CN116358584A

CN116358584A - Automatic driving vehicle path planning method, device, equipment and medium

Info

Publication number: CN116358584A
Application number: CN202310286469.5A
Authority: CN
Inventors: 姚志鹏; 李勇强; 吕强; 付一豪
Original assignee: Neolix Technologies Co Ltd
Current assignee: Neolix Technologies Co Ltd
Priority date: 2023-03-22
Filing date: 2023-03-22
Publication date: 2023-06-30

Abstract

The invention discloses a method, a device, equipment and a medium for planning an automatic driving vehicle path. The scheme relates to the unmanned field, and the method comprises the following steps: acquiring perception information of the unmanned vehicle; determining the safety coefficient of the vehicle and at least one dynamic obstacle according to the perception information; the safety coefficient is the ratio of the current distance between the vehicle and the dynamic obstacle to the safety distance; and determining the safety loss matched with each dynamic obstacle according to each safety coefficient, taking each safety loss as the prior characteristic of each dynamic obstacle, inputting the prior characteristic into a prediction model, and determining the planned path of the automatic driving vehicle. The technical scheme solves the problems of low accuracy, high time cost and the like caused by directly extracting the abstract features of the perception information by the prediction model, and can enhance the interpretability of the prediction model while improving the prediction accuracy of the prediction model and the generation efficiency of the model.

Description

Automatic driving vehicle path planning method, device, equipment and medium

Technical Field

The invention relates to the technical field of unmanned driving, in particular to a method, a device, equipment and a medium for planning an automatic driving vehicle path.

Background

With the continuous progress of technology, the demand for navigation ability of unmanned vehicles under various road conditions is increasingly urgent. The navigation system of the unmanned vehicle needs to acquire sensing information such as radar, vision and the like, and makes reasonable decisions based on the recognition result of the sensing information so as to arrive at a destination timely and accurately on the premise of ensuring driving safety.

At present, a navigation system of an unmanned vehicle mainly depends on predictive models such as Lyft, waymo and the like, the perception information is directly subjected to feature representation based on a deep learning network, and driving decisions are carried out according to abstract features extracted from the perception information.

However, in the prior art, the manner of directly characterizing the perception information through the deep learning network requires a great deal of time to train the deep learning network, and meanwhile, the stability of the training effect is difficult to ensure, and the interpretation of the prediction model is poor.

Disclosure of Invention

The invention provides an automatic driving vehicle path planning method, device, equipment and medium, which are used for solving the problems of low accuracy, high time cost and the like caused by directly extracting abstract features of perception information by a prediction model, and can enhance the interpretation of the prediction model while improving the prediction accuracy of the prediction model and the model generation efficiency.

According to an aspect of the present invention, there is provided a method of automatically driving a vehicle path planning, the method comprising:

obtaining perception information of the vehicle; the sensing information comprises speed information of the vehicle, speed information of the dynamic obstacle, distance information of the vehicle and the dynamic obstacle and response time;

determining the safety coefficient of the vehicle and at least one dynamic obstacle according to the perception information; the safety coefficient is the ratio of the current distance between the vehicle and the dynamic obstacle to the safety distance;

and determining the safety loss matched with each dynamic obstacle according to each safety coefficient, taking each safety loss as the prior characteristic of each dynamic obstacle, inputting the prior characteristic into a prediction model, and determining the planned path of the automatic driving vehicle.

According to another aspect of the present invention, there is provided an autonomous vehicle path planning apparatus comprising:

the perception information acquisition module is used for acquiring perception information of the vehicle; the sensing information comprises speed information of the vehicle, speed information of the dynamic obstacle, distance information of the vehicle and the dynamic obstacle and response time;

the safety coefficient determining module is used for determining the safety coefficient of the vehicle and at least one dynamic obstacle according to the perception information; the safety coefficient is the ratio of the current distance between the vehicle and the dynamic obstacle to the safety distance;

And the planned path determining module is used for determining the safety loss matched with each dynamic obstacle according to each safety coefficient, taking each safety loss as the prior characteristic of each dynamic obstacle, inputting the prior characteristic into the prediction model, and determining the planned path of the automatic driving vehicle.

According to another aspect of the present invention, there is provided an electronic apparatus including:

at least one processor; and

a memory communicatively coupled to the at least one processor; wherein,,

the memory stores a computer program executable by the at least one processor to enable the at least one processor to perform the autonomous vehicle path planning method according to any of the embodiments of the present invention.

According to another aspect of the present invention, there is provided a computer readable storage medium storing computer instructions for causing a processor to execute the method for autonomous vehicle path planning according to any of the embodiments of the present invention.

According to the technical scheme, the safety coefficients of the vehicle and at least one dynamic obstacle are determined according to the perception information by acquiring the perception information of the vehicle; and determining the safety loss matched with each dynamic obstacle according to each safety coefficient, taking each safety loss as the prior characteristic of each dynamic obstacle, inputting the prior characteristic into a prediction model, and determining the planned path of the automatic driving vehicle. The method solves the problems of low accuracy, high time cost and the like caused by directly extracting the abstract features of the perception information by the prediction model, and can enhance the interpretability of the prediction model while improving the prediction accuracy of the prediction model and the generation efficiency of the model.

It should be understood that the description in this section is not intended to identify key or critical features of the embodiments of the invention or to delineate the scope of the invention. Other features of the present invention will become apparent from the description that follows.

Drawings

In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings required for the description of the embodiments will be briefly described below, and it is apparent that the drawings in the following description are only some embodiments of the present invention, and other drawings may be obtained according to these drawings without inventive effort for a person skilled in the art.

FIG. 1 is a flow chart of a method for automatically driving a vehicle path planning in accordance with a first embodiment of the present invention;

FIG. 2 is a schematic diagram of a prediction model provided according to an embodiment of the present invention;

fig. 3 is a flowchart of an automatic driving vehicle path planning method according to a second embodiment of the present invention;

fig. 4 is a schematic view of candidate region division in a simple driving scenario provided according to an embodiment of the present invention;

fig. 5 is a schematic diagram of candidate region division in a complex driving scenario according to an embodiment of the present invention;

Fig. 6 is a schematic structural view of an automatic driving vehicle path planning apparatus according to a third embodiment of the present invention;

fig. 7 is a schematic structural diagram of an electronic device implementing a method for path planning of an autonomous vehicle according to an embodiment of the present invention.

Detailed Description

In order that those skilled in the art will better understand the present invention, a technical solution in the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings in which it is apparent that the described embodiments are only some embodiments of the present invention, not all embodiments. All other embodiments, which can be made by those skilled in the art based on the embodiments of the present invention without making any inventive effort, shall fall within the scope of the present invention.

It should be noted that the terms "first," "second," and the like in the description and the claims of the present invention and the above figures are used for distinguishing between similar objects and not necessarily for describing a particular sequential or chronological order. It is to be understood that the data so used may be interchanged where appropriate such that the embodiments of the invention described herein may be implemented in sequences other than those illustrated or otherwise described herein. Furthermore, the terms "comprises," "comprising," and "having," and any variations thereof, are intended to cover a non-exclusive inclusion, such that a process, method, apparatus, article, or apparatus that comprises a list of steps or elements is not necessarily limited to those steps or elements expressly listed or inherent to such process, method, article, or apparatus. The data acquisition, storage, use, processing and the like in the technical scheme meet the relevant regulations of national laws and regulations.

Example 1

Fig. 1 is a flowchart of an automatic vehicle path planning method according to an embodiment of the present invention, where the method may be implemented by an automatic vehicle path planning device, and the device may be implemented in hardware and/or software, and the device may be configured in an electronic device.

As shown in fig. 1, the method includes:

s110, obtaining perception information of the vehicle; the sensing information comprises speed information of the vehicle, speed information of the dynamic obstacle, distance information of the vehicle and the dynamic obstacle and response time.

The scheme can be executed by an automatic driving vehicle controller, and the controller can acquire multidimensional sensing information through sensors such as radars, cameras and the like deployed by the vehicle. The sensing information may include information such as a speed of the vehicle, a speed of the dynamic obstacle, a distance between the vehicle and the dynamic obstacle, and response time. The controller can identify the type of each target, such as static obstacle and dynamic obstacle, according to the driving scene image acquired by the camera. Further, the controller can further realize fine recognition of each target, for example, fine recognition of static targets, including green belts, buildings and the like, and fine recognition of dynamic obstacles, including vehicles, pedestrians, bicycles and the like. According to the radar acquisition data, the controller can determine the distance information of the vehicle and each dynamic obstacle and the speed information of the dynamic obstacle. It will be readily appreciated that the speed sensor controller of the host vehicle may determine speed information of the host vehicle and that the controller may obtain acceleration of the host vehicle via an inertial sensor, such as an IMU. The controller may also record the response time of the host vehicle to the control decision.

S120, determining the safety coefficient of the vehicle and at least one dynamic obstacle according to the perception information; the safety coefficient is the ratio of the current distance between the vehicle and the dynamic obstacle to the safety distance.

According to the perception information of the vehicle, the controller can calculate the safety coefficient of each dynamic obstacle in the vehicle and the perception range, wherein the safety coefficient can be used for evaluating the dangerous degree of the dynamic obstacle to the vehicle. Specifically, the safety coefficient may be represented by a ratio of a current distance between the host vehicle and the dynamic obstacle to a safety distance, where the safety distance may be calculated based on a speed of the host vehicle, a speed of the dynamic obstacle, and a response time of the host vehicle to a driving decision.

S130, according to the safety coefficients, determining the safety loss matched with each dynamic obstacle, taking each safety loss as the prior characteristic of each dynamic obstacle, inputting the prior characteristic into a prediction model, and determining the planned path of the automatic driving vehicle.

After the safety coefficients corresponding to the dynamic obstacles are obtained, the controller can normalize the safety coefficients to obtain the safety loss. And taking the safety loss matched with each dynamic obstacle as the prior characteristic of each dynamic obstacle, inputting the safety loss into a prediction model, and determining the planned path of the automatic driving vehicle by the controller according to the output result of the prediction model.

The prediction model can plan the path of the vehicle by taking the motion characteristics, the navigation map characteristics and the obstacle characteristics of the vehicle as the basis, and output the prediction information of the motion of the vehicle. The motion characteristics of the vehicle can comprise the position, the running direction, the motion speed and the like of the vehicle. The navigation map feature may include road features of the host vehicle driving environment, such as road boundary lines, reference lines, etc. The obstacle features may include a priori features of the dynamic obstacle, and may also include features such as identification, location, direction of movement, and speed of movement of the obstacle. The predictive model may be constructed based on a deep learning algorithm and the predictive information may include the position, heading angle, speed, acceleration, curvature, and acceleration of the host vehicle, and time intervals. According to the prediction information, the controller can generate a planned path of the automatic driving vehicle so as to ensure safe driving of the vehicle.

Specifically, the prediction model may be obtained by training sample data in advance, and the training sample data may include multiple sets, so that the prediction model achieves a good training effect. Each set of training sample data may include feature data and tag data. Similar to the application of the predictive model, the feature data may include a host vehicle motion feature, a navigational map feature, and at least one obstacle feature. The tag data may be own vehicle motion information matched with the feature data.

Fig. 2 is a schematic structural diagram of a prediction model according to an embodiment of the present invention. As shown in fig. 2, the predictive model may include an input branch structure, a multi-headed self-attention structure, a multi-layered perceptron structure, and a long-term memory structure. The input branch structure can be used for extracting characteristics of the motion characteristics, the obstacle characteristics and the navigation map characteristics of the vehicle. The multi-head self-attention structure can be used for extracting the relation features among the input features so as to improve the accuracy and the prediction efficiency of the prediction model. The multi-layer perceptron structure may compress the features. The long-short term memory structure can extract time sequence characteristics among multiple frames of input data.

In a preferred scheme, the long-short-term memory structure can take the current frame and the historical four frames as input data, and extract time sequence characteristics among the input data so as to achieve a good prediction effect.

In one possible implementation, the input branch structure includes a first input branch, a second input branch, and a third input branch; the first input branch, the second input branch and the third input branch are respectively used for extracting the characteristics of the motion characteristic, the obstacle characteristic and the navigation map characteristic of the vehicle; the input branch structure, the multi-head self-attention structure, the multi-layer perceptron structure and the long-period memory structure are sequentially connected.

It is easy to understand that each input branch in the input branch structure is used for extracting the characteristics of the motion characteristics, the obstacle characteristics and the navigation map characteristics of the vehicle respectively. Each input branch may include at least one full connection layer, and the structures of the input branches may be the same or different. The first input branch, the second input branch and the third input branch can respectively perform feature extraction on the motion feature, the obstacle feature and the navigation map feature of the vehicle to obtain a first feature extraction result, a second feature extraction result and a third feature extraction result.

The controller may input the first feature extraction result, the second feature extraction result, and the third feature extraction result to the multi-head self-attention structure according to a preset input manner, so as to extract a relationship feature between the input features. After obtaining the output characteristics with relation characteristics output by the multi-head self-attention structure, the controller can further extract deep characteristics of the output characteristics by utilizing the multi-layer perceptron mechanism, and input the deep characteristics obtained by extraction into the long-short-period memory structure so as to extract time sequence characteristics among multi-frame deep characteristics.

The prediction model structure of the scheme can realize multi-dimensional feature extraction of the motion features, the obstacle features and the navigation map features of the vehicle, and is beneficial to improving the robustness and the accuracy of the model.

Based on the above scheme, optionally, the multi-head self-attention structure may include three inputs; wherein the first input and the second input are each determined based on a first feature extraction result of the first input branch, a second feature extraction result of the second input branch, and a third feature extraction result of the third input branch; the third input is determined based on the first feature extraction result of the first input branch.

Specifically, the controller may fuse the first feature extraction result, the second feature extraction result, and the third feature extraction result according to a preset fusion manner, and use the fused feature as the first input or the second input. Wherein the first input and the second input may be the same or different. The controller can directly splice the first feature extraction result, the second feature extraction result and the third feature extraction result according to a certain arrangement sequence to obtain the fusion feature. For example, the first feature extraction result, the second feature extraction result, and the third feature extraction result are represented by A, B and C, respectively, and the fusion feature may be represented as [ a, B, C ].

The controller may also select a part of the feature extraction results in the first feature extraction result, the second feature extraction result, and the third feature extraction result sequentially, and the different feature extraction results are spliced alternately to generate the fusion feature. For example, the fused feature may be represented as [ A1, B1, C1, A2, B2, C2], where A1 represents a first partial feature extraction of the first feature extraction and A2 represents a second partial feature extraction of the first feature extraction, and B1, C1, B2, and C2 are the same.

It will be appreciated that the controller may also take the first feature extraction result as a third input to determine an output feature having a relationship between the host vehicle motion feature and the navigation map feature and the obstacle feature based on the first input, the second input, and the third input. The calculation formula of the output characteristic can be expressed as:

wherein K represents a first input, V represents a second input, Q represents a third input, d _k Representing the dimension of the first input, softmax represents the normalized exponential function, and Attention (Q, K, V) represents the output characteristics of the multi-headed attentional mechanism.

The prediction model with the multi-head self-attention structure can improve the prediction efficiency and accuracy.

According to the scheme, the perception information of the vehicle is not directly input into the prediction model, the abstract features of the obstacle are extracted, the driving decision is judged, the prior features of the obstacle are determined according to the perception information, the prior features are used as the basis of the driving decision and are input into the prediction model, and the driving decision of the vehicle is obtained. Therefore, the scheme can improve the decision accuracy and the decision efficiency of the prediction model.

According to the technical scheme, the safety coefficient of the vehicle and at least one obstacle is determined according to the perception information by acquiring the perception information of the vehicle; and determining the safety loss matched with each obstacle according to each safety coefficient, taking each safety loss as the prior characteristic of each obstacle, inputting a prediction model, and determining the planned path of the automatic driving vehicle. The method solves the problems of low accuracy, high time cost and the like caused by directly extracting the abstract features of the perception information by the prediction model, and can enhance the interpretability of the prediction model while improving the prediction accuracy of the prediction model and the generation efficiency of the model.

Example two

Fig. 3 is a flowchart of a method for planning a path of an autonomous vehicle according to a second embodiment of the present invention, which is based on the above embodiment. For the dynamic obstacle in the obstacle, the second embodiment further refines and calculates the safety coefficient thereof, as shown in fig. 3, and the method includes:

s210, obtaining perception information of the vehicle; the sensing information comprises speed information of the vehicle, speed information of the dynamic obstacle, distance information of the vehicle and the dynamic obstacle and response time.

In this embodiment, the speed information may include information such as a lateral speed, a longitudinal speed, a lateral acceleration, and a longitudinal acceleration, and the distance information may include information of a lateral distance and a longitudinal distance.

S220, determining the safety distance between the vehicle and each dynamic obstacle according to the speed information of the vehicle, the speed information of the dynamic obstacle and the response time.

It can be appreciated that, since the driving directions of the dynamic obstacles are different in the driving scene, the controller can analyze the vehicle and each dynamic obstacle from both a transverse angle and a longitudinal angle. According to the information such as the speed of the vehicle, the acceleration of the vehicle, the speed of the dynamic obstacle, the acceleration of the dynamic obstacle, the response time and the like, the controller can calculate the transverse safety distance between the vehicle and each dynamic obstacle. According to the transverse safety distance, the controller can calculate the transverse safety coefficient matched with each dynamic obstacle. According to the information such as the speed of the vehicle, the acceleration of the vehicle, the speed of the dynamic obstacle, the acceleration of the dynamic obstacle, the response time and the like, the controller can also calculate the longitudinal safety distance between the vehicle and each dynamic obstacle. According to the longitudinal safety distance, the controller can calculate the longitudinal safety coefficient matched with each dynamic obstacle.

In one possible solution, the safety distance comprises a longitudinal distance;

correspondingly, the determining the safety distance between the vehicle and each dynamic obstacle according to the speed information of the vehicle, the speed information of the dynamic obstacle and the response time comprises the following steps:

determining the relative movement direction of the vehicle and the dynamic obstacle according to the speed information of the vehicle and the speed information of the dynamic obstacle;

and determining the longitudinal safety distance between the vehicle and each dynamic obstacle according to the relative movement direction, the speed information of the vehicle, the speed information of the dynamic obstacle and the response time.

It is easy to understand that the speed information of the host vehicle may indicate the movement direction of the host vehicle, and the speed information of the dynamic obstacle may indicate the movement direction of the dynamic obstacle. For the longitudinal safety distance, the controller can judge the relative movement direction of the vehicle and the dynamic obstacle according to the running direction of the vehicle and the movement direction of the dynamic obstacle. Based on the transverse and longitudinal directions of the vehicle, the speed of the dynamic obstacle is decomposed, and the relative movement direction of the vehicle and the dynamic obstacle is judged according to the decomposition speed of the dynamic obstacle in the longitudinal direction of the vehicle. The controller can respectively set different longitudinal safety distance calculation modes according to the relative movement direction of the vehicle and the dynamic obstacle.

According to the scheme, the relative movement direction of the vehicle and the dynamic obstacles is determined according to the movement direction indicated by the speed information, and then the longitudinal safety distance between the vehicle and each dynamic obstacle is calculated according to different relative movement directions by utilizing the speed information of the vehicle, the speed information of the dynamic obstacles and the response time. The scheme realizes accurate longitudinal safety distance calculation, and is beneficial to ensuring the accuracy of safety coefficient calculation.

On the basis of the scheme, optionally, the relative movement direction comprises the same direction and different directions; the speed information comprises speed, transverse speed information and longitudinal speed information; wherein the longitudinal speed information includes a longitudinal speed, a longitudinal deceleration, and a longitudinal acceleration; the lateral velocity information includes a lateral velocity;

the determining the longitudinal safety distance between the vehicle and each dynamic obstacle according to the relative movement direction, the speed information of the vehicle, the speed information of the dynamic obstacle and the response time comprises the following steps:

if the relative movement direction of the vehicle and the dynamic barrier is the same direction, determining the longitudinal safety distance between the vehicle and the dynamic barrier according to the longitudinal speed of the preceding traffic target, the longitudinal speed of the following traffic target, the maximum longitudinal deceleration, the maximum longitudinal acceleration, the minimum longitudinal deceleration and the response time;

If the relative movement direction of the vehicle and the dynamic obstacle is different, determining the longitudinal safety distance between the vehicle and the dynamic obstacle according to the longitudinal speed of the vehicle, the longitudinal speed of the dynamic obstacle, the minimum longitudinal deceleration, the maximum longitudinal acceleration, the minimum longitudinal deceleration and the response time of the vehicle.

If the relative movement direction of the vehicle and the dynamic obstacle is the same direction, the controller can calculate the longitudinal safety distance between the vehicle and the dynamic obstacle according to the longitudinal speed of the preceding traffic object, the longitudinal speed of the following traffic object, the maximum longitudinal deceleration, the maximum longitudinal acceleration, the minimum longitudinal deceleration and the response time. Wherein the preceding traffic objective is different from the following traffic objective, which may be the host vehicle or a dynamic obstacle, and similarly, the following traffic objective may be one of the host vehicle or the dynamic obstacle. The maximum longitudinal deceleration, the maximum longitudinal acceleration, and the minimum longitudinal deceleration are determined based on statistics of traffic target attribute parameters. The traffic target attribute parameters may include parameters such as a category, model, size, load range, acceleration range, deceleration range, and the like of the traffic target. The controller can acquire attribute parameters of traffic targets in the traffic environment, and respectively perform parameter statistics on the traffic targets of each class to obtain the maximum longitudinal deceleration, the maximum longitudinal acceleration and the minimum longitudinal deceleration of the traffic targets of each class. The controller may determine the maximum longitudinal deceleration, the maximum longitudinal acceleration, and the minimum longitudinal deceleration of the dynamic obstacle according to the maximum longitudinal deceleration, the maximum longitudinal acceleration, and the minimum longitudinal deceleration corresponding to the traffic target of the category to which the dynamic obstacle belongs.

In one particular approach, the controller may determine a maximum displacement of the subsequent traffic target within the response time based on the longitudinal speed, the maximum longitudinal acceleration, and the response time of the subsequent traffic target. The controller may determine a maximum braking distance between the preceding traffic target and the following traffic target within the response time based on the longitudinal speed of the following traffic target, the longitudinal speed of the preceding traffic target, the maximum longitudinal acceleration, the minimum longitudinal deceleration, the maximum longitudinal deceleration, and the response time. And adding the maximum displacement and the maximum braking distance of the subsequent traffic targets as the safety distance. Specifically, when the relative motion direction of the vehicle and the dynamic obstacle is in the same direction, the calculation formula of the longitudinal safety distance can be expressed as follows:

wherein v is _r Representing longitudinal speed, v, of a following traffic object _f Representing the longitudinal speed of a preceding traffic target, t representing the response time, a _{lon,max,accel} Indicating maximum longitudinal acceleration, a _{lon,min,brake} Representing the minimum longitudinal deceleration, a _{lon,max,brake} Indicating the maximum longitudinal deceleration.

If the relative movement direction of the vehicle and the dynamic obstacle is different, the controller can determine the longitudinal safety distance between the vehicle and the dynamic obstacle according to the longitudinal speed of the vehicle, the longitudinal speed of the dynamic obstacle, the minimum longitudinal deceleration, the maximum longitudinal acceleration, the minimum longitudinal deceleration and the response time of the vehicle.

Taking the forward running of the vehicle and the reverse running of the dynamic obstacle as an example, the controller can determine the maximum speed of the vehicle in the response time according to the longitudinal speed, the maximum longitudinal acceleration and the response time of the vehicle. Similarly, the controller may determine a maximum velocity of the dynamic obstacle within the response time based on the longitudinal velocity, the maximum longitudinal acceleration, and the response time of the dynamic obstacle. The controller may calculate the maximum displacement of the host vehicle based on the host vehicle's longitudinal speed, the host vehicle's maximum speed within the response time, the host vehicle's minimum longitudinal deceleration, and the response time. Based on the longitudinal velocity of the dynamic obstacle, the maximum velocity of the dynamic obstacle within the response time, the minimum longitudinal deceleration, and the response time, the controller may calculate the maximum displacement of the dynamic obstacle. And adding the maximum displacement of the vehicle and the maximum displacement of the dynamic obstacle as a safety distance. When the vehicle runs reversely and the dynamic obstacle runs forward in the same way, and the relative movement direction of the vehicle and the dynamic obstacle is different, the calculation formula of the longitudinal safety distance can be expressed as follows:

v _1lon,t ＝v _1lon +ta _{lon,max,accel} ；

v _2lon,t ＝|v _2lon |+ta _{lon,max,accel} ；

wherein v is _1lon Representing the speed, v, of a traffic target travelling in forward direction _2lon Representing the speed of a traffic target traveling in reverse, t representing the response time, a _{lon,max,accel} Indicating maximum longitudinal acceleration, a _{lon,min,brake} Representing the minimum longitudinal deceleration, a _{lon,min,brake,correct} Representing the minimum longitudinal deceleration of the vehicle.

In another possible implementation, the safety distance further includes a lateral safety distance; the lateral velocity information further includes lateral acceleration and lateral deceleration;

and determining the lateral safety distance between the vehicle and each dynamic obstacle according to the lateral speed of the vehicle, the lateral speed of the dynamic obstacle, the maximum lateral acceleration, the minimum lateral deceleration and the response time.

For the lateral safety distance, the controller can determine the lateral safety distance between the vehicle and each dynamic obstacle according to the relative position between the vehicle and the dynamic obstacle. The controller may bring the two traffic targets close to each other with maximum lateral acceleration and brake with minimum lateral deceleration until lateral displacement of the motion stop as lateral safe distance.

In a specific scheme, the calculation formula of the lateral safety distance can be expressed as follows:

v _1lat,t ＝v _1lat +ta _{lat,max,accel} ；

v _2lat,t ＝v _2lat -ta _{lat,max,accel} ；

wherein μ represents a fault tolerant space, v _1lat Representing lateral speed, v, of traffic targets located on the left _2lat Represents the lateral speed of a traffic target located on the right, t represents the response time, a _{lat,max,accel} Indicating maximum lateral acceleration, a _{lat,min,brake} Representing the minimum lateral deceleration.

According to the scheme, the safety distances are calculated from the transverse angle and the longitudinal angle respectively, so that the accurate calculation of the safety coefficient is realized, and the driving safety of the vehicle is guaranteed to the greatest extent. In some embodiments, mu is 0.2m, and fault tolerance is increased on the basis of calculating the safety distance so as to ensure running safety.

S230, determining the safety coefficient of the vehicle and each dynamic obstacle according to the safety distance and the distance information of the vehicle and the dynamic obstacle.

It is readily understood that the safety factor may include a lateral safety factor and a longitudinal safety factor. The calculation formulas of the transverse safety coefficient and the longitudinal safety coefficient of the vehicle and the dynamic barrier can be respectively expressed as follows:

wherein lon_dis represents the current longitudinal distance between the vehicle and the dynamic obstacle, d _min Representing the longitudinal safety distance, lon_safe_coeff representing the longitudinal safety factor; lat_dis represents the own vehicle and a dynamic obstacleD is equal to the current lateral distance of _min,lat Represents the lateral safety distance, lat_safe_coeff represents the lateral safety factor.

S240, according to the safety coefficients, determining the safety loss matched with each dynamic obstacle, taking each safety loss as the prior characteristic of each dynamic obstacle, inputting the prior characteristic into a prediction model, and determining the planned path of the automatic driving vehicle.

In this solution, optionally, the determining, according to each safety coefficient, a safety loss matched with each dynamic obstacle includes:

and if the safety coefficient is smaller than a preset coefficient threshold value, determining the safety loss matched with the dynamic obstacle based on an inverse Gaussian model.

According to the calculation method of the safety factor in S230, the greater the current distance is with respect to the safety distance, the greater the safety factor is, and the lower the risk of the dynamic obstacle is. If the safety coefficient is greater than or equal to a preset coefficient threshold value, the dynamic obstacle corresponding to the safety coefficient is not dangerous or has low risk degree. The controller can directly set the safety loss of the dynamic barrier without danger or with low danger degree to 0, and the safety loss calculation is not needed.

If the safety coefficient is smaller than the preset coefficient threshold, the controller can restrict the safety coefficient matched with each dynamic obstacle to be between (0 and 1) according to the inverted Gaussian model, so that the convergence speed of the prediction model is increased in the training process, and the driving decision accuracy of the prediction model is improved.

On the basis of the scheme, optionally, the safety coefficient comprises a longitudinal safety coefficient and a transverse safety coefficient;

accordingly, the determining, based on the inverse gaussian model, a security loss matching the dynamic obstacle includes:

based on the inverted Gaussian model, determining longitudinal safety loss according to the longitudinal safety coefficient, and determining transverse safety loss according to the transverse safety coefficient;

determining a safety loss matched with the dynamic obstacle according to the longitudinal safety loss, the transverse safety loss and a predetermined weight coefficient; the weight coefficient is determined based on a safety loss change rate, and the safety loss change rate is determined based on the distance between the vehicle and the dynamic obstacle and the relative speed.

Specifically, the calculation formulas of the longitudinal safety loss and the transverse safety loss can be expressed as:

lon_safe_cost＝-1×[1-gaussian(K1-lon_safe_coeff)]；

lat_safe_cost＝-1×[1-gaussian(K2-lat_safe_coeff)]；

the method comprises the steps of determining a longitudinal safety loss of a vehicle, wherein the lon_safe_cost represents the longitudinal safety loss, the lon_safe_coeff represents the longitudinal safety coefficient, the lat_safe_cost represents the transverse safety loss, the lat_safe_coeff represents the transverse safety coefficient, the gaussian represents the gaussian distribution, K1 and K2 represent preset coefficient thresholds, and K1 and K2 can be the same or different.

The controller can determine the overall safety loss of the operation target according to the transverse safety loss, the longitudinal safety loss and the weight coefficient. Specifically, the calculation formula of the security loss can be expressed as:

safe_cost＝w_lon_safe×lon_safe_cost+w_lat_safe×lat_safe_cost；

where w_lon_safe represents the weight coefficient of the longitudinal safety loss, and w_lat_safe represents the weight coefficient of the transverse safety loss.

The weight coefficient may be statistically derived from historical security loss data, for example, the weight coefficient of lateral security loss may be set to 0.54, and the weight coefficient of longitudinal security loss may be set to 0.46. The weight coefficient may also be determined based on a security loss rate of change. The controller can determine the change rate of the safety loss according to the current distance between the vehicle and the dynamic obstacle and the current speed. The security loss rate of change may include a lateral security loss rate of change and a longitudinal security loss rate of change. The transverse safety loss change rate can be determined according to the ratio of the current transverse speed to the current transverse distance between the vehicle and the dynamic obstacle, and the longitudinal safety loss change rate can be determined according to the ratio of the current longitudinal speed to the current longitudinal distance between the vehicle and the dynamic obstacle.

The calculation formulas of the longitudinal and lateral loss change rates can be expressed as:

wherein lon_speed represents the relative speed of the vehicle and the dynamic obstacle in the longitudinal direction, and lat_speed represents the relative speed of the vehicle and the dynamic obstacle in the transverse direction.

According to the scheme, the matched weight coefficients can be set for the transverse safety loss and the longitudinal safety loss so as to calculate the overall safety loss of the dynamic obstacle, and the accurate evaluation of the safety of the dynamic obstacle is facilitated.

In order to achieve more reliable path planning, the controller can also input a prediction model to determine the planned path of the automatic driving vehicle by taking the safety loss and the safety loss rate as prior characteristics of each dynamic obstacle.

The motion characteristics, the navigation map characteristics and the obstacle characteristics of the vehicle are input into a pre-trained prediction model, and the controller can determine the prediction information of the vehicle. The predicted information may include position, orientation angle, speed, acceleration, curvature, acceleration, and time interval. Based on the prediction information, the vehicle control apparatus may determine a planned path of the host vehicle. Meanwhile, according to the prediction information and the current motion information output by the prediction model, the vehicle control equipment can generate a driving decision of the vehicle in the current decision period. For example, the speed of the vehicle output by the prediction model is 10m/s, the current speed of the vehicle is 8m/s, and the vehicle control device can generate an acceleration instruction to control the power device of the vehicle to increase the speed. According to the driving decision, the vehicle control equipment can control the vehicle to drive according to the predicted planning path, and the driving safety and reliability of the vehicle are ensured.

In complex driving scenes such as traffic jams, a large number of obstacles may exist in the perception range of the vehicle, and the prediction efficiency is greatly reduced by inputting the obstacle characteristics of all the obstacles into the prediction model, and sufficient hardware resource support is required. There may also be obstacles within the perception of the host vehicle that have no impact on the driving decisions of the host vehicle, such as obstacles that do not present a risk of collision with the host vehicle. Therefore, the vehicle control device can screen the obstacles in the perception range of the vehicle, and select the obstacles with guiding significance for the driving decision of the vehicle as the input of the prediction model.

Thus, in a preferred embodiment, the controller may determine the weight of the matching of the at least one candidate region based on the vehicle location division according to the motion state of the vehicle, and determine the ranking result of the obstacles according to the weight of each candidate region, the a priori characteristics of each obstacle, and each dynamic obstacle location. According to the sequencing result, the controller can determine at least one dangerous obstacle in each obstacle, and input the motion characteristics, the navigation map characteristics and the at least one dangerous obstacle characteristics of the vehicle into a pre-trained prediction model to determine the prediction information of the vehicle. The controller may generate a planned path for the autonomous vehicle based on the predictive information for the host vehicle.

The controller may divide one or more candidate areas according to the vehicle location. The controller can set the area size of each candidate area in the driving scene, and sets multiple groups of weights for the candidate areas according to the motion state of the vehicle. The motion state may include straight, right turn, left turn, head drop, etc. Each motion state may correspond to one or more sets of candidate region weights, e.g., the straight-going state may correspond to the weight set a.

The controller may determine, based on the positions of the obstacles, candidate regions to which the obstacles belong. And calculating risk assessment values of the obstacles according to the prior characteristics of the obstacles and the weights of the candidate areas. The risk evaluation values of the respective obstacles are ranked, and the vehicle control apparatus can obtain the ranking result of the obstacles.

The controller may select, according to the sorting result, a preset number of obstacles with a higher risk evaluation value as dangerous obstacles. For example, the obstacle at the first 64 bits of the risk assessment value in the ranking result is determined as a risk obstacle. The vehicle control apparatus may also select, as the dangerous obstacle, an obstacle whose risk evaluation value is greater than a preset evaluation threshold value, based on the ranking result. For example, an obstacle having a risk assessment value of greater than 0.8 in the ranked result is taken as a risk obstacle.

The vehicle control device can take the motion characteristics, the navigation map characteristics and the dangerous obstacle characteristics of the vehicle as the input of a prediction model to predict the running information of the vehicle so as to improve the prediction efficiency of the prediction model.

In a specific example, the dangerous object determination process may be as follows:

(1) At least one candidate region is determined based on the host vehicle location.

After the prior characteristics of each obstacle are obtained, the controller can divide candidate areas for the perception range of the vehicle according to the vehicle position. The region division may be different according to different driving scenarios. Fig. 4 is a schematic view of candidate region division in a simple driving scenario according to an embodiment of the present invention. As shown in fig. 4, in a narrow one-way driving scenario, the controller may divide 3 candidate areas, namely candidate area (1), candidate area (2) and candidate area (3), according to the current position of the host vehicle. The controller can also divide the perception range of the vehicle according to the current position of the vehicle in a gradient manner according to the distance, for example, the perception range is divided into 5 candidate areas, namely, a candidate area (1), a candidate area (2), a candidate area (3), a candidate area (4) and a candidate area (5).

Fig. 5 is a schematic diagram of candidate region division in a complex driving scenario according to an embodiment of the present invention. In a complex driving scenario such as traffic jam, the vehicle control apparatus may divide 9 candidate areas shown in fig. 5 with the candidate area (5) where the host vehicle is located as a center area, and the vehicle control apparatus may set the area size of each candidate area according to the driving scenario.

It should be noted that, the above candidate region division according to the complexity of the driving scenario is only one of the candidate region division modes, and the controller may also perform the candidate region division according to factors such as the lane where the host vehicle is located, the driving road segment of the host vehicle, and the like. The present embodiment does not limit the division manner of the candidate region. The areas of the candidate regions may be the same or different, and the shapes of the candidate regions may be regular or irregular.

(2) And determining the weight of each candidate area according to the motion state of the vehicle and a preset weight distribution principle.

The movement state of the autonomous vehicle may include straight, u-turn, left-turn, right-turn, reverse, etc. The controller may set weights of the candidate regions to which the motion states match according to the degree of influence of each motion state on the candidate regions. Taking the candidate region division manner as shown in fig. 5 as an example, in the straight-running state of the vehicle, the affected candidate regions may include (3), (5), (6) and (9), where the degree of influence of the candidate regions (3), (5), (6) and (9) may be ordered as (5), (6), (3) and (9), and then the weights of the candidate regions (1) - (9) that are matched in the straight-running state may be respectively: 1. 1, 1.5, 1, 2, 1.8, 1 and 1.5. The controller can also gradient the weight of the target candidate region according to the sequence of the candidate regions affected by the motion state of the vehicle. Still taking the candidate region division manner as shown in fig. 5 as an example, when the host vehicle sequentially affects the candidate regions (5), (6), (3), (2) and (1) in the left turn state, the weights of the candidate regions (1) to (9) matched in the left turn state may be respectively: 1.2, 1.4, 1.6, 1, 2, 1.8, 1 and 1.

(3) And determining the candidate area of each obstacle according to the position of each obstacle.

The controller may compare the positions of the obstacles with the ranges of the candidate regions to determine the candidate regions to which the obstacles belong.

(4) And determining the sequencing result of the obstacles according to the weight of each candidate region, the candidate region of each obstacle and the safety loss of each obstacle.

In one possible implementation, the determining the ranking result of the obstacles according to the weight of each candidate area, the candidate area of each obstacle, and the safety loss of each obstacle includes:

determining the weight of the candidate area of each obstacle, and determining the weighted safety loss according to the weight of the candidate area of each obstacle and the safety loss of each obstacle;

and determining the sequencing result of the barriers according to the weighted safety loss of each barrier.

The controller may determine the weight of each obstacle safety loss match according to the weight of each candidate region and the candidate region to which each obstacle belongs. Multiplying the safety loss of each obstacle by the matched weight, the controller can obtain the weighted safety loss of each obstacle. The weighted safety losses of the obstacles are ranked, and the controller can output the ranking result of the obstacles.

The scheme can determine the weighted safety loss of each obstacle, and is beneficial to realizing the reliability of obstacle risk degree evaluation.

(5) And determining a preset number of barriers as dangerous barriers in each barrier according to the sequencing result.

The controller can determine the dangerous degree of each obstacle according to the weighted safety loss in the sequencing result of the obstacles, and select a preset number of obstacles with relatively high dangerous degree from each obstacle as dangerous obstacles.

According to the scheme, a certain number of barriers are selected from the barriers to serve as dangerous barriers, so that the driving decision timeliness is guaranteed, and meanwhile, the safety and reliability of the driving decision are improved. According to the technical scheme, the safety coefficient of the vehicle and at least one obstacle is determined according to the perception information by acquiring the perception information of the vehicle; and determining the weighted safety loss matched with each obstacle according to the safety coefficient and the weight of the candidate area, taking each safety loss as the prior characteristic of each obstacle, inputting a prediction model, and determining the planned path of the automatic driving vehicle. The method solves the problems of low accuracy, high time cost and the like caused by directly extracting the abstract features of the perception information by the prediction model, and can enhance the interpretability of the prediction model while improving the prediction accuracy of the prediction model and the generation efficiency of the model.

Example III

Fig. 6 is a schematic structural diagram of an automatic driving vehicle path planning device according to a third embodiment of the present invention. As shown in fig. 6, the apparatus includes:

a sensing information obtaining module 310, configured to obtain sensing information of the host vehicle; the sensing information comprises speed information of the vehicle, speed information of the dynamic obstacle, distance information of the vehicle and the dynamic obstacle and response time;

the safety factor determining module 320 is configured to determine, according to the sensing information, a safety factor of the host vehicle and at least one dynamic obstacle; the safety coefficient is the ratio of the current distance between the vehicle and the dynamic obstacle to the safety distance;

the planned path determining module 330 is configured to determine, according to each safety coefficient, a safety loss matched with each dynamic obstacle, input each safety loss as a priori feature of each dynamic obstacle into the prediction model, and determine a planned path of the autonomous vehicle.

In this solution, optionally, the security coefficient determining module 320 includes:

the safety distance determining unit is used for determining the safety distance between the vehicle and each dynamic obstacle according to the speed information of the vehicle, the speed information of the dynamic obstacle and the response time;

And the safety coefficient determining unit is used for determining the safety coefficient of the vehicle and each dynamic obstacle according to the safety distance and the distance information of the vehicle and the dynamic obstacle.

correspondingly, the safe distance determining unit comprises:

the direction determining subunit is used for determining the relative movement direction of the vehicle and the dynamic obstacle according to the speed information of the vehicle and the speed information of the dynamic obstacle;

and the distance determining subunit is used for determining the longitudinal safety distance between the vehicle and each dynamic obstacle according to the relative movement direction, the speed information of the vehicle, the speed information of the dynamic obstacle and the response time.

On the basis of the scheme, the relative movement direction comprises the same direction and different directions; the speed information comprises speed, transverse speed information and longitudinal speed information; wherein the longitudinal speed information includes a longitudinal speed, a longitudinal deceleration, and a longitudinal acceleration; the lateral velocity information includes a lateral velocity;

the distance determining subunit is specifically configured to:

if the relative movement direction of the vehicle and the dynamic barrier is the same direction, determining the longitudinal safety distance between the vehicle and the dynamic barrier according to the longitudinal speed of the preceding traffic target, the longitudinal speed of the following traffic target, the maximum longitudinal deceleration, the maximum longitudinal acceleration, the minimum longitudinal deceleration and the response time; wherein the maximum longitudinal deceleration, the maximum longitudinal acceleration, and the minimum longitudinal deceleration are determined based on statistics of traffic target attribute parameters;

correspondingly, the safe distance determining unit is specifically configured to:

In a preferred embodiment, the planned path determining module 330 includes:

and the safety loss determining unit is used for determining the safety loss matched with the dynamic obstacle based on the inverted Gaussian model if the safety coefficient is smaller than a preset coefficient threshold value.

On the basis of the scheme, the safety coefficient comprises a longitudinal safety coefficient and a transverse safety coefficient;

correspondingly, the safety loss determining unit is specifically configured to:

determining a safety loss matched with the dynamic obstacle according to the longitudinal safety loss, the transverse safety loss and a predetermined weight coefficient; the weight coefficient is determined based on a safety loss change rate, and the safety loss change rate is determined based on distance information of the vehicle and the dynamic obstacle and the speed of the dynamic obstacle.

The automatic driving vehicle path planning device provided by the embodiment of the invention can execute the automatic driving vehicle path planning method provided by any embodiment of the invention, and has the corresponding functional modules and beneficial effects of the execution method.

Example IV

Fig. 7 shows a schematic diagram of an electronic device 410 that may be used to implement an embodiment of the invention. Electronic devices are intended to represent various forms of digital computers, such as laptops, desktops, workstations, personal digital assistants, servers, blade servers, mainframes, and other appropriate computers. Electronic equipment may also represent various forms of mobile devices, such as personal digital processing, cellular telephones, smartphones, wearable devices (e.g., helmets, glasses, watches, etc.), and other similar computing devices. The components shown herein, their connections and relationships, and their functions, are meant to be exemplary only, and are not meant to limit implementations of the inventions described and/or claimed herein.

As shown in fig. 7, the electronic device 410 includes at least one processor 411, and a memory, such as a Read Only Memory (ROM) 412, a Random Access Memory (RAM) 413, etc., communicatively connected to the at least one processor 411, wherein the memory stores a computer program executable by the at least one processor, and the processor 411 may perform various suitable actions and processes according to the computer program stored in the Read Only Memory (ROM) 412 or the computer program loaded from the storage unit 418 into the Random Access Memory (RAM) 413. In the RAM 413, various programs and data required for the operation of the electronic device 410 may also be stored. The processor 411, the ROM 412, and the RAM 413 are connected to each other through a bus 414. An input/output (I/O) interface 415 is also connected to bus 414.

Various components in the electronic device 410 are connected to the I/O interface 415, including: an input unit 416 such as a keyboard, a mouse, etc.; an output unit 417 such as various types of displays, speakers, and the like; a storage unit 418, such as a magnetic disk, optical disk, or the like; and a communication unit 419 such as a network card, modem, wireless communication transceiver, etc. The communication unit 419 allows the electronic device 410 to exchange information/data with other devices through a computer network such as the internet and/or various telecommunication networks.

The processor 411 may be a variety of general and/or special purpose processing components having processing and computing capabilities. Some examples of processor 411 include, but are not limited to, a Central Processing Unit (CPU), a Graphics Processing Unit (GPU), various specialized Artificial Intelligence (AI) computing chips, various processors running machine learning model algorithms, digital Signal Processors (DSPs), and any suitable processor, controller, microcontroller, etc. The processor 411 performs the various methods and processes described above, such as an autonomous vehicle path planning method.

In some embodiments, the autonomous vehicle path planning method may be implemented as a computer program tangibly embodied on a computer-readable storage medium, such as storage unit 418. In some embodiments, some or all of the computer program may be loaded and/or installed onto the electronic device 410 via the ROM 412 and/or the communication unit 419. When the computer program is loaded into RAM 413 and executed by processor 411, one or more steps of the autonomous vehicle path planning method described above may be performed. Alternatively, in other embodiments, the processor 411 may be configured to perform the autonomous vehicle path planning method in any other suitable manner (e.g., by means of firmware).

Various implementations of the systems and techniques described here above may be implemented in digital electronic circuitry, integrated circuit systems, field Programmable Gate Arrays (FPGAs), application Specific Integrated Circuits (ASICs), application Specific Standard Products (ASSPs), systems On Chip (SOCs), load programmable logic devices (CPLDs), computer hardware, firmware, software, and/or combinations thereof. These various embodiments may include: implemented in one or more computer programs, the one or more computer programs may be executed and/or interpreted on a programmable system including at least one programmable processor, which may be a special purpose or general-purpose programmable processor, that may receive data and instructions from, and transmit data and instructions to, a storage system, at least one input device, and at least one output device.

A computer program for carrying out methods of the present invention may be written in any combination of one or more programming languages. These computer programs may be provided to a processor of a general purpose computer, special purpose computer, or other programmable data processing apparatus, such that the computer programs, when executed by the processor, cause the functions/acts specified in the flowchart and/or block diagram block or blocks to be implemented. The computer program may execute entirely on the machine, partly on the machine, as a stand-alone software package, partly on the machine and partly on a remote machine or entirely on the remote machine or server.

In the context of the present invention, a computer-readable storage medium may be a tangible medium that can contain, or store a computer program for use by or in connection with an instruction execution system, apparatus, or device. The computer readable storage medium may include, but is not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any suitable combination of the foregoing. Alternatively, the computer readable storage medium may be a machine readable signal medium. More specific examples of a machine-readable storage medium would include an electrical connection based on one or more wires, a portable computer diskette, a hard disk, a Random Access Memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing.

To provide for interaction with a user, the systems and techniques described here can be implemented on an electronic device having: a display device (e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor) for displaying information to a user; and a keyboard and a pointing device (e.g., a mouse or a trackball) through which a user can provide input to the electronic device. Other kinds of devices may also be used to provide for interaction with a user; for example, feedback provided to the user may be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user may be received in any form, including acoustic input, speech input, or tactile input.

The systems and techniques described here can be implemented in a computing system that includes a background component (e.g., as a data server), or that includes a middleware component (e.g., an application server), or that includes a front-end component (e.g., a user computer having a graphical user interface or a web browser through which a user can interact with an implementation of the systems and techniques described here), or any combination of such background, middleware, or front-end components. The components of the system can be interconnected by any form or medium of digital data communication (e.g., a communication network). Examples of communication networks include: local Area Networks (LANs), wide Area Networks (WANs), blockchain networks, and the internet.

The computing system may include clients and servers. The client and server are typically remote from each other and typically interact through a communication network. The relationship of client and server arises by virtue of computer programs running on the respective computers and having a client-server relationship to each other. The server can be a cloud server, also called a cloud computing server or a cloud host, and is a host product in a cloud computing service system, so that the defects of high management difficulty and weak service expansibility in the traditional physical hosts and VPS service are overcome.

It should be appreciated that various forms of the flows shown above may be used to reorder, add, or delete steps. For example, the steps described in the present invention may be performed in parallel, sequentially, or in a different order, so long as the desired results of the technical solution of the present invention are achieved, and the present invention is not limited herein.

The above embodiments do not limit the scope of the present invention. It will be apparent to those skilled in the art that various modifications, combinations, sub-combinations and alternatives are possible, depending on design requirements and other factors. Any modifications, equivalent substitutions and improvements made within the spirit and principles of the present invention should be included in the scope of the present invention.

Claims

1. A method of automatically driving a vehicle path planning, the method comprising:

2. The method of claim 1, wherein determining the safety factor of the host vehicle and the at least one dynamic obstacle based on the perception information comprises:

determining the safety distance between the vehicle and each dynamic obstacle according to the speed information of the vehicle, the speed information of the dynamic obstacle and the response time;

and determining the safety coefficient of the vehicle and each dynamic obstacle according to the safety distance and the distance information of the vehicle and the dynamic obstacle.

3. The method of claim 2, wherein the safe distance comprises a longitudinal distance;

4. A method according to claim 3, wherein the relative movement direction comprises co-direction and counter-direction; the speed information comprises speed, transverse speed information and longitudinal speed information; wherein the longitudinal speed information includes a longitudinal speed, a longitudinal deceleration, and a longitudinal acceleration; the lateral velocity information includes a lateral velocity;

5. The method of claim 4, wherein the safety distance further comprises a lateral safety distance; the lateral velocity information further includes lateral acceleration and lateral deceleration;

6. The method of claim 1, wherein determining a security loss matching each dynamic obstacle based on each security coefficient comprises:

7. The method of claim 5 or 6, wherein the safety factor comprises a longitudinal safety factor and a transverse safety factor;

8. An autonomous vehicle path planning apparatus, the apparatus comprising:

9. An electronic device, the electronic device comprising:

at least one processor; and

a memory communicatively coupled to the at least one processor; wherein,,

the memory stores a computer program executable by the at least one processor to enable the at least one processor to perform the autonomous vehicle path planning method of any of claims 1-7.

10. A computer readable storage medium storing computer instructions for causing a processor to perform the autonomous vehicle path planning method of any of claims 1-7.