Driving control method and device for unmanned vehicle on bumpy road section and unmanned vehicle

Abstract

The invention provides a driving control method and device for an unmanned vehicle on a bumpy road section and the unmanned vehicle. The driving control method comprises the steps that the unmanned vehicle receives road bumpy information sent by other vehicles through Internet of Vehicles V2X communication; the road bumping information comprises position information of bumping points; according to the position information of the bumping point and a planned real-time track of the unmanned vehicle, determining a first bumping road section on a driving track in front of the unmanned vehicle; and according to the first bumpy road section and the road information of the first bumpy road section in the first preset range, determining a driving strategy of the unmanned vehicle passing through the first bumpy road section. According to the unmanned vehicle disclosed by the invention, the road bumping information is obtained through the V2X communication of the Internet of Vehicles and is combined with the planned real-time track of the unmanned vehicle, so that the bumpy road section on the driving track is accurately identified, and the driving strategy of passing through the bumpy road section is decided and planned in advance. And the timeliness and the stability are high, so that the safety risk when the unmanned vehicle runs on a bumpy road section is effectively reduced.

Description

Technical Field

[0001] The present disclosure relates to the field of unmanned driving technology, and in particular to a driving control method and device for an unmanned vehicle on a bumpy road section, and an unmanned vehicle. Background Art

[0002] In mine autonomous driving operations, the operating roads are usually dirt roads. Although they are flattened, the roads are often bumpy. Unmanned vehicles are prone to bumps when driving on these roads with poor road conditions. When the speed is too fast, it may cause significant damage to the balance shaft and body of the unmanned vehicle, and even cause safety risks. In the traditional way, the unmanned vehicle uses the pitch angle, vehicle posture information or road information during driving to determine whether it is driving on a bumpy road section, and brakes and slows down accordingly. However, this method has a lag. When the unmanned vehicle is driving on a bumpy road section, the speed may be already high, and it is too late to brake and slow down.

[0003] Therefore, it is urgent to design and implement a driving control method for unmanned vehicles on bumpy roads with strong timeliness and high stability to ensure the safe operation of unmanned vehicles on bumpy roads. Summary of the invention

[0004] The disclosed embodiments provide a driving control method and device for an unmanned vehicle on a bumpy road section, and an unmanned vehicle, so as to solve the problem of safety risks faced by existing unmanned vehicles when driving on bumpy roads.

[0005] Based on the above problems, in a first aspect, an embodiment of the present disclosure provides a driving control method for an unmanned vehicle on a bumpy road section, comprising:

[0006] The unmanned vehicle receives road bump information sent by other vehicles through vehicle-to-vehicle network V2X communication; the road bump information includes location information of bump points;

[0007] Determine a first bumpy road section on the front driving track of the unmanned vehicle according to the position information of the bumpy point and the real-time track planned by the unmanned vehicle;

[0008] A driving strategy for the unmanned vehicle to pass through the first bumpy road section is determined based on the first bumpy road section and road information within a first preset range of the first bumpy road section.

[0009] In combination with the first aspect, in a possible implementation manner, the road bump information further includes: first vehicle parameter information and / or first bump feature information of the other vehicle;

[0010] Determining the first bumpy road section on the front driving track of the unmanned vehicle according to the position information of the bumpy point and the real-time track planned by the unmanned vehicle includes:

[0011] When it is determined that the unmanned vehicle meets the specified conditions according to the first vehicle parameter information and / or the first bump feature information, determining a first bumpy road section on the driving trajectory ahead of the unmanned vehicle according to the position information of the bump point and the real-time trajectory planned by the unmanned vehicle; and / or,

[0012] The method also includes: when it is determined that the unmanned vehicle does not meet specified conditions based on the first vehicle parameter information and / or the first bump feature information, driving planning for the unmanned vehicle is performed according to the specified information, and the specified information does not include the location information of the bump point.

[0013] In combination with the first aspect, in a possible implementation manner, whether the unmanned vehicle meets a specified condition is determined by any one of the following methods:

[0014] determining whether the first bump feature information satisfies a bump feature threshold condition, and if so, determining that the unmanned vehicle satisfies a specified condition;

[0015] Predicting second bump characteristic information of the unmanned vehicle corresponding to the second vehicle parameter information under the same road condition according to the first vehicle parameter information and the first bump characteristic information, and determining whether the unmanned vehicle meets a specified condition according to whether the second bump characteristic information meets a bump characteristic threshold condition;

[0016] According to the first vehicle parameter information, it is determined whether there is a vehicle that meets the specified vehicle parameter conditions and is bumpy. If so, it is determined that the unmanned vehicle meets the specified conditions.

[0017] In combination with the first aspect, in a possible implementation manner, the road bump information further includes: bump level information of the bump point;

[0018] The step of determining a first bumpy road section on the front driving track of the unmanned vehicle according to the position information of the bumpy point and the real-time track planned by the unmanned vehicle comprises:

[0019] Determine a first bump point located within a second preset range of the track point according to the position information of the bump point and the position information of the track point on the front driving track of the unmanned vehicle;

[0020] Mapping the first bump point to the track point to determine a first track point mapped with the first bump point;

[0021] clustering the first trajectory points with the same bump level according to the bump level information of the first bump point corresponding to the first trajectory point and the position information of the first trajectory point, and determining the first bump interval according to the clustering result;

[0022] For the second bumpy intervals with the same bumpy level in the first bumpy interval, the second bumpy intervals that meet the preset position relationship are merged to obtain a first bumpy road section; wherein the first bumpy road section includes the section formed by the merged second bumpy intervals, and the second bumpy intervals that do not meet the preset position relationship and are not merged.

[0023] In combination with the first aspect, in a possible implementation manner, the method further includes:

[0024] Receiving information of a third bumpy interval sent by the cloud platform; wherein the third bumpy interval is determined by the cloud platform after clustering bumpy points after receiving the road bumpy information sent by the other vehicles; the information of the third bumpy interval includes location information and bumpy level information of the third bumpy interval;

[0025] Determine a fourth bumpy interval located within a third preset range of the track point according to the position information of the third bumpy interval and the position information of the track point on the front driving track of the unmanned vehicle;

[0026] The step of combining the second bumpy sections with the same bumpy level in the first bumpy section with the second bumpy sections that meet the preset position relationship to obtain the first bumpy road section includes:

[0027] For the second bumpy interval with the same bumpy level in the first bumpy interval, and the fifth bumpy interval with the same bumpy level in the fourth bumpy interval, the second bumpy interval and the fifth bumpy interval that meet the preset position relationship are merged to obtain a first bumpy road section; the first bumpy road section includes the section formed by the merged second bumpy interval and the fifth bumpy interval, as well as the second bumpy interval and the fifth bumpy interval that do not meet the preset position relationship and have not been merged.

[0028] In combination with the first aspect, in a possible implementation manner, after obtaining the first bumpy road section, the method further includes:

[0029] Traversing the front driving track of the unmanned vehicle, and filtering the first bumpy road section according to the position information and bump level information of the first bumpy road section on the driving track to obtain a valid first bumpy road section;

[0030] Determining a driving strategy for the unmanned vehicle to pass through the first bumpy road section according to the first bumpy road section and road information within a first preset range of the first bumpy road section includes:

[0031] According to the effective first bumpy road section and the road information within a first preset range of the effective first bumpy road section, a driving strategy of the unmanned vehicle passing through the first bumpy road section is determined.

[0032] In combination with the first aspect, in a possible implementation manner, traversing the front driving trajectory of the unmanned vehicle, filtering the first bumpy road section according to the position information and bump level information of the first bumpy road section on the driving trajectory to obtain a valid first bumpy road section includes:

[0033] Taking the real-time position of the unmanned vehicle as the origin, taking the distance between the trajectory point on the driving trajectory in front of the unmanned vehicle and the origin as the abscissa, taking the bump level information as the ordinate, and determining the waveform graph of each first bumpy road section on the driving trajectory according to the position information and bump level information of each first bumpy road section on the driving trajectory;

[0034] The waveform is filtered to obtain a valid first bumpy road section.

[0035] In combination with the first aspect, in a possible implementation manner, the driving strategy includes: a detour driving strategy and a deceleration driving strategy;

[0036] The determining, based on the first bumpy road section and the road information within a first preset range of the first bumpy road section, a driving strategy for the unmanned vehicle to pass through the first bumpy road section includes:

[0037] When the road information within the first preset range of the first bumpy road section indicates that there is a detour road, determining a first cost for the unmanned vehicle to pass the first bumpy road section using the detour driving strategy and a second cost for the unmanned vehicle to pass the first bumpy road section using the deceleration driving strategy;

[0038] Determining a driving strategy for the unmanned vehicle to pass through the first bumpy road section according to a magnitude relationship between the first cost and the second cost;

[0039] When the road information within the first preset range of the first bumpy road section indicates that there is no detour road, the deceleration driving strategy is determined as the driving strategy for the unmanned vehicle to pass through the first bumpy road section.

[0040] In combination with the first aspect, in a possible implementation manner, the road bump information further includes: first bump feature information of the other vehicles; the driving strategy includes: a detour driving strategy and a deceleration driving strategy;

[0041] The determining, based on the first bumpy road section and the road information within a first preset range of the first bumpy road section, a driving strategy for the unmanned vehicle to pass through the first bumpy road section includes:

[0042] When the road information within the first preset range of the first bumpy road section indicates the existence of a detour road, a target driving strategy to be adopted is determined based on the first bumpy characteristic information or the second bumpy characteristic information of the unmanned vehicle estimated based on the first bumpy characteristic information, and the target driving strategy is a detour driving strategy or a deceleration driving strategy.

[0043] In combination with the first aspect, in a possible implementation, the driving strategy includes: a deceleration driving strategy;

[0044] The deceleration driving strategy is determined in the following manner:

[0045] For each first bumpy road section, determining a first speed limit value of the unmanned vehicle on the first bumpy road section according to bump level information of the first bumpy road section and a correspondence between the bump level information and the speed limit value;

[0046] Determining a speed plan of the unmanned vehicle on a forward driving trajectory according to first speed limit values ​​corresponding to each first bumpy road section;

[0047] The deceleration driving strategy is determined according to the speed planning.

[0048] In combination with the first aspect, in a possible implementation manner, the method further includes:

[0049] After the unmanned vehicle detects the road bump information, the road bump information is sent to surrounding unmanned vehicles; and / or,

[0050] After the unmanned vehicle detects the road bump information, the road bump information is sent to the cloud platform.

[0051] In a second aspect, a driving control device for an unmanned vehicle on a bumpy road is provided, comprising:

[0052] The bump information acquisition module is used for the unmanned vehicle to receive road bump information sent by other vehicles through the vehicle network V2X communication; the road bump information includes the location information of the bump point;

[0053] A bumpy road section determination module, used to determine a first bumpy road section on the front driving track of the unmanned vehicle according to the position information of the bumpy point and the real-time track planned by the unmanned vehicle;

[0054] A driving control module is used to determine a driving strategy for the unmanned vehicle to pass through the first bumpy road section based on the first bumpy road section and road information within a first preset range of the first bumpy road section.

[0055] In a third aspect, an unmanned vehicle is provided, comprising: a driving control device for an unmanned vehicle on a bumpy road section as described in the second aspect.

[0056] The beneficial effects of the embodiments of the present disclosure include:

[0057] The present disclosure provides a driving control method, device and unmanned vehicle for an unmanned vehicle on a bumpy road section, including: the unmanned vehicle receives road bump information sent by other vehicles through vehicle-to-vehicle network V2X communication; the road bump information includes the location information of the bump point; according to the location information of the bump point and the real-time trajectory planned by the unmanned vehicle, the first bumpy road section on the driving trajectory ahead of the unmanned vehicle is determined; according to the first bumpy road section and the road information within the first preset range of the first bumpy road section, the driving strategy of the unmanned vehicle through the first bumpy road section is determined. The driving control method for an unmanned vehicle on a bumpy road section provided by the embodiment of the present disclosure, the unmanned vehicle directly obtains road bump information from surrounding vehicles in a timely manner through the vehicle-to-vehicle network V2X communication technology, and before entering the bumpy road section, it combines the road bump information with the real-time trajectory planned by the unmanned vehicle, accurately and timely identifies the bumpy road section on the driving trajectory, and makes decisions and plans for the driving strategy through the bumpy road section in advance. Compared with the prior art in which the vehicle enters a bumpy road section and then determines the driving strategy on the bumpy road section, this method can not only accurately locate the bumpy road section, but also plan the driving strategy in advance before entering the bumpy road section. It has strong timeliness and high stability, thereby effectively reducing the safety risks of unmanned vehicles when driving on bumpy roads. BRIEF DESCRIPTION OF THE DRAWINGS

[0058] Figure 1 A flow chart of a driving control method for an unmanned vehicle on a bumpy road provided by an embodiment of the present disclosure;

[0059] Figure 2 One of the schematic diagrams of the bumpy interval on the driving track of the unmanned vehicle provided in the embodiment of the present disclosure;

[0060] Figure 3 The second schematic diagram of the bumpy interval on the driving track of the unmanned vehicle provided in the embodiment of the present disclosure;

[0061] Figure 4 A schematic diagram of a first bumpy road section on the driving trajectory of an unmanned vehicle provided in an embodiment of the present disclosure;

[0062] Figure 5 A schematic diagram of a trajectory of an unmanned vehicle using a detour driving strategy provided by an embodiment of the present disclosure;

[0063] Figure 6 A schematic diagram of the speed limit value on the driving trajectory of the unmanned vehicle provided in an embodiment of the present disclosure;

[0064] Figure 7 A schematic diagram of a speed curve on a driving trajectory of an unmanned vehicle provided in an embodiment of the present disclosure;

[0065] Figure 8 A flow chart for determining a first bumpy road section and a driving strategy provided in an embodiment of the present disclosure;

[0066] Fig. 9 A structural diagram of a driving control device for an unmanned vehicle on a bumpy road provided in an embodiment of the present disclosure. DETAILED DESCRIPTION

[0067] The embodiments of the present disclosure provide a driving control method and device for an unmanned vehicle on a bumpy road section, and an unmanned vehicle. The preferred embodiments of the present disclosure are described below in conjunction with the drawings of the specification. It should be understood that the preferred embodiments described herein are only used to illustrate and explain the present disclosure, and are not used to limit the present disclosure. In addition, the embodiments and features in the embodiments of the present application can be combined with each other if there is no conflict.

[0068] The disclosed embodiment provides a driving control method for an unmanned vehicle on a bumpy road section. Figure 1 As shown, the following steps are included:

[0069] S101, the unmanned vehicle receives road bump information sent by other vehicles through vehicle-to-vehicle network V2X communication; the road bump information includes location information of bump points;

[0070] S102, determining a first bumpy road section on the driving trajectory ahead of the unmanned vehicle according to the location information of the bumpy point and the real-time trajectory planned by the unmanned vehicle;

[0071] S103: Determine a driving strategy for the unmanned vehicle to pass through the first bumpy road section based on the first bumpy road section and road information within a first preset range of the first bumpy road section.

[0072] In the disclosed embodiments, the unmanned vehicle may be a mining vehicle with unmanned transportation in mines. In a mining operation environment, there are often bumpy sections on operating roads. When an unmanned vehicle travels quickly on these bumpy sections, it may cause significant damage to the balance shaft and the vehicle body, and even cause safety risks. In the traditional way, unmanned vehicles are usually equipped with advanced sensors, such as inertial measurement units (IMUs), laser radars, cameras, etc., which are used to collect data such as the pitch angle, vehicle posture information or road information of the unmanned vehicle, and use machine learning, deep learning and other algorithms to process the collected data, so that the unmanned vehicle can detect and identify road bumps and perform corresponding braking and deceleration. However, this method has a lag. When the unmanned vehicle is traveling on a bumpy section, the vehicle speed may be already high, and it is too late to brake and decelerate at this time.

[0073] In another embodiment, after any unmanned vehicle in the operating area detects road bumps, the road bump information can be uploaded to the cloud platform. The cloud platform can cluster and generate bump intervals based on road bump information and map information, and send the bump intervals to the unmanned vehicles in the operating area or send the map layer with the bump intervals to the unmanned vehicles in the operating area. However, the process of the cloud platform sending the bump intervals to the unmanned vehicle has a certain lag, for example, the cloud platform's calculation of the bump information takes time and the communication delay between the cloud platform and the unmanned vehicle. In addition, in the weak network environment of the mine, the communication delay is large, and there is often even a situation where the communication link between the unmanned vehicle and the cloud platform is interrupted. These factors may cause the unmanned vehicle to be unable to pass smoothly through the bumpy road section.

[0074] In the disclosed embodiment, the unmanned vehicle can communicate with other unmanned vehicles in the mine operation area through the vehicle-to-everything (V2X) communication technology. The vehicle-to-everything (V2X) communication can use wireless communication technology to enable the unmanned vehicles to share information such as location, speed, direction, etc. in real time, and realize information interaction during the collaborative operation of the unmanned vehicles, such as when the front unmanned vehicle brakes, notifying the rear unmanned vehicle to slow down in advance. The vehicle-to-everything (V2X) communication has the characteristics of high bandwidth, low latency, and high reliability, and is suitable for the communication needs between unmanned vehicles in the mine operation environment. When other vehicles encounter bumps, the unmanned vehicle can receive road bump information sent by other vehicles through the vehicle-to-everything (V2X) communication. These road bump information may include one or more of: location information of the bump point, bump level information of the bump point, identification information of the bump point, timestamp, etc. The unmanned vehicle can filter the road bump information whose distance from the real-time location exceeds the preset distance range according to the real-time location and the location information of the bump point, and obtain effective road bump information. For example, the unmanned vehicle retains the road bump information within 200 meters from the vehicle. Based on the location information of the bumpy points and the real-time trajectory planned by the unmanned vehicle, the unmanned vehicle can map the trajectory with the bumpy points, and filter out the bumpy points that are beyond a preset distance range from the trajectory points, for example, filtering out the bumpy points outside a range of 20 meters from the trajectory points, and then determine the first bumpy section on the driving trajectory in front of the unmanned vehicle.

[0075] Furthermore, according to the first bumpy road section and the road information within the first preset range of the first bumpy road section, the driving strategy of the unmanned vehicle through the first bumpy road section is determined. For example, when the road information within the first preset range of the first bumpy road section indicates the existence of a detour road, illustratively, in an open space, the first bumpy road section can be regarded as an obstacle, and the unmanned vehicle can detour to avoid the obstacle in a sufficiently large open space. Or when there is no detour road, the unmanned vehicle can plan the driving speed in advance and smoothly pass through the first bumpy road section.

[0076] In the embodiment of the present application, unmanned vehicles share road bump information through high-bandwidth, low-latency, and high-reliability vehicle-to-vehicle (V2X) communication technology, and combine the road bump information with the real-time trajectory planned by the unmanned vehicle to accurately identify the bumpy sections on the driving trajectory. By identifying the bumpy sections on the driving trajectory in advance and planning the driving strategy in advance, the timeliness is strong and the stability is high, thereby effectively reducing the safety risks of unmanned vehicles when driving on bumpy sections.

[0077] In yet another embodiment of the present disclosure, the road bump information further includes: first vehicle parameter information and / or first bump feature information of other vehicles;

[0078] In the above step S102, according to the position information of the bumpy point and the real-time track planned by the unmanned vehicle, determining the first bumpy road section on the front driving track of the unmanned vehicle includes the following steps:

[0079] Step 1: when it is determined that the unmanned vehicle meets the specified conditions according to the first vehicle parameter information and / or the first bump feature information, determine the first bumpy road section on the driving trajectory ahead of the unmanned vehicle according to the location information of the bump point and the real-time trajectory planned by the unmanned vehicle; and / or,

[0080] The method may further include:

[0081] Step 2: When it is determined that the unmanned vehicle does not meet the specified conditions based on the first vehicle parameter information and / or the first bump characteristic information, driving planning is performed for the unmanned vehicle based on the specified information, and the specified information does not include location information of the bump point.

[0082] In the disclosed embodiment, since the vehicle parameter information of different unmanned vehicles during the operation process is different, it is possible to predict whether the vehicle will bump under the same road conditions by referring to the operation of other vehicles on bumpy sections and combining the vehicle parameter information of the vehicle. In the case where the bumpiness is slight or no bumpiness occurs, the driving plan is performed according to the specified information to improve the transportation efficiency. In the case where the degree of bumpiness poses a safety hazard to the operation, the first bumpy section on the driving trajectory ahead of the unmanned vehicle and the corresponding driving strategy are determined according to the location information of the bumpy point and the real-time trajectory planned by the unmanned vehicle. The road bump information includes: the first vehicle parameter information and / or the first bump feature information of other vehicles. The first vehicle parameter information may include at least one of the following: the load information of the unmanned vehicle, such as heavy load, empty load or specific load, the unmanned vehicle number identification, the speed when passing through the bumpy section, the driving mode, the vehicle grade and other parameter information. These parameter information can affect the degree of bumpiness of the unmanned vehicle when passing through the bumpy section. The first bump feature information may include at least one of the following: pitch angle, body vibration frequency, horizontal or vertical acceleration change, vehicle posture change, etc. of other vehicles traveling on bumpy sections. The first bump feature information can be used to intuitively measure the degree of bumps other vehicles experience when passing through bumpy sections under the state of the first vehicle parameter information.

[0083] For the above step 1, after the unmanned vehicle receives the first vehicle parameter information and the first bump feature information sent by other vehicles through the vehicle network V2X communication, it determines the specified conditions satisfied by the unmanned vehicle. The specified conditions may include the conditions under which the unmanned vehicle predicts bumps when driving on a road with the same road conditions based on the first vehicle parameter information and / or the first bump feature information. If the unmanned vehicle meets the specified conditions, it indicates that the unmanned vehicle may encounter bumps when passing through the bumpy section. Then, combined with the location information of the bump point and the real-time trajectory planned by the unmanned vehicle, the first bumpy section on the driving trajectory ahead of the unmanned vehicle and the corresponding driving strategy are determined.

[0084] For step 2 above, if the unmanned vehicle does not meet the specified conditions, it indicates that the unmanned vehicle will not bump on the road with the same road conditions. For example, the speed of the unmanned vehicle is slower than that of other vehicles that bump, or the unmanned vehicle is in a safe driving mode and the vehicle condition is more stable, so bumps will not occur on sections with less potholes. If the unmanned vehicle does not meet the specified conditions, the unmanned vehicle will make driving plans based on the specified information. The specified information does not include the location information of the bump point. The specified information may include the vehicle parameters of the unmanned vehicle, such as real-time speed, load, vehicle driving mode, vehicle grade, etc., as well as information such as operation tasks and real-time trajectories.

[0085] By learning from the performance of other vehicles passing through bumpy sections and combining it with information such as the vehicle parameters of the vehicle itself, it is possible to predict whether the unmanned vehicle will experience bumps when passing through bumpy sections, and take corresponding countermeasures to reduce misjudgment and improve the efficiency of transportation operations.

[0086] In another embodiment of the present disclosure, whether the unmanned vehicle meets the specified condition is determined by any one of the following methods:

[0087] Step 1: determining whether the first bump feature information meets the bump feature threshold condition, and if so, determining that the unmanned vehicle meets the specified condition;

[0088] Step 2: predicting the second bump characteristic information of the unmanned vehicle corresponding to the second vehicle parameter information under the same road condition based on the first vehicle parameter information and the first bump characteristic information, and determining whether the unmanned vehicle meets the specified condition based on whether the second bump characteristic information meets the bump characteristic threshold condition;

[0089] Step 3: Based on the first vehicle parameter information, determine whether there is a vehicle that meets the specified vehicle parameter conditions and is experiencing bumps. If so, determine that the unmanned vehicle meets the specified conditions.

[0090] In the disclosed embodiment, based on the first vehicle parameter information and the first bump feature information of other vehicles, it is determined whether the unmanned vehicle meets the specified conditions. For the above step 1, it is determined whether the first bump feature information meets the bump feature threshold condition. For example, the first bump feature information may include at least one of the following body vibration frequencies, and it is determined whether the body vibration frequency meets the upper limit of the body vibration frequency in the bump feature threshold condition. In the case of yes, it is determined that the unmanned vehicle meets the specified conditions, indicating that the degree of bumps that occur when other vehicles pass through bumpy sections is more severe, and it is determined that the unmanned vehicle may also experience bumps when passing through the same road conditions. In the case of no, it is determined that the unmanned vehicle does not meet the specified conditions, indicating that the degree of bumps that occur when other vehicles pass through bumpy sections is relatively mild, and the unmanned vehicle will not cause adverse effects when passing through the same road conditions.

[0091] For the above step 2, according to the first vehicle parameter information and the first bump characteristic information, the second bump characteristic information of the unmanned vehicle corresponding to the second vehicle parameter information under the same road condition is predicted. During the transportation operation of the unmanned vehicle, the vehicle parameters may be in different states. For example, the vehicle parameters may include at least one of the following parameter information: the load information of the unmanned vehicle, the number identification of the unmanned vehicle, the speed when passing through the bumpy road section, the driving mode, the vehicle grade, etc. Exemplarily, the first vehicle parameter information of other vehicles indicates that the vehicle is in an unloaded state, and the first bump characteristic information when passing through the bumpy road section indicates that the pitch angle of the vehicle changes little and slight bumps occur. After the unmanned vehicle receives the first vehicle parameter information and the first bump characteristic information, it is combined with the second vehicle parameter information of the vehicle to indicate that the vehicle is in a heavy-loaded state. When it is heavy-loaded, the suspension system is under greater pressure. It is predicted that under the same road condition, the second bump characteristic information indicates that the shock absorption effect is weakened, the bump feeling is more obvious, and the pitch angle change meets the upper limit of the pitch angle change in the bump characteristic threshold condition. When the second bump characteristic information meets the bump characteristic threshold condition, it is determined that the unmanned vehicle meets the specified condition.

[0092] For the above step 3, based on the first vehicle parameter information, determine whether there is a vehicle that meets the specified vehicle parameter conditions and experiences bumps. Exemplarily, the first vehicle parameter information indicates that the other vehicle is a small unmanned mining truck. When the unmanned vehicle is also a small unmanned mining truck, it meets the specified vehicle parameter conditions and is predicted to experience bumps, and the unmanned vehicle is determined to meet the specified conditions. In the case where the unmanned vehicle is a large unmanned mining truck, since the large unmanned mining truck is equipped with a more powerful suspension system, it can better absorb and alleviate the impact caused by bumps, and does not meet the specified vehicle parameter conditions, it is predicted that there will be no bumps, and the unmanned vehicle is determined to not meet the specified conditions. Through a comprehensive analysis of the first vehicle parameter information or the first bump feature information and the combination of the first vehicle parameter information and the first bump feature information, it is predicted whether the unmanned vehicle meets the conditions for bumps, and then it is determined whether the unmanned vehicle meets the specified conditions.

[0093] In yet another embodiment of the present disclosure, the road bump information further includes: bump level information of the bump point;

[0094] In the above step S102, according to the position information of the bumpy point and the real-time track planned by the unmanned vehicle, determining the first bumpy road section on the front driving track of the unmanned vehicle includes the following steps:

[0095] Step 1: determining a first bump point located within a second preset range of the track point according to the position information of the bump point and the position information of the track point on the driving track ahead of the unmanned vehicle;

[0096] Step 2: Map the first bump point to the track point to determine the first track point mapped with the first bump point;

[0097] Step 3: clustering the first trajectory points with the same bump level according to the bump level information of the first bump point corresponding to the first trajectory point and the position information of the first trajectory point, and determining the first bump interval according to the clustering result;

[0098] Step 4: For the second bumpy intervals with the same bumpy level in the first bumpy interval, merge the second bumpy intervals that meet the preset position relationship to obtain a first bumpy road section; wherein the first bumpy road section includes the section formed by the merged second bumpy intervals and the second bumpy intervals that do not meet the preset position relationship and are not merged.

[0099] In the disclosed embodiment, the first bumpy road section corresponding to the driving trajectory is obtained by clustering and merging the trajectory points with the same bump level on the driving trajectory. The road bump information includes: the location information of the bump points and the bump level information of the bump points. According to the road conditions, the bump levels can be divided into: mild bumps, moderate bumps and severe bumps. With respect to the above step one, firstly, according to the location information of the bump points and the location information of the trajectory points on the driving trajectory ahead of the unmanned vehicle, the bump points within the second preset range of the trajectory points are screened to obtain the first bump points. For example, Figure 2 As shown, along the driving trajectory, the bumpy points within a range of 20 meters in diameter with the trajectory point as the center are screened as the first bumpy points. For the above step two, the first bumpy point is mapped to the trajectory point according to the position information, so as to obtain the first trajectory point mapped with the first bumpy point. During implementation, for each trajectory point, if there is a corresponding first bumpy point for the trajectory point, for example, there is a trajectory point within a range of 20 meters in the above example, then the first trajectory point can be regarded as the corresponding first bumpy point. For the above step three, the first trajectory points with the same bumpy level are clustered according to the bumpy level information of the first bumpy point corresponding to the first trajectory point and the position information of the first trajectory point, and the first bumpy interval is obtained after clustering. The first bumpy points with the same bumpy level can also be clustered to obtain the first bumpy interval after clustering. For the above step four, for the second bumpy intervals with the same bumpy level in the first bumpy interval, the second bumpy intervals that meet the preset position relationship are merged, for example, as Figure 3As shown, on the driving trajectory of the unmanned vehicle traveling along the x direction, the second bumpy interval A and the second bumpy interval B in the first bumpy interval have the same bumpy level, both of which are light bumpy, and are adjacent in position, so the second bumpy intervals A and B are merged. The bumpy level of the second bumpy interval C in the first bumpy interval is moderate bumpy, and there is no other second bumpy interval with moderate bumpy level adjacent in position, so it is not merged. The first bumpy road section includes the section formed by the merged second bumpy intervals, and the second bumpy intervals that do not meet the preset position relationship and are not merged. By mapping the bumpy points to the trajectory points, and clustering and merging them, the position and bumpy level of each first bumpy road section on the driving trajectory in front of the unmanned vehicle can be obtained, so that the unmanned vehicle can obtain the bumpy road section information on the real-time trajectory in advance.

[0100] In another embodiment of the present disclosure, it also includes:

[0101] Step 1, receiving information of the third bumpy interval sent by the cloud platform; wherein the third bumpy interval is determined by the cloud platform after receiving road bumpy information sent by other vehicles and clustering the bumpy points; the information of the third bumpy interval includes location information and bumpy level information of the third bumpy interval;

[0102] Step 2: determining a fourth bumpy interval located within a third preset range of the track point according to the position information of the third bumpy interval and the position information of the track point on the front driving track of the unmanned vehicle;

[0103] In the above step 4, for the second bumpy sections with the same bumpy level in the first bumpy section, the second bumpy sections that meet the preset position relationship are merged to obtain the first bumpy road section, including:

[0104] For the second bumpy interval with the same bumpy level in the first bumpy interval, and the fifth bumpy interval with the same bumpy level in the fourth bumpy interval, the second bumpy interval and the fifth bumpy interval that meet the preset position relationship are merged to obtain a first bumpy road section; the first bumpy road section includes the section formed by the merged second bumpy interval and the fifth bumpy interval, as well as the second bumpy interval and the fifth bumpy interval that do not meet the preset position relationship and have not been merged.

[0105] In the disclosed embodiment, the information of the third bumpy interval sent by the cloud platform is combined with the first bumpy interval to jointly determine the first bumpy road section on the driving track ahead of the unmanned vehicle. For the above step 1, the unmanned vehicle can communicate with the cloud platform using the vehicle-to-network (V2N) communication technology. After receiving the road bump information sent by other vehicles, the cloud platform summarizes it and clusters the bump points that meet the position relationship and have the same bump level, for example, using convex polygons for clustering to form a third bumpy interval. And send the information of the third bumpy interval to the unmanned vehicle, or combine the third bumpy interval with the map module to generate a map layer, and send the map layer including the third bumpy interval information to the unmanned vehicle. The information of the third bumpy interval includes: the location information of the third bumpy interval and the bumpy level information. For the above step 2, according to the location information of the third bumpy interval and the location information of the track point on the driving track ahead of the unmanned vehicle, the third bumpy interval is mapped to the driving track to obtain a fourth bumpy interval within a third preset range from the track point.

[0106] Furthermore, the first bumpy interval is combined with the fourth bumpy interval to obtain a first bumpy road section. For the second bumpy interval with the same bumpy level in the first bumpy interval and the fifth bumpy interval with the same bumpy level in the fourth bumpy interval, the second bumpy interval and the fifth bumpy interval that meet the preset position relationship are merged. Figure 3 As shown, the second bumpy interval D in the first bumpy interval and the fifth bumpy interval E in the fourth bumpy interval have the same bumpy level, both of which are severe bumps, and are adjacent in position, so the second bumpy interval D and the fifth bumpy interval E are merged. The first bumpy road section includes the second bumpy interval that has been merged, the fifth bumpy interval that has been merged, the section formed by merging the second bumpy interval and the fifth interval, and the second bumpy interval and the fifth bumpy interval that do not meet the preset position relationship and are not merged. After receiving the third bumpy interval sent by the cloud platform, the unmanned vehicle can also clear the corresponding road bumpy information. When the cloud platform is working normally, the operator can control the protection strategy and duration of the third bumpy interval. After discovering that the safety hazard has disappeared, such as after on-site road repair, the third bumpy interval can be cancelled, thereby improving the traffic efficiency of the unmanned vehicle. By combining the information of the third bumpy interval sent by the received cloud platform with the first bumpy interval, the reliability of the first bumpy road section can be guaranteed, and the position and bumpy level of each first bumpy road section on the driving trajectory in front of the unmanned vehicle can be obtained more comprehensively.

[0107] In another embodiment of the present disclosure, after obtaining the first bumpy road section, the method further includes:

[0108] Step 1, traversing the driving track ahead of the unmanned vehicle, filtering the first bumpy road section according to the position information and bump level information of the first bumpy road section on the driving track, and obtaining a valid first bumpy road section;

[0109] In the above step S103, according to the first bumpy road section and the road information within the first preset range of the first bumpy road section, determining the driving strategy of the unmanned vehicle through the first bumpy road section includes:

[0110] According to the effective first bumpy road section and the road information within a first preset range of the effective first bumpy road section, a driving strategy for the unmanned vehicle to pass through the first bumpy road section is determined.

[0111] In the embodiment of the present disclosure, after obtaining the first bumpy road section, the first bumpy road section on the trajectory is filtered to remove the jump, thereby obtaining a valid first bumpy road section. Figure 4 As shown, the location information and bump level information of the first bumpy road section are displayed on the front driving track of the unmanned vehicle. The front driving track of the unmanned vehicle is traversed, and the first bumpy road section is filtered to remove the bump level jump on the track to obtain a valid first bumpy road section. Then, according to the valid first bumpy road section and the road information within the first preset range of the valid first bumpy road section, the driving strategy of the unmanned vehicle through the first bumpy road section is determined. Filtering the first bumpy road section is conducive to smoothing the speed curve in the subsequent speed planning process and improving the efficiency of transportation operations.

[0112] In another embodiment of the present disclosure, in the above step 1, the driving track ahead of the unmanned vehicle is traversed, and the first bumpy road section is filtered according to the position information and bump level information of the first bumpy road section on the driving track to obtain a valid first bumpy road section, including:

[0113] Step 1: Taking the real-time position of the unmanned vehicle as the origin, taking the distance between the track point on the driving track in front of the unmanned vehicle and the origin as the horizontal coordinate, taking the bump level information as the vertical coordinate, and determining the waveform of each first bumpy road section on the driving track according to the position information and bump level information of each first bumpy road section on the driving track;

[0114] Step 2: Filter the waveform to obtain an effective first bumpy road section.

[0115] In the embodiment of the present disclosure, the waveform of each first bumpy road section on the driving trajectory is filtered to obtain an effective first bumpy road section. Figure 4 As shown, for the above step 1, the real-time position of the unmanned vehicle is taken as the coordinate origin ( Figure 4The position of the middle mark 0), the distance between the track point on the driving track in front of the unmanned vehicle and the real-time position of the unmanned vehicle is used as the horizontal coordinate, and the bump level information is used as the vertical coordinate to obtain the waveform of each first bumpy road section on the driving track. For the above step 2, the waveform is filtered to remove the bump level jump on the track, smooth the waveform, and obtain an effective first bumpy road section. Filtering the first bumpy road section is conducive to smoothing the speed curve in the subsequent speed planning process and improving the transportation operation efficiency.

[0116] In yet another embodiment of the present disclosure, the driving strategy includes: a detour driving strategy and a deceleration driving strategy;

[0117] In the above step S103, determining the driving strategy of the unmanned vehicle through the first bumpy road section according to the first bumpy road section and the road information within the first preset range of the first bumpy road section includes the following steps:

[0118] Step 1: When the road information within the first preset range of the first bumpy road section indicates that there is a detour road, determine a first cost for the unmanned vehicle to pass the first bumpy road section using a detour driving strategy and a second cost for the unmanned vehicle to pass the first bumpy road section using a deceleration driving strategy;

[0119] Step 2: Determine the driving strategy adopted by the unmanned vehicle when passing through the first bumpy road section according to the relationship between the first cost and the second cost;

[0120] Step 3: When the road information within the first preset range of the first bumpy road section indicates that there is no detour road, the deceleration driving strategy is determined as the driving strategy for the unmanned vehicle to pass through the first bumpy road section.

[0121] In the embodiment of the present disclosure, by analyzing the road conditions around the first bumpy road section, a suitable driving strategy is selected to perform the transportation operation. The driving strategy includes: a detour driving strategy and a deceleration driving strategy. For the above step 1, for example, Figure 5As shown, the unmanned vehicle travels along the driving trajectory, obtains the road information within the first preset range of the first bumpy road section, and when the road information indicates that there is a detour road and the road space is sufficient, for example, the unmanned vehicle travels in a large open space, the first bumpy road section can be regarded as an obstacle, and the unmanned vehicle uses the sufficient space to adopt a detour driving strategy to plan a detour trajectory to avoid the obstacle. For the above step 2, the unmanned vehicle determines the system overhead of adopting the detour driving strategy to pass through the first bumpy road section based on factors such as detour time and transportation cost, and determines the system overhead as the first overhead, and at the same time determines the system overhead of the deceleration driving strategy to pass through the first bumpy road section, and determines it as the second overhead. Compare the size of the first overhead and the second overhead, and select the strategy with the smaller system overhead as the driving strategy adopted by the unmanned vehicle to pass through the first bumpy road section. For the above step 3, when the road information indicates that there is no detour road, the deceleration driving strategy is selected as the driving strategy for the unmanned vehicle to pass through the first bumpy road section. By analyzing the road conditions and comparing different driving strategies, the best driving strategy is selected to ensure the operation safety of the unmanned vehicle and reduce operating costs.

[0122] In another embodiment of the present disclosure, the road bump information further includes: first bump feature information of other vehicles; the driving strategy includes: a detour driving strategy and a deceleration driving strategy;

[0123] In the above step S103, according to the first bumpy road section and the road information within the first preset range of the first bumpy road section, determining the driving strategy of the unmanned vehicle through the first bumpy road section includes:

[0124] When the road information within the first preset range of the first bumpy road section indicates the existence of a detour road, a target driving strategy to be adopted is determined based on the first bumpy characteristic information or the second bumpy characteristic information of the unmanned vehicle estimated based on the first bumpy characteristic information. The target driving strategy is a detour driving strategy or a deceleration driving strategy.

[0125] In the disclosed embodiment, the driving strategy adopted by the unmanned vehicle when passing through the first bumpy road section is determined by the first bumpy characteristic information provided by other vehicles. The road bumpy information also includes: the first bumpy characteristic information of other vehicles. The first bumpy characteristic information may include at least one of the following: the pitch angle of other vehicles driving on the bumpy road section, the body vibration frequency, the acceleration change in the horizontal or vertical direction, the vehicle posture change and other information. According to the first bumpy characteristic information, the degree of bumps experienced by other vehicles when passing through the bumpy road section can be measured. The unmanned vehicle can estimate the second bumpy characteristic information when passing through the first bumpy road section based on the first bumpy characteristic information. The unmanned vehicle determines the target driving strategy to be adopted based on the first bumpy characteristic information or the second bumpy characteristic information. For example, if the road information within the first preset range of the first bumpy road section indicates that there is a detour road, and the unmanned vehicle adopts a deceleration driving strategy to pass through the first bumpy road section when the bumpiness is relatively mild according to the first bumpy feature information or the second bumpy feature information and does not affect vehicle safety, and adopts a detour driving strategy to pass through the first bumpy road section when the bumpiness is relatively severe according to the first bumpy feature information or the second bumpy feature information and affects vehicle safety. By intuitively judging the bumpy feature information, the driving strategy for passing through the first bumpy road section is selected to ensure driving safety.

[0126] In yet another embodiment of the present disclosure, the driving strategy includes: a deceleration driving strategy;

[0127] The deceleration driving strategy is determined as follows:

[0128] Step 1: for each first bumpy road section, according to the bumpiness level information of the first bumpy road section and the corresponding relationship between the bumpiness level information and the speed limit value, determine the first speed limit value of the unmanned vehicle on the first bumpy road section;

[0129] Step 2: determining a speed plan of the unmanned vehicle on the front driving trajectory according to the first speed limit values ​​corresponding to the first bumpy road sections;

[0130] Step 3: Determine the deceleration strategy based on the speed plan.

[0131] In the embodiment of the present disclosure, different bump levels correspond to different speed limits, and the speed limit is used to ensure driving safety when passing through the first bumpy road section. For the above step 1, there is a corresponding relationship between the bump level information and the speed limit value, for example, Figure 6As shown, when the unmanned vehicle is driving on the first bumpy section with slight bumps, the speed limit is 25km / h, when driving on the first bumpy section with moderate bumps, the speed limit is 15km / h, and when driving on the first bumpy section with severe bumps, the speed limit is 7.5km / h. When driving on a section without bumps, the speed is limited according to the maximum speed limit specified for that section. Determine the first speed limit for the unmanned vehicle when driving on each first bumpy section. For step 2 above, determine the speed plan of the unmanned vehicle on the driving trajectory ahead according to the first speed limit values ​​corresponding to each first bumpy section, for example, Figure 7 As shown, the speed of the unmanned vehicle on the speed curve planned on the front driving trajectory does not exceed the first speed limit value. For example, the unmanned vehicle with a lower current speed accelerates, and the maximum speed on the acceleration trajectory does not exceed the first speed limit value. The unmanned vehicle with a higher current speed decelerates, and the maximum speed on the deceleration trajectory does not exceed the first speed limit value. For the above step 3, according to the speed planning, a deceleration driving strategy is determined so that the unmanned vehicle can drive at a smooth speed, thereby ensuring driving safety. Through the speed limit values ​​corresponding to different bump levels, the unmanned vehicle can perform speed planning according to the real-time trajectory and the corresponding speed limit value on the trajectory, determine the best deceleration driving strategy for passing the first bumpy section, and ensure operation safety.

[0132] In another embodiment of the present disclosure, it also includes:

[0133] After the unmanned vehicle detects road bump information, the road bump information is sent to surrounding unmanned vehicles; and / or,

[0134] After the unmanned vehicle detects road bump information, it sends the road bump information to the cloud platform.

[0135] In the disclosed embodiment, the unmanned vehicle can use advanced sensors to detect road bump information. And send the detected road bump information to surrounding unmanned vehicles through the vehicle network V2X communication. After detecting the road bump information, the unmanned vehicle can also send the road bump information to the cloud platform through the vehicle network V2N communication. When bumps occur, the unmanned vehicle reports the road bump information using a dual-link transmission method. On the one hand, it reports to the cloud platform for clustering to generate bump intervals, and on the other hand, it broadcasts to surrounding vehicles, and surrounding vehicles generate bump intervals according to the vehicle's driving trajectory. Ensure that in weak network conditions, surrounding vehicles can also receive road bump information and plan driving strategies smoothly. Unmanned vehicles in the operating area can quickly obtain road bump information through vehicle network V2X communication, so as to implement driving strategies in a timely manner and avoid the occurrence of safety hazards. Under weak network conditions, even if the data transmission link of the cloud platform is interrupted or fails due to other reasons, the unmanned vehicles in the operating area still have the protection function of driving on bumpy roads.

[0136] Figure 8The flowchart of the embodiment of determining the first bumpy road section and the driving strategy provided by the embodiment of the present disclosure. Figure 8 As shown, the execution subject may be a system including a subject vehicle, other vehicles and a cloud platform, including the following steps:

[0137] S801, other vehicles share road bump information after detecting it; when sending it to surrounding unmanned vehicles, proceed to step S802, and when sending it to the cloud platform, proceed to step S803;

[0138] S802, the subject vehicle determines whether the unmanned vehicle meets the specified conditions according to the first vehicle parameter information and / or the first bump feature information; if so, proceed to step S804, otherwise, proceed to step S805;

[0139] S803, after receiving the road bump information sent by other vehicles, the cloud platform performs clustering processing on the bump points to determine the third bump interval, and sends it to the unmanned vehicle; proceed to step S806;

[0140] S804, mapping the first bump point to the track point, clustering the track points with the same bump level to obtain the first bump interval; proceeding to step S806;

[0141] S805. Perform driving planning for the unmanned vehicle according to the specified information; this process ends.

[0142] S806, obtaining a first bumpy road section according to the first bumpy interval and / or the third bumpy interval;

[0143] S807, determining whether there is a detour road within the first preset range of the first bumpy road section; if so, proceeding to step S808; otherwise, proceeding to step S809;

[0144] S808. Compare the costs of the deceleration driving strategy and the detour driving strategy to determine the driving strategy for the unmanned vehicle to pass through the first bumpy road section; or, determine the target driving strategy to be adopted based on the first bumpy feature information or the second bumpy feature information; this process ends.

[0145] S809: Determine the deceleration driving strategy as the driving strategy for the unmanned vehicle to pass through the first bumpy road section; this process ends.

[0146] Based on the same disclosed concept, the disclosed embodiments also provide a driving control device and an unmanned vehicle for an unmanned vehicle on a bumpy road section. Since the principles of the problems solved by these devices and the unmanned vehicle are similar to the aforementioned driving control method for an unmanned vehicle on a bumpy road section, the implementation of the device and the unmanned vehicle can refer to the implementation of the aforementioned method, and the repeated parts will not be repeated.

[0147] The disclosed embodiment provides a driving control device for an unmanned vehicle on a bumpy road section, such as Fig. 9Including:

[0148] The bump information acquisition module 901 is used for the unmanned vehicle to receive road bump information sent by other vehicles through the vehicle-to-vehicle network V2X communication; the road bump information includes the location information of the bump point;

[0149] A bumpy road section determination module 902 is used to determine a first bumpy road section on the front driving track of the unmanned vehicle according to the position information of the bumpy point and the real-time track planned by the unmanned vehicle;

[0150] The driving control module 903 is used to determine the driving strategy of the unmanned vehicle through the first bumpy road section according to the first bumpy road section and the road information within a first preset range of the first bumpy road section.

[0151] In yet another embodiment of the present disclosure, the road bump information further includes: first vehicle parameter information and / or first bump feature information of the other vehicle;

[0152] The bumpy road section determination module 902 is used to determine the first bumpy road section on the front driving trajectory of the unmanned vehicle according to the position information of the bumpy point and the real-time trajectory planned by the unmanned vehicle when it is determined that the unmanned vehicle meets the specified conditions according to the first vehicle parameter information and / or the first bumpy feature information; and / or,

[0153] The bumpy road section determination module 902 is also used to: when it is determined that the unmanned vehicle does not meet the specified conditions based on the first vehicle parameter information and / or the first bumpy feature information, plan the driving of the unmanned vehicle according to the specified information, and the specified information does not include the location information of the bumpy point.

[0154] In another embodiment of the present disclosure, the bumpy road section determination module 902 is used to determine whether the unmanned vehicle meets the specified condition by any one of the following methods:

[0155] determining whether the first bump feature information satisfies a bump feature threshold condition, and if so, determining that the unmanned vehicle satisfies a specified condition;

[0156] Predicting second bump characteristic information of the unmanned vehicle corresponding to the second vehicle parameter information under the same road condition according to the first vehicle parameter information and the first bump characteristic information, and determining whether the unmanned vehicle meets a specified condition according to whether the second bump characteristic information meets a bump characteristic threshold condition;

[0157] According to the first vehicle parameter information, it is determined whether there is a vehicle that meets the specified vehicle parameter conditions and is bumpy. If so, it is determined that the unmanned vehicle meets the specified conditions.

[0158] In yet another embodiment of the present disclosure, the road bump information further includes: bump level information of the bump point;

[0159] The bumpy road section determination module 902 is used to determine a first bumpy point located within a second preset range of the track point according to the position information of the bumpy point and the position information of the track point on the front driving track of the unmanned vehicle;

[0160] Mapping the first bump point to the track point to determine a first track point mapped with the first bump point;

[0161] clustering the first trajectory points with the same bump level according to the bump level information of the first bump point corresponding to the first trajectory point and the position information of the first trajectory point, and determining the first bump interval according to the clustering result;

[0162] For the second bumpy intervals with the same bumpy level in the first bumpy interval, the second bumpy intervals that meet the preset position relationship are merged to obtain a first bumpy road section; wherein the first bumpy road section includes the section formed by the merged second bumpy intervals, and the second bumpy intervals that do not meet the preset position relationship and are not merged.

[0163] In another embodiment of the present disclosure, the bump information acquisition module 901 is further used to receive information of a third bump interval sent by the cloud platform; wherein the third bump interval is determined by the cloud platform after clustering bump points after receiving the road bump information sent by the other vehicles; the information of the third bump interval includes location information and bump level information of the third bump interval;

[0164] The bumpy road section determination module 902 is used to determine a fourth bumpy section located within a third preset range of the track point according to the position information of the third bumpy section and the position information of the track point on the front driving track of the unmanned vehicle;

[0165] For the second bumpy interval with the same bumpy level in the first bumpy interval, and the fifth bumpy interval with the same bumpy level in the fourth bumpy interval, the second bumpy interval and the fifth bumpy interval that meet the preset position relationship are merged to obtain a first bumpy road section; the first bumpy road section includes the section formed by the merged second bumpy interval and the fifth bumpy interval, as well as the second bumpy interval and the fifth bumpy interval that do not meet the preset position relationship and have not been merged.

[0166] In yet another embodiment of the present disclosure, after obtaining the first bumpy road section, the bumpy road section determining module 902 is further configured to:

[0167] Traversing the front driving track of the unmanned vehicle, and filtering the first bumpy road section according to the position information and bump level information of the first bumpy road section on the driving track to obtain a valid first bumpy road section;

[0168] The driving control module 903 is used to determine the driving strategy of the unmanned vehicle through the first bumpy road section according to the valid first bumpy road section and the road information within the first preset range of the valid first bumpy road section.

[0169] In another embodiment of the present disclosure, the bumpy road section determination module 902 is used to determine a waveform diagram of each first bumpy road section on the driving trajectory according to the position information and bumpy road section information of each first bumpy road section on the driving trajectory, taking the real-time position of the unmanned vehicle as the origin, taking the distance between the trajectory point on the driving trajectory in front of the unmanned vehicle and the origin as the horizontal coordinate, and taking the bumpy road section information as the vertical coordinate;

[0170] The waveform is filtered to obtain a valid first bumpy road section.

[0171] In another embodiment of the present disclosure, the driving strategy includes: a detour driving strategy and a deceleration driving strategy;

[0172] The driving control module 903 is used to determine a first cost of the unmanned vehicle passing through the first bumpy section by adopting the detour driving strategy and a second cost of the unmanned vehicle passing through the first bumpy section by adopting the deceleration driving strategy when the road information within the first preset range of the first bumpy section indicates that there is a detour road;

[0173] Determining a driving strategy for the unmanned vehicle to pass through the first bumpy road section according to a magnitude relationship between the first cost and the second cost;

[0174] When the road information within the first preset range of the first bumpy road section indicates that there is no detour road, the deceleration driving strategy is determined as the driving strategy for the unmanned vehicle to pass through the first bumpy road section.

[0175] In another embodiment of the present disclosure, the road bump information further includes: first bump feature information of the other vehicles; the driving strategy includes: a detour driving strategy and a deceleration driving strategy;

[0176] The driving control module 903 is used to determine a target driving strategy to be adopted according to the first bumpy characteristic information or the second bumpy characteristic information of the unmanned vehicle estimated according to the first bumpy characteristic information when the road information within the first preset range of the first bumpy road section indicates the existence of a detour road, and the target driving strategy is a detour driving strategy or a deceleration driving strategy.

[0177] In yet another embodiment of the present disclosure, the driving strategy includes: a deceleration driving strategy;

[0178] The driving control module 903 is used to determine the deceleration driving strategy in the following manner:

[0179] For each first bumpy road section, determining a first speed limit value of the unmanned vehicle on the first bumpy road section according to bump level information of the first bumpy road section and a correspondence between the bump level information and the speed limit value;

[0180] Determining a speed plan of the unmanned vehicle on a forward driving trajectory according to first speed limit values ​​corresponding to each first bumpy road section;

[0181] The deceleration driving strategy is determined according to the speed planning.

[0182] In another embodiment of the present disclosure, the bump information acquisition module 901 is further used to

[0183] After the unmanned vehicle detects the road bump information, the road bump information is sent to surrounding unmanned vehicles; and / or,

[0184] After the unmanned vehicle detects the road bump information, the road bump information is sent to the cloud platform.

[0185] An embodiment of the present disclosure provides an unmanned vehicle, comprising: a driving control device for an unmanned vehicle on a bumpy road section as described in any of the above embodiments.

[0186] Through the description of the above implementation methods, those skilled in the art can clearly understand that the embodiments of the present disclosure can be implemented by hardware, or by means of software plus a necessary general hardware platform. Based on such an understanding, the technical solution of the embodiments of the present disclosure can be embodied in the form of a software product, which can be stored in a non-volatile storage medium (which can be a CD-ROM, a USB flash drive, a mobile hard disk, etc.), including a number of instructions for enabling a computer device (which can be a personal computer, a server, or a network device, etc.) to execute the methods described in each embodiment of the present disclosure.

[0187] Those skilled in the art will appreciate that the accompanying drawings are merely schematic diagrams of a preferred embodiment, and the modules or processes in the accompanying drawings are not necessarily required for implementing the present disclosure.

[0188] Those skilled in the art can understand that the modules in the device in the embodiment can be distributed in the device in the embodiment according to the description of the embodiment, or can be changed accordingly and located in one or more devices different from the present embodiment. The modules in the above embodiment can be combined into one module, or can be further divided into multiple sub-modules.

[0189] The serial numbers of the above-mentioned embodiments of the present disclosure are only for description and do not represent the advantages or disadvantages of the embodiments.

[0190] Obviously, those skilled in the art can make various changes and modifications to the present disclosure without departing from the spirit and scope of the present disclosure. Thus, if these modifications and variations of the present disclosure fall within the scope of the claims of the present disclosure and their equivalents, the present disclosure is also intended to include these modifications and variations.

Claims

1. A driving control method for an unmanned vehicle on a bumpy road, characterized in that: include: The unmanned vehicle receives road bump information sent by other vehicles through vehicle-to-vehicle network V2X communication; the road bump information includes location information of bump points; Determine a first bumpy road section on the front driving track of the unmanned vehicle according to the position information of the bumpy point and the real-time track planned by the unmanned vehicle; A driving strategy for the unmanned vehicle to pass through the first bumpy road section is determined based on the first bumpy road section and road information within a first preset range of the first bumpy road section.

2. The method according to claim 1, characterized in that The road bump information further includes: first vehicle parameter information and / or first bump feature information of the other vehicle; Determining the first bumpy road section on the front driving track of the unmanned vehicle according to the position information of the bumpy point and the real-time track planned by the unmanned vehicle includes: When it is determined that the unmanned vehicle meets a specified condition according to the first vehicle parameter information and / or the first bump feature information, a first bumpy road section on the driving trajectory ahead of the unmanned vehicle is determined according to the position information of the bump point and the real-time trajectory planned by the unmanned vehicle; and / or, The method also includes: when it is determined that the unmanned vehicle does not meet specified conditions based on the first vehicle parameter information and / or the first bump feature information, driving planning for the unmanned vehicle is performed according to the specified information, and the specified information does not include the location information of the bump point.

3. The method according to claim 2, characterized in that Determine whether the unmanned vehicle meets the specified conditions by any of the following methods: determining whether the first bump feature information satisfies a bump feature threshold condition, and if so, determining that the unmanned vehicle satisfies a specified condition; Predicting second bump characteristic information of the unmanned vehicle corresponding to the second vehicle parameter information under the same road condition according to the first vehicle parameter information and the first bump characteristic information, and determining whether the unmanned vehicle meets a specified condition according to whether the second bump characteristic information meets a bump characteristic threshold condition; Based on the first vehicle parameter information, it is determined whether there is a vehicle that meets the specified vehicle parameter conditions and is experiencing bumps. If so, it is determined that the unmanned vehicle meets the specified conditions.

4. The method according to claim 1, characterized in that The road bump information also includes: bump level information of bump points; The step of determining a first bumpy road section on the front driving track of the unmanned vehicle according to the position information of the bumpy point and the real-time track planned by the unmanned vehicle comprises: Determine a first bump point located within a second preset range of the track point according to the position information of the bump point and the position information of the track point on the front driving track of the unmanned vehicle; Mapping the first bump point to the track point to determine a first track point mapped with the first bump point; clustering the first trajectory points with the same bump level according to the bump level information of the first bump point corresponding to the first trajectory point and the position information of the first trajectory point, and determining the first bump interval according to the clustering result; For the second bumpy intervals with the same bumpy level in the first bumpy interval, the second bumpy intervals that meet the preset position relationship are merged to obtain a first bumpy road section; wherein the first bumpy road section includes the section formed by the merged second bumpy intervals, and the second bumpy intervals that do not meet the preset position relationship and are not merged.

5. The method according to claim 4, characterized in that Also includes: Receiving information of a third bumpy interval sent by the cloud platform; wherein the third bumpy interval is determined by the cloud platform after clustering bumpy points after receiving the road bumpy information sent by the other vehicles; the information of the third bumpy interval includes location information and bumpy level information of the third bumpy interval; Determine a fourth bumpy interval located within a third preset range of the track point according to the position information of the third bumpy interval and the position information of the track point on the front driving track of the unmanned vehicle; The step of combining the second bumpy sections with the same bumpy level in the first bumpy section with the second bumpy sections that meet the preset position relationship to obtain the first bumpy road section includes: For the second bumpy interval with the same bumpy level in the first bumpy interval, and the fifth bumpy interval with the same bumpy level in the fourth bumpy interval, the second bumpy interval and the fifth bumpy interval that meet the preset position relationship are merged to obtain a first bumpy road section; the first bumpy road section includes the section formed by the merged second bumpy interval and the fifth bumpy interval, as well as the second bumpy interval and the fifth bumpy interval that do not meet the preset position relationship and have not been merged.

6. The method according to claim 4, characterized in that After obtaining the first bumpy road section, the method further includes: Traversing the front driving track of the unmanned vehicle, and filtering the first bumpy road section according to the position information and bump level information of the first bumpy road section on the driving track to obtain a valid first bumpy road section; Determining a driving strategy for the unmanned vehicle to pass through the first bumpy road section according to the first bumpy road section and road information within a first preset range of the first bumpy road section includes: According to the effective first bumpy road section and the road information within a first preset range of the effective first bumpy road section, a driving strategy of the unmanned vehicle passing through the first bumpy road section is determined.

7. The method according to claim 6, characterized in that The traversing the front driving track of the unmanned vehicle and filtering the first bumpy road section according to the position information and bump level information of the first bumpy road section on the driving track to obtain a valid first bumpy road section includes: Taking the real-time position of the unmanned vehicle as the origin, taking the distance between the trajectory point on the driving trajectory in front of the unmanned vehicle and the origin as the abscissa, taking the bump level information as the ordinate, and determining the waveform graph of each first bumpy road section on the driving trajectory according to the position information and bump level information of each first bumpy road section on the driving trajectory; The waveform is filtered to obtain a valid first bumpy road section.

8. The method according to claim 1, characterized in that The driving strategies include: detour driving strategy and deceleration driving strategy; The determining, based on the first bumpy road section and the road information within a first preset range of the first bumpy road section, a driving strategy for the unmanned vehicle to pass through the first bumpy road section includes: When the road information within the first preset range of the first bumpy road section indicates that there is a detour road, determining a first cost for the unmanned vehicle to pass the first bumpy road section using the detour driving strategy and a second cost for the unmanned vehicle to pass the first bumpy road section using the deceleration driving strategy; Determining a driving strategy for the unmanned vehicle to pass through the first bumpy road section according to a magnitude relationship between the first cost and the second cost; When the road information within the first preset range of the first bumpy road section indicates that there is no detour road, the deceleration driving strategy is determined as the driving strategy for the unmanned vehicle to pass through the first bumpy road section.

9. The method according to claim 1, characterized in that The road bump information also includes: first bump feature information of the other vehicles; the driving strategy includes: detour driving strategy and deceleration driving strategy; The determining, based on the first bumpy road section and the road information within a first preset range of the first bumpy road section, a driving strategy for the unmanned vehicle to pass through the first bumpy road section includes: When the road information within the first preset range of the first bumpy road section indicates the existence of a detour road, a target driving strategy to be adopted is determined based on the first bumpy characteristic information or the second bumpy characteristic information of the unmanned vehicle estimated based on the first bumpy characteristic information, and the target driving strategy is a detour driving strategy or a deceleration driving strategy.

10. The method according to claim 1, characterized in that The driving strategy includes: a deceleration driving strategy; The deceleration driving strategy is determined in the following manner: For each first bumpy road section, determining a first speed limit value of the unmanned vehicle on the first bumpy road section according to bump level information of the first bumpy road section and a correspondence between the bump level information and the speed limit value; Determining a speed plan of the unmanned vehicle on a forward driving trajectory according to first speed limit values ​​corresponding to each first bumpy road section; The deceleration driving strategy is determined according to the speed planning.

11. The method according to claim 1, characterized in that Also includes: After the unmanned vehicle detects the road bump information, the road bump information is sent to surrounding unmanned vehicles; and / or, After the unmanned vehicle detects the road bump information, the road bump information is sent to the cloud platform.

12. A driving control device for an unmanned vehicle on a bumpy road, characterized in that: include: The bump information acquisition module is used for the unmanned vehicle to receive road bump information sent by other vehicles through the vehicle-to-vehicle network V2X communication; The road bump information includes location information of bump points; A bumpy road section determination module, used to determine a first bumpy road section on the front driving track of the unmanned vehicle according to the position information of the bumpy point and the real-time track planned by the unmanned vehicle; A driving control module is used to determine a driving strategy for the unmanned vehicle to pass through the first bumpy road section based on the first bumpy road section and road information within a first preset range of the first bumpy road section.

13. An unmanned vehicle, characterized in that: include: The driving control device for an unmanned vehicle on a bumpy road as claimed in claim 12.

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