CN116353610A

CN116353610A - Method and device for vehicle, vehicle and system

Info

Publication number: CN116353610A
Application number: CN202111567834.7A
Authority: CN
Inventors: 刘玉磊
Original assignee: Bayerische Motoren Werke AG
Current assignee: Bayerische Motoren Werke AG
Priority date: 2021-12-21
Filing date: 2021-12-21
Publication date: 2023-06-30

Abstract

The present disclosure relates to a method for a vehicle, the method comprising: acquiring a planned route to be driven by the vehicle; determining operational design domain parameters associated with the planned route, wherein the operational design domain parameters include operational design domain parameters associated with other vehicles that are traveling on the planned route and/or have traveled through the planned route; obtaining an evaluation index aiming at the planned route according to each operation design domain parameter; a suggested travel mode of the vehicle on the planned route is indicated based on the evaluation index. Furthermore, the disclosure relates to a device for a vehicle, a vehicle and a system.

Description

Method and device for vehicle, vehicle and system

Technical Field

The present disclosure relates generally to a method for a vehicle, an apparatus for a vehicle, and a system.

Background

Autopilot technology is a technical hotspot in the current vehicle industry. With the advancement of automatic driving technology, vehicle driving becomes more intelligent, and more potential safety hazards are brought. It is desirable that an autonomous vehicle be able to adaptively cope with the environment and road conditions that change in real time during traveling. For this reason, how to further improve the safety of an autonomous vehicle during driving is a technical problem to be solved by those skilled in the art.

Disclosure of Invention

It is an object of the present disclosure to provide a method for a vehicle, an apparatus for a vehicle, a vehicle and a system that overcome at least one of the drawbacks of the prior art. With this method, device, vehicle or system, the driving safety of the vehicle is improved and the user experience can be further improved.

According to a first aspect of the present disclosure, there is provided a method for a vehicle, characterized in that the method comprises:

acquiring a planned route to be driven by the vehicle;

determining operational design domain parameters associated with the planned route, wherein the operational design domain parameters include operational design domain parameters associated with other vehicles that are traveling on the planned route and/or have traveled through the planned route;

obtaining an evaluation index aiming at the planned route according to each operation design domain parameter;

a suggested driving pattern of the vehicle on the planned route is indicated based on the evaluation index.

In some embodiments, "determining operational design domain parameters associated with the planned route" includes: and acquiring road condition parameters on the planned route, wherein the road condition parameters comprise road surface parameters, road shape parameters and/or road type parameters.

In some embodiments, "obtaining road condition parameters on the planned route" includes: invoking a road surface friction coefficient on the planned route from a road surface friction coefficient map; calling the pothole parameters on the planned route from a road pothole map; and/or obtaining first characteristic data characterizing road condition parameters from the other vehicle, preferably the first characteristic data comprising braking parameters, speed parameters, steering parameters, vibration parameters, lidar data, camera data and/or driving modes of the other vehicle, preferably the driving modes comprising activation or deactivation of the body electronic stability system.

In some embodiments, "determining operational design domain parameters associated with the planned route" includes: and acquiring weather parameters on the planned route, wherein the weather parameters comprise a rainwater parameter, a temperature parameter, an illumination parameter and/or a visibility parameter.

In some embodiments, "obtaining weather parameters on the planned route" includes: acquiring weather parameters related to the planned route from a weather forecast center; second characteristic data characterizing weather parameters are obtained from the other vehicle, preferably including a rain parameter obtained from a wiper of the other vehicle and/or an illumination parameter and/or a visibility parameter obtained from a camera of the other vehicle.

In some embodiments, "determining operational design domain parameters associated with the planned route" includes: and acquiring traffic parameters on the planned route, wherein the traffic parameters comprise a vehicle running mode, traffic signs and/or traffic road conditions.

In some embodiments, "obtaining traffic parameters on the planned route" includes: acquiring traffic conditions, such as congestion parameters, accident areas and/or construction areas, related to the planned route from a traffic information center; third characteristic data characterizing traffic parameters are acquired from the other vehicle, wherein the third characteristic data comprise braking parameters, speed parameters, steering parameters, vibration parameters, lidar data, camera data and/or driving modes of the other vehicle, preferably driving modes comprise activation or deactivation of a specific automatic driving mode, preferably traffic conditions of the planned route can be evaluated from the third characteristic data.

In some embodiments, the "deriving an evaluation index for the planned route from each operational design domain parameter" includes: respectively configuring corresponding weight values for the determined operation design domain parameters; numerical processing, such as filtering and/or weighted averaging, is performed on the weighted individual operational design domain parameters to obtain an evaluation index.

In some embodiments, the "deriving an evaluation index for the planned route from each operational design domain parameter" includes: dividing the determined operational design domain parameters into a plurality of evaluation subsets, wherein the operational design domain parameters in each evaluation subset have an evaluation correlation with each other, preferably the operational design domain parameters in each evaluation subset are numerically processed, e.g. filtered and/or weighted averaged; giving a respective evaluation factor for each evaluation subset; the evaluation factors that combine the respective evaluation subsets provide an evaluation index for the planned route.

In some embodiments, the method comprises: one or more evaluation indicators are given for different route segments of the planned route, and each evaluation indicator is assigned a suggested driving pattern.

In some embodiments, one or more confidence levels are given for different road segments of the planned route, with higher confidence levels indicating higher recommendation of the automatic travel mode, preferably with different confidence levels associated with different levels of the automatic travel mode.

In some embodiments, the method comprises: the method includes the steps of acquiring from the vehicle own vehicle data representing operation design domain parameters of a current road section, evaluating an evaluation index for the current road section by considering the own vehicle data, and adapting the evaluation index for the current road section according to an evaluation result. Preferably, the vehicle data includes braking parameters, speed parameters, steering parameters, vibration parameters, laser radar data, camera data, and wiper parameters of the vehicle.

According to a second aspect of the present disclosure, there is provided an apparatus for a vehicle, characterized in that the apparatus comprises: a memory configured to store a series of computer-executable instructions; and a processor configured to execute the series of computer-executable instructions, wherein the series of computer-executable instructions, when executed by the processor, cause the processor to perform steps of the methods as described in some embodiments of the present disclosure.

According to a third aspect of the present disclosure, there is provided a vehicle comprising:

a navigation system configured to provide a planned route to be traveled by the vehicle;

a communication module configured to send the planned route to a server and to receive from the server an evaluation index or a suggested travel pattern, wherein the evaluation index indicates a suggested travel pattern of the vehicle on the planned route, preferably one or more evaluation indexes are given for different route segments of the planned route, each evaluation index being associated with one suggested travel pattern, preferably the server is configured for an apparatus according to some embodiments of the present disclosure,

a user input module configured to receive user decision instructions, and

And a control module configured to control the driving mode of the vehicle according to the evaluation index or the suggested driving mode received from the server and the user decision instruction.

In some embodiments, the vehicle comprises:

a first set of detection means configured to detect first characteristic data characterizing road condition parameters, preferably including braking parameters, speed parameters, steering parameters, vibration parameters, lidar data, camera data and/or driving modes of the vehicle;

a second set of detection means configured to detect second characteristic data characterizing weather parameters, preferably including a rain parameter from a wiper and/or an illumination parameter and/or a visibility parameter from a camera; and/or

A third detection device set configured to detect third characteristic data characterizing a traffic parameter, wherein the third characteristic data includes a braking parameter, a speed parameter, a steering parameter, a vibration parameter, lidar data, camera data, and/or a travel mode of the vehicle,

the communication module is further configured to send the first, second and/or third characteristic data to a server.

In some embodiments, the control module is further configured to evaluate the evaluation index or the suggested travel pattern received from the server according to the first, second and/or third feature data, and adapt the evaluation index or the suggested travel pattern for the current road segment according to the evaluation result.

In some embodiments, the vehicle further comprises a notification device configured to notify a user of the vehicle of the suggested travel pattern received from the server for the planned route.

In some embodiments, the user input module is configured to set a travel mode ultimately determined by the user, preferably the automatic travel mode level ultimately determined by the user is only allowed to be equal to or lower than the automatic travel mode level suggested by the server.

In some embodiments, the communication module is further configured to return the final activated travel mode of the vehicle to the server.

According to a fourth aspect of the present disclosure, there is provided a system for a vehicle, the system comprising a vehicle and a server, wherein a communication module of the vehicle establishes a communication connection with a communication module of the server, characterized in that the server is configured as an apparatus according to some embodiments of the present disclosure and/or the vehicle is configured as a vehicle according to some embodiments of the present disclosure.

Drawings

The foregoing and other aspects and advantages of the present disclosure will become apparent from the following detailed description of exemplary embodiments, taken in conjunction with the accompanying drawings, illustrating by way of example the principles of the disclosure. It is noted that the drawings are not necessarily drawn to scale.

Fig. 1 illustrates a schematic diagram of a system according to some exemplary embodiments of the present disclosure.

Fig. 2 shows a block diagram of a server of the system in fig. 1.

Fig. 3 illustrates a flow chart of a method according to some exemplary embodiments of the present disclosure.

Fig. 4 shows a functional block diagram of a processor within a server.

Fig. 5 illustrates a block diagram of a system according to some exemplary embodiments of the present disclosure, wherein the block diagram of a vehicle is specifically shown.

Detailed Description

The present disclosure will be described below with reference to the accompanying drawings, which illustrate several embodiments of the present disclosure. It should be understood, however, that the present disclosure may be presented in many different ways and is not limited to the embodiments described below; indeed, the embodiments described below are intended to provide a more complete disclosure and to fully illustrate the scope of the disclosure to those skilled in the art. It should also be understood that the embodiments disclosed herein can be combined in various ways to provide yet additional embodiments.

It should be understood that the terminology herein is for the purpose of describing particular embodiments only and is not intended to be limiting of the disclosure. All terms (including technical and scientific terms) used herein have the same meaning as commonly understood by one of ordinary skill in the art, unless otherwise defined. Well-known functions or constructions may not be described in detail for brevity and/or clarity.

In this document, the term "a or B" includes "a and B" and "a or B", and does not include exclusively only "a" or only "B", unless otherwise specifically indicated.

In this document, the term "exemplary" means "serving as an example, instance, or illustration. Any implementation described herein by way of example is not necessarily to be construed as preferred or advantageous over other implementations. Furthermore, this disclosure is not limited by any expressed or implied theory presented in the preceding technical field, background, brief summary or the detailed description.

In addition, for reference purposes only, the terms "first," "second," and the like may also be used herein, and the terms "first," "second," and the like may also refer to a plurality of the terms "first," "second," and the like. For example, the terms "first," "second," and other such numerical terms referring to structures or elements do not imply a sequence or order unless clearly indicated by the context.

It will be further understood that the terms "comprises" and/or "comprising," when used herein, specify the presence of stated features, integers, steps, operations, elements, and/or components, but do not preclude the presence or addition of one or more other features, integers, steps, operations, elements, and/or components, and/or groups thereof. Unless otherwise defined, all terms (including technical and scientific terms) are used herein to their ordinary meaning in the art to which examples belong.

It should be noted that: the order of the method steps may be flexibly configured in this document, and steps are labeled with numbers for convenience of description only and are not limiting.

Aspects in accordance with the present disclosure are described in detail below with reference to fig. 1-5.

Referring initially to FIG. 1, a schematic diagram of a system 10 is shown, according to some exemplary embodiments of the present disclosure. The system 10 may include a plurality of vehicles 12, 12-1, 12-2, 12-3, a network 14, 14-1, 14-2, 14-3, a remote server 16, and a traffic information communication system 18 (e.g., weather center, RTTI, TMC) that optionally establishes a communication connection with the remote server 16. The plurality of vehicles 12, 12-1, 12-2, 12-3 may include a host vehicle 12 and a plurality of other vehicles 12-1, 12-2, 12-3 that may register with a remote server and use services provided by the remote server 16. In this context, the vehicle may be a mobile vehicle, such as a car, a passenger car, a truck, a van, a train, a ship, a motorcycle, or other mobile vehicle. It should be appreciated that the vehicle user may be any person within the vehicle, either an occupant or a driver.

Each vehicle 12, 12-1, 12-2, 12-3 may establish communication with a remote server 16 via a respective network 14, 14-1, 14-2, 14-3. The network may be a wireless network supporting communication between the vehicle and a remote server. For example, the network may be a mobile communication network, such as a 3G, 4G or 5G network, e.g. a CDMA network, or may be a WLAN, such as a WiFi network. The remote server may be a distributed system maintained by a vehicle service provider. The remote server may comprise a cloud server.

Methods for a vehicle according to some exemplary embodiments of the present disclosure will be described in detail below in order to provide a user with travel mode advice for the vehicle, thereby allowing the user to be able to set a reliable and road-condition-compliant travel mode for a route to be traveled by the vehicle on the premises. Especially, reliable pre-judgment is provided for starting the automatic driving mode, so that the safety of automatic driving is ensured, and the automatic driving mode is reasonably set.

The steps of the methods presented below are intended to be illustrative. In some embodiments, the method may be accomplished with one or more additional undescribed steps, and/or without one or more discussed steps. Furthermore, the order in which the steps of a method are illustrated in the figures and described below is not intended to be limiting.

In some embodiments, the method may be implemented in one or more processing devices (e.g., a digital processor, an analog processor, a digital circuit designed to process information, an analog circuit designed to process information, a state machine, and/or other mechanisms for electronically processing information). The one or more processing devices may include one or more modules that perform some or all of the steps of the method in response to instructions stored electronically on an electronic storage medium. The one or more processing modules may include one or more devices configured by hardware, firmware, and/or software specifically designed for the execution of one or more steps of the method.

The method may be performed by an apparatus for a vehicle, such as a remote server 16, according to the present disclosure. Referring to FIG. 2, a block diagram of the server 16 of the system 10 of FIG. 1 is shown. The remote server 16 may include a communication module 20 that may be configured to interact with each vehicle 12, 12-1, 12-2, 12-3 and additionally with other available traffic information communication systems 18 to comprehensively obtain road network information. The remote server 16 may include a memory 22 configured to store a series of computer-executable instructions; and a processor 24 configured to execute the series of computer-executable instructions, wherein the series of computer-executable instructions, when executed by the processor, cause the processor to perform the steps of the method. The travel mode advice provided by the device, in particular by executing the steps of the method, can be used by the user to reference when using the vehicle in order to set a reliable and rational travel mode.

Referring to fig. 3, a flow chart of a method according to some exemplary embodiments of the present disclosure is shown. As shown in fig. 3, the method may include the steps of:

s10: acquiring a planned route to be traveled by the vehicle 12;

s20: determining operational design domain parameters associated with the planned route;

s30: obtaining an evaluation index aiming at the planned route according to each operation design domain parameter;

s40: a proposed driving mode of the vehicle 12 on the planned route is indicated based on the evaluation index.

The method may begin execution S0 by a request command sent by the host vehicle 12 or vehicle user to the server 16 as a trigger signal. The host-vehicle 12 may request that the server 16 give a travel pattern suggestion for the upcoming planned route of the host-vehicle 12 by a request instruction to determine one or more travel patterns that are viable for the vehicle 12 during the entire or a portion of the planned route prior to or during the start of the journey.

Regarding the classification of the running mode or the automatic driving, it is internationally generally accepted that the standard of SAE (society of automotive engineers) is classified into six stages L0 to L5. Level L0: the level is completely operated and driven by a driver, and comprises steering, braking, accelerator and the like which are judged by the driver, and the vehicle is only responsible for executing the command. Level L1: can assist the driver to perform certain driving tasks, such as adaptive cruise (ACC) functions of many vehicle models, radar real-time control of vehicle distance and vehicle acceleration and deceleration. L2 level: the vehicle driving system can automatically complete certain driving tasks, automatically adjust the vehicle state through processing and analysis, has the lane keeping function at the level, can control acceleration and deceleration, can control the steering wheel, and can provide vehicle safety operation for a driver to observe surrounding conditions. L3 level: the level controls the vehicle through a more logical driving computer, a driver does not need to stand by hands and feet, the vehicle can independently finish operation driving under a specific environment, but the driver cannot sleep or rest, and when the artificial intelligence cannot accurately judge, the driver still needs manual operation. L4 grade: the vehicle automatically makes an autonomous decision, and a driver does not need any operation, and the real scene of travel such as automatic vehicle taking and returning, automatic formation cruising, automatic obstacle avoidance and the like is generally realized by means of supporting road information data which can be updated in real time. L5 grade: the biggest difference with the L4 level is that the driver is not required to cooperate with any operation, all-weather and all-region automatic driving is realized, the change of environmental climate and geographic position can be dealt with, and the driver can pay attention to rest or other works.

In S10, the host-vehicle 12 may send the determined planned route, e.g., planned by the navigation system, to the server 16, such that the server may receive the planned route to be traveled by the host-vehicle 12. As shown in fig. 1, the planned route may be a complete route from the departure point a to the destination B of the host vehicle 12, which may preferably include a plurality of road segments having different road conditions. It should be understood that the planned route represented by the present disclosure may also be a portion of a complete route, such as one or more road segments in a complete route.

In S20, the server 16 invokes operational design domain parameters associated with the planned route by accessing or remotely accessing a database, memory and/or communication object based on the received planned route. According to the definition in SAE J3016, the operational design field or operational design field ODD (Operational Design Domain) is defined as: the operating conditions for which a particular driving automation system or function thereof is specifically designed include, but are not limited to, environmental, geographic, and time constraints, and/or the presence or absence of certain traffic or road features.

In some embodiments, the operational design domain parameters may include operational design domain parameters associated with other vehicles 12-1, 12-2, 12-3 that are traveling on and/or have traveled through the planned route. For example, in sub-step S201, the other vehicles 12-1, 12-2, 12-3 traveling on the planned route may send their own held design domain parameters to the server 16, and/or the other vehicles 12-1, 12-2, 12-3 having traveled through the planned route may send their own grasped design domain parameters to the server 16. It should be appreciated that the step of the other vehicles 12-1, 12-2, 12-3 sending their own learned design domain parameters to the server 16 may be independent of the travel of the host vehicle 12. That is, the design domain parameters may be randomly transmitted by the other vehicles 12-1, 12-2, 12-3 each independently. The server 16 may invoke stored association parameters based on the request instructions of the host vehicle 12.

In some embodiments of the present disclosure, the operational design domain parameters may include road condition parameters. The road condition parameters may include road surface parameters, road shape parameters, and/or road type parameters. The road surface parameters may include, for example, road material, road surface friction coefficient, pothole parameters. The road shape parameters may include, for example, curves, straight roads, three lanes, two lanes, uphill, downhill, three branches, crossroads, etc. The road type parameters may include, for example, highways, arterial roads, secondary arterial roads, branches, central area roads, literature area roads, administrative area roads, residential area roads, scenic tour area roads, and the like.

In some embodiments, the server 16 may access a local or remote road surface friction coefficient map and recall therefrom road surface friction coefficients on the planned route. In general, the road surface friction coefficient may be related to road type, road usage time, weather conditions. The level of autopilot is also related to the magnitude of the road surface friction coefficient. A suitable road surface friction coefficient is advantageous for reliable autopilot behaviour. Lower road surface coefficients of friction, e.g., due to road materials, rain, ice, etc., may negatively impact the safety of the automatic driving behavior.

In some embodiments, the server 16 may access a local or remote map of roadway indentations and invoke the parameters of indentations on the planned route therefrom. In general, the pothole parameters may relate to both the road type and the road usage time. The autopilot level is also closely related to the pothole parameters. Denser or larger indentations can negatively impact the safety of the autopilot behaviour.

In some embodiments, the server 16 may obtain first characteristic data characterizing road condition parameters from other vehicles. The first characteristic data may include braking parameters, speed parameters, steering parameters, vibration parameters, lidar data, camera data, and/or travel patterns of other vehicles.

For example, road surface friction coefficients and/or pothole parameters on the planned route may be inferred from braking parameters, speed parameters, steering parameters, and/or vibration parameters of the other vehicles 12-1, 12-2, 12-3, etc. For example, the higher the road surface friction coefficient, the shorter the braking distance. For example, the higher the vibration parameter, the larger the pothole parameter.

For example, the road surface friction coefficient and/or the pit parameter on the planned route, for example, the reflection intensity based on the point cloud data, etc. may be estimated from the laser radar data, for example, the point cloud data, of the other vehicles 12-1, 12-2, 12-3. For example, the road surface friction coefficient and/or the pit parameter on the planned route may be estimated from camera data of other vehicles, that is, from the image, for example, based on the image recognition technique.

For example, road surface friction coefficients and/or pothole parameters on the planned route may be inferred from the driving patterns of the other vehicles 12-1, 12-2, 12-3, such as particular driving patterns, in particular activation or deactivation of the vehicle body electronic stability system. For example, when the vehicle body electronic stability system is activated, it is presumed that the road may have a low coefficient of friction on the road surface due to factors such as road materials, rainwater, ice formation, and the like.

In some embodiments of the present disclosure, the operational design domain parameters may include weather parameters. The weather parameters may include, for example, rain parameters, temperature parameters, lighting parameters, and/or visibility parameters, among others.

In some embodiments, in sub-step S202, the server 16 may access the weather forecast center 18-1 and obtain weather data regarding the planned route therefrom. In general, rain level, wind level, visibility are closely related to autopilot behavior. As the level of rain and/or wind increases, the safety of the autopilot is lower. Conversely, the higher the visibility, the higher the safety of the autopilot behavior.

In some embodiments, the server 16 may obtain second characteristic data characterizing weather data from the other vehicles 12-1, 12-2, 12-3. For example, the server 16 may obtain rain parameters from the wipers of the other vehicles 12-1, 12-2, 12-3. The higher the wiping level of the wiper blade, i.e. the higher the wiping frequency, the larger the amount of the characterizing rain water. For example, the server 16 may obtain illumination parameters and/or visibility parameters from cameras of other vehicles 12-1, 12-2, 12-3. The illumination parameters and/or visibility parameters on the planned route and the like may also be estimated from the camera data, i.e., the images, of the other vehicles 12-1, 12-2, 12-3, for example, based on image recognition techniques.

In some embodiments of the present disclosure, the operational design domain parameters may include traffic parameters on the planned route. Traffic parameters may include, for example, vehicle travel patterns (e.g., L0-L5 six travel patterns), traffic signs, and/or traffic conditions.

In some embodiments, in substep S203, the server 16 may access the traffic information center 18-2 and obtain therefrom current congestion parameters, accident areas, and/or construction areas related to the planned route. The congested traffic, accident area or construction area may negatively affect the safety of the autopilot behavior.

In some embodiments, the server 16 may obtain third characteristic data characterizing traffic parameters from the other vehicles 12-1, 12-2, 12-3. The third characteristic data may for example comprise braking parameters, speed parameters, steering parameters, vibration parameters, laser radar data, camera data and/or driving modes of the other vehicle.

For example, the degree of congestion on the planned route may be inferred from braking parameters and/or speed parameters of the other vehicles 12-1, 12-2, 12-3, etc. For example, the higher the frequency of braking of other vehicles on a particular road segment, the higher the degree of congestion. For example, the lower the travel speed of other vehicles on a particular road segment, the higher the degree of congestion. In general, the higher the degree of congestion, the less safe the autopilot behaviour.

For example, traffic parameters on the planned route may also be inferred from lidar data, such as point cloud data, of the other vehicles 12-1, 12-2, 12-3. For example, a specific region such as an accident area and/or a construction area can be estimated based on the point cloud data. In addition, from camera data of other vehicles, that is, images, it is also possible to estimate specific traffic signs, accident areas, construction areas, and the like on the planned route based on, for example, image recognition techniques. It is also possible to estimate the degree of congestion on the planned route from the camera data of other vehicles, i.e. from the images, for example based on image recognition techniques.

For example, the travel pattern of the own vehicle may be set with reference to the history data of the other vehicles 12-1, 12-2, 12-3. That is, the traveling mode of the own vehicle may be set based on the other vehicles, referring to the history data of the other vehicles 12-1, 12-2, 12-3. That is, the travel mode of the own vehicle may be set based on the other vehicles, referring to the history data of the other vehicles 12-1, 12-2, 12-3. That is, the driving pattern of the own vehicle may be set based on the driving pattern performed by the other vehicles on the specific road section, in particular, after numerical analysis. Traffic parameters on the planned route may be inferred, for example, from activation or deactivation of other vehicle travel modes, such as a particular automatic travel mode. For example, when the driving pattern of other vehicles on a specific road segment is an automatic driving level of L3 or more, it can be inferred that the congestion on the road segment is low and that there are few or no special areas such as accident areas and/or construction areas. The driving pattern performed on a specific road section, in particular, the driving pattern of the own vehicle is set after numerical analysis. Traffic parameters on the planned route may be inferred, for example, from activation or deactivation of other vehicle travel modes, such as a particular automatic travel mode. For example, when the driving mode of other vehicles on a specific road segment is an automatic driving level of L3 or more, it can be inferred that the congestion on the road segment is low and that there is little or no specific area such as accident area and/or construction area. The driving pattern performed on a specific road section, in particular, the driving pattern of the own vehicle is set after numerical analysis. Traffic parameters on the planned route may be inferred, for example, from activation or deactivation of other vehicle travel modes, such as a particular automatic travel mode. For example, when the driving pattern of other vehicles on a specific road segment is an automatic driving level of L3 or more, it can be inferred that the congestion on the road segment is low and there is little or no specific area such as accident area and/or construction area.

In S30: the server 16 may perform analytical evaluation of the acquired operational design domain parameters to provide an evaluation index for the planned route. In S40: a suggested driving pattern of the vehicle, i.e. the own vehicle 12, on the planned route is indicated based on the evaluation index.

In the present disclosure, the evaluation index may be indicative data, such as confidence, after a series of numerical processes (e.g., normalization or normalization, filtering, weighting, averaging, etc.) are performed on each operational design domain parameter. The higher the evaluation index is, the higher the degree of recommendation of the automatic travel mode is. Preferably, different evaluation indexes are associated with different levels of automatic travel patterns. In some embodiments, the server may give one or more evaluation indicators for different route segments of the planned route, and assign each evaluation indicator a suggested travel pattern.

Fig. 4 shows a functional block division of the processor 24 of the server 16 to clarify exemplary analytical evaluation performed by the server on the acquired operational design domain parameters.

As shown in fig. 4, the input module 24-1 of the server 16 may obtain the following operational design domain parameters: road surface friction coefficient P10, road pothole parameter P20, automotive wiper parameter P30, weather parameter P40, other vehicle parameter P50, real-time traffic information P60 (e.g., real-time traffic information from RTTI/TMC).

In order to integrate the individual operating design domain parameters, a respective weight value may be assigned to each operating design domain parameter and a weighted average performed within the numerical integration module 24-2 of the server 16 to obtain the evaluation index. It is possible that different weights are specified according to the time-course of the operational design domain parameters. The degree of even the parameter is determined, for example, from a time stamp carried by the running design field parameter. It is possible to specify different weights depending on the reliability of the operational design domain parameters. For example, the reliability of data from a public platform is typically higher than the reliability of personal data. In some embodiments, the numerical integration module 24-2 of the server 16 may present one or more evaluation metrics for different route segments of the planned route.

It should be understood that the numerical processing within the numerical integration module 24-2 of the server may be varied and is not limited to the case described in the embodiments.

In other embodiments, the numerical integration module 24-2 of the server 16 may divide each operational design domain parameter into a plurality of assessment subsets. There is an evaluation correlation between the operational design domain parameters in each evaluation subset. As an example, the rain level from the weather forecast center (normalized 0.6), the wiper level from the wiper blade of other vehicles (normalized 0.8), the road surface friction coefficient (normalized 0.4) can be used as an evaluation subset; a corresponding evaluation factor can then be given for each evaluation subset, for example, the operating design domain parameters in the evaluation subset are numerically processed, for example, averaged to an evaluation factor of 0.6; finally, the evaluation factors of the evaluation subsets can be integrated to provide evaluation indexes for the planning route.

The evaluation module 24-3 of the server 16 may receive the evaluation index from the numerical integration module 24-2 and assign a suggested travel pattern to the evaluation index. As an example: the travel pattern L0 may be assigned 0-20% confidence, the travel pattern L1 may be assigned 20-40% confidence, the travel pattern L2 may be assigned 40-60% confidence, the travel pattern L3 may be assigned 60-80% confidence, the travel pattern L4 may be assigned 80-100% confidence, and the travel pattern L5 may be assigned 100% confidence. In some embodiments, the evaluation module of the server may assign a suggested travel pattern to each evaluation index for different segments of the planned route. It should be understood that the allocation rules within the evaluation module of the server may also be varied and are not limited to the case described in the embodiments.

The output module 24-4 of the server 16 may send the derived suggested travel pattern to the notification device of the host vehicle 12 in a predetermined data format. The predetermined data form may be adapted to a predetermined notification device in the host vehicle and may thus be varied. In some embodiments, the notification device predetermined from within the vehicle may be a navigation device or a display device. At this time, the predetermined data form may be a different color form assigned to a different recommended driving mode. As an example. The green road segment may represent the travel pattern L4, the yellow road segment may represent the travel pattern L3, the red road segment may represent the travel pattern L2, the purple road segment may represent the travel pattern L1, and the black road segment may represent the travel pattern L0. In some embodiments, the predetermined notification device within the host vehicle may be a voice playback device. At this time, the predetermined data form may be different voice forms assigned to different recommended traveling modes. For example, the voice playing device may directly play a voice similar to the "current link recommended on travel mode L2".

Additionally or alternatively, a user input module may be installed on the host vehicle 12 to obtain user decision instructions configured to set a travel mode ultimately determined by the user. Preferably, the automatic travel mode level finally determined by the user is only allowed to be equal to or lower than the automatic travel mode level suggested by the server.

It should be understood that each functional module is only functionally distinct and is not strictly limited in terms of physical location. In some embodiments, some of the functional modules may also be transferred into the host vehicle. An evaluation module for assigning a recommended driving pattern to the evaluation index may also be provided in the host vehicle, for example. In some embodiments, some of the functional modules may be configured as a single processor, while other functional modules may be configured as another single processor.

Additionally or alternatively, with continued reference to fig. 3, methods according to some example embodiments of the present disclosure may further include:

s50: from the host vehicle 12, host vehicle data is obtained that characterizes operational design domain parameters of the current road segment.

S60: the evaluation index for the current road section is evaluated by considering the own vehicle data, and the evaluation index for the current road section is adapted according to the evaluation result.

In step S50, the own vehicle data may include one or more of the following parameters of the own vehicle 12: braking parameters, speed parameters, steering parameters, vibration parameters, laser radar data, camera data and wiper parameters. The parameters involved in step S50 are parameters acquired by the host vehicle on the current road segment in real time, which can reflect the operation design domain parameters of the current road segment with high accuracy and real time. In step S60, the rationality or appropriateness of the evaluation index for the current road section can be evaluated by taking into account the own vehicle data. When the evaluation index or the suggested travel pattern estimated based on the own vehicle data deviates from the evaluation index or the suggested travel pattern given in step S30 by more than a threshold value, the evaluation index or the suggested travel pattern for the current road section may be adapted according to the evaluation result. It should be understood that method steps S50 and S60 may be performed in a server or in a host vehicle. No limitation is made herein.

Fig. 5 illustrates a schematic block diagram of a system 10 according to some exemplary embodiments of the present disclosure, the system 10 including a vehicle 12 and a server 16, a communication module 62 of the vehicle 12 establishing a communication connection with a communication module 20 of the server 16. As shown in fig. 5, the vehicle 12 may be configured as any one of a host vehicle and other vehicles to which the present disclosure relates, and the vehicle 12 may include:

A navigation system 60, which may be configured to provide a planned route to be travelled by the vehicle.

A communication module 62, which may be configured to send the planned route to a server and to receive an evaluation index from the server 16 (which evaluation index may indicate a suggested travel pattern of the vehicle on the planned route) or directly from the server. Preferably, one or more evaluation indicators may be provided for different road segments of the planned route, and each evaluation indicator is associated with a suggested driving pattern. In some embodiments, different confidence levels are given for different segments of the planned route, with higher confidence levels indicating a higher degree of recommendation for the automatic travel mode. Preferably, different confidence levels are associated with different levels of automatic travel patterns.

A user input module 64, which may be configured to receive user decision instructions.

A control module 66 that may be configured to control the travel mode of the vehicle 12 based on the evaluation index or suggested travel mode received from the server 16 and the user decision instruction.

Additionally or alternatively, the vehicle may comprise at least one of the following groups of detection devices:

A first set of detection means 68, which may be configured to detect first characteristic data characterizing a road condition parameter. Preferably, the first characteristic data may include: braking parameters, speed parameters, steering parameters, vibration parameters, lidar data, camera data, and/or travel patterns of the vehicle. Preferably, the driving mode may include a specific driving mode, such as activation or deactivation of the vehicle body electronic stability system.

A second set of detection means 70, which may be configured to detect second characteristic data characterizing a weather parameter. Preferably, the second characteristic data may comprise a rain parameter from a wiper and/or an illumination parameter and/or a visibility parameter from a camera.

A third set of detection means 72, which may be configured to detect third characteristic data characterizing the traffic parameter. Preferably, the third characteristic data may include: braking parameters, speed parameters, steering parameters, vibration parameters, lidar data, camera data, and/or travel modes of the vehicle. Preferably, the driving mode may comprise the activation or deactivation of a specific automatic driving mode, preferably the congestion parameter, the accident area and/or the construction area can be ascertained from the data characterizing the traffic parameter.

In some embodiments, the communication module 62 may be further configured to send the first, second, and/or third characteristic data to a server to supplement a database in the server.

In some embodiments, the control module 66 may be further configured to evaluate the evaluation index received from the server according to the first, second and/or third feature data, and adapt the evaluation index or the suggested driving pattern for the current road segment according to the evaluation result. When the evaluation index or the recommended travel pattern estimated based on the first feature data, the second feature data, and/or the third feature data deviates from the evaluation index or the recommended travel pattern received from the server by more than a threshold value, the evaluation index or the recommended travel pattern for the current road section may be adapted according to the evaluation result.

In some embodiments, the vehicle may further include a notification device 74, which may be configured to notify a user of the vehicle of the evaluation index received from the server 16 for the planned route. Preferably, the notification device is configured as a display device.

In some embodiments, user input module 64 may be configured to set a travel mode that is ultimately determined by the user. Preferably, the automatic travel mode level finally determined by the user is only allowed to be equal to or lower than the automatic travel mode level suggested by the server.

In some embodiments, the communication module 62 may also be configured to return the final activated travel mode of the vehicle to the server 16. The server 16 may adaptively adjust the processing mode for a particular vehicle based on the results of the feedback.

Having thus described the present disclosure, it will be apparent that the present disclosure may be varied in a number of ways. Such variations are not to be regarded as a departure from the spirit and scope of the present disclosure, and all such modifications as would be obvious to one skilled in the art are intended to be included within the scope of the following claims.

Claims

1. A method for a vehicle, the method comprising:

acquiring a planned route to be driven by the vehicle;

a suggested travel mode of the vehicle on the planned route is indicated based on the evaluation index.

2. The method of claim 1, wherein determining operational design domain parameters associated with the planned route comprises:

Acquiring road condition parameters on the planned route, wherein the road condition parameters comprise road surface parameters, road shape parameters and/or road type parameters;

preferably, "obtaining the road condition parameters on the planned route" includes:

invoking a road surface friction coefficient on the planned route from a road surface friction coefficient map;

calling the pothole parameters on the planned route from a road pothole map; and/or

First characteristic data characterizing road condition parameters are acquired from the other vehicle, preferably the first characteristic data comprising braking parameters, speed parameters, steering parameters, vibration parameters, lidar data, camera data and/or driving modes of the other vehicle, preferably the driving modes comprising activation or deactivation of the vehicle body electronic stability system.

3. The method of claim 1, wherein determining operational design domain parameters associated with the planned route comprises:

obtaining weather parameters on the planned route, wherein the weather parameters comprise rainwater parameters, temperature parameters, illumination parameters and/or visibility parameters,

preferably, "obtaining weather parameters on the planned route" includes:

Acquiring weather parameters related to the planned route from a weather forecast center;

second characteristic data characterizing weather parameters are acquired from the other vehicle, preferably including a rain parameter acquired from a wiper of the other vehicle and/or an illumination parameter and/or a visibility parameter acquired from a camera of the other vehicle.

4. The method of claim 1, wherein determining operational design domain parameters associated with the planned route comprises:

obtaining traffic parameters on the planned route, wherein the traffic parameters comprise vehicle driving modes, traffic signs and/or traffic road conditions,

preferably, "obtaining traffic parameters on the planned route" includes:

acquiring traffic conditions, such as congestion parameters, accident areas and/or construction areas, related to the planned route from a traffic information center;

third characteristic data characterizing traffic parameters are acquired from the other vehicle, wherein the third characteristic data comprise braking parameters, speed parameters, steering parameters, vibration parameters, lidar data, camera data and/or driving modes of the other vehicle, preferably driving modes comprise activation or deactivation of a specific automatic driving mode, preferably traffic conditions of the planned route can be evaluated from the third characteristic data.

5. The method according to one of claims 1 to 4, characterized in that "deriving an evaluation index for the planned route from each operational design domain parameter" comprises:

respectively configuring corresponding weight values for the determined operation design domain parameters;

numerical processing, such as filtering and/or weighted averaging, is performed on the weighted individual operational design domain parameters to obtain an evaluation index,

alternatively, "finding an evaluation index for the planned route from each operation design domain parameter" includes:

dividing the determined operational design domain parameters into a plurality of evaluation subsets, wherein the operational design domain parameters in each evaluation subset have an evaluation correlation with each other, preferably the operational design domain parameters in each evaluation subset are numerically processed, e.g. filtered and/or weighted averaged;

giving a respective evaluation factor for each evaluation subset;

and integrating the evaluation factors of each evaluation subset to provide an evaluation index for the planned route.

6. The method according to one of claims 1 to 4, characterized in that it comprises:

one or more evaluation indicators are given for different route segments of the planned route, and each evaluation indicator is assigned a suggested driving pattern,

Preferably, one or more confidence levels are given for different road segments of the planned route, a higher confidence level indicating a higher recommendation of the automatic driving mode, preferably different confidence levels being associated with different levels of automatic driving modes.

7. The method according to one of claims 1 to 4, characterized in that it comprises:

obtaining from the vehicle, vehicle data representing operational design domain parameters of a current road segment, preferably the vehicle data including braking parameters, speed parameters, steering parameters, vibration parameters, lidar data, camera data, wiper parameters of the vehicle,

and evaluating the evaluation index for the current road section by considering the own vehicle data, and adapting the evaluation index for the current road section according to the evaluation result.

8. An apparatus for a vehicle, the apparatus comprising:

a memory configured to store a series of computer-executable instructions; and

a processor configured to execute the series of computer-executable instructions,

wherein the series of computer executable instructions, when executed by a processor, cause the processor to perform the steps of the method of any one of claims 1-7.

9. A vehicle, characterized in that the vehicle comprises:

a communication module configured to send the planned route to a server and to receive from the server an evaluation index or a suggested travel pattern, wherein the evaluation index indicates a suggested travel pattern of the vehicle on the planned route, preferably one or more evaluation indexes are given for different route segments of the planned route, each evaluation index being associated with one suggested travel pattern, preferably the server is configured as an apparatus according to claim 8,

a user input module configured to receive user decision instructions, and

and a control module configured to control the travel mode of the vehicle according to the evaluation index or the suggested travel mode received from the server and the user decision instruction.

10. The vehicle of claim 9, characterized in that the vehicle comprises:

A second set of detection means configured to detect second characteristic data characterizing weather parameters, preferably comprising a rain parameter from a windscreen wiper and/or an illumination parameter and/or a visibility parameter from a camera; and/or

A third detection device group configured to detect third characteristic data characterizing a traffic parameter, wherein the third characteristic data includes a braking parameter, a speed parameter, a steering parameter, a vibration parameter, lidar data, camera data, and/or a travel mode of the vehicle,

11. The vehicle according to claim 10, characterized in that the control module is further configured to evaluate the evaluation index or the suggested travel pattern received from the server on the basis of the first, second and/or third characteristic data and to adapt the evaluation index or the suggested travel pattern for the current road section on the basis of the evaluation result.

12. The vehicle according to one of claims 9 to 11, characterized in that the vehicle further comprises a notification device configured to notify a user of the vehicle of a suggested travel pattern received from the server for the planned route.

13. The vehicle according to claim 12, characterized in that the user input module is configured to set a travel mode finalized by the user, preferably the automatic travel mode level finalized by the user is only allowed to be equal to or lower than the automatic travel mode level suggested by the server.

14. The vehicle of claim 13, wherein the communication module is further configured to return the final activated travel mode of the vehicle to the server.

15. A system for a vehicle, the system comprising a vehicle and a server, wherein a communication module of the vehicle establishes a communication connection with a communication module of the server, characterized in that the server is configured as a device according to claim 8 and/or the vehicle is configured as a vehicle according to one of claims 9 to 14.