Path planning method and device, computer device and storage medium
By identifying and matching the driving environment information of autonomous vehicles, a target path map is generated, which solves the problem of path planning misjudgment in special scenarios for autonomous vehicles and improves the accuracy of path planning and traffic safety.
Patent Information
- Authority / Receiving Office
- CN · China
- Patent Type
- Patents(China)
- Current Assignee / Owner
- GUANGZHOU XIAOMA ZHIKA TECH CO LTD
- Filing Date
- 2023-03-28
- Publication Date
- 2026-06-12
AI Technical Summary
In certain scenarios, autonomous vehicles may cause misjudgments in path prediction and planning due to other vehicles not following the roads planned on the map, thus endangering traffic safety.
By acquiring the current vehicle's driving environment information, identifying the target vehicle, confirming its trajectory information, and matching it with candidate path maps in the path map library, a target path map is generated. A reference coordinate system is established to perform trajectory point grouping and fitting, trajectory distance and probability are calculated, and the most suitable path map is selected for path planning.
It improves the accuracy of path planning for autonomous vehicles in special scenarios and enhances road traffic safety.
Smart Images
Figure CN116907523B_ABST
Abstract
Description
Technical Field
[0001] This application relates to the field of autonomous driving technology, and in particular to a path planning method, apparatus, computer device, and storage medium. Background Technology
[0002] Autonomous vehicles rely on the collaborative efforts of artificial intelligence, computer vision, radar, monitoring devices, and GPS to enable computers to operate motor vehicles automatically and safely without any human intervention.
[0003] When autonomous vehicles operate in autonomous mode, they rely heavily on high-precision road maps. However, in certain special scenarios, private vehicles do not always follow the roads planned on the map, which can lead to misjudgments in the autonomous vehicle's path prediction and planning, thereby endangering traffic safety. Summary of the Invention
[0004] Based on this, a route planning method, apparatus, computer device, and computer-readable storage medium are provided to improve road traffic safety by improving route planning methods.
[0005] On the one hand, a path planning method is provided, the method comprising:
[0006] Obtain the current vehicle's driving environment information and identify the target vehicle in the driving environment information;
[0007] Based on the location information of the target vehicle, the trajectory information corresponding to the target vehicle is confirmed;
[0008] The trajectory information is matched with candidate route maps in the route map library to obtain the target route map. The route map library includes pre-stored route maps, route maps shared by route-sharing vehicles, and real-time generated route maps.
[0009] The current vehicle's movement is controlled according to the target path map, and the target path map is shared.
[0010] In one embodiment, it further includes:
[0011] Establish a reference coordinate system;
[0012] The position information of the target vehicle is sampled according to the reference coordinate system to obtain vehicle trajectory points, and the number of paths corresponding to the target vehicle is confirmed according to the driving environment information.
[0013] The vehicle trajectory points are grouped according to the number of paths to obtain the grouping results;
[0014] The vehicle trajectory points in each grouping result are fitted to obtain the trajectory information corresponding to the target vehicle.
[0015] In one embodiment, it further includes:
[0016] The vehicle trajectory points in each grouping result are fitted to obtain the initial trajectory information;
[0017] Calculate the trajectory distance between each of the initial trajectory information;
[0018] If the trajectory distance does not meet the preset conditions, the number of paths will be modified.
[0019] The vehicle trajectory points are regrouped based on the modified number of paths to obtain the regrouping results.
[0020] The vehicle trajectory points in each of the regrouping results are fitted to obtain new initial trajectory information;
[0021] The new initial trajectory information is used as the trajectory information corresponding to the target vehicle until the trajectory distance between each new initial trajectory information meets the preset condition.
[0022] In one embodiment, it further includes:
[0023] Based on the trajectory information, the probability that the target vehicle travels on the candidate path map under the conditions of the trajectory information is calculated to obtain the predicted probability;
[0024] By comparing the predicted probabilities of each path map, the path map with the highest predicted probability is selected to obtain the target path map.
[0025] In one embodiment, it further includes:
[0026] At least one virtual lane is generated based on the trajectory information, and the reliability probability of the virtual lane is calculated.
[0027] The confidence probability and the probability threshold are compared, and the centerline of the lane with the confidence probability greater than the probability threshold is selected as the predicted trajectory of the target vehicle.
[0028] Calculate the probability that the target vehicle travels on the candidate path map under the predicted trajectory.
[0029] In one embodiment, it further includes:
[0030] Given the predicted trajectory, the mathematical expression for the probability P that the target vehicle travels on the candidate path map is:
[0031]
[0032] Wherein, when the target vehicle is traveling in the m-th lane on the candidate path map, there are n predicted trajectories j of the vehicle.
[0033] In one embodiment, it further includes:
[0034] Based on the time information corresponding to the candidate path map, the candidate path map is clustered to obtain a set of candidate path maps corresponding to multiple time periods;
[0035] A set of candidate path maps is selected based on the time period corresponding to the trajectory information;
[0036] The trajectory information is matched with the candidate path maps in the selected candidate path map set to obtain the target path map.
[0037] On the other hand, a path planning device is provided, the device comprising:
[0038] The identification module is used to acquire the driving environment information of the target vehicle and identify the target vehicle in the driving environment information.
[0039] The confirmation module is used to confirm the trajectory information corresponding to the target vehicle based on the location information of the target vehicle.
[0040] A matching module is used to match the trajectory information with candidate path maps in the path map library to obtain a target path map, wherein the path map library includes pre-stored path maps and path maps shared by path-sharing vehicles;
[0041] The control module is used to control the driving of the current vehicle according to the target path map and to share the target path map.
[0042] In another aspect, a computer device is provided, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to perform the following steps:
[0043] Obtain the current vehicle's driving environment information and identify the target vehicle in the driving environment information;
[0044] Based on the location information of the target vehicle, the trajectory information corresponding to the target vehicle is confirmed;
[0045] The trajectory information is matched with candidate route maps in the route map library to obtain the target route map. The route map library includes pre-stored route maps, route maps shared by route-sharing vehicles, and real-time generated route maps.
[0046] The current vehicle's movement is controlled according to the target path map, and the target path map is shared.
[0047] In another aspect, a computer-readable storage medium is provided having a computer program stored thereon, which, when executed by a processor, performs the following steps:
[0048] Obtain the current vehicle's driving environment information and identify the target vehicle in the driving environment information;
[0049] Based on the location information of the target vehicle, the trajectory information corresponding to the target vehicle is confirmed;
[0050] The trajectory information is matched with candidate route maps in the route map library to obtain the target route map. The route map library includes pre-stored route maps, route maps shared by route-sharing vehicles, and real-time generated route maps.
[0051] The current vehicle's movement is controlled according to the target path map, and the target path map is shared.
[0052] The aforementioned path planning method, apparatus, computer equipment, and storage medium acquire the current vehicle's driving environment information, identify the target vehicle within the driving environment information, and obtain information about other vehicles in the surrounding driving environment; based on the target vehicle's location information, confirm the trajectory information corresponding to the target vehicle to obtain the target vehicle's driving trajectory; match the trajectory information with candidate path maps in a path map library to obtain a target path map, thereby completing the analysis of the road path; then control the current vehicle's driving based on the target path map, and share the target path map to complete the path planning for the current vehicle. This path planning method improves the path planning process, enabling more accurate and reasonable path planning for autonomous vehicles during operation, thus enhancing road traffic safety. Attached Figure Description
[0053] Figure 1 This is a flowchart illustrating a path planning method in one embodiment;
[0054] Figure 2 This is a structural block diagram of a path planning device in one embodiment;
[0055] Figure 3 This is an internal structural diagram of a computer device in one embodiment. Detailed Implementation
[0056] To make the objectives, technical solutions, and advantages of this application clearer, the following detailed description is provided in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative and not intended to limit the scope of this application.
[0057] The flowcharts shown in the accompanying drawings are merely illustrative and do not necessarily include all content and operations / steps, nor do they necessarily have to be performed in the described order. For example, some operations / steps can be broken down, while others can be combined or partially combined; therefore, the actual execution order may change depending on the specific circumstances.
[0058] When autonomous vehicles are driving in certain special scenarios, they may encounter other vehicles that do not always follow the roads planned on the map. This can lead to misjudgments in the autonomous vehicle's prediction and planning of the route, thereby endangering traffic safety.
[0059] For example, in areas without lane markings, such as before and after toll stations, ramp entrances and exits, industrial parks, and urban villages, multiple vehicles may drive side by side in one lane.
[0060] Or, when the lane and surrounding space are wide enough to accommodate multiple vehicles side by side, social vehicles may form multiple queues side by side within the lane;
[0061] In emergency situations, other vehicles may need to make way for police cars, ambulances, fire trucks, and other rescue vehicles. These situations can all affect the path planning of autonomous vehicles. Therefore, in order to make the path planning of autonomous vehicles more accurate and reasonable and improve road traffic safety, a path planning method is proposed.
[0062] In one exemplary embodiment, such as Figure 1 As shown, a path planning method is provided. This path planning method can be executed by a device with computing power on an autonomous vehicle, or by other devices with computing power such as a server. This embodiment does not limit the execution subject to which the method is applicable.
[0063] The following uses the driving scenario of an autonomous vehicle as an example to illustrate the path planning method in this embodiment, including the following steps:
[0064] Step 101: Obtain the current vehicle's driving environment information and identify the target vehicle in the driving environment information.
[0065] Among them, driving environment information refers to the information about the surrounding environment of the vehicle during its current driving process.
[0066] Specifically, it is necessary to identify and extract other vehicles traveling in the surrounding environment from the driving environment information to obtain information about the target vehicle.
[0067] Step 102: Based on the location information of the target vehicle, confirm the trajectory information corresponding to the target vehicle.
[0068] Specifically, obtaining the location information of a target vehicle over a period of time can be understood as allowing the calculation of the target vehicle's trajectory to a certain extent based on changes in this location information, thus obtaining the target vehicle's trajectory information.
[0069] Step 103: Match the trajectory information with the candidate path maps in the path map library to obtain the target path map. The path map library includes pre-stored path maps, path maps shared by path-sharing vehicles, and real-time generated path maps.
[0070] Among them, the candidate route map is one or more route maps in the route map library. The pre-stored route map can be a high-precision map with annotations or a map from other platforms. The route map shared by vehicles refers to the map shared by vehicles that have traveled the current route and / or are currently traveling the current route. The real-time generated route map refers to the map generated in real time by the driving trajectories of other vehicles in the surrounding area and / or the trajectory of the current vehicle.
[0071] Specifically, the trajectory information is matched with candidate path maps in the path map library to determine which path map best matches the trajectory information of the target vehicle, and then that path map is used as the target path map.
[0072] Step 104: Control the driving of the current vehicle according to the target path map, and share the target path map.
[0073] Specifically, the system controls the current vehicle to travel on the road based on the target path map and shares the target path map with the server and / or other vehicles.
[0074] In one embodiment, it further includes:
[0075] Establish a reference coordinate system;
[0076] The position information of the target vehicle is sampled according to the reference coordinate system to obtain vehicle trajectory points, and the number of paths corresponding to the target vehicle is confirmed according to the driving environment information.
[0077] The vehicle trajectory points are grouped according to the number of paths to obtain the grouping results;
[0078] The vehicle trajectory points in each grouping result are fitted to obtain the trajectory information corresponding to the target vehicle.
[0079] For example, the centerline of the current vehicle's driving route can be used as the s-axis, and the normal to the s-axis can be used as the d-axis to establish a Frenet coordinate system. It should be noted that the Frenet coordinate system is suitable for path planning in autonomous driving, and can reduce the dimensionality of map data, reduce the amount of computation, and improve computational efficiency.
[0080] The target vehicle's position information is obtained based on its geometric center, and then converted to the Frenet coordinate system for representation, thus obtaining the target vehicle's trajectory points. The trajectory points are grouped and fitted to obtain the corresponding trajectory information. Based on the trajectory information and driving environment information, the number of virtual lanes is estimated. Here, virtual lanes refer to lane information generated based on real-time collected and calculated information (including trajectory information and driving environment information), and are one type of the real-time generated path map mentioned above.
[0081] It should be noted that the number of virtual lanes is not necessarily the same as the number of lanes obtained from the map (route library). For example, on a two-lane road with sufficient lane spacing in the same direction, if more than N vehicles are traveling side by side, where N≥3 and N is an integer, then the number of lanes obtained from the map may be 2, but the number of virtual lanes may be N.
[0082] Preferably, based on the geometric center of the target vehicle observed over multiple consecutive frames, it is sampled onto the Frenet coordinate system to obtain trajectory points. These trajectory points can be grouped, linearly interpolated, or resampled according to the sampling precision ds to fit the trajectory points and obtain the corresponding trajectory, i.e., the trajectory information of the target vehicle. For example, for the nth target vehicle j, the geometric center observed over t consecutive frames is sampled in the Frenet coordinate system as {(s_n_1,d_n_1),(s_n_2,d_n_2),…,(s_n_t,d_n_t)}. After linear interpolation and resampling according to the sampling precision ds for each trajectory, a corresponding set S = {0,ds,2ds,…} represents the trajectory of target vehicle j as {(s_1',d_n_1'),(s_2',d_n_2'),…}, where s_1',s_2',...∈S.
[0083] In one embodiment, it further includes:
[0084] The vehicle trajectory points in each grouping result are fitted to obtain the initial trajectory information;
[0085] Calculate the trajectory distance between each of the initial trajectory information;
[0086] If the trajectory distance does not meet the preset conditions, the number of paths will be modified.
[0087] The vehicle trajectory points are regrouped based on the modified number of paths to obtain the regrouping results.
[0088] The vehicle trajectory points in each of the regrouping results are fitted to obtain new initial trajectory information;
[0089] The new initial trajectory information is used as the trajectory information corresponding to the target vehicle until the trajectory distance between each new initial trajectory information meets the preset condition.
[0090] Specifically, after obtaining at least two initial trajectory information, the number of virtual lane paths is determined based on the distance between the trajectories to see if it meets the preset conditions. If it does not meet the preset conditions, the number of virtual lanes is modified until the number of virtual lanes meets the safe distance standard between trajectories.
[0091] For example, in conjunction with this embodiment and the above embodiments, the k-means clustering algorithm can be used to generate a corresponding road map based on the vehicle trajectory. The target vehicle trajectory is randomly divided into k groups. For the i-th group of trajectories, for each trajectory s_i'∈S, the average value of all d_n_i' corresponding to the trajectory containing s_i' is calculated, where i = 1, 2, 3, ... k. Linear interpolation is used to ensure that each s_i'∈S has a corresponding average value.
[0092] For each group of trajectories, calculate the squared distance to the above average value, and take the minimum value to regroup the trajectories until the trajectory grouping is stable.
[0093] Calculate the maximum distance between the average trajectories of adjacent groups. If the maximum distance is less than the empirical width, it indicates that the two groups of trajectories are too close. The two groups of trajectories can be merged, the number of groups k can be reduced, and the groups can be re-clustered. The empirical width refers to the vehicle width data, which can be preset, calculated based on actual driving conditions, or obtained through real-time data collection. Otherwise, a virtual lane can be generated based on the average value of the two groups of trajectories. If the width of the virtual lane is less than the vehicle width, the width of the virtual lane can be compensated based on the empirical width to ensure that the generated virtual lane can accommodate autonomous vehicles.
[0094] In one embodiment, it further includes:
[0095] Based on the trajectory information, the probability that the target vehicle travels on the candidate path map under the conditions of the trajectory information is calculated to obtain the predicted probability;
[0096] By comparing the predicted probabilities of each path map, the path map with the highest predicted probability is selected to obtain the target path map.
[0097] Based on the candidate path map, the corresponding lane information can be obtained. If the trajectory of each target vehicle in each lane follows a high-dimensional normal distribution with the corresponding mean, the covariance of the normal distribution can be calculated by the prior distribution and the observed values of the parameter tuning. Then, the probability of the trajectory matching the lane of each path map can be obtained based on the normal distribution, that is, the prediction probability. Then, the most suitable path map can be selected based on the prediction probability for the current vehicle's path planning.
[0098] In one embodiment, it further includes:
[0099] At least one virtual lane is generated based on the trajectory information, and the reliability probability of the virtual lane is calculated.
[0100] The confidence probability and the probability threshold are compared, and the centerline of the lane with the confidence probability greater than the probability threshold is selected as the predicted trajectory of the target vehicle.
[0101] Calculate the probability that the target vehicle travels on the candidate path map under the predicted trajectory.
[0102] In one embodiment, it further includes:
[0103] Given the predicted trajectory, the mathematical expression for the probability P that the target vehicle travels on the candidate path map is:
[0104]
[0105] Wherein, when the target vehicle is traveling in the m-th lane on the candidate path map, there are n predicted trajectories j of the vehicle.
[0106] In conjunction with this embodiment and the above embodiments, the probability of trajectory j matching lanes with each path map (map) is obtained according to the normal distribution, that is, the prediction probability P(j|m). Because in the actual driving process of the vehicle, high-precision maps are usually the most reliable, as are maps obtained through the network from other platforms or maps shared by the route-sharing vehicle. Secondly, the reliability of maps generated in real time is relatively low. Therefore, corresponding prior probabilities P(m) can be set for these maps.
[0107] The calculation method is transformed by Bayesian inference to obtain the matching probability P of each trajectory and each map, which represents the probability that the vehicle can travel on each path under the provided trajectory conditions, and then determines which map's lane is more suitable for the driving trajectory.
[0108] Preferably, the numerical format can be converted to logarithmic format during the calculation process to calculate its logarithmic probability, which can avoid the calculation accuracy problem caused by multiplication.
[0109] In one embodiment, it further includes:
[0110] Based on the time information corresponding to the candidate path map, the candidate path map is clustered to obtain a set of candidate path maps corresponding to multiple time periods;
[0111] A set of candidate path maps is selected based on the time period corresponding to the trajectory information;
[0112] The trajectory information is matched with the candidate path maps in the selected candidate path map set to obtain the target path map.
[0113] It should be noted that the candidate path map also includes network path maps obtained through the network. These network path maps are similar to the real-time generated path maps, but they typically use driving data from multiple times and multiple vehicles, providing more complete data. Since traffic conditions on some lanes differ at different times, they can be clustered again along the time dimension to generate network path maps for multiple time periods for selection and use in path planning for autonomous vehicles.
[0114] In one embodiment, such as Figure 2 As shown, a path planning device is provided, including: an identification module, a confirmation module, a matching module, and a control module, wherein:
[0115] The identification module is used to acquire the driving environment information of the target vehicle and identify the target vehicle in the driving environment information.
[0116] The confirmation module is used to confirm the trajectory information corresponding to the target vehicle based on the location information of the target vehicle.
[0117] A matching module is used to match the trajectory information with candidate path maps in the path map library to obtain a target path map, wherein the path map library includes pre-stored path maps and path maps shared by path-sharing vehicles;
[0118] The control module is used to control the driving of the current vehicle according to the target path map and to share the target path map.
[0119] In one embodiment, it further includes:
[0120] Establish a reference coordinate system;
[0121] The position information of the target vehicle is sampled according to the reference coordinate system to obtain vehicle trajectory points, and the number of paths corresponding to the target vehicle is confirmed according to the driving environment information.
[0122] The vehicle trajectory points are grouped according to the number of paths to obtain the grouping results;
[0123] The vehicle trajectory points in each grouping result are fitted to obtain the trajectory information corresponding to the target vehicle.
[0124] In one embodiment, it further includes:
[0125] The vehicle trajectory points in each grouping result are fitted to obtain the initial trajectory information;
[0126] Calculate the trajectory distance between each of the initial trajectory information;
[0127] If the trajectory distance does not meet the preset conditions, the number of paths will be modified.
[0128] The vehicle trajectory points are regrouped based on the modified number of paths to obtain the regrouping results.
[0129] The vehicle trajectory points in each of the regrouping results are fitted to obtain new initial trajectory information;
[0130] The new initial trajectory information is used as the trajectory information corresponding to the target vehicle until the trajectory distance between each new initial trajectory information meets the preset condition.
[0131] In one embodiment, it further includes:
[0132] Based on the trajectory information, the probability that the target vehicle travels on the candidate path map under the conditions of the trajectory information is calculated to obtain the predicted probability;
[0133] By comparing the predicted probabilities of each path map, the path map with the highest predicted probability is selected to obtain the target path map.
[0134] In one embodiment, it further includes:
[0135] At least one virtual lane is generated based on the trajectory information, and the reliability probability of the virtual lane is calculated.
[0136] The confidence probability and the probability threshold are compared, and the centerline of the lane with the confidence probability greater than the probability threshold is selected as the predicted trajectory of the target vehicle.
[0137] Calculate the probability that the target vehicle travels on the candidate path map under the predicted trajectory.
[0138] In one embodiment, it further includes:
[0139] Given the predicted trajectory, the mathematical expression for the probability P that the target vehicle travels on the candidate path map is:
[0140]
[0141] Wherein, when the target vehicle is traveling in the m-th lane on the candidate path map, there are n predicted trajectories j of the vehicle.
[0142] In one embodiment, it further includes:
[0143] Based on the time information corresponding to the candidate path map, the candidate path map is clustered to obtain a set of candidate path maps corresponding to multiple time periods;
[0144] A set of candidate path maps is selected based on the time period corresponding to the trajectory information;
[0145] The trajectory information is matched with the candidate path maps in the selected candidate path map set to obtain the target path map.
[0146] For specific limitations regarding the path planning device, please refer to the limitations of the path planning method above, which will not be repeated here. Each module in the aforementioned path planning device can be implemented entirely or partially through software, hardware, or a combination thereof. These modules can be embedded in or independent of the processor in a computer device in hardware form, or stored in the memory of a computer device in software form, so that the processor can call and execute the operations corresponding to each module.
[0147] In one embodiment, a computer device is provided, which may be a server, and its internal structure diagram may be as follows: Figure 3 As shown, the computer device includes a processor, memory, network interface, and database connected via a system bus. The processor provides computational and control capabilities. The memory includes non-volatile storage media and internal memory. The non-volatile storage media stores the operating system, computer programs, and database. The internal memory provides an environment for the operation of the operating system and computer programs stored in the non-volatile storage media. The database stores path planning-related data. The network interface communicates with external terminals via a network connection. When executed by the processor, the computer program implements a path planning method.
[0148] Those skilled in the art will understand that Figure 3 The structure shown is merely a block diagram of a portion of the structure related to the present application and does not constitute a limitation on the computer device to which the present application is applied. Specific computer devices may include more or fewer components than those shown in the figure, or combine certain components, or have different component arrangements.
[0149] In one embodiment, a computer device is provided, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to perform the following steps:
[0150] Obtain the current vehicle's driving environment information and identify the target vehicle in the driving environment information;
[0151] Based on the location information of the target vehicle, the trajectory information corresponding to the target vehicle is confirmed;
[0152] The trajectory information is matched with candidate route maps in the route map library to obtain the target route map. The route map library includes pre-stored route maps, route maps shared by route-sharing vehicles, and real-time generated route maps.
[0153] The current vehicle's movement is controlled according to the target path map, and the target path map is shared.
[0154] In one embodiment, the processor, when executing a computer program, also performs the following steps:
[0155] Establish a reference coordinate system;
[0156] The position information of the target vehicle is sampled according to the reference coordinate system to obtain vehicle trajectory points, and the number of paths corresponding to the target vehicle is confirmed according to the driving environment information.
[0157] The vehicle trajectory points are grouped according to the number of paths to obtain the grouping results;
[0158] The vehicle trajectory points in each grouping result are fitted to obtain the trajectory information corresponding to the target vehicle.
[0159] In one embodiment, the processor, when executing a computer program, also performs the following steps:
[0160] The vehicle trajectory points in each grouping result are fitted to obtain the initial trajectory information;
[0161] Calculate the trajectory distance between each of the initial trajectory information;
[0162] If the trajectory distance does not meet the preset conditions, the number of paths will be modified.
[0163] The vehicle trajectory points are regrouped based on the modified number of paths to obtain the regrouping results.
[0164] The vehicle trajectory points in each of the regrouping results are fitted to obtain new initial trajectory information;
[0165] The new initial trajectory information is used as the trajectory information corresponding to the target vehicle until the trajectory distance between each new initial trajectory information meets the preset condition.
[0166] In one embodiment, the processor, when executing a computer program, also performs the following steps:
[0167] Based on the trajectory information, the probability that the target vehicle travels on the candidate path map under the conditions of the trajectory information is calculated to obtain the predicted probability;
[0168] By comparing the predicted probabilities of each path map, the path map with the highest predicted probability is selected to obtain the target path map.
[0169] In one embodiment, the processor, when executing a computer program, also performs the following steps:
[0170] At least one virtual lane is generated based on the trajectory information, and the reliability probability of the virtual lane is calculated.
[0171] The confidence probability and the probability threshold are compared, and the centerline of the lane with the confidence probability greater than the probability threshold is selected as the predicted trajectory of the target vehicle.
[0172] Calculate the probability that the target vehicle travels on the candidate path map under the predicted trajectory.
[0173] In one embodiment, the processor, when executing a computer program, also performs the following steps:
[0174] Given the predicted trajectory, the mathematical expression for the probability P that the target vehicle travels on the candidate path map is:
[0175]
[0176] Wherein, when the target vehicle is traveling in the m-th lane on the candidate path map, there are n predicted trajectories j of the vehicle.
[0177] In one embodiment, the processor, when executing a computer program, also performs the following steps:
[0178] Based on the time information corresponding to the candidate path map, the candidate path map is clustered to obtain a set of candidate path maps corresponding to multiple time periods;
[0179] A set of candidate path maps is selected based on the time period corresponding to the trajectory information;
[0180] The trajectory information is matched with the candidate path maps in the selected candidate path map set to obtain the target path map.
[0181] In one embodiment, a computer-readable storage medium is provided having a computer program stored thereon, the computer program performing the following steps when executed by a processor:
[0182] Obtain the current vehicle's driving environment information and identify the target vehicle in the driving environment information;
[0183] Based on the location information of the target vehicle, the trajectory information corresponding to the target vehicle is confirmed;
[0184] The trajectory information is matched with candidate route maps in the route map library to obtain the target route map. The route map library includes pre-stored route maps, route maps shared by route-sharing vehicles, and real-time generated route maps.
[0185] The current vehicle's movement is controlled according to the target path map, and the target path map is shared.
[0186] In one embodiment, when the computer program is executed by a processor, it also performs the following steps:
[0187] Establish a reference coordinate system;
[0188] The position information of the target vehicle is sampled according to the reference coordinate system to obtain vehicle trajectory points, and the number of paths corresponding to the target vehicle is confirmed according to the driving environment information.
[0189] The vehicle trajectory points are grouped according to the number of paths to obtain the grouping results;
[0190] The vehicle trajectory points in each grouping result are fitted to obtain the trajectory information corresponding to the target vehicle.
[0191] In one embodiment, when the computer program is executed by a processor, it also performs the following steps:
[0192] The vehicle trajectory points in each grouping result are fitted to obtain the initial trajectory information;
[0193] Calculate the trajectory distance between each of the initial trajectory information;
[0194] If the trajectory distance does not meet the preset conditions, the number of paths will be modified.
[0195] The vehicle trajectory points are regrouped based on the modified number of paths to obtain the regrouping results.
[0196] The vehicle trajectory points in each of the regrouping results are fitted to obtain new initial trajectory information;
[0197] The new initial trajectory information is used as the trajectory information corresponding to the target vehicle until the trajectory distance between each new initial trajectory information meets the preset condition.
[0198] In one embodiment, when the computer program is executed by a processor, it further performs the following steps:
[0199] Based on the trajectory information, the probability that the target vehicle travels on the candidate path map under the conditions of the trajectory information is calculated to obtain the predicted probability;
[0200] By comparing the predicted probabilities of each path map, the path map with the highest predicted probability is selected to obtain the target path map.
[0201] In one embodiment, when the computer program is executed by a processor, it further performs the following steps:
[0202] At least one virtual lane is generated based on the trajectory information, and the reliability probability of the virtual lane is calculated.
[0203] The confidence probability and the probability threshold are compared, and the centerline of the lane with the confidence probability greater than the probability threshold is selected as the predicted trajectory of the target vehicle.
[0204] Calculate the probability that the target vehicle travels on the candidate path map under the predicted trajectory.
[0205] In one embodiment, when the computer program is executed by a processor, it further performs the following steps:
[0206] Given the predicted trajectory, the mathematical expression for the probability P that the target vehicle travels on the candidate path map is:
[0207]
[0208] Wherein, when the target vehicle is traveling in the m-th lane on the candidate path map, there are n predicted trajectories j of the vehicle.
[0209] In one embodiment, when the computer program is executed by a processor, it further performs the following steps:
[0210] Based on the time information corresponding to the candidate path map, the candidate path map is clustered to obtain a set of candidate path maps corresponding to multiple time periods;
[0211] A set of candidate path maps is selected based on the time period corresponding to the trajectory information;
[0212] The trajectory information is matched with the candidate path maps in the selected candidate path map set to obtain the target path map.
[0213] Those skilled in the art will understand that all or part of the processes in the methods of the above embodiments can be implemented by a computer program instructing related hardware. The computer program can be stored in a non-volatile computer-readable storage medium, and when executed, it can include the processes of the embodiments of the above methods. Any references to memory, storage, databases, or other media used in the embodiments provided in this application can include non-volatile and / or volatile memory. Non-volatile memory can include read-only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM), or flash memory. Volatile memory can include random access memory (RAM) or external cache memory. By way of illustration and not limitation, RAM is available in various forms, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), dual data rate SDRAM (DDRSDRAM), enhanced SDRAM (ESDRAM), synchronous link DRAM (SLDRAM), Rambus direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM), and memory bus dynamic RAM (RDRAM), etc.
[0214] The technical features of the above embodiments can be combined in any way. For the sake of brevity, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.
[0215] The embodiments described above are merely illustrative of several implementation methods of this application, and while the descriptions are relatively specific and detailed, they should not be construed as limiting the scope of the invention patent. It should be noted that those skilled in the art can make various modifications and improvements without departing from the concept of this application, and these all fall within the protection scope of this application. Therefore, the protection scope of this patent application should be determined by the appended claims.
Claims
1. A path planning method, characterized in that, include: Obtain the current vehicle's driving environment information and identify the target vehicle in the driving environment information; Based on the location information of the target vehicle, the trajectory information corresponding to the target vehicle is confirmed; The trajectory information is matched with candidate route maps in the route map library to obtain the target route map. The route map library includes pre-stored route maps, route maps shared by route-sharing vehicles, and real-time generated route maps. The candidate route maps are one or more route maps in the route map library. The step of matching the trajectory information with candidate path maps in the path map library to obtain the target path map includes: Based on the trajectory information, the probability that the target vehicle travels on the candidate path map under the conditions of the trajectory information is calculated to obtain the predicted probability; By comparing the predicted probabilities of each path map, the path map with the highest predicted probability is selected to obtain the target path map; The current vehicle's movement is controlled according to the target path map, and the target path map is shared.
2. The method according to claim 1, characterized in that, The step of confirming the trajectory information corresponding to the target vehicle based on the location information of the target vehicle includes: Establish a reference coordinate system; The position information of the target vehicle is sampled according to the reference coordinate system to obtain vehicle trajectory points, and the number of paths corresponding to the target vehicle is confirmed according to the driving environment information. The vehicle trajectory points are grouped according to the number of paths to obtain the grouping results; The vehicle trajectory points in each grouping result are fitted to obtain the trajectory information corresponding to the target vehicle.
3. The method according to claim 2, characterized in that, The process of fitting the vehicle trajectory points in each grouping result to obtain the trajectory information corresponding to the target vehicle includes: The vehicle trajectory points in each grouping result are fitted to obtain the initial trajectory information; Calculate the trajectory distance between each of the initial trajectory information; If the trajectory distance does not meet the preset conditions, the number of paths will be modified. The vehicle trajectory points are regrouped based on the modified number of paths to obtain the regrouping results. The vehicle trajectory points in each of the regrouping results are fitted to obtain new initial trajectory information; The new initial trajectory information is used as the trajectory information corresponding to the target vehicle until the trajectory distance between each new initial trajectory information meets the preset condition.
4. The method according to claim 1, characterized in that, The step of calculating the probability that the target vehicle travels on the candidate path map based on the trajectory information includes: At least one virtual lane is generated based on the trajectory information, and the reliability probability of the virtual lane is calculated. The confidence probability and the probability threshold are compared, and the centerline of the lane with the confidence probability greater than the probability threshold is selected as the predicted trajectory of the target vehicle. Calculate the probability that the target vehicle travels on the candidate path map under the predicted trajectory.
5. The method according to claim 4, characterized in that, The calculation of the probability that the target vehicle travels on the candidate path map under the predicted trajectory includes: Given the predicted trajectory, the mathematical expression for the probability P that the target vehicle travels on the candidate path map is: ； in, P represents the posterior probability of trajectory j matching the lanes in each path graph; P(m) represents the likelihood probability of trajectory j matching lanes in each path graph; P(m) represents the prior probability of lanes in each path graph; when the target vehicle is traveling in the m-th lane of the candidate path graph, there are n predicted trajectories j of the vehicle.
6. The method according to claim 1, characterized in that, The step of matching the trajectory information with candidate path maps in the path map library to obtain the target path map includes: Based on the time information corresponding to the candidate path map, the candidate path map is clustered to obtain a set of candidate path maps corresponding to multiple time periods; A set of candidate path maps is selected based on the time period corresponding to the trajectory information; The trajectory information is matched with the candidate path maps in the selected candidate path map set to obtain the target path map.
7. A path planning device, characterized in that, include: The identification module is used to acquire the current vehicle's driving environment information and identify the target vehicle in the driving environment information; The confirmation module is used to confirm the trajectory information corresponding to the target vehicle based on the location information of the target vehicle. A matching module is used to match the trajectory information with candidate path maps in a path map library to obtain a target path map. The path map library includes pre-stored path maps, path maps shared by path-sharing vehicles, and real-time generated path maps. The candidate path maps are one or more path maps in the path map library. The step of matching the trajectory information with candidate path maps in the path map library to obtain the target path map includes: Based on the trajectory information, the probability that the target vehicle travels on the candidate path map under the conditions of the trajectory information is calculated to obtain the predicted probability; By comparing the predicted probabilities of each path map, the path map with the highest predicted probability is selected to obtain the target path map; The control module is used to control the driving of the current vehicle according to the target path map and to share the target path map.
8. A computer device, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that, When the processor executes the computer program, it implements the steps of the path planning method according to any one of claims 1 to 6.
9. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by a processor, it implements the steps of the path planning method according to any one of claims 1 to 6.