CN114590248A

CN114590248A - Method and device for determining driving strategy, electronic equipment and automatic driving vehicle

Info

Publication number: CN114590248A
Application number: CN202210168805.1A
Authority: CN
Inventors: 羊野; 张晔
Original assignee: Apollo Intelligent Technology Beijing Co Ltd
Current assignee: Apollo Intelligent Technology Beijing Co Ltd
Priority date: 2022-02-23
Filing date: 2022-02-23
Publication date: 2022-06-07
Anticipated expiration: 2042-02-23
Also published as: CN114590248B

Abstract

The disclosure provides a method and a device for determining a driving strategy, electronic equipment and an automatic driving vehicle, relates to the technical field of automatic driving, and particularly relates to artificial intelligence, computer vision, automatic driving, intelligent transportation and the like. The specific implementation scheme is as follows: determining the traffic coefficient of the barrier according to the initial information of the barrier; the traffic coefficient is a coefficient value determined based on the traffic intention of the obstacle; determining the safety factor of the obstacle according to the relative speed information between the vehicle and the obstacle; optimizing the initial speed of the barrier by using the traffic coefficient and the safety coefficient to obtain an optimized speed; and determining the driving strategy of the vehicle according to the optimized speed.

Description

Method and device for determining driving strategy, electronic equipment and automatic driving vehicle

Technical Field

The present disclosure relates to the technical field of automatic driving, and particularly to the technical fields of artificial intelligence, computer vision, automatic driving, intelligent transportation, etc.

Background

In the field of autonomous driving, vehicles are planned, decided and controlled based on the state of motion of obstacles. The road obstacle has strong mobility due to movement of the road obstacle, so that the vehicle often has large deviation on the speed estimation of the vehicle, and unreasonable driving decision is made by the automatic driving vehicle.

Therefore, the technical problem to be solved is how to improve the accuracy of the estimated barrier speed of the automatic driving vehicle and make a more reasonable driving decision by the automatic driving vehicle.

Disclosure of Invention

The disclosure provides a method and a device for determining a driving strategy, electronic equipment and a storage medium.

According to an aspect of the present disclosure, there is provided a method of determining a driving strategy, which may include the steps of:

determining the traffic coefficient of the barrier according to the initial information of the barrier; the traffic coefficient is a coefficient value determined based on the traffic intention of the obstacle;

determining the safety factor of the obstacle according to the relative speed information between the vehicle and the obstacle;

optimizing the initial speed of the barrier by using the traffic coefficient and the safety coefficient to obtain an optimized speed;

and determining the driving strategy of the vehicle according to the optimized speed.

According to another aspect of the present disclosure, there is provided a driving strategy determination apparatus, which may include:

the traffic coefficient determining module is used for determining the traffic coefficient of the barrier according to the initial information of the barrier; the traffic coefficient is a coefficient value determined based on the traffic intention of the obstacle;

the safety coefficient determining module is used for determining the safety coefficient of the obstacle according to the relative speed information between the vehicle and the obstacle;

the speed optimization module is used for optimizing the initial speed of the barrier by utilizing the traffic coefficient and the safety coefficient to obtain the optimized speed;

and the driving strategy determining module is used for determining the driving strategy of the vehicle according to the optimized speed.

According to another aspect of the present disclosure, there is provided an electronic device including:

at least one processor; and

a memory communicatively coupled to the at least one processor; wherein the content of the first and second substances,

the memory stores instructions executable by the at least one processor to enable the at least one processor to perform a method according to any one of the embodiments of the present disclosure.

According to another aspect of the present disclosure, there is provided a non-transitory computer readable storage medium storing computer instructions for causing a computer to perform a method in any embodiment of the present disclosure.

According to another aspect of the present disclosure, a computer program product is provided, comprising a computer program which, when executed by a processor, implements the method in any of the embodiments of the present disclosure.

According to another aspect of the present disclosure, there is provided an autonomous vehicle comprising an electronic device that performs a method in accordance with any of the embodiments of the present disclosure.

According to the technical scheme of the invention, the motion state of the obstacle can be estimated more finely, and more reasonable driving decision can be taken according to the motion state.

It should be understood that the statements in this section are not intended to identify key or critical features of the embodiments of the present disclosure, nor are they intended to limit the scope of the present disclosure. Other features of the present disclosure will become apparent from the following description.

Drawings

The drawings are included to provide a better understanding of the present solution and are not to be construed as limiting the present disclosure. Wherein:

FIG. 1 is a flow chart of a method of determining a driving strategy according to the present disclosure;

FIG. 2 is a flow chart of a method of determining a traffic factor according to the present disclosure;

FIG. 3 is a flow chart of a method of determining a cross-road probability value according to the present disclosure;

FIG. 4 is a flow chart of a method of determining a safety factor according to the present disclosure;

FIG. 5 is a flow chart of a speed optimization method according to the present disclosure;

FIG. 6 is a block diagram of a determination device of a driving strategy according to the present disclosure;

fig. 7 is a block diagram of an electronic device implementing a driving strategy determination method according to an embodiment of the present disclosure.

Detailed Description

Exemplary embodiments of the present disclosure are described below with reference to the accompanying drawings, in which various details of the embodiments of the disclosure are included to assist understanding, and which are to be considered as merely exemplary. Accordingly, those of ordinary skill in the art will recognize that various changes and modifications of the embodiments described herein can be made without departing from the scope and spirit of the present disclosure. Also, descriptions of well-known functions and constructions are omitted in the following description for clarity and conciseness.

As shown in fig. 1, the present disclosure relates to a method of determining a driving strategy, which may include the steps of:

s101: determining the traffic coefficient of the barrier according to the initial information of the barrier; the traffic coefficient is a coefficient value determined based on the traffic intention of the obstacle;

s102: determining the safety factor of the obstacle according to the relative speed information between the vehicle and the obstacle;

s103: optimizing the initial speed of the barrier by using the traffic coefficient and the safety coefficient to obtain an optimized speed;

s104: and determining the driving strategy of the vehicle according to the optimized speed.

The embodiment can be applied to computer equipment, and specifically can include a vehicle-mounted running computer or a server in communication connection with a vehicle.

Wherein the vehicle is a host vehicle in an autonomous driving scenario. Specifically, the vehicle with the automatic driving function can be used for carrying out assistant decision-making based on a high-precision map, a vehicle-mounted camera and a vehicle-mounted radar. The obstacle may be a pedestrian, a pet, or the like included in the image captured by the vehicle-mounted camera, and specifically may be an obstacle in a stationary state or an obstacle in a moving state. The initial information of the obstacle includes image information including the obstacle, speed information, moving direction information, distance information, and the like, and is not limited herein.

The pass coefficient of the obstacle is determined from the initial information of the obstacle, which may be a coefficient value determined based on the pass intention of the obstacle. The intended use of the barrier may include standing in place, walking along a lane line, traversing a road, etc., without limitation. Different pass coefficient values can be determined according to different pass intentions, and the pass coefficient can range from 0 to 1, for example, 0.15, 0.59, and the like, which is not exhaustive here.

And determining the safety factor of the obstacle according to the relative speed information between the main vehicle and the obstacle. Wherein, the safety factor can be a safety level coefficient, such as primary, secondary, etc., and a higher level represents a safer barrier; the safety factor may also be a safety factor value representing safety between the vehicle and an obstacle. The safety coefficient value is between 0 and 1, and the larger the safety coefficient value is, the safer the barrier is. E.g. 0.15, 0.57 etc., which are not exhaustive here.

The relative speed information may be a speed value determined by using a relative motion state of the vehicle and the obstacle, specifically, may be obtained by calculating using an absolute speed of the vehicle and an absolute speed of the obstacle, or may be obtained by determining speed components of the vehicle and the obstacle in a direction perpendicular to a lane line, and calculating using the speed components of the vehicle and the obstacle.

And optimizing the initial speed of the barrier by using the traffic coefficient and the safety coefficient to obtain the optimized speed of the barrier. The initial speed of the obstacle may be obtained by the vehicle in advance by using a sensing device, and specifically may be an initial speed vector and a speed value of the obstacle, or a speed component value of the obstacle in a direction perpendicular to a lane line, which is not limited herein.

Based on the initial speed of the obstacle, in the case where it is determined that the obstacle does not have an intention to cross the road or that the safety factor of the obstacle is high, the initial speed of the obstacle may be optimized by reducing the initial speed value below a first preset speed threshold value, and taking the reduced value as the optimized speed. The first preset speed threshold may be 5m/s, 6m/s, etc., which are not exhaustive here.

Conversely, where it is determined that an obstacle has an intention to cross the road or that the safety factor of the obstacle is low, the optimization may be to increase the initial speed value above a second preset speed threshold. The second preset speed threshold is greater than the first preset speed threshold, e.g., 10m/s, 15m/s, etc., and is not exhaustive herein.

And determining the driving strategy of the vehicle according to the optimized speed. The driving strategy may be a driving choice made by the vehicle based on a real-time condition of the road obstacle, such as deceleration and avoidance, acceleration of traffic, or keeping the vehicle speed to continue traffic, and is not limited herein.

Through the process, the initial speed of the obstacle is optimized by combining two dimensions of the traffic coefficient and the safety factor of the obstacle, and the driving strategy of the vehicle is determined according to the optimized result. Thus, the motion state of the obstacle can be estimated more finely, and more reasonable driving decisions can be taken accordingly.

As shown in fig. 2, in one embodiment, the determining manner of the pass coefficient includes:

s201: determining a probability value of the obstacle crossing the road according to the initial information of the obstacle;

s202: determining a traffic coefficient by using the probability value and the initial included angle; the initial included angle is the included angle between the initial speed direction of the obstacle and the lane line.

An implementation of determining the probability value of the obstacle crossing the road according to the initial information of the obstacle may be: and determining long-term vision prediction semantics related to the obstacle according to the initial information of the obstacle, and then determining a probability value of the obstacle crossing the road based on the prediction semantics. The long-term vision prediction semantics can be obstacle state information determined based on video information or multi-frame image information containing obstacles. For example, the long-term vision prediction semantics may be that within a preset time period, the obstacle is "walking along a non-motorized lane", or the obstacle is "opening the door outside the lane line", the obstacle is "crossing the road along the zebra crossing", the obstacle is "looking to pass the vehicle while walking", etc. The preset time period may be set as required, for example, 3s, 5s, and the like, and is not limited herein.

The probability value of an obstacle crossing a road may be any decimal between 0 and 1, e.g. 0.15, 0.39, 084, etc., which is not exhaustive here. The initial angle may be an angle between the initial velocity of the obstacle and the lane line, for example, 0 degree, 5 degrees, 30 degrees, 90 degrees, etc., which is not exhaustive.

The traffic coefficient of the obstacle can be determined by using the probability value and the initial included angle, and the formula (1) can be referred to specifically:

wherein the content of the first and second substances,

may represent the traffic coefficient of the obstacle, theta may represent the initial angle, and P may represent the probability value of the obstacle crossing the road.

Through the process, the traffic coefficient of the obstacle can be more accurately determined by combining the probability value of the obstacle crossing the road and the initial included angle.

As shown in fig. 3, in one embodiment, in the case where the initial information includes initial image information and initial motion information, the determination manner of the cross road probability value includes;

s301: determining a predicted trajectory of the obstacle using at least one of the initial image information and the initial motion information;

s302: using the predicted trajectory, a cross-road probability value is determined.

The determination method of the predicted track may be that at least one of the initial image information and the initial motion information is input to a track prediction model trained in advance, and the predicted track is output by the track prediction model. The predicted trajectory may be a trajectory of action of the obstacle for a next time period.

The trajectory prediction model may be a neural network model, such as a fully-connected (MLP) neural network, a convolutional neural network, a cyclic neural network, and the like, which is not limited herein. The track prediction model may adopt a sequence-to-sequence learning manner, specifically, may adopt a long-time memory network encoder to learn a vector of a historical track sequence to encode a historical track, and decode the vector to obtain a predicted future track.

The input of the trajectory prediction model may be at least one of initial image information and initial motion information, that is, only the initial image information acquired by the vehicle or the initial motion information of the obstacle may be input to the trajectory prediction model, or both of them may be input to the trajectory prediction model, which is not limited herein.

The initial image information may be image information including an obstacle acquired by the vehicle through the vehicle-mounted camera. The initial movement information may be information describing a state parameter of the obstacle. For example, the distance between the obstacle and the vehicle, the speed of the obstacle, the vertical distance between the obstacle and the boundary of the lane line, and the like.

The predicted trajectory is used to determine a cross-road probability value. The probability value is used to indicate the possibility that an obstacle crosses a road, and the value range of the probability value may be any value between 0 and 1, for example, 0.15, 0.38, and the like, which is not exhaustive here.

Through the above process, the cross road probability value can be determined more accurately, thereby improving the accuracy of the obstacle speed optimization.

As shown in fig. 4, in one embodiment, the determining of the safety factor includes:

s401: determining a predicted collision point of the vehicle and the obstacle according to the relative speed information;

s402: determining a predicted collision time and a predicted collision distance based on the location of the predicted collision point;

s403: and determining a safety factor by using the predicted collision time and the predicted collision distance.

The relative speed information may include speed direction information, speed magnitude information, and the like of the vehicle and the obstacle. An implementation of determining the predicted collision point of the vehicle with the obstacle may be: the intersection point of the two directions is determined using the speed direction of the vehicle and the obstacle, and the intersection point is used as a predicted collision point of the vehicle and the obstacle.

The predicted time to collision and the predicted distance to collision are determined based on the location of the predicted collision point, and the predicted time to collision and the predicted distance to collision of the obstacle with respect to the predicted collision point may be calculated based on the location of the predicted collision point, and the speed of the obstacle and the position of the obstacle. Meanwhile, the calculation may also be performed based on the position of the predicted collision point, the vehicle speed, and the vehicle time, which will not be described herein.

The safety factor can be determined by calculation using a preset formula. For example, the product of the predicted collision time and the predicted collision distance may be used as the safety factor. At this time, the smaller the safety factor is, the greater the possibility that the obstacle and the vehicle collide after continuing to run according to the current situation is; conversely, the smaller the probability of collision. In addition, the safety factor may be determined by other calculation methods, which are not limited herein.

Through the process, the safety factor between the barrier and the vehicle can be determined, and the accuracy of barrier speed optimization can be further improved based on the safety factor.

As shown in fig. 5, in one embodiment, the determining of the optimized speed includes:

s501: determining an optimization coefficient by using the traffic coefficient and the safety coefficient;

s502: and determining an optimization speed by using the optimization coefficient and the initial speed.

The traffic coefficient and the safety coefficient can be used for obtaining the optimization coefficient through calculation according to a preset calculation mode. The preset calculation method may be multiplication, weighted summation, or the like, and is not limited herein.

The optimized velocity of the obstacle can be obtained by multiplying the optimized coefficient by the initial velocity of the obstacle. Wherein the initial velocity and the optimized velocity may be a velocity component of the obstacle in a direction perpendicular to the lane line.

In one embodiment, the driving strategy is determined in such a way that the vehicle keeps the original vehicle speed and continues to pass when the optimized speed is less than the preset threshold value.

The preset threshold may be set as needed, for example, 3m/s, 5m/s, and the like, which is not limited herein.

As shown in fig. 6, the present disclosure relates to a driving strategy determination apparatus, which may include:

a traffic coefficient determining module 601, configured to determine a traffic coefficient of an obstacle according to initial information of the obstacle; the traffic coefficient is a coefficient value determined based on the traffic intention of the obstacle;

the safety factor determining module 602 is configured to determine a safety factor of the obstacle according to the relative speed information between the vehicle and the obstacle;

the speed optimization module 603 is configured to optimize the initial speed of the obstacle by using the traffic coefficient and the safety factor to obtain an optimized speed;

and a driving strategy determining module 604 for determining a driving strategy of the vehicle according to the optimized speed.

In one embodiment, the pass coefficient determination module 601 includes:

the probability value determining submodule is used for determining the probability value of the obstacle crossing the road according to the initial information of the obstacle;

the traffic coefficient determining submodule is used for determining a traffic coefficient by utilizing the probability value and the initial included angle; the initial included angle is the included angle between the initial speed direction of the obstacle and the lane line.

In one embodiment, a probability value determination submodule includes;

a predicted trajectory determination sub-module for determining a predicted trajectory of the obstacle using at least one of the initial image information and the initial motion information;

and the probability value execution submodule is used for determining the probability value of crossing the road by utilizing the predicted track.

In one embodiment, the safety factor determination module includes:

the first collision determining submodule is used for determining a predicted collision point of the vehicle and the obstacle according to the relative speed information;

a first collision determination submodule for determining a predicted collision time and a predicted collision distance based on the position of the predicted collision point;

and the safety coefficient determining submodule is used for determining the safety coefficient by utilizing the predicted collision time and the predicted collision distance.

In one embodiment, a speed optimization module includes:

the optimization coefficient determination submodule is used for determining an optimization coefficient by utilizing the traffic coefficient and the safety coefficient;

and the optimization speed execution submodule is used for determining the optimization speed by utilizing the optimization coefficient and the initial speed.

In one embodiment, the driving strategy determining module is used for keeping the vehicle to keep passing the original vehicle speed when the optimized speed is smaller than the preset threshold value.

In the technical scheme of the disclosure, the acquisition, storage, application and the like of the personal information of the related user all accord with the regulations of related laws and regulations, and do not violate the good customs of the public order.

The present disclosure also provides an electronic device, a readable storage medium, and a computer program product according to embodiments of the present disclosure.

According to an embodiment of the present disclosure, there is also provided an autonomous vehicle comprising an electronic device performing the method in any of the embodiments of the present disclosure.

FIG. 7 illustrates a schematic block diagram of an example electronic device 700 that can be used to implement embodiments of the present disclosure. 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. The electronic device may also represent various forms of mobile devices, such as personal digital processing, cellular phones, smart phones, wearable devices, and other similar computing devices. The components shown herein, their connections and relationships, and their functions, are meant to be examples only, and are not meant to limit implementations of the disclosure described and/or claimed herein.

As shown in fig. 7, the device 700 comprises a computing unit 701, which may perform various suitable actions and processes according to a computer program stored in a Read Only Memory (ROM)702 or a computer program loaded from a storage unit 708 into a Random Access Memory (RAM) 703. In the RAM 703, various programs and data required for the operation of the device 700 can also be stored. The computing unit 701, the ROM 702, and the RAM 703 are connected to each other by a bus 704. An input/output (I/O) interface 705 is also connected to bus 704.

Various components in the device 700 are connected to the I/O interface 705, including: an input unit 706 such as a keyboard, a mouse, or the like; an output unit 707 such as various types of displays, speakers, and the like; a storage unit 708 such as a magnetic disk, optical disk, or the like; and a communication unit 709 such as a network card, modem, wireless communication transceiver, etc. The communication unit 709 allows the device 700 to exchange information/data with other devices via a computer network, such as the internet, and/or various telecommunication networks.

Computing unit 701 may be a variety of general purpose and/or special purpose processing components with processing and computing capabilities. Some examples of the computing unit 701 include, but are not limited to, a Central Processing Unit (CPU), a Graphics Processing Unit (GPU), various specialized Artificial Intelligence (AI) computing chips, various computing units running machine learning model algorithms, a Digital Signal Processor (DSP), and any suitable processor, controller, microcontroller, and so forth. The calculation unit 701 executes the respective methods and processes described above, such as the method of determination of the running strategy. For example, in some embodiments, the method of determining a driving strategy may be implemented as a computer software program tangibly embodied in a machine-readable medium, such as storage unit 708. In some embodiments, part or all of a computer program may be loaded onto and/or installed onto device 700 via ROM 702 and/or communications unit 709. When the computer program is loaded into the RAM 703 and executed by the computing unit 701, one or more steps of the method of determination of a driving strategy described above may be performed. Alternatively, in other embodiments, the computing unit 701 may be configured by any other suitable means (e.g., by means of firmware) to perform the determination method of the driving strategy.

Various implementations of the systems and techniques described here above may be implemented in digital electronic circuitry, integrated circuitry, Field Programmable Gate Arrays (FPGAs), Application Specific Integrated Circuits (ASICs), Application Specific Standard Products (ASSPs), system on a 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 that are executable and/or interpretable on a programmable system including at least one programmable processor, which may be special or general purpose, receiving data and instructions from, and transmitting data and instructions to, a storage system, at least one input device, and at least one output device.

Program code for implementing the methods of the present disclosure may be written in any combination of one or more programming languages. These program codes may be provided to a processor or controller of a general purpose computer, special purpose computer, or other programmable data processing apparatus, such that the program codes, when executed by the processor or controller, cause the functions/operations specified in the flowchart and/or block diagram to be performed. The program code 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 this disclosure, a machine-readable medium may be a tangible medium that can contain, or store a program for use by or in connection with an instruction execution system, apparatus, or device. The machine-readable medium may be a machine-readable signal medium or a machine-readable storage medium. A machine-readable 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. 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 a computer 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) by which a user can provide input to the computer. Other kinds of devices may also be used to provide for interaction with a user; for example, feedback provided to the user can 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, speech, or tactile input.

The systems and techniques described here can be implemented in a computing system that includes a back-end 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 back-end, 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), and the Internet.

The computer system may include clients and servers. A client and server are generally 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 may be a cloud server, a server of a distributed system, or a server with a combined blockchain.

It should be understood that various forms of the flows shown above may be used, with steps reordered, added, or deleted. For example, the steps described in the present disclosure may be executed in parallel, sequentially, or in different orders, as long as the desired results of the technical solutions disclosed in the present disclosure can be achieved, and the present disclosure is not limited herein.

The above detailed description should not be construed as limiting the scope of the disclosure. It should be understood by those skilled in the art that various modifications, combinations, sub-combinations and substitutions may be made in accordance with design requirements and other factors. Any modification, equivalent replacement, and improvement made within the spirit and principle of the present disclosure should be included in the scope of protection of the present disclosure.

Claims

1. A method of determining a driving strategy, comprising:

determining a traffic coefficient of the obstacle according to initial information of the obstacle; the passage coefficient is a coefficient value determined based on the passage intention of the obstacle;

determining a safety factor of the obstacle according to the relative speed information between the vehicle and the obstacle;

2. The method of claim 1, wherein the pass coefficient is determined by:

determining a probability value of the obstacle crossing the road according to the initial information of the obstacle;

determining the traffic coefficient by using the probability value and the initial included angle; the initial included angle is an included angle between the initial speed direction of the barrier and a lane line.

3. The method of claim 2, wherein, in the case that the initial information includes initial image information and initial motion information, the determination of the probability value of crossing the road comprises:

determining a predicted trajectory of the obstacle using at least one of the initial image information and the initial motion information;

determining the cross-road probability value using the predicted trajectory.

4. The method of claim 1, wherein the determination of the safety factor comprises:

determining a predicted collision point of the vehicle with the obstacle according to the relative speed information;

determining a predicted collision time and a predicted collision distance based on the location of the predicted collision point;

and determining a safety factor by using the predicted collision time and the predicted collision distance.

5. The method of claim 4, wherein the optimal speed determination comprises:

determining an optimization coefficient by using the traffic coefficient and the safety coefficient;

determining the optimized speed using the optimized coefficient and the initial speed.

6. The method of any of claims 1-5, wherein said determining a driving strategy for said vehicle based on said optimized speed comprises:

and under the condition that the optimized speed is smaller than a preset threshold value, the vehicle keeps the original speed and continues to pass.

7. A travel strategy determination apparatus comprising:

the traffic coefficient determining module is used for determining the traffic coefficient of the barrier according to the initial information of the barrier; the passage coefficient is a coefficient value determined based on the passage intention of the obstacle;

the safety factor determining module is used for determining the safety factor of the obstacle according to the relative speed information between the vehicle and the obstacle;

the speed optimization module is used for optimizing the initial speed of the barrier by utilizing the traffic coefficient and the safety coefficient to obtain an optimized speed;

8. The apparatus of claim 7, wherein the pass coefficient determination module comprises:

a traffic coefficient determining submodule for determining the traffic coefficient by using the probability value and the initial included angle; the initial included angle is an included angle between the initial speed direction of the barrier and a lane line.

9. The apparatus of claim 8, wherein in the case that the initial information includes initial image information and initial motion information, the probability value determination sub-module comprises:

a probability value execution submodule for determining the cross road probability value using the predicted trajectory.

10. The apparatus of claim 7, wherein the safety factor determination module comprises:

a first collision determination submodule for determining a predicted collision point of the vehicle with the obstacle based on the relative speed information;

and the safety coefficient determining submodule is used for determining a safety coefficient by utilizing the estimated collision time and the estimated collision distance.

11. The apparatus of claim 10, wherein the speed optimization module comprises:

12. The device according to any one of claims 7-11, wherein the driving strategy determination module is used for keeping the original vehicle speed for continuing the traffic under the condition that the optimized speed is smaller than a preset threshold value.

13. An electronic device, comprising:

at least one processor; and

the memory stores instructions executable by the at least one processor to enable the at least one processor to perform the method of any one of claims 1-6.

14. A non-transitory computer readable storage medium having stored thereon computer instructions for causing the computer to perform the method of any one of claims 1-6.

15. A computer program product comprising a computer program which, when executed by a processor, implements the method according to any one of claims 1-6.

16. An autonomous vehicle comprising the electronic device of claim 13.