Driving information generation method, automatic driving method, device and electronic device

Abstract

The present disclosure provides a method for generating driving information, an automatic driving method, a device and an electronic device, and relates to the field of intelligent transportation technology, and in particular to the field of driving technology. The specific implementation scheme is: the method for generating driving information includes: obtaining the coordinate position and driving direction of the vehicle; taking the coordinate position as the starting point and the first distance as the termination condition, and performing path planning in a preset map along the driving direction to obtain a target path; generating road condition information corresponding to the target path, and the road condition information includes the first driving environment information in the target path. The present disclosure can avoid the problem of pathfinding blind spots that occur when driving without a destination, thereby improving the pathfinding effect.

Description

Technical Field

[0001] The present disclosure relates to the field of intelligent transportation technology, in particular to the field of driving technology, and specifically to a method for generating driving information, an automatic driving method, a device and an electronic device. Background Art

[0002] With the development of driving technology, pathfinding technology has been applied in many scenarios. Existing pathfinding methods usually include the following process: the vehicle-side device receives the starting position and the end position input by the driver, the vehicle-side device sends the input starting position and the end position to the cloud platform, the cloud platform performs path planning based on the starting position and the end position, obtains the optimal path, and sends the optimal path to the vehicle-side device. Summary of the invention

[0003] The present disclosure provides a method for generating driving information, an automatic driving method, a device, and an electronic device.

[0004] According to a first aspect of the present disclosure, there is provided a method for generating driving information, comprising:

[0005] Get the coordinate position and driving direction of the vehicle;

[0006] Taking the coordinate position as a starting point and the first distance as a termination condition, a path is planned in a preset map along the driving direction to obtain a target path;

[0007] Road condition information corresponding to the target path is generated, wherein the road condition information includes first driving environment information in the target path.

[0008] According to a second aspect of the present disclosure, there is provided an automatic driving method, comprising:

[0009] Get the coordinate position and driving direction of the vehicle;

[0010] Taking the coordinate position as a starting point and the first distance as a termination condition, a path is planned in a preset map along the driving direction to obtain a target path;

[0011] generating road condition information corresponding to the target path, the road condition information including first driving environment information in the target path;

[0012] Automatic driving is performed based on the road condition information.

[0013] According to a third aspect of the present disclosure, there is provided a device for generating driving information, comprising:

[0014] A first acquisition module is used to acquire the coordinate position and driving direction of the vehicle;

[0015] A first path planning module is used to perform path planning in a preset map along the travel direction with the coordinate position as a starting point and the first distance as a termination condition to obtain a target path;

[0016] The first generating module is used to generate road condition information corresponding to the target path, wherein the road condition information includes first driving environment information in the target path.

[0017] According to a fourth aspect of the present disclosure, there is provided an automatic driving device, comprising:

[0018] The second acquisition module is used to obtain the coordinate position and driving direction of the vehicle;

[0019] A second path planning module is used to perform path planning in a preset map along the travel direction with the coordinate position as a starting point and the first distance as a termination condition to obtain a target path;

[0020] A second generating module, configured to generate road condition information corresponding to the target path, wherein the road condition information includes first driving environment information in the target path;

[0021] A control module is used to perform automatic driving based on the road condition information.

[0022] According to a fifth aspect of the present disclosure, there is provided an electronic device, including:

[0023] at least one processor; and

[0024] a memory communicatively connected to the at least one processor; wherein,

[0025] The memory stores instructions that can be executed by the at least one processor, and the instructions are executed by the at least one processor to enable the at least one processor to perform the method described in the first aspect or the second aspect.

[0026] According to a sixth aspect of the present disclosure, a non-transitory computer-readable storage medium storing computer instructions is provided, wherein the computer instructions are used to enable the computer to execute the method described in the first aspect or the second aspect.

[0027] According to a seventh aspect of the present disclosure, a computer program product is provided, including a computer program, wherein the computer program implements the method described in the first aspect or the second aspect when executed by a processor.

[0028] In the disclosed embodiment, the target path is obtained by taking the coordinate position as the starting point, the first distance as the termination condition, and performing path planning in the preset map along the driving direction, and the road condition information corresponding to the target path is generated. In this way, when the vehicle is driving without a destination, the path finding process can also be completed, avoiding the problem of path finding blind spots when driving without a destination, thereby improving the path finding effect. BRIEF DESCRIPTION OF THE DRAWINGS

[0029] The accompanying drawings are used to better understand the present solution and do not constitute a limitation of the present disclosure.

[0030] Figure 1 is a flow chart of a method for generating driving information provided by an embodiment of the present disclosure;

[0031] Figure 2 is a schematic diagram of generating different road network view frames at different time points in an embodiment of the present disclosure;

[0032] Figure 3 is one of the road network schematic diagrams of the road network where the vehicle is located in the embodiment of the present disclosure;

[0033] Figure 4 is a second schematic diagram of a road network where a vehicle is located in an embodiment of the present disclosure;

[0034] Figure 5 is one of the schematic diagrams at the target intersection in the embodiment of the present disclosure;

[0035] Figure 6 This is the second schematic diagram of the target intersection in the embodiment of the present disclosure;

[0036] Figure 7 This is the third schematic diagram of the target intersection in the embodiment of the present disclosure;

[0037] Figure 8 is a schematic diagram of a target area in an embodiment of the present disclosure;

[0038] Fig. 9 This is the fourth schematic diagram of the target intersection in the embodiment of the present disclosure;

[0039] Fig.10 It is a schematic diagram of constructing a road network vision frame in sections in an embodiment of the present disclosure;

[0040] Fig.11 is a flow chart of an automatic driving method provided by an embodiment of the present disclosure;

[0041] Fig.12 is a structural schematic diagram of a driving information generating device provided by an embodiment of the present disclosure;

[0042] Fig.13is a structural schematic diagram of an automatic driving device provided by an embodiment of the present disclosure;

[0043] Fig.14 It is a block diagram of an electronic device for implementing a method for generating driving information or an automatic driving method provided by an embodiment of the present disclosure. DETAILED DESCRIPTION

[0044] The following is a description of exemplary embodiments of the present disclosure in conjunction with the accompanying drawings, including various details of the embodiments of the present disclosure to facilitate understanding, which should be considered as merely exemplary. Therefore, it should be recognized by those of ordinary skill in the art that various changes and modifications may be made to the embodiments described herein without departing from the scope and spirit of the present disclosure. Similarly, for the sake of clarity and conciseness, descriptions of well-known functions and structures are omitted in the following description.

[0045] See also Figure 1 , Figure 1 A flowchart of a method for generating driving information provided in an embodiment of the present disclosure, wherein the method for generating driving information comprises the following steps:

[0046] Step S101, obtaining the coordinate position and driving direction of the vehicle;

[0047] Step S102, taking the coordinate position as a starting point and the first distance as a termination condition, performing path planning in a preset map along the driving direction to obtain a target path;

[0048] Step S103: Generate road condition information corresponding to the target path, wherein the road condition information includes first driving environment information in the target path.

[0049] The above-mentioned vehicle may include a global positioning system (GPS) module, and the GPS module may obtain the coordinate position of the vehicle in real time, and the GPS module may generate the driving direction of the vehicle based on the coordinate position of the vehicle. For example, the GPS module may obtain the coordinate position of the vehicle at two adjacent moments, and determine the direction from the coordinate position at the previous moment to the coordinate position at the next moment as the driving direction of the vehicle.

[0050] The above-mentioned method for generating driving information can be applied to an on-board processing unit, or to a specific server or cloud platform. The following takes the method for generating driving information as an example of being applied to an on-board processing unit to further explain the method for generating driving information provided by the embodiment of the present disclosure.

[0051] It can be understood that, by generating the road condition information on the vehicle-mounted processing unit, the real-time performance of the road condition information obtained by the vehicle can be improved compared to generating the road condition information on a specific server or cloud platform.

[0052] The vehicle-mounted processing unit may be communicatively connected with the GPS module, and the vehicle-mounted processing unit may obtain the coordinate position and driving direction of the vehicle from the GPS module.

[0053] The first distance can be preset, and the first distance can be determined specifically according to the data processing capability of the vehicle-mounted processing unit. For example, the first distance can be 1000 meters or 2000 meters, etc. Accordingly, the first distance as the termination condition can mean that the length of the path planning is the first distance, that is, the length of the target path is the first distance.

[0054] Specifically, starting from the coordinate position, a first distance can be explored along the driving direction to obtain a target path, wherein the pathfinding means can be a conventional pathfinding means in the prior art.

[0055] In one embodiment of the present disclosure, the coordinate position may be the coordinate position of the vehicle on a specific road, and the driving direction may be the driving direction of the vehicle along the specific road. Specifically, after acquiring the position and direction of the vehicle from the GPS module, the road and driving direction of the vehicle in the preset map may be determined based on the position and direction of the vehicle. Specifically, the position and direction of the vehicle may be used as algorithm inputs, and a binding road algorithm in related technologies may be used to determine the road and driving direction of the vehicle. Then, path planning may be performed in the preset map along the road where the vehicle is located, with the coordinate position as the starting point, the first distance as the termination condition, and in the driving direction, so as to obtain the target path.

[0056] The above-mentioned generation of the road condition information corresponding to the target path may specifically include: constructing a field of view image of the target path based on the topological relationship of the road network in the preset map, and obtaining the constructed road network field of view frame. The road condition information may include the road network field of view frame, and the road network field of view frame may include the target path, and the driving environment information such as traffic lights and road signs in the target path. In addition, the real-time driving environment information of the target path may be obtained from an external service platform, and the real-time driving environment information may be added to the road network field of view frame, wherein the real-time driving environment information may include: real-time information such as the congestion situation of the target path or whether the target path is currently open.

[0057] In one embodiment of the present disclosure, since the position of the vehicle will change with time in the driving direction, Figure 2 As shown, road network view frames at different positions can be generated at a certain frequency to form continuous road network view frames.

[0058] After the road condition information corresponding to the target path is generated, the road condition information can be sent to the control unit of the vehicle so that the control unit can automatically control the vehicle based on the road condition information to achieve the control process of automatic driving or assisted driving. In addition, the road condition information corresponding to the target path can also be displayed on the vehicle terminal of the vehicle to further remind the driver of the road condition information ahead.

[0059] In the related art, when a vehicle is driving without a destination, it is usually unable to perform pathfinding based on existing pathfinding means, which results in the vehicle being unable to obtain information about the external driving environment, posing a safety hazard during autonomous driving or assisted driving. The driving without a destination may refer to a scenario where the user has set the location of the destination and has performed path planning, but before the vehicle deviates from the planned path and a new path is planned; or the driving without a destination may also refer to a scenario where the user has not set the location of the destination.

[0060] In this embodiment, by taking the coordinate position as the starting point and the first distance as the termination condition, and performing path planning in the preset map along the driving direction, a target path is obtained, and road condition information corresponding to the target path is generated. In this way, when the vehicle is driving without a destination, the path finding process can also be completed, avoiding the problem of path finding blind spots when driving without a destination, thereby improving the path finding effect.

[0061] Optionally, the step of taking the coordinate position as a starting point, taking the first distance as a termination condition, and performing path planning in a preset map along the driving direction to obtain a target path includes:

[0062] Taking the coordinate position as a starting point and the first distance as a termination condition, a path exploration is performed in a preset map along the driving direction to obtain n candidate paths;

[0063] When n is equal to 1, determining the candidate path as the target path;

[0064] When n is greater than 1, the target path is determined from the n candidate paths.

[0065] Specifically, due to the relatively complex structure of the existing road network, during the process of path exploration, multiple candidate paths may be explored. For example, see Figure 3 When encountering a fork in the road during the path exploration process, there are usually at least two optional paths.

[0066] Based on this, in the embodiment of the present disclosure, when performing path exploration, all the explored paths can be obtained first, and when only one path is explored, that is, when n is equal to 1, the explored candidate path can be directly determined as the target path. When more than two paths are explored, that is, when n is greater than 1, a relatively better path can be determined from the n candidate paths based on a preset means, and it can be determined as the target path.

[0067] In this implementation, by taking the coordinate position as the starting point and the first distance as the termination condition, path exploration is performed in the preset map along the driving direction to obtain n candidate paths, and a relatively better path is determined from the n candidate paths as the target path, so that the effect of path exploration can be improved.

[0068] Optionally, the preset map includes a first road section and n second road sections, the first road section intersects with the n second road sections at a target intersection, when n is greater than 1, the first road section and any second road section are spliced ​​to form a candidate path, the vehicle is located on the first road section, and determining the target path among the n candidate paths includes:

[0069] Calculating a weight value of each second road section among the n second road sections;

[0070] A candidate path formed by concatenating the first road segment and the target road segment is determined as the target path, wherein the target road segment is the second road segment with the largest weight value among the n second road segments.

[0071] Understandably, see Figure 4 , the target intersection Y is located in the driving direction of the vehicle, and since the vehicle can enter any second road section among the n second road sections when traveling to the target intersection Y, n candidate paths can be formed. In this way, the target path can be determined by only determining one target road section among the n second road sections.

[0072] Based on this, weight values ​​can be set for the n second road sections according to a preset weight calculation method, wherein the higher the weight value of the second road section, the greater the probability that the vehicle may travel to the second road section. Figure 5 When n is equal to 3, after calculating the weight values ​​of the three second road sections respectively, the weight values ​​of the three second road sections can be represented by w1, w2 and w3 respectively. Specifically, the weight values ​​of the n second road sections can be determined based on the historical traffic data of the vehicle.

[0073] In this implementation, the weight value of each second road segment is calculated respectively, and the candidate path formed by splicing the first road segment and the second road segment with the largest weight value is determined as the target path, so that the effect of path exploration can be further improved.

[0074] Optionally, the weight value is determined based on at least one of the following weight parameters:

[0075] A first weight parameter determined based on historical traffic data of the target intersection;

[0076] a second weight parameter determined based on the angle between the first road section and the second road section;

[0077] A third weight parameter is determined based on whether the first road section and the second road section belong to the same road.

[0078] The historical traffic data of the target intersection may include: historical traffic data of all vehicles entering the target intersection from the first road section within a period of time, so that the probability that a vehicle may enter the corresponding second road section can be determined according to the number of vehicles entering different second road sections. It is understandable that in the historical traffic data, the more vehicles enter a second road section from the first road section, the greater the parameter value of the first weight parameter corresponding to the second road section. For example, see Figure 6 When the vehicle travels from the first section a to the target intersection, the first weight parameter of the second section b can be determined to be 10%, the first weight parameter of the second section c can be determined to be 40%, and the first weight parameter of the second section d can be determined to be 50% based on the historical traffic data.

[0079] The angle between the first road section and any second road section can be within the interval: (0°, 180°]. Since the larger the angle between the second road section and the first road section, the smaller the turning angle of the vehicle at today's target intersection, when the angle between the second road section and the first road section is 180°, the vehicle does not need to turn. Therefore, it can be set that the larger the angle between the second road section and the first road section, the larger the second weight value of the second road section. For example, see Figure 7 , the angle between the second section b and the extension line of the first section a is r1, that is, the angle between the second section b and the first section a is (180°-r1). The angle between the second section c and the extension line of the first section a is r2, that is, the angle between the second section c and the first section a is (180°-r2).

[0080] In addition, since different road names are usually set for different roads in the road network of the preset map, according to a large amount of data statistics, the possibility of a vehicle changing roads when passing a road intersection is relatively small. Therefore, the third weight parameter of the second road section among the n second road sections that has the same road name as the first road section can be set to 1; at the same time, the third weight parameter of the second road section among the n second road sections that has a different road name from the first road section can be set to 0.

[0081] In one embodiment of the present disclosure, the weight value may be calculated based on the following formula:

[0082] w=at+br+cm

[0083] Wherein, t is the first weight parameter, r is the second weight value, m is the third weight parameter, a, b, c are coefficients, and the values ​​of a, b, c can be determined based on historical experience. When the historical traffic data cannot be obtained, t can be set to 0.

[0084] In this embodiment, when determining the weight of the second section, by considering factors such as historical traffic data, the angle between the first section and the second section, and whether the first section and the second section belong to the same road, the rationality of the determined weight value can be further improved, thereby further improving the path exploration effect.

[0085] Optionally, the road condition information also includes second driving environment information within a target range, and the target range includes: a range formed by position points located on the side of the target path and whose distance from the target path is less than a second distance, and the second distance is less than the first distance.

[0086] The target range may refer to the range formed by the areas on both sides of the road of the target path, and the second distance may be preset, for example, it may be a relatively small distance such as 50 meters or 100 meters. It is understood that when the target path includes an intersection, the target range includes a partial range of each branch road branched from the intersection, so that the road condition information may include partial road condition information of each branch road branched from the intersection. For example, see Figure 8 The area enclosed by the dotted frame in the figure is the target area.

[0087] Specifically, since the vehicle does not necessarily follow the pre-planned target path when passing through the intersection, in this case, if only the road condition information of the target path is obtained, when the vehicle passes through the intersection and does not follow the target path, it will cause the problem of blind spot in the pathfinding.

[0088] Based on this, in the embodiment of the present disclosure, when generating the road condition information corresponding to the target path, the second driving environment information within the target range can be obtained, and the road condition information can be jointly generated based on the first driving environment information and the second driving environment information. That is, the above-mentioned road network vision frame includes the target path and the driving environment information within the target range. In this way, the problem of blind spots in pathfinding can be avoided when the vehicle passes through an intersection and does not follow the target path.

[0089] In this implementation, by making the road condition information also include the second driving environment information within the target range, the problem of pathfinding blind spots when the vehicle passes through an intersection and does not travel along the target path can be avoided, thereby further improving the pathfinding effect.

[0090] Optionally, the generating the road condition information corresponding to the target path includes:

[0091] Reading the first driving environment information in a target database;

[0092] The road condition information is generated based on the first driving environment information.

[0093] The target database may be a storage module of the vehicle-mounted terminal, and the preset map may be stored in the target database. In addition, the real-time traffic information on the target path acquired by the vehicle-mounted terminal may also be stored in the target database.

[0094] In this implementation, after the target path is determined, the target field of view frame is constructed by reading the driving environment information corresponding to the target path from the target database, thereby generating the road condition information corresponding to the target path.

[0095] Optionally, the target path includes at least two sub-segments, the at least two sub-segments are sequentially connected to form the target path, the road condition information includes at least two sub-road condition information, the at least two sub-segments correspond one-to-one to the at least two sub-road condition information, and the reading of the first driving environment information in the target database includes:

[0096] Starting from the starting point, sequentially reading the driving environment information in the at least two sub-segments in the target database;

[0097] The generating the road condition information based on the driving environment information in the target path includes:

[0098] Based on the driving environment information in the at least two sub-road sections, the at least two sub-road condition information are generated in sequence.

[0099] Specifically, since the data processing capability of the on-board processing unit is usually low, if the road condition information corresponding to the target path is generated at one time, the generation process of the road condition information may cause the occupancy rate of the central processing unit (CPU) of the on-board processing unit to be too high, which may affect the working process of the on-board processing unit.

[0100] Based on this, in the embodiment of the present disclosure, the road condition information of the target path can be constructed in segments to reduce the CPU occupancy rate of the vehicle processing unit during the route information generation process.

[0101] For example, see Fig.10 , when the length of the first distance is 1000m, that is, when the length of the target path is 1000m, the target path can be equally divided into 10 sub-segments. And the 10 sub-road condition information corresponding to the 10 sub-segments can be generated in sequence according to the distance from the coordinate position, from near to far. That is, the driving environment information within 100m from the vehicle can be obtained first, and the first frame of the road network field of view frame can be generated based on the obtained driving environment information. Then, the driving environment information within the range of 100m to 200m is obtained to generate the second frame of the road network field of view frame. Fig.10 As shown, at time t0, sub-road condition information within a range of 100m is generated, at time t1, sub-road condition information within a range of 100m to 200m is generated, ..., at time t9, sub-road condition information within a range of 900m to 1000m is generated, thereby completing the generation process of a frame of road network vision frame. It can be understood that since the time occupied by this process is relatively short, it can be equivalent to consider that the vehicle is in a stationary state.

[0102] In this implementation, the road condition information is generated by segmenting the target path. Thus, in the process of generating the road condition information, the CPU occupancy rate of the vehicle-mounted processing unit can be reduced, thereby ensuring that the vehicle-mounted processing unit can operate normally.

[0103] Optionally, reading the first driving environment information in a target database includes:

[0104] Performing K data readings on the target database to obtain the first driving environment information, wherein the first driving environment information includes: data read by the K data readings;

[0105] The K is greater than 1, and in the K data readings, any two adjacent data readings are separated by a preset time length.

[0106] Specifically, since it is usually necessary to read multiple types of data from the target database during the process of constructing the road network vision frame, it is often necessary to read data from the target database dozens or hundreds of times to generate one road network vision frame. During the data reading process, a large amount of CPU resources are required, which increases the CPU peak value.

[0107] Based on this, in the embodiment of the present disclosure, the peak value of the CPU can be reduced by reducing the data reading speed of the target database. For example, when generating a frame of the road network vision frame requires reading the target database 100 times, it is possible to sleep for 10ms after each reading of the target database, that is, the preset duration is 10ms, so that the reading process takes 1s to complete, thereby effectively reducing the data reading speed, and further reducing the peak value of the CPU.

[0108] In this implementation, by setting a sleep time between two data readings, the data reading speed of the on-board processing unit for the target database can be reduced, thereby reducing the peak value of the CPU.

[0109] Optionally, after generating the road condition information corresponding to the target path, the method further includes:

[0110] generating first target traffic condition information based on the traffic condition information, wherein the first target traffic condition information includes the traffic condition information in a read-only format;

[0111] The first target road condition information is transmitted to a control unit of the vehicle, so that the control unit controls the vehicle based on the first target road condition information.

[0112] Among them, the first target road condition information can be a read-only copy of the road condition information. Since it is impossible to read and write data to the same data at the same time. After generating the road condition information, on the one hand, the control unit needs to read the road condition information, and on the other hand, the on-board processing unit needs to further generate new road condition information based on the road condition information. Therefore, by passing the read-only copy of the road condition information to the control unit, the data reading process and the data writing process can be realized in parallel, and a lock-free structure can be realized, so that the CPU occupancy rate can be further reduced.

[0113] In this implementation, by generating first target road condition information based on the road condition information and transmitting the first target road condition information to the control unit of the vehicle, multi-thread parallelism can be achieved, which is beneficial to further reduce the CPU occupancy rate.

[0114] Optionally, after generating the road condition information corresponding to the target path, the method further includes:

[0115] storing the road condition information in a storage unit of the vehicle;

[0116] The second target road condition information in the storage unit is cleared, wherein the second target road condition information includes: road condition information corresponding to a road section that the vehicle has passed and that is more than a third distance away from the location of the vehicle.

[0117] Specifically, due to the possibility of inaccurate GPS positioning during vehicle driving, for example, see Fig. 9 When a vehicle passes through an intersection and moves to section c, since the distance between section b and section c is relatively close, at this time, GPS may locate the vehicle's position as section b. In this case, the on-board processing unit will construct the road condition information based on section b. At this time, if the on-board processing unit directly deletes the road network view frame of the traversed section a, when the GPS positioning signal is updated to section c, it is necessary to reconstruct the road network view frame of section a, and construct the road network view frame of section c based on the constructed road network view frame of section a, which leads to the problem of repeated construction.

[0118] In this implementation, the generated road condition information is cached in the storage unit of the vehicle, and the road condition information corresponding to the road section that the vehicle has passed and is more than a third distance away from the vehicle's position is cleared, that is, the road condition information corresponding to the road section that the vehicle has passed and is no more than the third distance away from the vehicle's position is delayed. In this way, when the GPS positioning is inaccurate, the road condition information of the passed road section can be directly obtained in the storage unit, thereby avoiding the problem of repeatedly constructing the road network vision frame, which is conducive to further reducing the CPU occupancy rate.

[0119] In addition, since the higher the frequency of building the road network view frame, the more timely the road condition information is refreshed, and the higher the system resource usage is, the higher the system resource usage can be. Therefore, the refresh frequency of the road network view frame can be reduced to reduce the system resource usage. However, the construction frequency of the road network view frame cannot be lower than the frequency of the incoming GPS coordinates, otherwise there will be a significant delay in refreshing the road information.

[0120] See also Fig.11 , is a flow chart of an automatic driving method provided by an embodiment of the present disclosure, the automatic driving method comprising the following steps:

[0121] Step S1101, obtaining the coordinate position and driving direction of the vehicle;

[0122] Step S1102, taking the coordinate position as a starting point and the first distance as a termination condition, performing path planning in a preset map along the driving direction to obtain a target path;

[0123] Step S1103: generating road condition information corresponding to the target path, wherein the road condition information includes first driving environment information in the target path;

[0124] Step S1104: performing automatic driving based on the road condition information.

[0125] It can be understood that this implementation is an automatic driving method further provided on the basis of the above-mentioned method for generating driving information. Therefore, the automatic driving method can implement each process of the above-mentioned method for generating driving information and has the same beneficial effects. To avoid repetition, it will not be described here.

[0126] In this implementation, a target path is obtained by taking the coordinate position as a starting point and the first distance as a termination condition, and performing path planning in a preset map along the driving direction, and road condition information corresponding to the target path is generated, and then automatic driving is performed based on the road condition information. In this way, when the vehicle is driving without a destination, the path finding process can also be completed, avoiding the problem of path finding blind spots when driving without a destination, thereby improving the path finding effect.

[0127] See also Fig.12 , is a driving information generating device 1200 provided in an embodiment of the present disclosure, the driving information generating device 1200 comprising:

[0128] The first acquisition module 1201 is used to acquire the coordinate position and driving direction of the vehicle;

[0129] A first path planning module 1202 is used to perform path planning in a preset map along the travel direction with the coordinate position as the starting point and the first distance as the termination condition to obtain a target path;

[0130] The first generating module 1203 is used to generate road condition information corresponding to the target path, where the road condition information includes first driving environment information in the target path.

[0131] Optionally, the first path planning module 1202 includes:

[0132] A path exploration submodule is used to explore a path in a preset map along the driving direction with the coordinate position as a starting point and the first distance as a termination condition, so as to obtain n candidate paths;

[0133] a determination submodule, configured to determine the candidate path as the target path when n is equal to 1;

[0134] The determination submodule is further configured to determine the target path from among the n candidate paths when n is greater than 1.

[0135] Optionally, the preset map includes a first road section and n second road sections, the first road section intersects with the n second road sections at a target intersection, when n is greater than 1, the first road section and any second road section are spliced ​​to form a candidate path, the vehicle is located on the first road section, and the determination submodule includes:

[0136] A calculation unit, used for calculating a weight value of each second road section among the n second road sections;

[0137] A determination unit is used to determine a candidate path formed by splicing the first segment and a target segment as the target path, wherein the target segment is the second segment with the largest weight value among the n second segments.

[0138] Optionally, the weight value is determined based on at least one of the following weight parameters:

[0139] A first weight parameter determined based on historical traffic data of the target intersection;

[0140] a second weight parameter determined based on the angle between the first road section and the second road section;

[0141] A third weight parameter is determined based on whether the first road section and the second road section belong to the same road.

[0142] Optionally, the road condition information also includes second driving environment information within a target range, and the target range includes: a range formed by position points located on the side of the target path and whose distance from the target path is less than a second distance, and the second distance is less than the first distance.

[0143] Optionally, the first generating module 1203 includes:

[0144] A reading submodule, used for reading the first driving environment information in a target database;

[0145] A generating submodule is used to generate the road condition information based on the first driving environment information.

[0146] Optionally, the target path includes at least two sub-segments, the at least two sub-segments are sequentially connected to form the target path, the road condition information includes at least two sub-road condition information, the at least two sub-segments correspond to the at least two sub-road condition information in a one-to-one manner, and the reading submodule is specifically configured to start from the starting point and sequentially read the driving environment information in the at least two sub-segments in the target database;

[0147] The generating submodule is specifically configured to sequentially generate the at least two sub-road condition information based on the driving environment information in the at least two sub-road sections.

[0148] Optionally, the reading submodule is specifically used to perform K data readings on the target database to obtain the first driving environment information, wherein the first driving environment information includes: data read by the K data readings, K is greater than 1, and in the K data readings, the interval between any two adjacent data readings is a preset time length.

[0149] Optionally, the first generating module 1203 is further configured to generate first target road condition information based on the road condition information, wherein the first target road condition information includes the road condition information in a read-only format;

[0150] The device also includes:

[0151] A transmission module is used to transmit the first target road condition information to a control unit of the vehicle, so that the control unit controls the vehicle based on the first target road condition information.

[0152] Optionally, the device further comprises:

[0153] A storage module, used for storing the road condition information in a storage unit of the vehicle;

[0154] A cleaning module is used to clean up the second target road condition information in the storage unit, wherein the second target road condition information includes: the road condition information corresponding to the road section that the vehicle has passed and is more than a third distance away from the vehicle's location.

[0155] It should be noted that the driving information generating device 1200 provided in this embodiment can implement all the technical solutions of the above-mentioned driving information generating method embodiment, and therefore can at least achieve all the above-mentioned technical effects, which will not be repeated here.

[0156] See also Fig.13 , is a schematic diagram of the structure of an automatic driving device 1300 provided in an embodiment of the present disclosure, wherein the automatic driving device 1300 includes:

[0157] The second acquisition module 1301 is used to acquire the coordinate position and driving direction of the vehicle;

[0158] A second path planning module 1302 is used to perform path planning in a preset map along the travel direction with the coordinate position as the starting point and the first distance as the termination condition to obtain a target path;

[0159] A second generating module 1303 is used to generate road condition information corresponding to the target path, wherein the road condition information includes first driving environment information in the target path;

[0160] The control module 1304 is used to perform automatic driving based on the road condition information.

[0161] It should be noted that the automatic driving device 1300 provided in this embodiment can implement all the technical solutions of the above-mentioned automatic driving method embodiment, and therefore can at least achieve all the above-mentioned technical effects, which will not be repeated here.

[0162] In the technical solution disclosed herein, the acquisition, storage and application of user personal information involved are in compliance with the provisions of relevant laws and regulations and do not violate public order and good morals.

[0163] According to an embodiment of the present disclosure, the present disclosure also provides an electronic device, a readable storage medium and a computer program product.

[0164] Fig.14 A schematic block diagram of an example electronic device 1400 that can be used to implement an embodiment of the present disclosure is shown. The electronic device is intended to represent various forms of digital computers, such as laptop computers, desktop computers, workstations, personal digital assistants, servers, blade servers, mainframe computers, and other suitable computers. The electronic device can also represent various forms of mobile devices, such as personal digital processing, cellular phones, smart phones, wearable devices, and other similar computing devices. The components shown herein, their connections and relationships, and their functions are merely examples and are not intended to limit the implementation of the present disclosure described and / or required herein.

[0165] like Fig.14 As shown, the electronic device 1400 includes a computing unit 1401, which can perform various appropriate actions and processes according to a computer program stored in a read-only memory (ROM) 1402 or a computer program loaded from a storage unit 1408 into a random access memory (RAM) 1403. In the RAM 1403, various programs and data required for the operation of the device 1400 can also be stored. The computing unit 1401, the ROM 1402, and the RAM 1403 are connected to each other via a bus 1404. An input / output (I / O) interface 1405 is also connected to the bus 1404.

[0166] Multiple components in the electronic device 1400 are connected to the I / O interface 1405, including: an input unit 1406, such as a keyboard, a mouse, etc.; an output unit 1407, such as various types of displays, speakers, etc.; a storage unit 1408, such as a disk, an optical disk, etc.; and a communication unit 1409, such as a network card, a modem, a wireless communication transceiver, etc. The communication unit 1409 allows the device 1400 to exchange information / data with other devices through a computer network such as the Internet and / or various telecommunication networks.

[0167] The computing unit 1401 may be a variety of general and / or special processing components with processing and computing capabilities. Some examples of the computing unit 1401 include, but are not limited to, a central processing unit (CPU), a graphics processing unit (GPU), various dedicated artificial intelligence (AI) computing chips, various computing units running machine learning model algorithms, digital signal processors (DSPs), and any appropriate processors, controllers, microcontrollers, etc. The computing unit 1401 performs the various methods and processes described above, such as a method for generating driving information, or an automatic driving method. For example, in some embodiments, the method for generating driving information, or the automatic driving method may be implemented as a computer software program, which is tangibly contained in a machine-readable medium, such as a storage unit 1408. In some embodiments, part or all of the computer program may be loaded and / or installed on the device 1400 via the ROM 1402 and / or the communication unit 1409. When the computer program is loaded into the RAM 1403 and executed by the computing unit 1401, one or more steps of the method for generating driving information, or the automatic driving method described above are executed. Alternatively, in other embodiments, the computing unit 1401 may be configured to execute the driving information generation method, or configured to execute the automatic driving method, in any other appropriate manner (for example, by means of firmware).

[0168] Various implementations of the systems and techniques described above herein can be implemented in digital electronic circuit systems, integrated circuit systems, field programmable gate arrays (FPGAs), application specific integrated circuits (ASICs), application specific standard products (ASSPs), systems on chips (SOCs), load programmable logic devices (CPLDs), computer hardware, firmware, software, and / or combinations thereof. These various implementations can include: being implemented in one or more computer programs that can be executed and / or interpreted on a programmable system including at least one programmable processor, which can be a special purpose or general purpose programmable processor that can receive data and instructions from a storage system, at least one input device, and at least one output device, and transmit data and instructions to the storage system, the at least one input device, and the at least one output device.

[0169] The program code for implementing the method of the present disclosure may be written in any combination of one or more programming languages. These program codes may be provided to a processor or controller of a general-purpose computer, a special-purpose computer, or other programmable data processing device, so that the program code, when executed by the processor or controller, enables the functions / operations specified in the flow chart and / or block diagram to be implemented. The program code may be executed entirely on the machine, partially on the machine, partially on the machine and partially on a remote machine as a stand-alone software package, or entirely on a remote machine or server.

[0170] In the context of the present disclosure, a machine-readable medium may be a tangible medium that may contain or store a program for use by or in conjunction with an instruction execution system, device, or equipment. A machine-readable medium may be a machine-readable signal medium or a machine-readable storage medium. A machine-readable medium may include, but is not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, device, or equipment, or any suitable combination of the foregoing. A more specific example of a machine-readable storage medium may include an electrical connection based on one or more lines, a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing.

[0171] To provide interaction with a user, the systems and techniques described herein can be implemented on a computer having: a display device (e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor) for displaying information to the user; and a keyboard and pointing device (e.g., a mouse or trackball) through which the user can provide input to the computer. Other types of devices can also be used to provide interaction with the user; for example, the feedback provided to the user can be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user can be received in any form (including acoustic input, voice input, or tactile input).

[0172] The systems and techniques described herein may be implemented in a computing system that includes back-end components (e.g., as a data server), or a computing system that includes middleware components (e.g., an application server), or a computing system that includes front-end components (e.g., a user computer with a graphical user interface or a web browser through which a user can interact with implementations of the systems and techniques described herein), or a computing system that includes any combination of such back-end components, middleware components, or front-end components. The components of the system may be interconnected by any form or medium of digital data communication (e.g., a communication network). Examples of communication networks include: a local area network (LAN), a wide area network (WAN), and the Internet.

[0173] A computer system may include a client and a server. The client and the server are generally remote from each other and usually interact through a communication network. The relationship of client and server is generated by computer programs running on respective computers and having a client-server relationship with each other. The server may be a cloud server, a server of a distributed system, or a server combined with a blockchain.

[0174] It should be understood that the various forms of processes shown above can be used to reorder, add or delete steps. For example, the steps recorded in this disclosure can be executed in parallel, sequentially or in different orders, as long as the desired results of the technical solutions disclosed in this disclosure can be achieved, and this document does not limit this.

[0175] The above specific implementations do not constitute a limitation on the protection scope of the present disclosure. It should be understood by those skilled in the art that various modifications, combinations, sub-combinations and substitutions can be made according to design requirements and other factors. Any modification, equivalent substitution and improvement made within the spirit and principle of the present disclosure shall be included in the protection scope of the present disclosure.

Claims

1. A method for generating driving information, applied to a scenario where a vehicle drives without a destination, comprising: Get the coordinate position and driving direction of the vehicle; Taking the coordinate position as a starting point and the first distance as a termination condition, a path is planned in a preset map along the driving direction to obtain a target path; generating road condition information corresponding to the target path, the road condition information including first driving environment information in the target path; The method of taking the coordinate position as the starting point, taking the first distance as the termination condition, and performing path planning in a preset map along the driving direction to obtain a target path includes: Taking the coordinate position as a starting point and the first distance as a termination condition, a path exploration is performed in a preset map along the driving direction to obtain n candidate paths; When n is equal to 1, determining the candidate path as the target path; When n is greater than 1, a weight value of each second road segment among the n second road segments is calculated; and a candidate path formed by splicing the first road segment and the target road segment is determined as the target path; The preset map includes the first road section and the n second road sections, the first road section and the n second road sections intersect at a target intersection, and when n is greater than 1, the first road section and any second road section are spliced ​​to form a candidate path, and the target road section is the second road section with the largest weight value among the n second road sections; The weight value is determined based on at least one of the following weight parameters: a first weight parameter determined based on historical traffic data of the target intersection, wherein the first weight parameter of the second road section is proportional to the number of vehicles passing through the second road section within a time period; a second weight parameter determined based on the angle between the first section and the second section, wherein the larger the angle between the second section and the first section, the larger the second weight value of the second section; The third weight parameter is determined based on whether the first road segment and the second road segment belong to the same road, wherein the third weight parameter of the second road segment having the same road name as the first road segment is greater than the third weight parameter of the second road segment having a different road name from the first road segment.

2. The method according to claim 1, wherein: The road condition information also includes second driving environment information within a target range, and the target range includes: a range formed by position points located on the side of the target path and whose distance from the target path is less than a second distance, and the second distance is less than the first distance.

3. The method according to claim 1, wherein: The generating of the road condition information corresponding to the target path includes: Reading the first driving environment information in a target database; The road condition information is generated based on the first driving environment information.

4. The method according to claim 3, wherein: The target path includes at least two sub-segments, the at least two sub-segments are sequentially connected to form the target path, the road condition information includes at least two sub-road condition information, the at least two sub-segments correspond to the at least two sub-road condition information in a one-to-one manner, and the reading of the first driving environment information in the target database includes: Starting from the starting point, sequentially reading the driving environment information in the at least two sub-segments in the target database; The generating the road condition information based on the driving environment information in the target path includes: Based on the driving environment information in the at least two sub-road sections, the at least two sub-road condition information are generated in sequence.

5. The method according to claim 3, wherein: The reading the first driving environment information in the target database includes: The target database is read K times to obtain the first driving environment information, wherein the first driving environment information includes: data read by the K times of data reading, K is greater than 1, and in the K times of data reading, an interval of a preset time length is between any two adjacent data readings.

6. The method according to claim 1, wherein: After generating the road condition information corresponding to the target path, the method further includes: generating first target traffic condition information based on the traffic condition information, wherein the first target traffic condition information includes the traffic condition information in a read-only format; The first target road condition information is transmitted to a control unit of the vehicle, so that the control unit controls the vehicle based on the first target road condition information.

7. The method according to claim 1, wherein: After generating the road condition information corresponding to the target path, the method further includes: storing the road condition information in a storage unit of the vehicle; The second target road condition information in the storage unit is cleared, wherein the second target road condition information includes: road condition information corresponding to a road section that the vehicle has passed and that is more than a third distance away from the location of the vehicle.

8. An automatic driving method, applied to a scenario where a vehicle is driving without a destination, comprising: Get the coordinate position and driving direction of the vehicle; Taking the coordinate position as a starting point and the first distance as a termination condition, a path is planned in a preset map along the driving direction to obtain a target path; generating road condition information corresponding to the target path, the road condition information including first driving environment information in the target path; Performing autonomous driving based on the road condition information; The method of taking the coordinate position as the starting point, taking the first distance as the termination condition, and performing path planning in a preset map along the driving direction to obtain a target path includes: Taking the coordinate position as a starting point and the first distance as a termination condition, a path exploration is performed in a preset map along the driving direction to obtain n candidate paths; When n is equal to 1, determining the candidate path as the target path; When n is greater than 1, a weight value of each second road segment among the n second road segments is calculated; and a candidate path formed by splicing the first road segment and the target road segment is determined as the target path; The preset map includes the first road section and the n second road sections, the first road section and the n second road sections intersect at a target intersection, and when n is greater than 1, the first road section and any second road section are spliced ​​to form a candidate path, and the target road section is the second road section with the largest weight value among the n second road sections; The weight value is determined based on at least one of the following weight parameters: a first weight parameter determined based on historical traffic data of the target intersection, wherein the first weight parameter of the second road section is proportional to the number of vehicles passing through the second road section within a time period; a second weight parameter determined based on the angle between the first section and the second section, wherein the larger the angle between the second section and the first section, the larger the second weight value of the second section; The third weight parameter is determined based on whether the first road segment and the second road segment belong to the same road, wherein the third weight parameter of the second road segment having the same road name as the first road segment is greater than the third weight parameter of the second road segment having a different road name from the first road segment.

9. A driving information generating device, applied to a scenario where a vehicle is driving without a destination, the device comprising: A first acquisition module is used to acquire the coordinate position and driving direction of the vehicle; A first path planning module is used to perform path planning in a preset map along the travel direction with the coordinate position as a starting point and the first distance as a termination condition to obtain a target path; A first generating module, configured to generate road condition information corresponding to the target path, wherein the road condition information includes first driving environment information in the target path; The first path planning module includes: A path exploration submodule is used to explore a path in a preset map along the driving direction with the coordinate position as a starting point and the first distance as a termination condition, so as to obtain n candidate paths; a determination submodule, configured to determine the candidate path as the target path when n is equal to 1; The determination submodule is further used to calculate the weight value of each second road segment among the n second road segments; and determine the candidate path formed by splicing the first road segment and the target road segment as the target path; The preset map includes the first road section and the n second road sections, the first road section and the n second road sections intersect at a target intersection, and when n is greater than 1, the first road section and any second road section are spliced ​​to form a candidate path, and the target road section is the second road section with the largest weight value among the n second road sections; The weight value is determined based on at least one of the following weight parameters: a first weight parameter determined based on historical traffic data of the target intersection, wherein the first weight parameter of the second road section is proportional to the number of vehicles passing through the second road section within a time period; a second weight parameter determined based on the angle between the first section and the second section, wherein the larger the angle between the second section and the first section, the larger the second weight value of the second section; The third weight parameter is determined based on whether the first road segment and the second road segment belong to the same road, wherein the third weight parameter of the second road segment having the same road name as the first road segment is greater than the third weight parameter of the second road segment having a different road name from the first road segment.

10. The device according to claim 9, wherein: The road condition information also includes second driving environment information within a target range, and the target range includes: a range formed by position points located on the side of the target path and whose distance from the target path is less than a second distance, and the second distance is less than the first distance.

11. The device according to any one of claims 9 to 10, wherein: The first generating module comprises: A reading submodule, used for reading the first driving environment information in a target database; A generating submodule is used to generate the road condition information based on the first driving environment information.

12. The device according to claim 11, wherein The target path includes at least two sub-segments, the at least two sub-segments are sequentially connected to form the target path, the road condition information includes at least two sub-road condition information, the at least two sub-segments correspond to the at least two sub-road condition information in a one-to-one manner, and the reading submodule is specifically used to start from the starting point and sequentially read the driving environment information in the at least two sub-segments in the target database; The generating submodule is specifically configured to sequentially generate the at least two sub-road condition information based on the driving environment information in the at least two sub-road sections.

13. The device according to claim 11, wherein: The reading submodule is specifically used to perform K data readings on the target database to obtain the first driving environment information, wherein the first driving environment information includes: data read by the K data readings, K is greater than 1, and in the K data readings, a preset time interval is between any two adjacent data readings.

14. The device according to claim 9, wherein: The first generating module is further used to generate first target road condition information based on the road condition information, wherein the first target road condition information includes the road condition information in a read-only format; The device also includes: A transmission module is used to transmit the first target road condition information to a control unit of the vehicle, so that the control unit controls the vehicle based on the first target road condition information.

15. The device according to claim 9, wherein: The device also includes: A storage module, used for storing the road condition information in a storage unit of the vehicle; A cleaning module is used to clean up the second target road condition information in the storage unit, wherein the second target road condition information includes: the road condition information corresponding to the road section that the vehicle has passed and is more than a third distance away from the vehicle's location.

16. An automatic driving device, applied to a scenario where a vehicle is driving without a destination, the device comprising: The second acquisition module is used to obtain the coordinate position and driving direction of the vehicle; A second path planning module is used to perform path planning in a preset map along the travel direction with the coordinate position as a starting point and the first distance as a termination condition to obtain a target path; A second generating module, configured to generate road condition information corresponding to the target path, wherein the road condition information includes first driving environment information in the target path; A control module, configured to perform automatic driving based on the road condition information; The method of taking the coordinate position as the starting point, taking the first distance as the termination condition, and performing path planning in a preset map along the driving direction to obtain a target path includes: Taking the coordinate position as a starting point and the first distance as a termination condition, a path exploration is performed in a preset map along the driving direction to obtain n candidate paths; When n is equal to 1, determining the candidate path as the target path; When n is greater than 1, a weight value of each second road segment among the n second road segments is calculated; and a candidate path formed by splicing the first road segment and the target road segment is determined as the target path; The preset map includes the first road section and the n second road sections, the first road section and the n second road sections intersect at a target intersection, and when n is greater than 1, the first road section and any second road section are spliced ​​to form a candidate path, and the target road section is the second road section with the largest weight value among the n second road sections; The weight value is determined based on at least one of the following weight parameters: a first weight parameter determined based on historical traffic data of the target intersection, wherein the first weight parameter of the second road section is proportional to the number of vehicles passing through the second road section within a time period; a second weight parameter determined based on the angle between the first section and the second section, wherein the larger the angle between the second section and the first section, the larger the second weight value of the second section; The third weight parameter is determined based on whether the first road segment and the second road segment belong to the same road, wherein the third weight parameter of the second road segment having the same road name as the first road segment is greater than the third weight parameter of the second road segment having a different road name from the first road segment.

17. An electronic device comprising: at least one processor; as well as a memory communicatively connected to the at least one processor; wherein, The memory stores instructions that can be executed by the at least one processor, and the instructions are executed by the at least one processor so that the at least one processor can execute the method for generating driving information described in any one of claims 1-7, or execute the automatic driving method described in claim 8.

18. A non-transitory computer-readable storage medium storing computer instructions, wherein: The computer instructions are used to enable the computer to execute the method for generating driving information described in any one of claims 1 to 7, or to execute the automatic driving method described in claim 8.

19. A computer program product, comprising a computer program, which, when executed by a processor, implements the method for generating driving information described in any one of claims 1 to 7, or implements the automatic driving method described in claim 8.

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