CN115257812A - Automatic driving confidence obtaining and reminding method and equipment - Google Patents

Abstract

The invention provides a method and a device for acquiring and reminding an automatic driving confidence, which can combine dynamic road data and static road data in a high-precision map, influence of weather data on road conditions, automatic driving data of other vehicles with the same automatic driving level and other big data, plan an automatic driving task of a vehicle on a driving road, such as pre-planning the overall speed, calculate the success rate of the automatic driving task of the vehicle on the driving road section in advance, convert the success rate into a confidence symbol and display the confidence symbol on a screen to tell a driver, so that the driver can predict the safety risk of automatic driving on the following road section in advance and take over and other control processes in a planned way, thereby relieving the panic and tension of a user.

Description

Automatic driving confidence obtaining and reminding method and equipment

Technical Field

The invention relates to an automatic driving confidence obtaining and reminding method and device.

Background

At present, automatic driving is still in a stage of driving by people and vehicles together, although the automatic driving technology on the market can control the vehicle speed and take over the reminding, most of the automatic driving technology on the market is controlled based on real-time sensing calculation of a sensor.

When the vehicle senses that the automatic driving task cannot be smoothly completed and needs to take over, a take-over signal is suddenly sent out, and urgent take-over information reminding often causes the tension and uneasiness of a driver, so that the driver is careful even when carrying out automatic driving, the mind and body are in a tense state, the effect of reducing the driving burden of the driver by automatic driving cannot be really achieved, the experience is poor, and even the risk of driving safety can be caused if the user does not take over in time.

Disclosure of Invention

Aiming at the problems in the prior art, the invention provides an automatic driving confidence obtaining and reminding method and equipment.

In order to achieve the purpose, the invention adopts the following technical scheme:

the invention provides an automatic driving confidence obtaining and reminding method, which comprises the following steps:

acquiring dynamic road data and static road data of a road on which a vehicle runs;

acquiring accuracy data of sensor identification of a vehicle;

the method comprises the steps of calculating a target speed of a road, the recognition definition of a lane line, the traffic flow and the vehicle collision risk based on dynamic road data and static road data of the road on which a vehicle runs and accuracy data of sensor recognition of the vehicle, obtaining a risk node with an automatic driving confidence coefficient smaller than a preset threshold value on the road on which the vehicle runs based on the target speed of the road, the recognition definition of the lane line, the traffic flow and the vehicle collision risk, and sending prompt information for prompting a user to take over the vehicle when the automatic driving vehicle approaches the risk node.

Further, the method for obtaining and reminding the automatic driving confidence coefficient obtains accuracy data of sensor identification of the vehicle, and includes:

acquiring weather forecast data of a vehicle in a driving process from a departure place to a destination;

and obtaining accuracy data of sensor identification of the vehicle based on the weather forecast data.

Further, the method for obtaining and reminding the automatic driving confidence coefficient obtains dynamic road data and static road data of a road on which a vehicle runs, and comprises the following steps:

acquiring a departure place and a driving destination of a vehicle;

acquiring dynamic road data and static road data from a high-precision map according to a departure place and a driving destination of a vehicle;

based on the dynamic road data and the static road data of the road on which the vehicle runs and the accuracy data of the sensor identification of the vehicle, calculating the target vehicle speed, the identification definition of a lane line, the vehicle flow and the vehicle collision risk of the road, and based on the target vehicle speed, the identification definition of the lane line, the vehicle flow and the vehicle collision risk of the road, obtaining a risk node of which the automatic driving confidence coefficient on the road on which the vehicle runs is smaller than a preset threshold value, the method comprises the following steps:

calculating a target speed of the road, a recognition definition of a lane line, a traffic flow and a vehicle collision risk based on dynamic road data and static road data of the road on which the vehicle runs and accuracy data of sensor recognition of the vehicle, obtaining a success rate of automatic driving of each node on the road on which the vehicle runs based on the target speed of the road, the recognition definition of the lane line, the traffic flow and the vehicle collision risk, and converting the success rate of automatic driving of each node into a corresponding confidence coefficient;

and planning the automatic driving task of the vehicle on the driving road based on the automatic driving success rate of each node, and marking the nodes with the confidence degrees smaller than a preset threshold value as risk nodes.

Further, in the automatic driving confidence obtaining and reminding method, the dynamic road data of the road on which the vehicle runs includes: curvature, gradient, lane line definition, and accident-prone information of a road on which the vehicle is traveling.

Further, in the above automatic driving confidence obtaining and reminding method, the static road data of the road on which the vehicle runs includes: congestion condition of a road on which the vehicle is traveling, traffic accident information, and road maintenance information.

Further, the method for obtaining and reminding the automatic driving confidence coefficient includes the steps of calculating a target speed of the road, a recognition definition of a lane line, a traffic flow and a vehicle collision risk based on dynamic road data and static road data of the road where the vehicle runs and accuracy data of sensor recognition of the vehicle, and obtaining a success rate of automatic driving of each node on the road where the vehicle runs based on the target speed of the road, the recognition definition of the lane line, the traffic flow and the vehicle collision risk, and includes the steps of:

acquiring historical data of automatic driving tasks of other vehicles which have the same automatic driving capability as the vehicle and the same driving road;

the method comprises the steps of calculating a target speed of the road, recognition definition of a lane line, a traffic flow and a vehicle collision risk based on dynamic road data and static road data of the road on which the vehicle runs, accuracy data of sensor recognition of the vehicle and historical data of automatic driving tasks of other vehicles, and obtaining the success rate of automatic driving of each node on the road on which the vehicle runs based on the target speed of the road, the recognition definition of the lane line, the traffic flow and the vehicle collision risk.

Further, the above method for obtaining and reminding the automatic driving confidence level, when the automatic driving vehicle approaches the risk node, sending a prompt message for reminding the user to take over the vehicle, includes:

and when the distance between the current position of the automatic driving vehicle and the risk node is smaller than a preset distance threshold value, or the time from the automatic driving vehicle to the risk node is smaller than a preset time threshold value, sending out prompt information for reminding a user to take over the vehicle.

According to another aspect of the present invention, there is also provided an automatic driving confidence obtaining and reminding apparatus, including:

the analysis module is used for acquiring dynamic road data and static road data of a road on which the vehicle runs; acquiring accuracy data of sensor identification of the vehicle;

the automatic driving module is used for calculating the target speed of the road, the recognition definition of the lane lines, the traffic flow and the vehicle collision risk based on the dynamic road data and the static road data of the road where the vehicle runs and the accuracy data of the sensor recognition of the vehicle, obtaining a risk node with automatic driving confidence coefficient smaller than a preset threshold value on the road where the vehicle runs based on the target speed of the road, the recognition definition of the lane lines, the traffic flow and the vehicle collision risk, and sending prompt information for prompting a user to take over the vehicle when the automatic driving vehicle approaches the risk node.

According to another aspect of the present invention, there is also provided a computing-based device, including:

a processor; and

a memory arranged to store computer executable instructions that, when executed, cause the processor to:

acquiring dynamic road data and static road data of a road on which a vehicle runs; acquiring accuracy data of sensor identification of a vehicle;

the method comprises the steps of calculating a target speed of a road, the recognition definition of a lane line, the traffic flow and the vehicle collision risk based on dynamic road data and static road data of the road on which a vehicle runs and accuracy data of sensor recognition of the vehicle, obtaining a risk node with an automatic driving confidence coefficient smaller than a preset threshold value on the road on which the vehicle runs based on the target speed of the road, the recognition definition of the lane line, the traffic flow and the vehicle collision risk, and sending prompt information for prompting a user to take over the vehicle when the automatic driving vehicle approaches the risk node.

According to another aspect of the present invention, there is also provided a computer-readable storage medium having stored thereon computer-executable instructions, wherein the computer-executable instructions, when executed by a processor, cause the processor to:

acquiring dynamic road data and static road data of a road on which a vehicle runs;

acquiring accuracy data of sensor identification of a vehicle;

the method comprises the steps of calculating a target speed of a road, the recognition definition of a lane line, the traffic flow and the vehicle collision risk based on dynamic road data and static road data of the road on which a vehicle runs and accuracy data of sensor recognition of the vehicle, obtaining a risk node with an automatic driving confidence coefficient smaller than a preset threshold value on the road on which the vehicle runs based on the target speed of the road, the recognition definition of the lane line, the traffic flow and the vehicle collision risk, and sending prompt information for prompting a user to take over the vehicle when the automatic driving vehicle approaches the risk node.

In summary, the invention can calculate the risk node that the confidence of automatic driving on the road on which the vehicle runs is smaller than the preset threshold value based on the dynamic road data, the static road data and the accuracy data identified by the sensor of the vehicle, and when the automatic driving vehicle approaches the risk node, send out the prompt information for reminding the user to take over the vehicle, so that the driver can predict the safety risk of automatic driving on the following road section in advance and take over and other control processes are planned, thereby reducing the panic and tension of the user.

In addition, the invention can combine the dynamic road data and static road data in the high-precision map, the influence of weather data on road conditions, and other big data such as automatic driving data of vehicles with the same automatic driving level, plan the automatic driving task of the vehicle on the driving road, such as pre-planning the overall speed, calculate the success rate of the automatic driving task of the vehicle on the driving road section in advance, convert the success rate into a confidence symbol to display on a screen to inform the driver, so that the driver can predict the safety risk of automatic driving on the rear road section in advance and take over the control process in a planned way, thereby reducing the feeling of panic and tension of the user.

Drawings

FIG. 1 is a flow chart of an automated driving confidence acquisition and reminder method according to an embodiment of the invention;

FIG. 2 is a schematic diagram of an automated driving confidence acquisition and reminder method in accordance with an embodiment of the present invention;

FIG. 3 is a schematic diagram of a full-route low confidence point marker in accordance with an embodiment of the present invention;

FIG. 4 is a diagram illustrating a low confidence alert to a user, in accordance with an embodiment of the present invention.

Detailed Description

The present invention is described in further detail below with reference to the attached drawing figures.

In a typical configuration of the present application, the terminal, the device serving the network, and the trusted party each include one or more processors (CPUs), input/output interfaces, network interfaces, and memory.

The memory may include forms of volatile memory in a computer readable medium, random Access Memory (RAM) and/or non-volatile memory, such as Read Only Memory (ROM) or flash memory (flash RAM). Memory is an example of a computer-readable medium.

Computer-readable media, including both non-transitory and non-transitory, removable and non-removable media, may implement information storage by any method or technology. The information may be computer readable instructions, data structures, modules of a program, or other data. Examples of computer storage media include, but are not limited to, phase change memory (PRAM), static Random Access Memory (SRAM), dynamic Random Access Memory (DRAM), other types of Random Access Memory (RAM), read Only Memory (ROM), electrically Erasable Programmable Read Only Memory (EEPROM), flash memory or other memory technology, compact disc read only memory (CD-ROM), digital Versatile Disks (DVD) or other optical storage, magnetic cassettes, magnetic tape, magnetic disk storage or other magnetic storage devices, or any other non-transmission medium, which can be used to store information that can be accessed by a computing device. As defined herein, computer readable media does not include non-transitory computer readable media (transient media), such as modulated data signals and carrier waves.

As shown in fig. 1 and 2, the present invention provides an automatic driving confidence obtaining and reminding method, including:

step S1, acquiring dynamic road data and static road data of a road on which a vehicle runs;

here, the static road data refers to road condition data that does not change for a long time; the dynamic road data refers to road condition data which can change along with time;

s2, acquiring accuracy data of sensor identification of the vehicle;

here, the sensor of the vehicle includes: cameras, radars, vehicle speed sensors, temperature sensors, shaft rotational speed sensors, pressure sensors, etc., rotational angle sensors, torque sensors, hydraulic pressure sensors, acceleration sensors, vehicle height sensors, roll angle sensors, rotational angle sensors, etc. Accuracy data of sensor identification of the vehicle can influence the confidence of automatic driving on a road on which the vehicle runs;

and S3, calculating a target speed of the road, the recognition definition of the lane line, the traffic flow and the vehicle collision risk based on the dynamic road data and the static road data of the road on which the vehicle runs and the accuracy data of the sensor recognition of the vehicle, obtaining a risk node with an automatic driving confidence coefficient smaller than a preset threshold value on the road on which the vehicle runs based on the target speed of the road, the recognition definition of the lane line, the traffic flow and the vehicle collision risk, and sending prompt information for prompting a user to take over the vehicle when the automatic driving vehicle approaches the risk node.

The automatic driving is a technology which does not need user intervention, can sense the external environment in real time by utilizing various integrated sensors, reasonably plans a path by combining a navigation positioning technology and controls vehicle components such as a steering wheel, an accelerator and the like to perform vehicle autonomous control.

Specifically, in the automatic driving process, the vehicle can dynamically adjust the automatic driving task and the takeover reminder of the remaining route according to the navigation dynamic data information and the change of the meteorological data.

Firstly, in the aspect of vehicle speed planning of the whole route, an automatic driving unit calculates the target vehicle speed of each road section according to the speed limit information of each road section in the navigation route, and if a vehicle exists in front, the automatic driving unit drives along with the speed of the vehicle in front; secondly, in the aspect of vehicle transverse control, the automatic driving unit can identify the lane line of a vehicle driving lane through a sensor of the vehicle and keep driving in the middle of the lane line; thirdly, the automatic driving unit can monitor and sense surrounding obstacles and the like through a sensor of the vehicle in the driving process of the vehicle, dynamic information such as pedestrians, other vehicles, animals and the like also exists, and the automatic driving unit can brake and stop the vehicle in time when the surrounding dynamic information and the self vehicle are in collision risk.

If the lane line of the road section is clear, the visibility of the driving environment of the road is good, the traffic flow on the road is low, and almost no collision risk exists, the automatic driving confidence coefficient is high under the condition, the navigation interface has no confidence coefficient icon display, and a confidence coefficient high icon (such as blue three-grid display) can be displayed above the model in the MR region; if the lane line cannot be accurately sensed, when factors such as unclear lane line, low visibility of weather and the like influence the vehicle to keep normal running in a specified lane and the like, or when collision risks exist, such as too fast running speed, traffic jam and the like cause that the vehicle cannot timely stop or change lanes and other danger avoiding operations, normal automatic driving of the vehicle is influenced, at the moment, the automatic driving success rate under the road condition is low, an icon (for example, two blue grids are displayed) with low confidence coefficient is marked on the road section on a navigation interface, the icon with low confidence coefficient is marked on an MR area vehicle model 1 kilometer before the road section with low confidence coefficient is reached, and a text popup window and a prompt tone are accompanied, so that a user is reminded in advance that the automatic driving confidence coefficient in front is low, and the road condition needs to be noticed and is ready to be taken over at any time. If the road section without the lane line information in front is too long, such as a large intersection, a country road and a small road, the vehicle in the road section cannot keep driving in a specified lane, namely the transverse direction of the vehicle cannot be controlled, so that the vehicle cannot finish automatic driving, at the moment, the automatic driving confidence coefficient of the vehicle in the road section is almost zero, an icon with low confidence coefficient (such as yellow display) is identified on the road section on a navigation interface, the icon with very low confidence coefficient (such as yellow display) is identified 2 kilometers before the road section with low confidence coefficient is reached, an icon with very low confidence coefficient is identified above a vehicle model in an MR area (such as yellow display), and a text popup window and an emergency prompt sound are accompanied, so that a user is reminded in advance that the automatic driving confidence coefficient in front is very low, and the user needs to take over manual driving.

According to the method and the system, based on the dynamic road data and the static road data of the road on which the vehicle runs and the accuracy data identified by the sensor of the vehicle, the risk node with the confidence coefficient of automatic driving on the road on which the vehicle runs being smaller than the preset threshold value is obtained through calculation, when the automatic driving vehicle approaches the risk node, the prompt information for reminding the user to take over the vehicle is sent out, the driver is enabled to predict the safety risk of automatic driving on the rear road section in advance and control processing such as taking over is carried out in a planned way, and the panic and the stress of the user are relieved.

In an embodiment of the method for obtaining and reminding an automatic driving confidence, step S2 of obtaining accuracy data of sensor identification of a vehicle includes:

step S21, acquiring weather forecast data of a vehicle in a driving process from a departure place to a destination;

here, as shown in fig. 2, the weather forecast data may be, for example, weather condition data of sunny days, rainy days, snowy days, storm winds, heavy fog, and the like;

and S22, obtaining accuracy data of sensor identification of the vehicle based on the weather forecast data.

In this case, different weather conditions can influence the accuracy of the sensor detection of the vehicle. For example, in heavy fog weather, the accuracy of the image of the road environment of the vehicle collected by the camera sensor is reduced due to low visibility.

The present embodiment may determine, based on weather data, an accuracy impact of a sensor of the vehicle on road condition identification; and then, calculating the success rate of the automatic driving task of the vehicle on the driving road section in advance by combining the dynamic road data, the static road data and the weather data of the road on which the vehicle runs.

As shown in fig. 2, in an embodiment of the method for obtaining and reminding an automatic driving confidence level of the present invention, step S1, obtaining dynamic road data and static road data of a road on which a vehicle is traveling includes:

step S11, obtaining a departure place and a running destination of the vehicle;

step S12, acquiring dynamic road data and static road data from a high-precision map according to the departure place and the driving destination of the vehicle;

the high-precision map refers to a high-precision and fine-defined map, and the precision of the high-precision map can be distinguished only by reaching a decimeter level. The high-precision map stores various traffic elements in a traffic scene, including road network data, lane grid data, lane lines, traffic signs and other data of a traditional map.

Step S3, calculating the target speed of the road, the recognition definition of the lane lines, the traffic flow and the vehicle collision risk based on the dynamic road data and the static road data of the road on which the vehicle runs and the accuracy data of the sensor recognition of the vehicle, and obtaining a risk node of which the automatic driving confidence coefficient on the road on which the vehicle runs is smaller than a preset threshold value based on the target speed of the road, the recognition definition of the lane lines, the traffic flow and the vehicle collision risk, wherein the method comprises the following steps:

step S31, calculating a target speed of the road, the recognition definition of a lane line, a traffic flow and a vehicle collision risk based on the dynamic road data and the static road data of the road on which the vehicle runs and the accuracy data of the sensor recognition of the vehicle, obtaining the automatic driving success rate of each node on the road on which the vehicle runs based on the target speed of the road, the recognition definition of the lane line, the traffic flow and the vehicle collision risk, and converting the automatic driving success rate of each node into a corresponding confidence coefficient;

and step S32, planning an automatic driving task of the vehicle on a driving road based on the automatic driving success rate of each node, and marking the node with the confidence coefficient smaller than a preset threshold value as a risk node.

The success rate of automatic driving of the vehicle on a driving road section can be calculated in advance by combining dynamic road data and static road data in a high-precision map and the influence of weather data on road conditions, the automatic driving task of the vehicle on the driving road is planned based on the success rate, such as global vehicle speed preplanning is carried out, the success rate is converted into a confidence symbol to be displayed on a screen to inform a driver, the driver can predict the safety risk of automatic driving on a rear road section in advance and take over and other control processes are planned, and the panic and tension of the user are relieved.

Specifically, the global vehicle speed is preplanned, for example, different vehicle speeds may be set for each node according to the success rate of automatic driving of each node on the road on which the vehicle is running, for example, a higher automatic driving vehicle speed may be set for a node with a higher success rate of automatic driving; and aiming at the node with lower automatic driving success rate, the lower automatic driving speed can be set.

In an embodiment of the method for obtaining and reminding an automatic driving confidence, the dynamic road data of the road on which the vehicle runs includes: curvature, gradient, lane line definition, and accident-prone information of a road on which the vehicle is traveling.

The invention uses the global data capability of the high-precision map, combines road information such as curvature, gradient, lane line definition, accident frequently-occurring information and the like of a road which needs to be traveled by the automatic driving of the vehicle, weather information and automatic driving information of other vehicles on the road, and uses big data to predict the success rate of smooth passing when the automatic driving passes through the road section.

As shown in fig. 3, the success rate can be displayed on the display screen of the vehicle in real time in a simple and easy-to-understand manner, so that the user is informed of the next control information in advance, the panic feeling of the user in driving is reduced, and the trust level and the safety feeling of the user on the automatic driving system are enhanced.

In an embodiment of the automatic driving confidence obtaining and reminding method of the present invention, the static road data of the road on which the vehicle is running includes: congestion of the road on which the vehicle is traveling, traffic accidents, and road maintenance information.

The invention uses the global data capability of the high-precision map, combines road information such as congestion, traffic accidents and road maintenance information of the road which the vehicle needs to pass through automatically, weather information and other automatic driving information of the vehicle on the road, and uses big data to predict the success rate of successfully passing the road section when the vehicle passes through automatically next. As shown in fig. 3, the success rate can be displayed on the display screen of the vehicle in real time in a concise and understandable manner, so that the user is informed of the next control information taking over in advance, the panic feeling of the user in driving is reduced, and the trust and the safety of the user on the automatic driving system are enhanced.

In addition, in the automatic driving process, the vehicle dynamically adjusts the automatic driving task and the taking over reminding of the remaining route according to the navigation dynamic data information and the change of the meteorological data.

In an embodiment of the method for obtaining and reminding an automatic driving confidence level, step S31, based on dynamic road data and static road data of a road on which a vehicle is traveling and accuracy data of sensor identification of the vehicle, calculates a target vehicle speed, identification definition of a lane line, a traffic flow and a risk of vehicle collision of the road, and obtains a success rate of automatic driving of each node on the road on which the vehicle is traveling based on the target vehicle speed, the identification definition of the lane line, the traffic flow and the risk of vehicle collision, including:

step S311 of acquiring history data of an autonomous driving task of another vehicle which has the same autonomous driving capability as the vehicle and the same road on which the vehicle travels;

here, the history data of the autonomous driving tasks of the other vehicles may be acquired from a database of large data. The big data (big data), or huge data, refers to historical data of a large number of automatic driving tasks of vehicles, which are obtained, managed, processed and collected in a reasonable time, and the large amount of data cannot be collected, managed, processed and collected through a mainstream software tool, and the large amount of historical data can be classified according to the automatic driving capacity and the driving roads, so that the matching is performed according to the automatic driving capacity and the driving roads which are the same as those of the vehicles, and the historical data of the automatic driving tasks of other corresponding vehicles can be conveniently obtained.

Step S322, calculating the target speed of the road, the recognition definition of the lane line, the traffic flow and the vehicle collision risk based on the dynamic road data, the static road data, the accuracy data of the sensor recognition of the vehicle and the historical data of the automatic driving task of other vehicles, and obtaining the automatic driving success rate of each node on the road on which the vehicle runs based on the target speed of the road, the recognition definition of the lane line, the traffic flow and the vehicle collision risk.

Referring to fig. 1 to 4, after the navigation completes the route planning, static data information (curvature, gradient, lane definition, frequent accident, etc.) and dynamic data information (congestion, accident, road maintenance, etc.) of the whole route are transmitted to the big data processing center, meanwhile, the weather forecasting unit transmits weather information (rain, visibility, etc.) to the big data processing center, the big data processing center can start to calculate and analyze the automatic driving success rate of the whole route by combining with the automatic driving history data of other vehicles with the same automatic driving capability, then the automatic driving unit plans the automatic driving task of the whole route based on the automatic driving success rate of the whole route, and marks a route node with low confidence as shown in fig. 3, and reminds the user to take over the vehicle in advance in the display screen as shown in fig. 4.

The embodiment can combine dynamic road data and static road data in a high-precision map, influence of weather data on road conditions and big data such as automatic driving data of vehicles with other equal automatic driving levels, plan automatic driving tasks of the vehicles on driving roads, such as pre-planning of global vehicle speed, calculate success rate of the automatic driving tasks of the vehicles on driving road sections in advance, convert the success rate into confidence symbols to be displayed on a screen to inform a driver, enable the driver to predict safety risks of automatic driving of the following road sections in advance and take over and other control processes in a planned way, and reduce the feeling of panic and tension of the user.

In an embodiment of the method for obtaining and reminding an automatic driving confidence coefficient, step S3 is to send out a prompt message for reminding a user to take over a vehicle when the automatic driving vehicle approaches the risk node, and the method includes:

and when the distance between the current position of the automatic driving vehicle and the risk node is smaller than a preset distance threshold value, or the time for the automatic driving vehicle to travel to the risk node is smaller than a preset time threshold value, sending prompt information for reminding a user to take over the vehicle.

Here, in order to timely remind the driver of the vehicle to take over the vehicle, it may be set that the distance between the current position of the autonomous vehicle and the risk node is less than a preset distance threshold, or the time that the autonomous vehicle travels to the risk node is less than a preset time threshold, where the personnel adjustment is satisfied that the prompt information for taking over the vehicle is sent to the driver of the vehicle, so that the driver of the vehicle has sufficient time to switch the autonomous driving state of the vehicle into the manual driving state.

As shown in fig. 2, according to another aspect of the present invention, there is also provided an automatic driving confidence obtaining and reminding apparatus, including:

the analysis module is used for acquiring dynamic road data and static road data of a road on which the vehicle runs; acquiring accuracy data of sensor identification of the vehicle;

here, the dynamic road data refers to road condition data that does not change for a long time; the static road data refers to road condition data which can change along with time;

the sensor of the vehicle includes: cameras, radars, vehicle speed sensors, temperature sensors, shaft rotational speed sensors, pressure sensors, etc., rotational angle sensors, torque sensors, hydraulic pressure sensors, acceleration sensors, vehicle height sensors, roll angle sensors, rotational angle sensors, etc. Accuracy data of sensor identification of the vehicle can influence the confidence of automatic driving on a road where the vehicle runs;

the automatic driving module is used for calculating a target speed of a road, the recognition definition of a lane line, the traffic flow and the vehicle collision risk based on the dynamic road data and the static road data of the road where the vehicle runs and the accuracy data of the sensor recognition of the vehicle, obtaining a risk node with an automatic driving confidence coefficient smaller than a preset threshold value on the road where the vehicle runs based on the target speed of the road, the recognition definition of the lane line, the traffic flow and the vehicle collision risk, and sending prompt information for reminding a user to take over the vehicle when the automatic driving vehicle approaches the risk node.

The automatic driving is a technology which does not need user intervention, can sense the external environment in real time by utilizing various integrated sensors, reasonably plans a path by combining a navigation positioning technology and controls vehicle components such as a steering wheel, an accelerator and the like to perform vehicle autonomous control.

According to the method and the system, based on the dynamic road data and the static road data of the road on which the vehicle runs and the accuracy data identified by the sensor of the vehicle, the risk node with the automatic driving confidence coefficient smaller than the preset threshold value on the road on which the vehicle runs is obtained through calculation, when the automatic driving vehicle approaches the risk node, prompt information for reminding a user to take over the vehicle is sent out, the driver is enabled to predict the safety risk of automatic driving of the road section behind in advance and take over and other control processing are planned, and the panic and tension of the user are relieved.

As shown in fig. 2, in an embodiment of the automatic driving confidence obtaining and reminding device of the present invention, the automatic driving confidence obtaining and reminding device further includes: the navigation module is used for acquiring weather forecast data of a vehicle in a driving process from a departure place to a destination;

and the analysis module is used for obtaining accuracy data of sensor identification of the vehicle based on the weather forecast data.

Here, as shown in fig. 2, the weather forecast data may be, for example, weather condition data of weather, rain, snow, storm, fog, and the like;

different weather conditions may affect the accuracy of the sensor identification of the vehicle. For example, in heavy fog weather, the accuracy of the image of the road environment of the vehicle collected by the camera sensor is reduced due to low visibility.

The present embodiment may determine, based on weather data, an accuracy impact of a sensor of the vehicle on road condition identification; and then, the success rate of the automatic driving task of the vehicle on the driving road section is calculated in advance by combining the dynamic road data, the static road data and the weather data of the road on which the vehicle runs.

As shown in fig. 2, in an embodiment of the automatic driving confidence obtaining and reminding device of the present invention, the navigation module is configured to obtain a departure location and a driving destination of a vehicle;

the analysis module is used for acquiring dynamic road data and static road data from the high-precision map according to the departure place and the driving destination of the vehicle;

the high-precision map refers to a high-precision and fine-defined map, and the precision of the high-precision map can be distinguished only by reaching a decimeter level. The high-precision map stores various traffic elements in a traffic scene, including road network data, lane grid data, lane lines, traffic signs and other data of a traditional map.

The automatic driving module is used for calculating the target speed of the road, the recognition definition of the lane lines, the traffic flow and the vehicle collision risk based on the dynamic road data and the static road data of the road on which the vehicle runs and the accuracy data of the sensor recognition of the vehicle, obtaining the automatic driving success rate of each node on the road on which the vehicle runs based on the target speed of the road, the recognition definition of the lane lines, the traffic flow and the vehicle collision risk, and converting the automatic driving success rate of each node into corresponding confidence; and planning the automatic driving task of the vehicle on the driving road based on the automatic driving success rate of each node, and marking the nodes with the confidence degrees smaller than a preset threshold value as risk nodes.

The success rate of automatic driving of the vehicle on the driving road section can be calculated in advance by combining dynamic road data and static road data in a high-precision map and the influence of weather data on road conditions, the automatic driving task of the vehicle on the driving road is planned based on the success rate, such as global vehicle speed preplanning is carried out, the success rate is converted into a confidence symbol to be displayed on a screen to inform a driver, the driver can predict the safety risk of automatic driving on the following road section in advance and take over and other control processes are carried out in a planned way, and the panic and tension of the user are relieved.

Specifically, the global vehicle speed is preplanned, for example, different vehicle speeds may be set for each node according to the success rate of automatic driving of each node on a road on which a vehicle is traveling, for example, a higher automatic driving vehicle speed may be set for a node with a higher success rate of automatic driving; and aiming at the node with lower automatic driving success rate, the lower automatic driving speed can be set.

In an embodiment of the automatic driving confidence obtaining and reminding device of the present invention, the dynamic road data of the road on which the vehicle runs includes: curvature, gradient, lane line definition, and accident-prone information of a road on which the vehicle is traveling.

The invention uses the global data capability of the high-precision map, combines road information such as curvature, gradient, lane line definition, accident frequently-occurring information and the like of a road which needs to be traveled by the automatic driving of the vehicle, weather information and automatic driving information of other vehicles on the road, and uses big data to predict the success rate of smooth passing when the automatic driving passes through the road section.

As shown in fig. 3, the success rate can be displayed on the display screen of the vehicle in real time in a simple and easy-to-understand manner, so that the user is informed of the next control information in advance, the panic feeling of the user in driving is reduced, and the trust level and the safety feeling of the user on the automatic driving system are enhanced.

In an embodiment of the automatic driving confidence obtaining and reminding device of the present invention, the static road data of the road on which the vehicle runs includes: congestion of the road on which the vehicle is traveling, traffic accidents, and road maintenance information.

The invention utilizes the global data capability of the high-precision map, combines road information such as congestion, traffic accidents and road maintenance information of the road on which the vehicle automatically drives to travel, weather information and automatic driving information of other vehicles on the road, and utilizes big data to predict the success rate of smooth passing when the vehicle automatically drives to pass the road section in advance. As shown in fig. 3, the success rate can be displayed on the display screen of the vehicle in real time in a concise and understandable manner, so that the user is informed of the next control information taking over in advance, the panic feeling of the user in driving is reduced, and the trust and the safety of the user on the automatic driving system are enhanced.

In addition, in the automatic driving process, the vehicle dynamically adjusts the automatic driving task and the taking over reminding of the remaining route according to the navigation dynamic data information and the change of the meteorological data.

In an embodiment of the automatic driving confidence obtaining and reminding device, the automatic driving module is configured to calculate a target speed of a road, a recognition definition of a lane line, a traffic flow and a vehicle collision risk based on dynamic road data of the road on which the vehicle is traveling, static road data, accuracy data of sensor recognition of the vehicle and historical data of automatic driving tasks of other vehicles, and obtain a success rate of automatic driving of each node on the road on which the vehicle is traveling based on the target speed of the road, the recognition definition of the lane line, the traffic flow and the vehicle collision risk.

Here, the history data of the autonomous driving tasks of the other vehicles may be acquired from a large data database. The big data (big data), or huge data, refers to historical data of a large number of automatic driving tasks of vehicles, which are related to the big data and cannot be collected, managed, processed and arranged through a mainstream software tool in a reasonable time, and the large number of historical data can be classified according to the automatic driving capacity and the driving roads, so that the matching is performed according to the automatic driving capacity and the driving roads which are the same as those of the vehicles, and the historical data of the automatic driving tasks of other corresponding vehicles can be conveniently obtained.

Referring to fig. 2 to 4, after the navigation completes the route planning, static data information (curvature, gradient, lane definition, frequent accident, etc.) and dynamic data information (congestion, accident, road maintenance, etc.) of the whole route are transmitted to the big data processing center, meanwhile, the weather forecasting unit transmits weather information (rain, visibility, etc.) to the big data processing center, the big data processing center can start to calculate and analyze the automatic driving success rate of the whole route by combining with the automatic driving history data of other vehicles with the same automatic driving capability, then the automatic driving unit plans the automatic driving task of the whole route based on the automatic driving success rate of the whole route, and marks a route node with low confidence as shown in fig. 3, and reminds the user to take over the vehicle in advance in the display screen as shown in fig. 4.

The embodiment can combine dynamic road data and static road data in a high-precision map, influence of weather data on road conditions and big data such as automatic driving data of vehicles with other equal automatic driving levels, plan automatic driving tasks of the vehicles on driving roads, such as pre-planning of global vehicle speed, calculate success rate of the automatic driving tasks of the vehicles on driving road sections in advance, convert the success rate into confidence symbols to be displayed on a screen to inform a driver, enable the driver to predict safety risks of automatic driving of the following road sections in advance and take over and other control processes in a planned way, and reduce the feeling of panic and tension of the user.

In an embodiment of the automatic driving confidence obtaining and reminding device of the present invention, the automatic driving module is configured to send a prompt message reminding a user to take over the vehicle when a distance between a current position of the automatic driving vehicle and the risk node is smaller than a preset distance threshold, or a time taken by the automatic driving vehicle to travel to the risk node is smaller than a preset time threshold.

Here, in order to timely remind the driver of the vehicle to take over the vehicle, it may be set that the distance between the current position of the automatically driven vehicle and the risk node is less than a preset distance threshold, or the time for the automatically driven vehicle to travel to the risk node is less than a preset time threshold, where the personnel adjustment is that a prompt message for taking over the vehicle is sent to the driver of the vehicle, so that the driver of the vehicle has sufficient time to switch the automatic driving state of the vehicle into the manual driving state.

According to another aspect of the present invention, there is also provided a computing-based device, including:

a processor; and

a memory arranged to store computer executable instructions that, when executed, cause the processor to:

acquiring dynamic road data and static road data of a road on which a vehicle runs;

acquiring accuracy data of sensor identification of a vehicle;

the method comprises the steps of calculating a target speed of a road, the recognition definition of a lane line, the traffic flow and the vehicle collision risk based on dynamic road data and static road data of the road on which a vehicle runs and accuracy data of sensor recognition of the vehicle, obtaining a risk node with an automatic driving confidence coefficient smaller than a preset threshold value on the road on which the vehicle runs based on the target speed of the road, the recognition definition of the lane line, the traffic flow and the vehicle collision risk, and sending prompt information for prompting a user to take over the vehicle when the automatic driving vehicle approaches the risk node.

According to another aspect of the present invention, there is also provided a computer-readable storage medium having stored thereon computer-executable instructions, wherein the computer-executable instructions, when executed by a processor, cause the processor to:

acquiring dynamic road data and static road data of a road on which a vehicle runs;

acquiring accuracy data of sensor identification of a vehicle;

the method comprises the steps of calculating a target speed of a road, identification definition of a lane line, a traffic flow and a vehicle collision risk based on dynamic road data and static road data of the road on which a vehicle runs and accuracy data of sensor identification of the vehicle, obtaining a risk node with an automatic driving confidence coefficient smaller than a preset threshold value on the road on which the vehicle runs based on the target speed of the road, the identification definition of the lane line, the traffic flow and the vehicle collision risk, and sending prompt information for reminding a user to take over the vehicle when the automatic driving vehicle approaches the risk node.

The method of the invention may be integrated in a specific control device.

The control device of the present invention may be a computer program product, wherein the computer program product, when run on a computer, causes the computer to perform some or all of the steps of the method as in the above method embodiments.

The control device of the present invention may be an application distribution platform for distributing a computer program product, wherein the computer program product, when run on a computer, causes the computer to perform part or all of the steps of the method as in the above method embodiments.

In various embodiments of the present invention, it should be understood that the sequence numbers of the above-mentioned processes do not imply a necessary order of execution, and the order of execution of each process should be determined by its function and inherent logic, and should not constitute any limitation to the implementation process of the embodiments of the present invention.

The various systems and elements of the invention, if implemented as software functional elements, may be stored in a computer accessible memory. With this understanding, part or all of the technical solutions of the present invention may be embodied in the form of a software product, which is stored in a memory and includes several requests to enable one or more computer devices (such as a personal computer, a server, or a network device, and may specifically be a processor in the computer device) to execute part or all of the steps of the above methods according to the embodiments of the present invention.

Those skilled in the art will appreciate that all or a portion of the steps of the various embodiments recited herein may be performed by associated hardware as instructed by a computer program that may be stored centrally or distributed on one or more computer devices, such as on a readable storage medium. The computer device includes a Read-Only Memory (ROM), a Random Access Memory (RAM), a Programmable Read-Only Memory (PROM), an Erasable Programmable Read-Only Memory (EPROM), a One-time Programmable Read-Only Memory (OTPROM), an Electrically Erasable rewritable Read-Only Memory (EEPROM), a compact disc Read-Only Memory (CD-ROM) or other optical disc Memory, a magnetic disk Memory, a tape Memory, or any other medium readable by a computer capable of carrying or storing data.

It should be understood by those skilled in the art that the above embodiments are only for illustrating the present invention and are not to be used as a limitation of the present invention, and that changes and modifications to the above described embodiments are within the scope of the claims of the present invention as long as they are within the spirit and scope of the present invention.

It will be apparent to those skilled in the art that various changes and modifications may be made in the present application without departing from the spirit and scope of the application. Thus, if such modifications and variations of the present application fall within the scope of the claims of the present application and their equivalents, the present application is intended to include such modifications and variations as well.

It should be noted that the present invention may be implemented in software and/or in a combination of software and hardware, for example, as an Application Specific Integrated Circuit (ASIC), a general purpose computer or any other similar hardware device. In one embodiment, the software program of the present invention may be executed by a processor to implement the steps or functions described above. Likewise, the software programs (including associated data structures) of the present invention can be stored in a computer readable recording medium, such as RAM memory, magnetic or optical drive or diskette and the like. Further, some of the steps or functions of the present invention may be implemented in hardware, for example, as circuitry that cooperates with the processor to perform various steps or functions.

In addition, some of the present invention can be applied as a computer program product, such as computer program instructions, which when executed by a computer, can invoke or provide the method and/or technical solution according to the present invention through the operation of the computer. Program instructions which invoke the methods of the present invention may be stored on a fixed or removable recording medium and/or transmitted via a data stream on a broadcast or other signal-bearing medium and/or stored within a working memory of a computer device operating in accordance with the program instructions. An embodiment according to the invention herein comprises an apparatus comprising a memory for storing computer program instructions and a processor for executing the program instructions, wherein the computer program instructions, when executed by the processor, trigger the apparatus to perform a method and/or solution according to embodiments of the invention as described above.

It will be evident to those skilled in the art that the invention is not limited to the details of the foregoing illustrative embodiments, and that the present invention may be embodied in other specific forms without departing from the spirit or essential attributes thereof. The present embodiments are therefore to be considered in all respects as illustrative and not restrictive, the scope of the invention being indicated by the appended claims rather than by the foregoing description, and all changes which come within the meaning and range of equivalency of the claims are therefore intended to be embraced therein. Any reference sign in a claim should not be construed as limiting the claim concerned. Furthermore, it is obvious that the word "comprising" does not exclude other elements or steps, and the singular does not exclude the plural. A plurality of units or means recited in the apparatus claims may also be implemented by one unit or means in software or hardware. The terms first, second, etc. are used to denote names, but not to denote any particular order.

Claims (10)

1. An automatic driving confidence obtaining and reminding method is characterized by comprising the following steps:

acquiring dynamic road data and static road data of a road on which a vehicle runs;

acquiring accuracy data of sensor identification of a vehicle;

the method comprises the steps of calculating a target speed of a road, the recognition definition of a lane line, the traffic flow and the vehicle collision risk based on dynamic road data and static road data of the road on which a vehicle runs and accuracy data of sensor recognition of the vehicle, obtaining a risk node with an automatic driving confidence coefficient smaller than a preset threshold value on the road on which the vehicle runs based on the target speed of the road, the recognition definition of the lane line, the traffic flow and the vehicle collision risk, and sending prompt information for prompting a user to take over the vehicle when the automatic driving vehicle approaches the risk node.

2. The automated driving confidence acquisition and alert method of claim 1, wherein acquiring accuracy data of sensor identification of a vehicle comprises:

acquiring weather forecast data of a vehicle in a driving process from a departure place to a destination;

and obtaining accuracy data of sensor identification of the vehicle based on the weather forecast data.

3. The automatic driving confidence acquiring and reminding method according to claim 1 or 2, wherein acquiring dynamic road data and static road data of a road on which a vehicle is running includes:

acquiring a departure place and a driving destination of a vehicle;

acquiring dynamic road data and static road data from a high-precision map according to a departure place and a driving destination of a vehicle;

based on the accuracy data of the sensor discernment of the dynamic road data, the static road data and the vehicle of the road that the vehicle traveles, calculate the target speed of a motor vehicle of road, the discernment definition of lane line, traffic flow and vehicle collision risk, based on the target speed of a motor vehicle of road, the discernment definition of lane line, traffic flow and vehicle collision risk, obtain the vehicle and travel the road on the automatic driving confidence degree be less than the risk node of presetting the threshold value, include:

calculating a target speed of a road, a recognition definition of a lane line, a traffic flow and a vehicle collision risk based on dynamic road data and static road data of the road on which the vehicle runs and accuracy data of sensor recognition of the vehicle, obtaining a success rate of automatic driving of each node on the road on which the vehicle runs based on the target speed of the road, the recognition definition of the lane line, the traffic flow and the vehicle collision risk, and converting the success rate of automatic driving of each node into a corresponding confidence coefficient;

and planning the automatic driving task of the vehicle on the driving road based on the automatic driving success rate of each node, and marking the nodes with the confidence degrees smaller than a preset threshold value as risk nodes.

4. The automated driving confidence retrieval and alert method of claim 1, wherein the dynamic road data for the road on which the vehicle is traveling includes: curvature, gradient, lane line definition, and accident-prone information of a road on which the vehicle is traveling.

5. The automatic driving confidence obtaining and reminding method according to claim 3, wherein the method includes calculating a target speed of the road, a recognition definition of a lane line, a traffic flow and a vehicle collision risk based on dynamic road data, static road data and accuracy data of sensor recognition of the vehicle on the road on which the vehicle is traveling, and obtaining a success rate of automatic driving of each node on the road on which the vehicle is traveling based on the target speed of the road, the recognition definition of the lane line, the traffic flow and the vehicle collision risk, and includes:

acquiring historical data of automatic driving tasks of other vehicles which have the same automatic driving capability as the vehicle and are on the same driving road;

the method comprises the steps of calculating a target speed of the road, recognition definition of a lane line, a traffic flow and a vehicle collision risk based on dynamic road data and static road data of the road on which the vehicle runs, accuracy data of sensor recognition of the vehicle and historical data of automatic driving tasks of other vehicles, and obtaining the success rate of automatic driving of each node on the road on which the vehicle runs based on the target speed of the road, the recognition definition of the lane line, the traffic flow and the vehicle collision risk.

6. The automated driving confidence retrieval and alert method of claim 1, wherein the static road data for the road on which the vehicle is traveling includes: congestion condition of a road on which the vehicle is traveling, traffic accident information, and road maintenance information.

7. The automated driving confidence acquisition and prompting method of claim 1, wherein when an automated driving vehicle approaches the risk node, issuing a prompt message prompting a user to take over the vehicle comprises:

and when the distance between the current position of the automatic driving vehicle and the risk node is smaller than a preset distance threshold value, or the time for the automatic driving vehicle to travel to the risk node is smaller than a preset time threshold value, sending prompt information for reminding a user to take over the vehicle.

8. An automatic driving confidence obtaining and reminding device, comprising:

the analysis module is used for acquiring dynamic road data and static road data of a road on which a vehicle runs; acquiring accuracy data of sensor identification of the vehicle;

the automatic driving module is used for calculating a target speed of a road, the recognition definition of a lane line, the traffic flow and the vehicle collision risk based on the dynamic road data and the static road data of the road where the vehicle runs and the accuracy data of the sensor recognition of the vehicle, obtaining a risk node with an automatic driving confidence coefficient smaller than a preset threshold value on the road where the vehicle runs based on the target speed of the road, the recognition definition of the lane line, the traffic flow and the vehicle collision risk, and sending prompt information for reminding a user to take over the vehicle when the automatic driving vehicle approaches the risk node.

9. A computing-based device, comprising:

a processor; and

a memory arranged to store computer executable instructions that, when executed, cause the processor to:

acquiring dynamic road data and static road data of a road on which a vehicle runs;

acquiring accuracy data of sensor identification of a vehicle;

the method comprises the steps of calculating a target speed of a road, the recognition definition of a lane line, the traffic flow and the vehicle collision risk based on dynamic road data and static road data of the road on which a vehicle runs and accuracy data of sensor recognition of the vehicle, obtaining a risk node with an automatic driving confidence coefficient smaller than a preset threshold value on the road on which the vehicle runs based on the target speed of the road, the recognition definition of the lane line, the traffic flow and the vehicle collision risk, and sending prompt information for prompting a user to take over the vehicle when the automatic driving vehicle approaches the risk node.

10. A computer-readable storage medium having computer-executable instructions stored thereon, wherein the computer-executable instructions, when executed by a processor, cause the processor to:

acquiring dynamic road data and static road data of a road on which a vehicle runs;

acquiring accuracy data of sensor identification of a vehicle;

the method comprises the steps of calculating a target speed of a road, the recognition definition of a lane line, the traffic flow and the vehicle collision risk based on dynamic road data and static road data of the road on which a vehicle runs and accuracy data of sensor recognition of the vehicle, obtaining a risk node with an automatic driving confidence coefficient smaller than a preset threshold value on the road on which the vehicle runs based on the target speed of the road, the recognition definition of the lane line, the traffic flow and the vehicle collision risk, and sending prompt information for prompting a user to take over the vehicle when the automatic driving vehicle approaches the risk node.

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