Path planning method and device of vehicle, electronic equipment and autonomous vehicle

Abstract

The present disclosure provides a path planning method and device of a vehicle, an electronic device and an autonomous vehicle, relates to the technical field of artificial intelligence, and further relates to the field of autonomous driving, and aims to at least solve the technical problem of low prediction accuracy in related technologies for predicting the driving trajectory of a vehicle. The specific implementation scheme is as follows: in the process of driving along the current driving trajectory, if an obstacle vehicle is sensed, the cut-in position of the obstacle vehicle cutting into the current driving trajectory is predicted, and the cut-in time point when cutting into the cut-in position is predicted; according to the predicted cut-in position and cut-in time point, driving prediction information currently predicted by the host vehicle is generated; the driving prediction information currently predicted and the driving prediction information accumulated in the historical time range are fused to obtain driving fusion information; the target cut-in distance of the obstacle vehicle is determined based on the driving fusion information; and the current driving trajectory of the host vehicle is updated by using the target cut-in distance.

Description

Technical Field

[0001] This disclosure relates to the field of artificial intelligence technology, and in particular to a method, apparatus, electronic device, and autonomous vehicle for path planning. Background Technology

[0002] With the rapid development of autonomous driving functions in automobiles, artificial intelligence technology is being applied more and more widely to these functions. For example, it can make safety judgments about the surrounding environment of the vehicle to enable the vehicle to drive safely, predict traffic conditions so that the vehicle can travel along the planned optimal driving path, and predict the driving trajectories of obstacles around the vehicle to ensure that the vehicle can always maintain a safe distance from the obstacles.

[0003] However, in existing technologies, predicting the trajectory of obstacle vehicles relies solely on the prediction line output by the prediction module. When the obstacle vehicle brakes or turns suddenly, the accuracy of the prediction becomes low, resulting in the current vehicle being unable to maintain a safe distance from the obstacle vehicle, which in turn reduces the safety performance of autonomous driving. Summary of the Invention

[0004] This disclosure provides a method, apparatus, electronic device, and autonomous vehicle for path planning, to at least address the technical problem of low prediction accuracy in predicting vehicle trajectories in related technologies.

[0005] According to one aspect of this disclosure, a path planning method for a vehicle is provided, comprising: when a main vehicle is traveling along a current driving trajectory, if an obstacle vehicle is sensed, predicting the entry position of the obstacle vehicle into the current driving trajectory and the entry time point at the entry position; generating driving prediction information of the main vehicle based on the predicted entry position and entry time point; fusing the currently predicted driving prediction information with driving prediction information accumulated within a historical time range to obtain driving fusion information; determining the target entry distance of the obstacle vehicle based on the driving fusion information; and updating the current driving trajectory of the main vehicle using the target entry distance.

[0006] According to another aspect of this disclosure, a path planning device for a vehicle is provided, comprising: a first prediction module, configured to predict, when a main vehicle is traveling along its current driving trajectory, the entry position of an obstacle vehicle into the current driving trajectory and the entry time point at which the obstacle vehicle enters the current driving trajectory if an obstacle vehicle is detected; a generation module, configured to generate driving prediction information of the main vehicle based on the predicted entry position and entry time point; a fusion module, configured to fuse the currently predicted driving prediction information with driving prediction information accumulated within a historical time range to obtain driving fusion information; a first determination module, configured to determine the target entry distance of the obstacle vehicle based on the driving fusion information; and a path planning module, configured to update the current driving trajectory of the main vehicle using the target entry distance.

[0007] According to another aspect of this disclosure, an electronic device is provided, comprising: at least one processor; and a memory communicatively connected to the at least one processor; wherein the memory stores instructions executable by the at least one processor, the instructions being executed by the at least one processor to enable the at least one processor to perform the vehicle path planning method proposed in this disclosure.

[0008] According to another aspect of this disclosure, a non-transitory computer-readable storage medium is provided storing computer instructions, wherein the computer instructions are used to cause a computer to execute the vehicle path planning method proposed in this disclosure.

[0009] According to another aspect of this disclosure, a computer program product is provided, including a computer program that is executed by a processor to perform the vehicle path planning method proposed in this disclosure.

[0010] According to another aspect of this disclosure, an autonomous vehicle is provided, including the aforementioned electronic device, wherein the electronic device includes: at least one processor; and a memory communicatively connected to the at least one processor; wherein the memory stores instructions executable by the at least one processor, the instructions being executed by the at least one processor to enable the at least one processor to perform any of the methods described above.

[0011] In this disclosure, if an obstacle vehicle is detected while the main vehicle is traveling along its current trajectory, the entry position and timing of the obstacle vehicle entering the current trajectory are predicted. Based on the predicted entry position and timing, the main vehicle's current predicted travel information is generated. The current predicted travel information is then fused with historical travel prediction information to obtain fused travel information. The target entry distance of the obstacle vehicle is determined based on the fused travel information. The main vehicle's current trajectory is updated using the target entry distance. This method achieves the goal of accurately predicting the vehicle's travel trajectory. It is noteworthy that the vehicle's travel trajectory prediction is achieved by fusing the predicted main vehicle's travel trajectory information with historical travel prediction information, determining the obstacle vehicle's entry distance based on the fused information, and then updating the main vehicle's current travel trajectory based on the entry distance. This fully considers the driver's driving habits, generates dynamic travel prediction information, and achieves the technical effect of improving the vehicle's travel trajectory prediction rate, thereby solving the technical problem of low prediction accuracy in related technologies.

[0012] It should be understood that the description in this section is not intended to identify key or essential features of the embodiments of this disclosure, nor is it intended to limit the scope of this disclosure. Other features of this disclosure will become readily apparent from the following description. Attached Figure Description

[0013] The accompanying drawings are provided to better understand this solution and do not constitute a limitation of this disclosure. Wherein:

[0014] Figure 1 This is a hardware structure block diagram of a computer terminal (or mobile device) for implementing a vehicle path planning method according to an embodiment of the present disclosure.

[0015] Figure 2 This is a flowchart of a vehicle path planning method according to an embodiment of the present disclosure;

[0016] Figure 3 This is a schematic diagram of an optional obstacle vehicle and a main vehicle's driving prediction trajectory according to an embodiment of this disclosure;

[0017] Figure 4 This is a flowchart of an optional algorithm for introducing lane line intent according to an embodiment of the present disclosure;

[0018] Figure 5 This is a structural block diagram of a vehicle path planning device according to an embodiment of the present disclosure. Detailed Implementation

[0019] The exemplary embodiments of this disclosure are described below with reference to the accompanying drawings, including various details of the embodiments to aid understanding, and should be considered merely exemplary. Therefore, those skilled in the art will recognize that various changes and modifications can be made to the embodiments described herein without departing from the scope and spirit of this disclosure. Similarly, for clarity and brevity, descriptions of well-known functions and structures are omitted in the following description.

[0020] It should be noted that the terms "first," "second," etc., in the specification, claims, and accompanying drawings of this disclosure are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that such data can be interchanged where appropriate so that the embodiments of this disclosure described herein can be implemented in orders other than those illustrated or described herein. Furthermore, the terms "comprising" and "having," and any variations thereof, are intended to cover non-exclusive inclusion; for example, a process, method, system, product, or apparatus that comprises a series of steps or units is not necessarily limited to those steps or units explicitly listed, but may include other steps or units not explicitly listed or inherent to such processes, methods, products, or apparatus.

[0021] Autonomous driving systems are generally divided into two modules: prediction and planning. The prediction module is responsible for providing the future trajectory of the obstacle vehicle, while the planning module uses the information from the prediction module to specifically control the accelerator, brakes, and steering wheel of the main vehicle.

[0022] Existing methods for determining the interaction area of ​​obstacle vehicles rely entirely on the prediction line in each frame (autonomous driving systems typically update the prediction and planning results at least every 0.1 seconds), and the stability of the interaction area size depends excessively on the stability of the prediction line.

[0023] Prediction modules are typically data-driven black-box neural network models that output a prediction line (composed of a bunch of points), which makes it difficult to guarantee stability and accuracy between frames. The planning module's reliance on the prediction line, which is prone to jumps, makes it difficult to improve the safety and stability of the autonomous driving system.

[0024] According to embodiments of this disclosure, a method for planning the path of a vehicle is provided. It should be noted that the steps shown in the flowcharts in the accompanying drawings can be executed in a computer system such as a set of computer-executable instructions. Furthermore, although a logical order is shown in the flowcharts, in some cases, the steps shown or described may be executed in a different order than that shown here.

[0025] The method embodiments provided in this disclosure can be performed in a mobile terminal, computer terminal, or similar electronic device. The electronic device is intended to represent various forms of digital computers, such as laptop computers, desktop computers, workstations, personal digital assistants, servers, blade servers, mainframe computers, and other suitable computers. The electronic device can also represent various forms of mobile devices, such as personal digital processors, cellular phones, smartphones, wearable devices, and other similar computing devices. The components shown herein, their connections and relationships, and their functions are merely examples and are not intended to limit the implementation of the disclosure described and / or claimed herein. Figure 1 This is a hardware structure block diagram of a computer terminal (or mobile device) for implementing a vehicle path planning method according to an embodiment of the present disclosure.

[0026] like Figure 1 As shown, the computer terminal 100 includes a computing unit 101, which can perform various appropriate actions and processes according to a computer program stored in a read-only memory (ROM) 102 or a computer program loaded from a storage unit 108 into a random access memory (RAM) 103. The RAM 103 may also store various programs and data required for the operation of the computer terminal 100. The computing unit 101, ROM 102, and RAM 103 are interconnected via a bus 104. An input / output (I / O) interface 105 is also connected to the bus 104.

[0027] Multiple components in the computer terminal 100 are connected to the I / O interface 105, including: an input unit 106, such as a keyboard and mouse; an output unit 107, such as various types of displays and speakers; a storage unit 108, such as a hard disk and optical disk; and a communication unit 109, such as a network interface card (NIC), a modem, or a wireless transceiver. The communication unit 109 allows the computer terminal 100 to exchange information / data with other devices through computer networks such as the Internet and / or various telecommunications networks.

[0028] The computing unit 101 can be a variety of general-purpose and / or special-purpose processing components with processing and computing capabilities. Some examples of the computing unit 101 include, but are not limited to, a central processing unit (CPU), a graphics processing unit (GPU), various special-purpose artificial intelligence (AI) computing chips, various computing units running machine learning model algorithms, a digital signal processor (DSP), and any suitable processor, controller, microcontroller, etc. The computing unit 101 executes the vehicle path planning method described herein. For example, in some embodiments, the vehicle path planning method may be implemented as a computer software program tangibly contained in a machine-readable medium, such as storage unit 108. In some embodiments, part or all of the computer program may be loaded and / or installed on the computer terminal 100 via ROM 102 and / or communication unit 109. When the computer program is loaded into RAM 103 and executed by the computing unit 101, one or more steps of the vehicle path planning method described herein may be performed. Alternatively, in other embodiments, the computing unit 101 may be configured to execute the vehicle path planning method by any other suitable means (e.g., by means of firmware).

[0029] Various implementations of the systems and techniques described herein can be realized in digital electronic circuit systems, integrated circuit systems, field-programmable gate arrays (FPGAs), application-specific integrated circuits (ASICs), application-specific standard products (ASSPs), systems-on-a-chip (SoCs), complex programmable logic devices (CPLDs), computer hardware, firmware, software, and / or combinations thereof. These various implementations may include: implementations in one or more computer programs that can be executed and / or interpreted on a programmable system including at least one programmable processor, which may be a dedicated or general-purpose programmable processor, capable of receiving data and instructions from a storage system, at least one input device, and at least one output device, and transferring data and instructions to the storage system, the at least one input device, and the at least one output device.

[0030] It should be noted here that, in some optional embodiments, the above... Figure 1 The electronic device shown may include hardware elements (including circuitry), software elements (including computer code stored on a computer-readable medium), or a combination of both hardware and software elements. It should be noted that... Figure 1 This is only one instance of a specific particular example, and is intended to illustrate the types of components that may exist in the aforementioned electronic devices.

[0031] Under the aforementioned operating environment, this disclosure provides, for example... Figure 2 The method shown is a path planning method for vehicles, which can be derived from... Figure 1The computer terminal or similar electronic device shown is used for execution. Figure 2 This is a flowchart of a vehicle path planning method according to an embodiment of the present disclosure. Figure 2 As shown, the method may include the following steps:

[0032] Step S20: During the process of the main vehicle traveling along the current driving trajectory, if an obstacle vehicle is sensed, predict the entry position of the obstacle vehicle into the current driving trajectory, as well as the entry time point when the obstacle vehicle enters the entry position.

[0033] The aforementioned main vehicle can be an autonomous vehicle, also referred to as the vehicle itself, and is the subject of the method.

[0034] The aforementioned current driving trajectory can be the driving trajectory that the main vehicle is currently traveling on based on the map, or it can be the predicted driving trajectory that the main vehicle will travel on, but it is not limited to these two.

[0035] The aforementioned obstacle vehicles can be any one or more vehicles that affect the driving trajectory of the main vehicle.

[0036] The aforementioned entry point can be the location where the predicted trajectory of the obstacle vehicle intersects with the current trajectory of the main vehicle, indicating that the obstacle vehicle will affect the trajectory of the main vehicle.

[0037] The aforementioned entry point can be the time point at which the entry position is obtained, or the time point at which the predicted driving trajectory of the obstacle vehicle and the current driving trajectory of the main vehicle intersect.

[0038] In one optional embodiment, when the main vehicle is traveling along the driving trajectory obtained from the map, if the radar on the vehicle senses an obstacle vehicle, the prediction module can predict the entry position and entry time of the obstacle vehicle into the current driving trajectory of the main vehicle.

[0039] In another optional embodiment, when the main vehicle is traveling along the predicted driving trajectory, if the radar on the vehicle senses an obstacle vehicle, the prediction module can predict the entry position and entry time of the obstacle vehicle into the current driving trajectory of the main vehicle.

[0040] It should be noted that sensing of obstacle vehicles is not limited to radar, but can also be any one or more processors, modules, devices, systems, and servers capable of sensing obstacle vehicles; predicting the entry position and entry time of obstacle vehicles is not limited to prediction modules, but can also be any one or more processors, modules, devices, systems, and servers capable of predicting the entry position and entry time.

[0041] In this step, by sensing the obstacle vehicle, the cutting position and cutting time of the obstacle vehicle can be predicted so that the cutting distance can be obtained later.

[0042] Step S21: Generate the current predicted driving information of the main vehicle based on the predicted cut-in position and cut-in time.

[0043] The aforementioned driving prediction information can be information that informs the driver of the entry position and entry time of the main vehicle and the obstacle vehicle. It can be text information, voice information, or video information, but is not limited to these.

[0044] In one optional embodiment, once the entry position and timing of the obstacle vehicle are predicted, the main vehicle's current driving prediction information can be generated through the main vehicle's generation module. For example, the central control system can display the message "This vehicle will collide with the obstacle vehicle ahead of us in 5 seconds. Please slow down" to the driver.

[0045] In another optional embodiment, once the entry position and entry time of the obstacle vehicle are predicted, the driving prediction information currently predicted by the main vehicle can be generated by the main vehicle's generation module. For example, the driver can be informed via the vehicle's audio system that "This vehicle will collide with the obstacle vehicle ahead of us in 5 seconds. Please slow down."

[0046] In another optional embodiment, once the entry position and entry time of the obstacle vehicle are predicted, the main vehicle's current driving prediction information can be generated through the main vehicle's generation module. For example, the vehicle's central control system can play a video message to the user indicating that a collision with the obstacle vehicle will occur ahead of the vehicle in 5 seconds, reminding the user to slow down.

[0047] In this step, the predicted entry position and entry time are used to generate the current predicted driving information of the main vehicle, which can ensure the safe driving of the main vehicle.

[0048] Step S22: The driving prediction information obtained from the current prediction is fused with the driving prediction information accumulated within the historical time range to obtain driving fusion information.

[0049] The driving prediction information accumulated within the aforementioned historical time range can be the driving prediction information obtained from the previous moment of the current moment.

[0050] The aforementioned driving fusion information can be the average of the summation of the cut-in location information and cut-in time information in the current driving prediction information and the cut-in location information and cut-in time information accumulated in the historical range.

[0051] In one optional embodiment, after the current driving prediction information is obtained, the average value of the current driving prediction information and the previous driving prediction information can be obtained by summing the current driving prediction information and the previous driving prediction information (i.e., fusion), thus obtaining the driving fusion information.

[0052] It should be noted that the summation can be obtained by taking the Gaussian distribution of the current driving prediction information and the cumulative driving prediction information in the historical range, and then obtaining the convolution sum of the Gaussian distribution, but it is not limited to this.

[0053] In this step, by fusing current driving prediction information and historical driving prediction information, driving fusion information can be obtained. Then, the subsequent target entry distance can be obtained through driving fusion information, and the driving path of the main vehicle can be replanned to achieve the goal of safe driving.

[0054] Step S23: Determine the target cutting distance of the obstacle vehicle based on the driving fusion information.

[0055] The aforementioned target entry distance can be the distance at which the predicted trajectories of the obstacle vehicle and the main vehicle will overlap, determined by the target entry position and target entry point in the driving fusion information.

[0056] In one optional embodiment, since the driving fusion information includes information on the cut-in position and the cut-in time, the cut-in position and the cut-in time can be calculated based on a preset formula, that is, the target cut-in distance of the obstacle vehicle can be obtained.

[0057] It should be noted that the formulas for calculating the entry position and entry time can include, but are not limited to, Gaussian distribution.

[0058] In this step, determining the target cutting distance of the obstacle vehicle based on the driving fusion information ensures that the main vehicle can replan its driving trajectory based on the target cutting distance, thereby ensuring the driving safety of the main vehicle.

[0059] Step S24: Update the current driving trajectory of the main vehicle using the target cut-in distance.

[0060] In one optional embodiment, once the target cut-in distance of the obstacle vehicle is obtained, the current driving trajectory of the main vehicle can be updated based on the target cut-in distance.

[0061] In another optional embodiment, after obtaining the target cutting distance of the obstacle vehicle, a new driving trajectory can be generated by the vehicle generation module. For example, when the driving trajectory of the main vehicle is a straight line, and the target cutting distance is obtained as a cutting position after continuing to drive along the current straight trajectory for 100 meters, the generation module can update the current driving trajectory of the vehicle to drive along the straight line for 50 meters and then turn right or left.

[0062] It should be noted that updating the current driving trajectory of the main vehicle is not limited to the generation module, but can also be any one or more processors, modules, devices, systems, and servers capable of updating the driving trajectory.

[0063] In this step, updating the main vehicle's trajectory based on the target entry distance ensures that the main vehicle can be driven safely.

[0064] According to steps S20 to S24 of this disclosure, if an obstacle vehicle is sensed while the main vehicle is traveling along the current driving trajectory, the entry position of the obstacle vehicle into the current driving trajectory and the entry time point at the entry position are predicted; based on the predicted entry position and entry time point, the current predicted driving prediction information of the main vehicle is generated; the current predicted driving prediction information is fused with the driving prediction information accumulated within the historical time range to obtain driving fusion information; the target entry distance of the obstacle vehicle is determined based on the driving fusion information; and the current driving trajectory of the main vehicle is updated using the target entry distance. This achieves the goal of accurately predicting the vehicle's driving trajectory. It is worth noting that the prediction of the vehicle's driving trajectory is achieved by fusing the predicted main vehicle driving trajectory information with historical driving prediction information, determining the entry distance of the obstacle vehicle based on the fused information, and then updating the current driving trajectory of the main vehicle based on the entry distance. This fully considers the driver's driving habits, generates dynamic driving prediction information, and achieves the technical effect of improving the vehicle driving trajectory prediction rate, thereby solving the technical problem of low prediction accuracy in related technologies for predicting vehicle driving trajectories.

[0065] The method described in this embodiment will be further described below.

[0066] Optionally, based on the predicted cut-in position and cut-in time, the current predicted driving prediction information of the main vehicle is generated, including: predicting the cut-in distance of the obstacle vehicle based on the current position of the main vehicle and the predicted cut-in position, wherein the cut-in distance is used to represent the distance between the cut-in position and the main vehicle; and generating driving prediction information based on the cut-in position and cut-in distance.

[0067] The aforementioned current position can be the location of the main vehicle at the current moment.

[0068] In one optional embodiment, after predicting the cutting position of the obstacle vehicle, the difference between the current position and the cutting position can be obtained based on the current position of the main vehicle, thereby predicting the cutting distance of the obstacle vehicle, wherein the cutting distance is used to represent the distance between the cutting position and the current position of the main vehicle.

[0069] In another alternative embodiment, after obtaining the cut-in distance, the generation module can generate driving prediction information based on the cut-in distance, cut-in position, and cut-in time.

[0070] It should be noted that the generation of driving prediction information is not limited to the generation module, but can also be any one or more processors, modules, devices, systems, and servers capable of generating driving prediction information.

[0071] In this step, driving prediction information is generated by cutting in distance, cutting in position, and cutting in time. This ensures that the cutting in position of the obstacle vehicle can be accurately predicted, thereby ensuring the driving safety of the main vehicle.

[0072] Optionally, the driving prediction information obtained from the current prediction and the driving prediction information accumulated within the historical time range are fused to obtain driving fusion information, including: transforming the driving prediction information obtained from the current prediction to obtain a predicted Gaussian distribution; obtaining the historical Gaussian distribution corresponding to the driving prediction information accumulated within the historical time range; and determining the driving fusion information based on the convolution sum of the predicted Gaussian distribution and the historical Gaussian distribution.

[0073] The predicted Gaussian distribution mentioned above can be N(u2,σ2) 2 Where u2 represents the predicted cut-in distance at the current time (i.e., the mean of the Gaussian distribution), and σ2 2 This represents the predicted cut-in time at the current moment (i.e., the variance of the Gaussian distribution).

[0074] The aforementioned historical Gaussian distribution can be N(u1,σ1) 2 Where u1 represents the predicted cut-in distance of the historical moment (i.e., the mean of the Gaussian distribution), σ1 2 The cut-in time of the predicted historical moment (i.e., the variance of the Gaussian distribution) can be obtained from the vehicle's memory, but is not limited to this.

[0075] The convolution sum of the predicted Gaussian distribution and the historical Gaussian distribution mentioned above can be expressed by the following formula:

[0076] σ2 2 / (σ1 2 +σ2 2 )*u1+σ1 2 / (σ1 2+σ2 2 )*u2

[0077] The above formula represents the cut-in distance after fusion.

[0078] σ1 2 *σ2 2 / (σ1 2 +σ2 2 )

[0079] The above formula represents the cut-in time after fusion.

[0080] In one optional embodiment, the currently predicted driving information can first be Gaussian transformed to obtain a predicted Gaussian distribution N(u2,σ2). 2 ).

[0081] In another alternative embodiment, the historical Gaussian distribution N(u1,σ1) can be obtained from the vehicle's memory. 2 ).

[0082] In another alternative embodiment, after obtaining the predicted Gaussian distribution and the historical Gaussian distribution, the convolution sum of the Gaussian distributions can be obtained, which yields the driving fusion information.

[0083] Optionally, the driving prediction information obtained from the current prediction is transformed to obtain a predicted Gaussian distribution, including: taking the predicted cutting distance of the obstacle vehicle as the first mean; determining the first variance based on the product of the preset value and the cutting time point; and generating a predicted Gaussian distribution based on the first mean and the first variance.

[0084] The first mean mentioned above could be u2.

[0085] The aforementioned preset value can be k, where k is a constant value that can be set by the user in advance. The specific value is not specifically limited in this embodiment.

[0086] The aforementioned entry point can be the time point at which the entry position is obtained, which can be t. The specific time point is determined by the predicted entry position, and is not specifically limited in this embodiment.

[0087] In an optional embodiment, the predicted cut-in distance of the obstacle vehicle can be used as the first mean, i.e., the first mean can be u2.

[0088] In another alternative embodiment, the first variance can be determined by multiplying a preset value by the cut-in time point, thus obtaining k*t = σ2. 2 .

[0089] In another alternative embodiment, a predicted Gaussian distribution can be generated based on a first mean and a first variance, i.e., the predicted Gaussian distribution can be obtained as N(u2,σ2). 2 ).

[0090] Optionally, if an obstacle vehicle is detected while the main vehicle is traveling along the current driving trajectory, the entry position of the obstacle vehicle into the current driving trajectory is predicted, including: predicting the first driving trajectory of the obstacle vehicle, wherein the first driving trajectory is the currently predicted driving trajectory of the obstacle vehicle; and determining the intersection position of the current driving trajectory and the first driving trajectory as the entry position.

[0091] In one optional embodiment, when the main vehicle is traveling along the driving trajectory obtained from the map (i.e., the current driving trajectory), if the radar on the vehicle senses an obstacle vehicle, the first driving trajectory of the obstacle vehicle can be predicted by the prediction module, wherein the first driving trajectory can be the currently predicted driving trajectory of the obstacle vehicle.

[0092] In another alternative embodiment, when the main vehicle is traveling along the predicted driving trajectory (i.e. the current driving trajectory), if the radar on the vehicle senses an obstacle vehicle, the prediction module can predict the first driving trajectory of the obstacle vehicle, wherein the first driving trajectory may be the currently predicted driving trajectory of the obstacle vehicle.

[0093] It should be noted that sensing of obstacle vehicles is not limited to radar, but can also be any one or more processors, modules, devices, systems, and servers capable of sensing obstacle vehicles; predicting the entry position and entry time of obstacle vehicles is not limited to prediction modules, but can also be any one or more processors, modules, devices, systems, and servers capable of predicting the entry position and entry time.

[0094] In another alternative embodiment, the generation module can determine whether the current driving trajectory and the first driving trajectory will intersect. If it is determined that they will intersect, the intersection position can be determined as the cutting position.

[0095] It should be noted that determining the entry point is not limited to the generation module, but can also be any one or more processors, modules, devices, systems, and servers that can determine the entry point.

[0096] Optionally, determining the target cutting distance of the obstacle vehicle based on the driving fusion information includes: transforming the driving fusion information to obtain a target Gaussian distribution; and determining the second mean of the target Gaussian distribution as the target cutting distance.

[0097] The target Gaussian distribution mentioned above can be the Gaussian distribution obtained by Gaussian transformation of the driving fusion information.

[0098] The aforementioned target cut-in distance can be the second mean of the target's Gaussian distribution, i.e., it can be σ². 2 / (σ1 2 +σ2 2 )*u1+σ1 2 / (σ1 2 +σ2 2 )*u2.

[0099] In one optional embodiment, the driving fusion information can first be Gaussian transformed to obtain the target Gaussian distribution; secondly, the second mean of the target Gaussian distribution can be determined as the target cut-in distance, i.e., the target cut-in distance is σ². 2 / (σ1 2 +σ2 2 )*u1+σ1 2 / (σ1 2 +σ2 2 )*u2.

[0100] Optionally, the current driving trajectory of the main vehicle is updated using the target cut-in distance, including: predicting the driving lane of the obstacle vehicle to obtain the predicted lane; determining the predicted lane distance based on the predicted lane and the current position of the main vehicle, wherein the predicted lane distance is used to represent the distance between the main vehicle and the predicted lane; determining the target avoidance distance based on the current speed of the main vehicle, wherein the target avoidance distance is the distance required for the main vehicle to avoid the obstacle vehicle under a preset state; and updating the current driving trajectory of the main vehicle based on the predicted lane distance, the target avoidance distance, and the target cut-in distance.

[0101] The predicted lane mentioned above can be the lane that the obstacle vehicle will travel in, obtained by predicting the lane in which the obstacle vehicle will travel.

[0102] The predicted lane distance mentioned above can be the distance between the main vehicle and the predicted lane, which is based on the current position of the main vehicle and the position of the predicted lane.

[0103] The aforementioned target avoidance distance can be the distance that enables the main vehicle to safely avoid the obstacle vehicle under preset conditions.

[0104] The aforementioned preset state can be a state that makes the current vehicle smooth and comfortable, but it is not limited to this.

[0105] In one alternative embodiment, the predicted lane of the obstacle vehicle can first be predicted by the prediction module to obtain the predicted lane, wherein the predicted lane is used to represent the lane that the obstacle vehicle will travel in.

[0106] In another alternative embodiment, the predicted lane distance can be obtained by acquiring the difference between the current position of the predicted lane and the current position of the primary vehicle, wherein the predicted lane distance is used to represent the distance between the primary vehicle and the predicted lane.

[0107] In another alternative embodiment, after determining the predicted lane distance, a target avoidance distance can be determined based on the predicted lane and the current speed of the main vehicle, wherein the target avoidance distance can be the distance required to enable the main vehicle to avoid the obstacle vehicle in a preset state.

[0108] In another alternative embodiment, after obtaining the predicted lane distance, target avoidance distance, and target cut-in distance, the current driving trajectory of the main vehicle can be updated based on the predicted lane distance, target avoidance distance, and target cut-in distance.

[0109] Optionally, the predicted lane distance is determined based on the predicted lane and the current position of the main vehicle, including: determining the intersection of the current driving trajectory and the predicted lane as the lane entry point; and determining the predicted lane distance based on the lane entry point and the current position of the main vehicle.

[0110] In one optional embodiment, the intersection of the current driving trajectory of the main vehicle and the predicted lane can first be determined by the generation module as the lane entry position. Then, the difference between the lane entry position and the current position of the main vehicle can be obtained, which is the predicted lane distance.

[0111] It should be noted that determining the entry point is not limited to the generation module, but can also be any one or more processors, modules, devices, systems, and servers that can determine the entry point.

[0112] Optionally, the target driving path of the main vehicle is determined based on the predicted lane distance, the target cut-in distance, and the target avoidance distance, including: determining whether the predicted lane distance is less than the target cut-in distance; in response to the predicted lane distance being less than the target cut-in distance, determining whether the target avoidance distance is less than the predicted lane distance; and in response to the target avoidance distance being less than the predicted lane distance, updating the current driving trajectory of the main vehicle based on the predicted lane distance.

[0113] The aforementioned target entry distance can be the distance at which the predicted trajectories of the obstacle vehicle and the main vehicle will overlap, determined by the target entry position and target entry point in the driving fusion information.

[0114] The predicted lane distance mentioned above can be the distance between the main vehicle and the predicted lane, which is based on the current position of the main vehicle and the position of the predicted lane.

[0115] The aforementioned target avoidance distance can be the distance that enables the main vehicle to safely avoid the obstacle vehicle under preset conditions.

[0116] In one alternative embodiment, the target cut-in distance has the highest safety priority because it is the distance at which the predicted trajectories of the obstacle vehicle and the master vehicle will overlap. Secondly, the predicted lane distance has a higher safety priority than the target avoidance distance because it is the distance between the master vehicle and the predicted lane, and the target avoidance distance is the distance at which the master vehicle can safely avoid the obstacle vehicle under a preset state.

[0117] In another optional embodiment, it can first be determined whether the predicted lane distance is less than the target cut-in distance. In response to the predicted lane distance being less than the target cut-in distance, in order to ensure the driving safety of the main vehicle, the distance between the main vehicle and the obstacle vehicle should be based on the predicted lane distance. Therefore, it can be further determined whether the target avoidance distance is less than the predicted lane distance. In response to the target safe avoidance distance being less than the predicted lane distance, it proves that the main vehicle can successfully avoid the obstacle vehicle at a smaller safe distance. Therefore, in order to further ensure the safety of the main vehicle, the driving trajectory of the main vehicle can be replanned based on the predicted lane distance.

[0118] Optionally, the method further includes: in response to the predicted lane distance being greater than or equal to the target cut-in distance, determining whether the target avoidance distance is less than the target cut-in distance; and in response to the target avoidance distance being less than the target cut-in distance, updating the current driving trajectory of the main vehicle based on the target cut-in distance.

[0119] In one optional embodiment, in response to the predicted lane distance being greater than or equal to the target cut-in distance, in order to ensure the driving safety of the main vehicle, the distance between the main vehicle and the obstacle vehicle should be based on the target cut-in distance. Therefore, it can be further determined whether the target cut-in distance is less than the target comfortable distance. In response to the target comfortable distance being less than the target cut-in distance, it proves that the main vehicle can successfully avoid the obstacle vehicle at a smaller safe distance. Therefore, in order to further ensure the safety of the main vehicle, the driving trajectory of the main vehicle can be replanned based on the predicted target cut-in distance.

[0120] Optionally, the method further includes: updating the current driving trajectory of the master vehicle based on the target avoidance distance in response to the target avoidance distance being greater than or equal to the target cut-in distance.

[0121] In one alternative embodiment, in response to a target avoidance distance being greater than or equal to a target cut-in distance, the driving trajectory of the main vehicle can be replanned based on the target avoidance distance in order to enable the main vehicle to safely and comfortably avoid obstacle vehicles.

[0122] Optionally, the method further includes: in response to the target avoidance distance being greater than or equal to the predicted lane distance, determining whether the target avoidance distance is less than the target cut-in distance; and in response to the target avoidance distance being less than the target cut-in distance, updating the current driving trajectory of the main vehicle based on the target avoidance distance.

[0123] In one alternative embodiment, in response to the target avoidance distance being greater than or equal to the predicted lane distance, in order to enable the main vehicle to avoid the obstacle vehicle safely and comfortably, it can be further determined whether the target avoidance distance is less than the target cut-in distance; in response to the target avoidance distance being less than the target cut-in distance, it is proven that the main vehicle can avoid the obstacle vehicle comfortably and safely with a smaller distance, so the driving trajectory of the main vehicle can be replanned based on the target avoidance distance.

[0124] Optionally, the method further includes: updating the current driving trajectory of the main vehicle based on the target cut-in distance in response to the target avoidance distance being greater than or equal to the target cut-in distance.

[0125] In one alternative embodiment, in response to a target avoidance distance greater than or equal to a target cut-in distance, the main vehicle's trajectory can be replanned based on a smaller target cut-in distance to ensure that the main vehicle does not collide with the obstacle vehicle.

[0126] In this disclosure, if an obstacle vehicle is detected while the main vehicle is traveling along its current trajectory, the entry position and timing of the obstacle vehicle entering the current trajectory are predicted. Based on the predicted entry position and timing, the main vehicle's current predicted travel information is generated. The current predicted travel information is then fused with historical travel prediction information to obtain fused travel information. The target entry distance of the obstacle vehicle is determined based on the fused travel information. The main vehicle's current trajectory is updated using the target entry distance. This method achieves the goal of accurately predicting the vehicle's travel trajectory. It is noteworthy that the vehicle's travel trajectory prediction is achieved by fusing the predicted main vehicle's travel trajectory information with historical travel prediction information, determining the obstacle vehicle's entry distance based on the fused information, and then updating the main vehicle's current travel trajectory based on the entry distance. This fully considers the driver's driving habits, generates dynamic travel prediction information, and achieves the technical effect of improving the vehicle's travel trajectory prediction rate, thereby solving the technical problem of low prediction accuracy in related technologies.

[0127] In the process of autonomous driving, a crucial piece of information is determining the interaction position between the obstacle vehicle and the driver vehicle. The accuracy and stability of this positioning play a vital role in the ultimate safety and comfort of autonomous driving.

[0128] Figure 3 This is a schematic diagram of an optional obstacle vehicle and a main vehicle's predicted driving trajectory according to an embodiment of this disclosure, such as... Figure 3As shown, the vehicle traveling along the straight path is the main vehicle, and the vehicle traveling along the curved path is the obstacle vehicle. It can be seen that if both vehicles continue to travel along the fixed path, they will collide in the area enclosed by 1, 2, 3, and 4 in the figure.

[0129] Therefore, in order to solve the above problems, this disclosure proposes a vehicle path planning method, the specific implementation method of which is as follows:

[0130] Considering prediction noise, assume that `trajcutin_s` follows a Gaussian distribution. In the autonomous driving system, the prediction module is responsible for estimating the future trajectory of obstacles and providing a corresponding predicted line. Since the prediction module cannot be 100% accurate, there will be inconsistencies between the predicted line and the actual trajectory of the obstacle; this inconsistency is called prediction noise. Here, `trajcutin_s` represents the position where the obstacle's predicted line cuts into the driver's path.

[0131] The Gaussian distribution is as follows:

[0132] N(u,σ n 2 )

[0133] Where u is the position of the perfect prediction line, which is the mean, and σ n Let be the standard deviation of the nth frame. Based on current big data statistics, the shorter the entry time, the more accurate the entry position, leading to the conclusion that the standard deviation decreases. It's important to note that a perfect prediction line represents the actual future trajectory of the obstacle vehicle, i.e., a completely accurate prediction line. Entry time represents the time when the obstacle vehicle enters the main vehicle's trajectory, as provided by the prediction line. Entry position represents the location where the obstacle vehicle enters the main vehicle's trajectory, as provided by the prediction line. The standard deviation indicates that if the obstacle enters 1 second later, the predicted entry position is relatively accurate. If the obstacle enters 8 seconds later, the predicted entry position is less accurate because the time is too far in the future to predict accurately. If we consider a Gaussian distribution, it represents the magnitude of the standard deviation.

[0134] σ n <σ n-1

[0135] Here, a recursive temporal Gaussian kernel is designed, assuming that the cumulative historical prediction distribution is N(u1, σ1). 2 The latest predicted distribution is N(u2,σ2). 2 ).

[0136] Inspired by Kalman filtering, the normalized Gaussian kernel is designed as shown in the following formula, which is inversely proportional to the variance (the larger the variance, the greater the noise, and the smaller the weight should be):

[0137] Historical predicted distribution:

[0138] σ1 2 / (σ1 2 +σ2 2 )

[0139] Latest predicted distribution:

[0140] σ2 2 / (σ1 2 +σ2 2 )

[0141] The mean of the merged new Gaussian distribution is:

[0142] σ2 2 / (σ1 2 +σ2 2 )*u1+σ1 2 / (σ1 2 +σ2 2 )*u2

[0143] Further calculations reveal that the variance of the merged new Gaussian distribution is:

[0144] σ1 2 *σ2 2 / (σ1 2 +σ2 2 )

[0145] The derivation yields:

[0146] σ1 2 *σ2 2 / (σ1 2 +σ2 2 )<σ1 2 *(σ1 2 +σ2 2 ) / (σ1 2 +σ2 2 )=σ1 2

[0147] σ1 2 *σ2 2 / (σ1 2 +σ2 2 )<σ2 2

[0148] It can be seen that the variance is reduced, similar to Gaussian filtering in image processing, achieving the effect of removing high-frequency noise. Therefore, the probability of sudden braking is lower after fusion. Fusion involves combining historically accumulated prediction information with the latest prediction information. The reduction in variance can also be understood using the statistical method of maximum likelihood estimation.

[0149] Therefore, through the above process, the prediction line information of multiple historical frames was fused to obtain the filtered entry position with smaller variance, denoted as s_filter.

[0150] Each frame's prediction line can provide an entry position. By fusing the entry position of the new frame with the historically accumulated entry positions, a comprehensive entry position is obtained.

[0151] To further improve stability and comfort, predicted lane intention information has been introduced. The predicted lane intention is provided by the upstream prediction module. This information means which lane the obstacle vehicle will travel in next. Compared with the predicted line, although some flexibility is lost (because a small number of obstacle vehicles will not stay in the lane), the stability is greatly improved (in most cases, the obstacle vehicle will stay in the lane).

[0152] Here, the interaction area position calculated using lane intent is denoted as s_lane. Finally, the algorithm flowchart that simultaneously considers s_lane, the entry position (s_filter), and the comfort avoidance position (s_comfort) is as follows: Figure 4 As shown, Figure 4 Here is a flowchart of an optional algorithm for introducing lane line intent according to an embodiment of this disclosure:

[0153] Step S41: Gaussian filtering is used to obtain the cut-in position;

[0154] Step S42: The lane line intention is to obtain the location of the interaction area;

[0155] Step S43: Determine whether the cut-in position and the interaction area position have been calculated. If yes, proceed to step S45; otherwise, proceed to step S44.

[0156] Step S44 returns the result that the calculated result does not exist;

[0157] Step S45: Estimate the comfortable avoidance position;

[0158] Step S46: Determine whether the interactive area position is smaller than the cut-in position. If yes, proceed to step S410; otherwise, proceed to step S47.

[0159] Step S47: Determine whether the comfort avoidance position is smaller than the cutting position. If yes, proceed to step S49; otherwise, proceed to step S48.

[0160] Step S48, the result returned is the cut-in position;

[0161] Step S49, the result is the comfortable avoidance position;

[0162] Step S410: Determine whether the comfortable avoidance distance is less than the interaction area position. If yes, proceed to step S411; otherwise, proceed to step S412.

[0163] Step S411, the result returned is the location of the interactive area;

[0164] Step S412: Determine whether the comfort avoidance position is smaller than the cutting position. If yes, proceed to step S413; otherwise, proceed to step S414.

[0165] Step S413 returns the comfortable avoidance distance;

[0166] Step S414 returns the cut-in position.

[0167] The collection, storage, use, processing, transmission, provision, and disclosure of user personal information involved in the technical solution disclosed herein comply with the provisions of relevant laws and regulations and do not violate public order and good morals.

[0168] Through the above description of the embodiments, those skilled in the art can clearly understand that the methods according to the above embodiments can be implemented by means of software plus necessary general-purpose hardware platforms. Of course, they can also be implemented by hardware, but in many cases the former is a better implementation method. Based on this understanding, the technical solution of this disclosure, in essence, or the part that contributes to the prior art, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a terminal device (which may be a mobile phone, computer, server, or network device, etc.) to execute the methods described in the various embodiments of this disclosure.

[0169] This disclosure also provides a vehicle path planning device for implementing the above embodiments and preferred embodiments, which will not be repeated hereafter. As used below, the term "module" can refer to a combination of software and / or hardware that performs a predetermined function. Although the device described in the following embodiments is preferably implemented in software, hardware implementation, or a combination of software and hardware, is also possible and contemplated.

[0170] Figure 5 This is a structural block diagram of a vehicle path planning device according to an embodiment of the present disclosure, such as... Figure 5As shown, a vehicle path planning device 500 includes: a first prediction module 50, used to predict the entry position of an obstacle vehicle into the current driving trajectory and the entry time point when the obstacle vehicle enters the current driving trajectory if an obstacle vehicle is sensed during the process of the main vehicle traveling along the current driving trajectory; a generation module 52, used to generate the currently predicted driving prediction information of the main vehicle based on the predicted entry position and the entry time point; a fusion module 54, used to fuse the currently predicted driving prediction information with the driving prediction information accumulated within a historical time range to obtain driving fusion information; a first determination module 56, used to determine the target entry distance of the obstacle vehicle based on the driving fusion information; and a path planning module 58, used to update the current driving trajectory of the main vehicle using the target entry distance.

[0171] Optionally, the generation module includes: a first determining unit, configured to predict the cutting distance of the obstacle vehicle based on the current position of the master vehicle and the predicted cutting position, wherein the cutting distance is used to represent the distance between the cutting position and the master vehicle; and a generation unit, configured to generate the driving prediction information based on the cutting position and the cutting distance.

[0172] Optionally, the fusion module includes: a first conversion unit, used to convert the currently predicted driving prediction information to obtain a predicted Gaussian distribution; a first acquisition unit, used to acquire the historical Gaussian distribution corresponding to the cumulative driving prediction information within the historical time range; and a second determination unit, used to determine the driving fusion information based on the convolution sum of the predicted Gaussian distribution and the historical Gaussian distribution.

[0173] Optionally, the first conversion unit includes: a processing subunit, configured to use the predicted cut-in distance of the obstacle vehicle as a first mean; a first determining subunit, configured to determine a first variance based on the product of a preset value and the cut-in time point; and a generating subunit, configured to generate the predicted Gaussian distribution based on the first mean and the first variance.

[0174] Optionally, the first prediction module includes: a first prediction unit, configured to predict a first driving trajectory of the obstacle vehicle, wherein the first driving trajectory is the currently predicted driving trajectory of the obstacle vehicle; and a third determination unit, configured to determine the intersection position of the current driving trajectory and the first driving trajectory as the cutting position.

[0175] Optionally, the first determining module includes: a second conversion unit, used to convert the driving fusion information to obtain a target Gaussian distribution; and a fourth determining unit, used to determine the second mean of the target Gaussian distribution as the target cutting distance.

[0176] Optionally, the path planning module includes: a second prediction unit, configured to predict the driving lane of the obstacle vehicle to obtain a predicted lane; a fifth determination unit, configured to determine the predicted lane distance based on the predicted lane and the current position of the main vehicle, wherein the predicted lane distance represents the distance between the main vehicle and the predicted lane; a sixth determination unit, configured to determine a target avoidance distance based on the current speed of the main vehicle, wherein the target avoidance distance is the distance required for the main vehicle to avoid the obstacle vehicle under a preset state; and a path planning unit, configured to update the current driving trajectory of the main vehicle based on the predicted lane distance, the target avoidance distance, and the target cut-in distance.

[0177] Optionally, the fifth determining unit includes: a second determining subunit, used to determine the intersection of the current driving trajectory and the predicted lane as the lane entry position; and a third determining subunit, used to determine the predicted lane distance based on the lane entry position and the current position of the main vehicle.

[0178] Optionally, the path planning unit includes: a first judgment subunit, configured to determine whether the predicted lane distance is less than the target cut-in distance; a second judgment subunit, configured to determine whether the target avoidance distance is less than the predicted lane distance in response to the predicted lane distance being less than the target cut-in distance; and a first path planning subunit, configured to update the current driving trajectory of the main vehicle based on the predicted lane distance in response to the target avoidance distance being less than the predicted lane distance.

[0179] Optionally, the path planning unit further includes: a third judgment subunit, configured to determine whether the target avoidance distance is less than the target avoidance distance in response to the predicted lane distance being greater than or equal to the target cut-in distance; and a second path planning subunit, configured to update the current driving trajectory of the main vehicle based on the target avoidance distance in response to the target avoidance distance being less than the target cut-in distance.

[0180] Optionally, the path planning unit further includes: a fourth determining subunit, used to update the current driving trajectory of the master vehicle based on the target avoidance distance in response to the target avoidance distance being greater than or equal to the target cutting distance.

[0181] Optionally, the path planning unit further includes: a fourth judgment subunit, used to determine whether the target avoidance distance is less than the target cut-in distance in response to the target avoidance distance being greater than or equal to the predicted lane distance; and a third path planning subunit, used to update the current driving trajectory of the main vehicle based on the target avoidance distance in response to the target avoidance distance being less than the target cut-in distance.

[0182] Optionally, the path planning unit further includes: a fourth path planning subunit, used to update the current driving trajectory of the master vehicle based on the target avoidance distance in response to the target avoidance distance being greater than or equal to the target cut-in distance.

[0183] It should be noted that the above modules can be implemented by software or hardware. For the latter, they can be implemented in the following ways, but are not limited to: all the above modules are located in the same processor; or, the above modules are located in different processors in any combination.

[0184] According to embodiments of this disclosure, this disclosure also provides an electronic device including a memory and at least one processor, the memory storing computer instructions, the processor being configured to execute the computer instructions to perform the steps in any of the above method embodiments.

[0185] Optionally, the electronic device may further include a transmission device and an input / output device, wherein the transmission device is connected to the processor and the input / output device is connected to the processor.

[0186] Optionally, in this disclosure, the processor described above can be configured to perform the following steps via a computer program:

[0187] S1, while the main vehicle is traveling along the current driving trajectory, if an obstacle vehicle is sensed, predict the entry point of the obstacle vehicle into the current driving trajectory, as well as the entry time point when the obstacle vehicle enters the entry point.

[0188] S2, Based on the predicted entry position and entry time, generate the current predicted driving information of the main vehicle;

[0189] S3, the driving prediction information obtained from the current prediction is fused with the driving prediction information accumulated within the historical time range to obtain the driving fusion information;

[0190] S4, determine the target cutting distance of the obstacle vehicle based on driving fusion information;

[0191] S5 updates the current driving trajectory of the main vehicle using the target cut-in distance.

[0192] Optionally, specific examples in this embodiment can refer to the examples described in the above embodiments and optional implementations, and will not be repeated here.

[0193] According to embodiments of the present disclosure, the present disclosure also provides a non-transitory computer-readable storage medium storing computer instructions, wherein the computer instructions are configured to perform the steps in any of the above method embodiments at runtime.

[0194] Optionally, in this embodiment, the non-volatile storage medium described above can be configured to store a computer program for performing the following steps:

[0195] S1, while the main vehicle is traveling along the current driving trajectory, if an obstacle vehicle is sensed, predict the entry point of the obstacle vehicle into the current driving trajectory, as well as the entry time point when the obstacle vehicle enters the entry point.

[0196] S2, Based on the predicted entry position and entry time, generate the current predicted driving information of the main vehicle;

[0197] S3, the driving prediction information obtained from the current prediction is fused with the driving prediction information accumulated within the historical time range to obtain the driving fusion information;

[0198] S4, determine the target cutting distance of the obstacle vehicle based on driving fusion information;

[0199] S5 updates the current driving trajectory of the main vehicle using the target cut-in distance.

[0200] Optionally, in this embodiment, the aforementioned non-transitory computer-readable storage medium may include, but is not limited to, electronic, magnetic, optical, electromagnetic, infrared, or semiconductor systems, devices, or equipment, or any suitable combination of the foregoing. More specific examples of readable storage media include electrical connections based on one or more wires, portable computer disks, hard disks, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fibers, portable compact disk read-only memory (CD-ROM), optical storage devices, magnetic storage devices, or any suitable combination of the foregoing.

[0201] According to embodiments of this disclosure, a computer program product is also provided. Program code for implementing the method embodiments of this disclosure can be written in any combination of one or more programming languages. This program code can be provided to a processor or controller of a general-purpose computer, special-purpose computer, or other programmable data processing apparatus, such that when executed by the processor or controller, the program code causes the functions / operations specified in the flowcharts and / or block diagrams to be implemented. The program code can be executed entirely on a machine, partially on a machine, partially on a remote machine as a standalone software package, or entirely on a remote machine or server.

[0202] According to embodiments of this disclosure, this disclosure also provides an autonomous driving vehicle, including an electronic device involved in the above embodiments, wherein the electronic device includes: at least one processor; and a memory communicatively connected to the at least one processor; wherein the memory stores instructions executable by the at least one processor, the instructions being executed by the at least one processor to enable the at least one processor to perform any of the methods in the above embodiments.

[0203] In the above embodiments of this disclosure, the descriptions of each embodiment have different focuses. For parts not described in detail in a certain embodiment, please refer to the relevant descriptions of other embodiments.

[0204] In the several embodiments provided in this disclosure, it should be understood that the disclosed technical content can be implemented in other ways. The device embodiments described above are merely illustrative; for example, the division of units can be a logical functional division, and in actual implementation, there may be other division methods. For instance, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the displayed or discussed mutual couplings, direct couplings, or communication connections may be through some interfaces; indirect couplings or communication connections between units or modules may be electrical or other forms.

[0205] The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple units. Some or all of the units can be selected to achieve the purpose of this embodiment according to actual needs.

[0206] Furthermore, the functional units in the various embodiments of this disclosure can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated unit can be implemented in hardware or as a software functional unit.

[0207] If the integrated unit is implemented as a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of this disclosure, in essence, or the part that contributes to the prior art, or all or part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of this disclosure. The aforementioned storage medium includes various media capable of storing program code, such as a USB flash drive, read-only memory (ROM), random access memory (RAM), portable hard disk, magnetic disk, or optical disk.

[0208] The above description is only a preferred embodiment of this disclosure. It should be noted that for those skilled in the art, several improvements and modifications can be made without departing from the principles of this disclosure, and these improvements and modifications should also be considered within the scope of protection of this disclosure.

Claims

1. A method for vehicle path planning, comprising: While the main vehicle is traveling along the current driving trajectory, if an obstacle vehicle is detected, the entry point of the obstacle vehicle into the current driving trajectory and the entry time point when it enters the entry point are predicted. Based on the predicted cut-in position and cut-in time, the current predicted driving information of the main vehicle is generated; The driving prediction information obtained from the current prediction is fused with the driving prediction information accumulated within the historical time range to obtain driving fusion information; The target cut-in distance of the obstacle vehicle is determined based on the driving fusion information, wherein the target cut-in distance is the distance at which the predicted driving trajectory of the obstacle vehicle and the main vehicle overlaps, determined by the target cut-in position and the target cut-in time point in the driving fusion information. The current driving trajectory of the main vehicle is updated using the target cut-in distance; The step of fusing the currently predicted driving prediction information with the driving prediction information accumulated over a historical time range to obtain driving fusion information includes: transforming the currently predicted driving prediction information to obtain a predicted Gaussian distribution, wherein the mean of the predicted Gaussian distribution is the predicted entry distance of the obstacle vehicle, and the variance of the predicted Gaussian distribution is the entry time point; obtaining the historical Gaussian distribution corresponding to the driving prediction information accumulated over the historical time range; and determining the driving fusion information based on the convolution sum of the predicted Gaussian distribution and the historical Gaussian distribution. The step of updating the current driving trajectory of the main vehicle using the target cut-in distance includes: updating the current driving trajectory of the main vehicle using the safety priority among the target cut-in distance, the predicted lane distance, and the target avoidance distance, wherein the safety priority of the target cut-in distance is greater than the safety priority of the predicted lane distance, the safety priority of the predicted lane distance is greater than the safety priority of the target avoidance distance, the predicted lane distance is used to represent the distance between the main vehicle and the predicted lane, the predicted lane is obtained by predicting the driving lane of the obstacle vehicle, and the target avoidance distance is used to represent the distance required for the main vehicle to avoid the obstacle vehicle under a preset state.

2. The method according to claim 1, wherein, Based on the predicted cut-in position and cut-in time, the currently predicted driving prediction information of the main vehicle is generated, including: Based on the current position of the main vehicle and the predicted cut-in position, the cut-in distance of the obstacle vehicle is predicted; The driving prediction information is generated based on the cut-in position, the cut-in time point, and the cut-in distance.

3. The method according to claim 1, wherein, The driving prediction information obtained from the current prediction is transformed to obtain a predicted Gaussian distribution, including: The predicted cut-in distance of the obstacle vehicle is used as the first mean. The first variance is determined based on the product of the preset value and the entry time point; The predicted Gaussian distribution is generated based on the first mean and the first variance.

4. The method according to claim 1, wherein, While the main vehicle is traveling along its current trajectory, if an obstacle vehicle is detected, the predicted entry point of the obstacle vehicle into the current trajectory is included: Predict the first driving trajectory of the obstacle vehicle, wherein the first driving trajectory is the currently predicted driving trajectory of the obstacle vehicle; The intersection point of the current driving trajectory and the first driving trajectory is determined as the cutting position.

5. The method according to claim 1, wherein, Determining the target cut-in distance of the obstacle vehicle based on the driving fusion information includes: The driving fusion information is transformed to obtain the target Gaussian distribution; The second mean of the Gaussian distribution of the target is determined as the target cutting distance.

6. The method according to claim 1, wherein, The method further includes: Predict the lane in which the obstructing vehicle will travel, and obtain the predicted lane; The predicted lane distance is determined based on the predicted lane and the current position of the main vehicle; The target avoidance distance is determined based on the current speed of the main vehicle.

7. The method according to claim 6, wherein, Determining the predicted lane distance based on the predicted lane and the current position of the main vehicle includes: The intersection of the current driving trajectory and the predicted lane is determined as the lane entry point; The predicted lane distance is determined based on the lane entry position and the current position of the main vehicle.

8. The method according to claim 6, wherein, Based on the predicted lane distance, the target cut-in distance, and the target avoidance distance, the target driving path of the main vehicle is determined, including: Determine whether the predicted lane distance is less than the target cut-in distance; In response to the predicted lane distance being less than the target cut-in distance, determine whether the target avoidance distance is less than the predicted lane distance; In response to the target avoidance distance being less than the predicted lane distance, the current driving trajectory of the master vehicle is updated based on the predicted lane distance.

9. The method according to claim 8, wherein, The method further includes: In response to the predicted lane distance being greater than or equal to the target cut-in distance, it is determined whether the target avoidance distance is less than the target cut-in distance; In response to the target avoidance distance being less than the target cut-in distance, the current driving trajectory of the master vehicle is updated based on the target cut-in distance.

10. The method according to claim 9, wherein, The method further includes: In response to the target avoidance distance being greater than or equal to the target cut-in distance, the current driving trajectory of the master vehicle is updated based on the target avoidance distance.

11. The method according to claim 8, wherein, The method further includes: In response to the target avoidance distance being greater than or equal to the predicted lane distance, it is determined whether the target avoidance distance is less than the target cut-in distance; In response to the target avoidance distance being less than the target cut-in distance, the current driving trajectory of the master vehicle is updated based on the target avoidance distance.

12. The method according to claim 9, wherein, The method further includes: In response to the target avoidance distance being greater than or equal to the target cut-in distance, the current driving trajectory of the master vehicle is updated based on the target cut-in distance.

13. A path planning device for a vehicle, comprising: The first prediction module is used to predict the entry position of the obstacle vehicle into the current driving trajectory and the entry time when the obstacle vehicle enters the current driving trajectory if the obstacle vehicle is sensed while the main vehicle is traveling along the current driving trajectory. The generation module is used to generate the current predicted driving prediction information of the main vehicle based on the predicted cut-in position and the cut-in time point; The fusion module is used to fuse the driving prediction information obtained from the current prediction with the driving prediction information accumulated within the historical time range to obtain driving fusion information, wherein the driving prediction information accumulated within the historical time range is the driving prediction information predicted at the previous time of the current time. The first determining module is used to determine the target cutting distance of the obstacle vehicle based on the driving fusion information, wherein the target cutting distance is the distance at which the predicted driving trajectory of the obstacle vehicle and the main vehicle coincides, determined by the target cutting position and the target cutting time point in the driving fusion information. The path planning module is used to update the current driving trajectory of the main vehicle using the target cutting distance; The fusion module is further configured to fuse the currently predicted driving prediction information and the driving prediction information accumulated over a historical time range through the following steps to obtain driving fusion information: The currently predicted driving prediction information is transformed to obtain a predicted Gaussian distribution, wherein the mean of the predicted Gaussian distribution is the predicted entry distance of the obstacle vehicle, and the variance of the predicted Gaussian distribution is the entry time point; the historical Gaussian distribution corresponding to the driving prediction information accumulated over the historical time range is obtained; and the driving fusion information is determined based on the convolution sum of the predicted Gaussian distribution and the historical Gaussian distribution. The path planning module is further configured to update the current driving trajectory of the master vehicle using the target cut-in distance through the following steps: updating the current driving trajectory of the master vehicle using the safety priority among the target cut-in distance, the predicted lane distance, and the target avoidance distance, wherein the safety priority of the target cut-in distance is greater than the safety priority of the predicted lane distance, the safety priority of the predicted lane distance is greater than the safety priority of the target avoidance distance, the predicted lane distance is used to represent the distance between the master vehicle and the predicted lane, the predicted lane is obtained by predicting the driving lane of the obstacle vehicle, and the target avoidance distance is used to represent the distance required for the master vehicle to avoid the obstacle vehicle under a preset state.

14. An electronic device, comprising: At least one processor; as well as A memory communicatively connected to the at least one processor; wherein, The memory stores instructions that can be executed by the at least one processor to enable the at least one processor to perform the method of any one of claims 1-12.

15. A non-transitory computer-readable storage medium storing computer instructions, wherein, The computer instructions are used to cause the computer to perform the method according to any one of claims 1-12.

16. A computer program product comprising a computer program that, when executed by a processor, implements the method according to any one of claims 1-12.

17. An autonomous vehicle, including the electronic equipment as claimed in claim 14.

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