Specifically, the objectives of the embodiment of this application include the following: First, improve navigation accuracy: By generating a lane-level logical map that includes lane connection relationships and lane change relationships, it provides more detailed path guidance for autonomous vehicles, especially in scenarios such as multi-lane roads and complex intersections.

[0031] Second, optimize path planning: introduce constraints on lane-changing relationships to reduce frequent or continuous lane changes in path planning, thereby reducing the risk of traffic conflicts and improving driving safety.

[0032] Third, enhance dynamic adaptability: When the vehicle deviates or the environment changes (such as road closures or sudden traffic incidents), it can quickly replan the optimal path and guide the vehicle to complete lane changes at the appropriate time to ensure the real-time and reliability of navigation.

[0033] Fourth, it provides multi-scenario solutions: applicable to various complex traffic scenarios such as urban roads, highways, and rural roads, providing reliable navigation support for unmanned vehicles in different environments.

[0034] Fifth, providing path constraints: Different levels of automation have different requirements for paths. The present invention can be expanded to add various constraints to the path to ensure that the output path is executable by the unmanned vehicle.

[0035] Furthermore, the method further comprises the following steps: Vehicle control is performed based on the real-time driving path and the lane change prompt information in combination with the real-time vehicle position.

[0036] Furthermore, the method further comprises the following steps: Based on the real-time position of the vehicle and the shortest planned driving path, a vehicle position check is performed, and when the real-time position of the vehicle deviates from the shortest planned driving path, it is determined that the vehicle is yawed.

[0037] Furthermore, before obtaining the road information corresponding to the driving destination and setting the lane change permission information, the method further includes the following steps: It is determined whether the driving destination is located in the area corresponding to the road information in the database. If so, it is determined that the coordinates are within the range; otherwise, it is determined that the coordinates are out of range.

[0038] Furthermore, after controlling the vehicle based on the real-time driving path and the lane change prompt information in combination with the real-time vehicle position, the method further includes the following steps: When the real-time position of the vehicle is consistent with the driving end point, it is determined that the driving is terminated.

[0039] Based on the technical solution of the embodiment of this application, during specific implementation, the situation is as follows: In the first stage, OSM files are processed: OSM files are in XML format, which contains three basic information: Node, Way, and Relation. Node: A point in the map. Its attributes include the Node number (n_id) and coordinate information (x, y).

[0040] Way: Lane line. Each lane line consists of several Nodes. It is an ordered set of Nodes. Its attributes include Way number (w_id), solid or dashed line, and Node set.

[0041] Relation: Road information. Each road has a left lane line lWay and a right lane line rWay. The center lane line cWay can be generated based on the left and right lane lines. For example, if the coordinates of the first point of the left lane line are (x1, y1) and the coordinates of the first point of the right lane line are (x2, y2), then the coordinates of the first point of the generated center line are , whether it is a straight road; Based on the above basic information, we need to organize it into the following data structure. Name it OsmRoad. The data structure is as follows:

[0042] The final generated road information is as follows: { "id": 17, "length": 79.32144519740704, "nextRoad": [3427, 1696, 3480], "rightRoad":[], "leftRoad" : [ { "key": 2810 "value": 79.18971452528731 } ], "wide": 3.45125978, "startX": 507585.9498299385, "startY": 3369924.4297370007, "endX": 507598.3669299385, "endY": 3369846.0869370005 } The above data structure describes the attribute information of road number 17, named R17. Its length is 79.32144519740704 meters, its maximum width is 3.45125978 meters, and its endpoints connect to roads 3427, 1696, and 3480. There is no right lane change information; a left lane change to road 2810 is possible, and the collinear distance between the dashed lines is 79.18971452528731 meters.

[0043] Generating the above data structure for each road completes the construction of the logical map, which is convenient for the subsequent development of the path algorithm.

[0044] In the second stage, the vehicle’s current position is matched: The vehicle will report the vehicle information in real time based on the Internet of Vehicles equipment, including the vehicle's current location (x, y).

[0045] After receiving the coordinates reported by the vehicle, the platform traverses the center lines of all roads, treating two adjacent points in the center as line segments and calculating the shortest distance Di between (x, y) and that line segment. The minimum value of the shortest distances between all pairwise line segments in the center is considered the distance between the vehicle position and the road centerline.

[0046] The distance between the vehicle and each road is calculated as follows: The coordinates of two consecutive points on the center line are A(x i ,y i )、B(x i+1 ,y i+1 ), the vehicle coordinates are (x, y). The steps to calculate the shortest distance between (x, y) and the AB segment are: Vector V = (x- x i )*(x i+1 -x i )+(yy i )*(y i+1 -y i ); Length of line segment AB = ; ; Traverse and calculate D1, D2, ...D N-1 (n is the number of centerline nodes), take D=Min(D i ) is the actual distance between the vehicle and the lane line.

[0047] Through the above steps, the distance PR between the vehicle and each road can be obtained (R is the road number), and Min(PR) is taken as the road R where the vehicle is currently located.

[0048] The third stage is the optimal path algorithm planning: Plan an optimal route based on the vehicle's current road PA and the mission destination PB.

[0049] Due to the limitations of the autonomous vehicle's intelligence, the planned route has the following requirements: (1) Priority is given to the shortest distance; (2) Continuous lane changes are not allowed; (3) Change lanes as early as possible to avoid route adjustments caused by lane changes; (4) The lateral speed of the vehicle is low and the lane change distance cannot be too short; (5) The vehicle must be informed of the road on which it is starting to change lanes and the direction of the lane change must be provided.

[0050] Based on the above requirements, the A* algorithm employs the same principles as above, adding constraints. G(n) represents the actual distance from the starting point to the current path (n). H(n) is a heuristic function used to estimate the optimal path distance from the current path (n). F(n) = G(n) + H(n) is the total distance estimate, and P(n) is the preceding path of path n. This invention uses the Euclidean distance as the value of the heuristic function H(n) to balance the algorithm's search efficiency with path optimality.

[0051] The above is the core technology of the technical solution of the embodiment of this application. The specific implementation steps for implementing the technical solution of this application are as follows: The first step is initialization: 1. Set the starting road A and the target road B 2. Create two lists: Open List and Closed List The open list is used to store roads to be expanded.

[0052] A closed list is used to store roads that have been extended.

[0053] 3. Add the starting road A to the development list and set its G(A) to 0. H(A) is the Euclidean distance between the target road B and the starting point. You can query the startX, startY of the starting point of road A and the endX, endY of the end point of road B in OsmRoad. H(A) = .

[0054] Step 2: Road expansion: 1. Select the road (n) with the minimum evaluation function value F(n) from the open list as the current road.

[0055] 2. Remove the current road (n) from the development list and add it to the closure list.

[0056] 3. Query the OsmRoad structure for the next path that can be reached by road (n). For each path m, perform the following processing: Calculate the actual distance G(m) = G(A) + Rm from the starting road A to the path m.

[0057] Calculate the Euclidean distance H(m) from road m to the target road.

[0058] ·F(m)=G(m)+H(m).

[0059] 4. Is lane change allowed? If so, query the left and right lane change paths of road (n) in the OsmRoad structure. Perform the following processing on each lane change path q: Determine whether P(n) is a lane change path. If so, continuous lane changes are not allowed, so extension is not allowed.

[0060] Determine whether the lane change distance between Rn and road q is greater than a threshold. If so, treat the route as a reachable path and calculate G(p), H(p), and F(p).

[0061] If the lane change distance is less than the threshold, the algorithm loops through the next path on road n to determine whether there is a path on the next path on road p that can also change lanes. If so, the lane change distances are accumulated and compared with the threshold.

[0062] 5. Add the next path and the lane change path that meets the constraints to the open list.

[0063] Step 3: Road selection and update: 1. Sort all roads in the development list according to the value of F(m), and give priority to expanding the nodes with the smallest F(m).

[0064] 2. If the road m already exists in the closed list, skip expanding the node.

[0065] 3. If road m already exists in the development list, compare the G(m) of the new and old roads. If the new path is smaller, update the G(m) and parent road information of the new path.

[0066] Step 4: Road termination conditions: 1. When the target road B is added to the open list, remove it from the open list and calculate its evaluation function value F(B).

[0067] 2. Backtrack from target road B to starting point A to generate a complete path.

[0068] Step 5: Path processing to meet the needs of autonomous vehicles: In the previous step, the shortest path that meets the constraints was found. Assume the optimal route from A to B is [1, 2, 3, 4, 5, 6]. Querying OsmRoad confirms the dependencies between connected roads. For example, Road 1 leads directly to Road 2, while Road 5 requires a left turn to Road 6.

[0069] In the planned path sent to the autonomous vehicle, the type is added to the end of the road number: 1: No lane change, 2: Left lane change, 3: Right lane change. Therefore, the path the autonomous vehicle receives is [11, 21, 31, 41, 52, 61]. When the autonomous vehicle receives this planned path, it understands that the platform has planned it to change lanes left on Road 5.

[0070] Step 6: Change lanes in advance: The target destination is 6. If the unmanned vehicle has reached 5 but there is no lane change condition, it may not be able to reach the target destination and needs to re-plan a detour. Therefore, the lane change timing should be advanced as much as possible.

[0071] 1. Find the lane change road 5 and change lanes to the left.

[0072] 2. Traverse from the starting point to determine whether a left lane change is possible on Road 1. If not, traverse Road 2.

[0073] 3. If possible, obtain the left lane change road No. 1 and traverse it to determine whether it meets the lane change requirements (threshold requirements, the method is the same as the third step of "road expansion" in the optimal path algorithm planning). If not, traverse other lane change paths.

[0074] 4. If the threshold condition is met, the lane change path q, is regarded as the starting point and 5 as the end point, and the optimal path algorithm planning is re-executed, and lane change is set to be prohibited.

[0075] 5. If a path can be found, and the number of paths in the new path is no more than 120% of the current path and the length is no more than 150%, the new path will replace the current path.

[0076] Step 7: Vehicle yaw processing: When the vehicle receives a path such as [11,21,31,41,52,61], it may deviate from the route planned by the platform due to obstacle avoidance, detour, or unknown reasons during driving, and the platform needs to make corrections.

[0077] The platform records the path segments that have been issued.

[0078] The vehicle reports its location information in real time.

[0079] Calculate the distance to the delivered path segment based on the positioning information. Record the distance using key-value pairs, for example, 1-0.2, 2-50.9.

[0080] If the distance is less than half the width of the road itself, for example, if the width of Road 1 is 3.5 meters and the current distance between the vehicle and Road 1 is 0.2, the platform will determine that the vehicle is on Road 1 and has not deviated.

[0081] If the vehicle's distance from the shortest path in the platform's assigned routes is greater than half the road width, the platform deems the vehicle off course and requires rerouting. Repeat steps 2 to 5.

[0082] This application embodiment proposes a lane-level navigation technology for autonomous vehicles based on OSM file processing. By generating a logical map, path planning, and dynamic adjustment that can be used for scheduling, it significantly improves the navigation accuracy, path planning efficiency, and safety and comfort of autonomous vehicles during driving. The specific technical effects are as follows: First, improve the accuracy and practicality of navigation functions: By processing OSM files, the present embodiment can generate a high-precision logical map that includes road connectivity and lane change relationships. Compared to traditional navigation systems that only provide road-level navigation, the present invention implements lane-level navigation, providing more detailed route guidance for autonomous vehicles. For example, at complex intersections or multi-lane road scenarios, the system can clearly indicate the specific lane the vehicle should travel in, thereby avoiding route deviations or safety hazards caused by improper lane selection.

[0083] Second, optimize the efficiency and safety of path planning: During the path planning process, the embodiments of this application not only consider the connectivity of roads but also introduce constraints on lane changes. In this way, the system can plan an optimal path for the autonomous vehicle that complies with actual driving rules and avoids frequent lane changes. For example, on urban roads, the system will prioritize paths that allow turns or lane changes within the same lane, thereby reducing the risk of traffic conflicts caused by continuous lane changes and improving driving safety.

[0084] Third, achieve real-time dynamic path adjustment: During the actual operation of autonomous vehicles, environmental changes (such as road closures, unexpected traffic incidents, etc.) or vehicle deviations may require real-time rerouting. This invention utilizes a dynamic path planning algorithm (such as an improved version of the A* algorithm) to rapidly generate a new optimal path in the event of vehicle deviation and guide the vehicle to optimal lane change timing. For example, if the vehicle deviates from the planned path, the system can rapidly calculate a new lane-level path and, through the lane change timing guidance module, ensure that the vehicle completes the lane change maneuver safely, thereby improving the system's real-time performance and adaptability.

[0085] Fourth, enhance the security and reliability of the system: By generating logical maps and implementing path planning constraints (such as preventing continuous lane changes and providing guidance on lane change timing), the present embodiment significantly reduces safety risks for autonomous vehicles in complex traffic environments. For example, on multi-lane roads, the system can avoid traffic conflicts caused by continuous lane changes and rapidly replan the path if the vehicle veers off course, ensuring it always stays in a safe lane. Furthermore, through in-depth processing of OSM files, the system can generate more accurate logical maps, thereby improving the reliability of navigation functions.

[0086] Fifth, it has strong applicability and supports multiple scenarios: The method of the embodiment of the present application is not only applicable to urban roads, but can also cope with a variety of complex traffic scenarios such as highways and rural roads. By processing the road connection relationships and lane change relationships in the OSM file, the system can provide flexible navigation support for unmanned vehicles. For example, in a highway scenario, the system can accurately plan the path for the vehicle to enter the main road from the ramp and guide the vehicle to complete the lane change operation at the appropriate time; in an urban road scenario, the system can plan the optimal lane-level path for the vehicle to avoid congestion or construction sections.

[0087] Sixth, improve system operation efficiency: The present invention significantly improves the efficiency of path planning by improving the A algorithm, incorporating Euclidean distance as a heuristic function, and introducing lane-level constraints. For example, in the traditional A algorithm, path planning may generate paths with frequent lane changes. However, the present invention optimizes the search process through constraints, reducing the exploration of invalid paths, thereby improving the algorithm's operational efficiency and the system's responsiveness.

[0088] Seventh, support intelligent decision-making for driverless vehicles: The methods of the embodiments of this application provide more intelligent decision-making support for autonomous vehicles. By generating logical maps and optimizing path planning, the system can help vehicles make more reasonable driving decisions in complex traffic environments. For example, when an obstacle or traffic jam appears on the road ahead, the system can quickly plan an alternative path and guide the vehicle to complete lane changes at the appropriate time, thereby ensuring smooth driving.

[0089] The present invention processes OSM files to generate a logical map containing road connection relationships and lane change relationships, and combines this with an improved A* algorithm for path planning, thereby achieving lane-level navigation for unmanned vehicles. This method not only improves navigation accuracy and path planning efficiency, but also significantly enhances the safety and reliability of the system, providing strong support for unmanned vehicles in complex traffic environments. Through dynamic path adjustment and lane change timing guidance, the present invention can also respond to vehicle deviation or environmental changes in real time, ensuring that the vehicle always travels on the optimal path, thereby improving the overall driving experience and traffic efficiency.

[0090] The realization of the above technical effects makes the present invention have important innovation and practicality in the field of unmanned driving navigation.

[0091] Second, see Figure 3 As shown, an embodiment of the present application provides an unmanned driving path planning device based on OSM data, the device comprising: Data acquisition module, which is used to obtain database road information based on OSM files; A road identification module, which is used to obtain the current road information of the vehicle based on the real-time position of the vehicle and the center lines of all roads in the road information database; A planning setting module, which is used to obtain the road information corresponding to the set driving destination and set lane change permission information; A road planning module is used to set the shortest planned driving path and corresponding lane change prompt information based on the real-time position of the vehicle, the current road information of the vehicle, the road information of the driving destination, the lane change permission information, and the driving destination position corresponding to the driving destination.

[0092] In the embodiment of the present application, road information and vehicle information are integrated based on the information of OSM data, so as to perform path planning in combination with preset route planning setting conditions, which has certain convenience and reliability.

[0093] Furthermore, the device further comprises: A vehicle control module is used to control the vehicle based on the real-time driving path and the lane change prompt information in combination with the real-time vehicle position.

[0094] Furthermore, the device further comprises: The yaw determination module is used to perform vehicle position verification based on the real-time position of the vehicle and the shortest planned driving path, and determine that the vehicle is yawed when the real-time position of the vehicle deviates from the shortest planned driving path.

[0095] Furthermore, the device further comprises: The end point range determination module is used to determine whether the driving end point is located in the area corresponding to the road information in the database. If so, it is determined that the coordinates are within the range; otherwise, it is determined that the coordinates are out of range.

[0096] Furthermore, the device further comprises: The driving termination determination module is used to determine that the driving is terminated when the real-time position of the vehicle is consistent with the driving end point.

[0097] To sum up, the unmanned driving path planning device based on OSM data provided in the embodiment of the present application has the same technical principles as the unmanned driving path planning method based on OSM data provided in the first aspect in terms of technical problems, technical solutions and technical effects, so they will not be elaborated here.

[0098] In the description of this application, it should be noted that the terms "upper" and "lower" and the like indicate orientations or positional relationships based on the orientations or positional relationships shown in the accompanying drawings, and are only for the convenience of describing this application and simplifying the description, and do not indicate or imply that the device or element referred to must have a specific orientation, be constructed and operated in a specific orientation, and therefore cannot be understood as a limitation on this application. Unless otherwise clearly specified and limited, the terms "installed", "connected", and "connected" should be understood in a broad sense, for example, it can be a fixed connection, a detachable connection, or an integral connection; it can be a mechanical connection or an electrical connection; it can be a direct connection, or an indirect connection through an intermediate medium, or it can be internal communication between two elements. For those of ordinary skill in the art, the specific meanings of the above terms in this application can be understood according to the specific circumstances.

[0099] It should be noted that, in this application, relational terms such as "first" and "second" are used only to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any actual relationship or order between these entities or operations. Moreover, the terms "include", "comprise" or any other variants thereof are intended to cover non-exclusive inclusion, so that a process, method, article or device comprising a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, article or device. In the absence of further restrictions, an element defined by the sentence "comprising a ..." does not exclude the presence of other identical elements in the process, method, article or device comprising the element.

[0100] The foregoing is merely a list of specific embodiments of the present application, intended to enable those skilled in the art to understand or implement the present application. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of the present application. Therefore, the present application is not limited to the embodiments shown herein, but is intended to conform to the broadest scope consistent with the principles and novel features of the present application.

Claims

1. A driverless vehicle path planning method based on OSM data, characterized in that: The method comprises the following steps: Based on OSM files, obtain database road information; Obtaining information about the road the vehicle is currently on based on the vehicle's real-time position and the center lines of all roads in the road information database; Obtaining the road information corresponding to the set driving destination and setting the lane change permission information; Based on the real-time position of the vehicle, the current road information of the vehicle, the road information of the driving destination, the lane change permission information and the driving destination position corresponding to the driving destination, the shortest planned driving path and the corresponding lane change prompt information are set.

2. The unmanned driving path planning method based on OSM data according to claim 1, characterized in that: The method further comprises the following steps: Vehicle control is performed based on the real-time driving path and the lane change prompt information in combination with the real-time vehicle position.

3. The unmanned driving path planning method based on OSM data according to claim 1, characterized in that: The method further comprises the following steps: Based on the real-time position of the vehicle and the shortest planned driving path, a vehicle position check is performed, and when the real-time position of the vehicle deviates from the shortest planned driving path, it is determined that the vehicle is yawed.

4. The unmanned driving path planning method based on OSM data according to claim 1, characterized in that: Before obtaining the road information corresponding to the driving destination and setting the lane change permission information, the method further includes the following steps: It is determined whether the driving destination is located in the area corresponding to the road information in the database. If so, it is determined that the coordinates are within the range; otherwise, it is determined that the coordinates are out of range.

5. The unmanned driving path planning method based on OSM data according to claim 2, characterized in that: After controlling the vehicle based on the real-time driving path and the lane change prompt information in combination with the real-time vehicle position, the method further includes the following steps: When the real-time position of the vehicle is consistent with the driving end point, it is determined that the driving is terminated.

6. An unmanned driving path planning device based on OSM data, characterized in that: The device comprises: Data acquisition module, which is used to obtain database road information based on OSM files; A road identification module, which is used to obtain the current road information of the vehicle based on the real-time position of the vehicle and the center lines of all roads in the road information database; A planning setting module, which is used to obtain the road information corresponding to the set driving destination and set lane change permission information; A road planning module is used to set the shortest planned driving path and corresponding lane change prompt information based on the real-time position of the vehicle, the current road information of the vehicle, the road information of the driving destination, the lane change permission information, and the driving destination position corresponding to the driving destination.

7. The unmanned driving path planning device based on OSM data according to claim 6, characterized in that: The device further comprises: A vehicle control module is used to control the vehicle based on the real-time driving path and the lane change prompt information in combination with the real-time vehicle position.

8. The unmanned driving path planning device based on OSM data according to claim 6, characterized in that: The device further comprises: The yaw determination module is used to perform vehicle position verification based on the real-time position of the vehicle and the shortest planned driving path, and determine that the vehicle is yawed when the real-time position of the vehicle deviates from the shortest planned driving path.

9. The unmanned driving path planning device based on OSM data according to claim 6, characterized in that: The device further comprises: The end point range determination module is used to determine whether the driving end point is located in the area corresponding to the road information in the database. If so, it is determined that the coordinates are within the range; otherwise, it is determined that the coordinates are out of range.

10. The unmanned driving path planning device based on OSM data according to claim 7, characterized in that: The device further comprises: The driving termination determination module is used to determine that the driving is terminated when the real-time position of the vehicle is consistent with the driving end point.

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