Driving planning method and device, vehicle-mounted equipment and vehicle

Abstract

The invention provides a driving planning method and device, vehicle-mounted equipment and a vehicle. The driving planning method comprises the following steps: acquiring environment sensing data of the vehicle and a planned driving path of the vehicle; determining a target obstacle disappearing at the current time and attribute information of the target obstacle before disappearance according to the environment perception data; according to the attribute information, predicting the intersection condition of the target obstacle in the future time and the planned driving path to obtain a prediction result; and according to the prediction result, vehicle driving planning is carried out. For the target obstacle disappearing at the current time, the vehicle driving planning is carried out based on the prediction result of the intersection condition of the target obstacle and the planned driving path of the vehicle at the future time, and the vehicle driving safety is improved.

Description

Technical Field

[0001] This application relates to the technical field of vehicles, and in particular, to a driving planning method, device, vehicle-mounted device, and vehicle. Background Art

[0002] As a tool for people's daily travel, the driving safety of vehicles is of great importance. Perceiving the surrounding environment of a vehicle through sensors on the vehicle is one of the main ways to improve driving safety.

[0003] In related technologies, for a detected obstacle, if the obstacle suddenly disappears from the detection result of the sensor, it is determined that the obstacle has left the vehicle surroundings.

[0004] However, the obstacle may be in an area where the sensor cannot perceive, or the sensor may have a short-term failure and fail to detect the obstacle. If it is determined that the obstacle has left the vehicle surroundings, it will lead to certain potential safety hazards in vehicle driving. Summary of the Invention

[0005] Based on the above technical status quo, this application provides a driving planning method, device, vehicle-mounted device, and vehicle, which can improve the driving safety of the vehicle.

[0006] To achieve the above technical objectives, this application specifically proposes the following technical solutions:

[0007] According to a first aspect of an embodiment of this application, a driving planning method is provided, including: obtaining environmental perception data of a vehicle and a planned driving path of the vehicle; determining a target obstacle that disappears at the current time and attribute information of the target obstacle before disappearance according to the environmental perception data; predicting the crossing situation between the target obstacle and the planned driving path of the vehicle at a future time according to the attribute information to obtain a prediction result; and performing vehicle driving planning according to the prediction result.

[0008] In some implementation manners, the attribute information includes a motion attribute and an obstacle position. The predicting the crossing situation between the target obstacle and the planned driving path of the vehicle at a future time according to the attribute information to obtain a prediction result includes: when the motion attribute indicates that the target obstacle is in a stationary state, determining whether the obstacle position is on the planned driving path; and determining the prediction result according to whether the obstacle position is on the planned driving path.

[0009] In some implementations, the attribute information further includes size information. Determining whether the obstacle position of the target obstacle is on the planned driving path includes: according to the size information, determining whether the target obstacle is an avoidance obstacle that the vehicle cannot drive through; if the target obstacle is the avoidance obstacle, then determining whether the obstacle position is on the planned driving path.

[0010] In some implementations, the attribute information includes a motion attribute and an obstacle position. Predicting the crossing situation of the target obstacle with the planned driving path of the vehicle at a future time according to the attribute information to obtain a prediction result includes: when the motion attribute indicates that the target obstacle is in a non - stationary state, predicting the motion range of the target obstacle at the future time according to the obstacle position to obtain the predicted motion range of the target obstacle; determining the prediction result according to whether the predicted motion range intersects with the planned driving path.

[0011] In some implementations, the attribute information further includes an obstacle type. Predicting the motion range of the target obstacle at the future time according to the obstacle position to obtain the predicted motion range of the target obstacle includes: when the obstacle type is a pedestrian, determining the predicted motion range with the obstacle position as the center and the walking distance of the pedestrian at the future time as the radius.

[0012] In some implementations, the motion attribute further indicates the driving speed of the target obstacle, and the attribute information further includes an obstacle type. Predicting the motion range of the target obstacle at the future time according to the obstacle position to obtain the predicted motion range of the target obstacle includes: when the obstacle type is a motor vehicle or a non - motor vehicle, determining the uniform motion trajectory of the target obstacle at the future time according to the driving speed; determining the predicted motion range as the trajectory range corresponding to the uniform motion trajectory.

[0013] In some implementations, performing vehicle driving planning according to the prediction result includes: updating the obstacle tracking information according to the prediction result; performing vehicle driving planning according to the obstacle tracking information.

[0014] In some implementations, updating the obstacle tracking information according to the prediction result includes: if the prediction result indicates that the target obstacle intersects with the planned driving path at the future time, updating the obstacle tracking information according to the intersection situation between the target obstacle and the planned driving path; if the prediction result indicates that the target obstacle does not intersect with the planned driving path at the future time, deleting the tracking information of the target obstacle from the obstacle tracking information.

[0015] According to a second aspect of the embodiments of the present application, a driving planning device is provided, including: an acquisition unit configured to acquire the environmental perception data of the vehicle and the planned driving path of the vehicle; a determination unit configured to determine, according to the environmental perception data, a target obstacle that disappears at the current time and the attribute information of the target obstacle before disappearance; a prediction unit configured to predict, according to the attribute information, the intersection situation between the target obstacle and the planned driving path of the vehicle at a future time, to obtain a prediction result; and a planning unit configured to perform vehicle driving planning according to the prediction result.

[0016] According to a third aspect of the embodiments of the present application, an in-vehicle device is provided, including a memory and a processor; the memory is connected to the processor and configured to store a program; the processor is configured to implement the driving planning method as described in the first aspect or any implementation manner of the first aspect by running the program in the memory.

[0017] According to a fourth aspect of the embodiments of the present application, a vehicle is provided, and the vehicle is equipped with the in-vehicle device described in the third aspect.

[0018] According to a fifth aspect of the present application, a computer program product is provided, including a computer program, and when the computer program is executed by a processor, the driving planning method as described in the first aspect or any embodiment of the first aspect is implemented.

[0019] According to a sixth aspect of the present application, a storage medium is provided, and a computer program is stored on the storage medium, and when the computer program is run by a processor, the driving planning method as described in the first aspect or any embodiment of the first aspect is implemented.

[0020] A driving planning method, device, vehicle-mounted device and vehicle provided by an embodiment of the present application determine a target obstacle that disappears at the current time and attribute information of the target obstacle before disappearance based on the environmental perception data of the vehicle; predict the intersection situation between the target obstacle and the planned driving path of the vehicle at a future time based on the attribute information, and the obtained prediction result reflects the potential safety hazard brought by the target obstacle to the vehicle driving; perform vehicle driving planning based on the prediction result, so that the vehicle driving planning is carried out on the premise of mastering the potential safety hazard of the vehicle driving brought by the target obstacle, and reduce the potential safety hazard of the vehicle driving through the vehicle driving planning, thereby improving the driving safety. BRIEF DESCRIPTION OF THE DRAWINGS

[0021] In order to more clearly illustrate the technical solutions in the embodiments of the present application or the prior art, the following will briefly introduce the drawings required for use in the description of the embodiments or the prior art. Obviously, the drawings in the following description are only the embodiments of the present application. For those of ordinary skill in the art, other drawings can be obtained according to the provided drawings without creative efforts.

[0022] Figure 1 The flowchart of a driving planning method provided by an embodiment of the present application Figure 1 ;

[0023] Figure 2 The flowchart of a driving planning method provided by an embodiment of the present application Figure 2 ;

[0024] Figure 3 The flowchart of a driving planning method provided by an embodiment of the present application Figure 3 ;

[0025] Figure 4 is a schematic structural diagram of a driving planning device provided by an embodiment of the present application;

[0026] Figure 5 is a schematic structural diagram of a vehicle-mounted device provided by an embodiment of the present application. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0027] The technical solution proposed by the embodiment of the present application is applicable to the vehicle driving scenario, aiming to significantly improve the driving safety of the vehicle by improving the driving planning method in the vehicle driving scenario. By adopting the technical solution described in the embodiment of the present application, it is possible to predict the potential safety hazard brought by the disappearing obstacle to the vehicle driving, improve the risk avoidance ability of the vehicle driving planning, and improve the driving safety of the vehicle.

[0028] Next, the technical solutions in the embodiments of the present application will be clearly and completely described in conjunction with the accompanying drawings in the embodiments of the present application. Obviously, the described embodiments are only a part of the embodiments of the present application, rather than all the embodiments. Based on the embodiments in the present application, all other embodiments obtained by those of ordinary skill in the art without creative efforts shall fall within the protection scope of the present application.

[0029] Generally, sensors for environmental perception are deployed on a vehicle. An obstacle detected by the sensor at a previous time may disappear from the detection result of the sensor at the next time.

[0030] In the related art, for the situation where an obstacle detected at a previous time disappears from the detection result of the sensor, one way is to determine that the obstacle has left the vicinity of the vehicle and will not pose a safety hazard to the vehicle's driving, and the obstacle will no longer be considered in the vehicle driving plan; another way is to directly trigger the vehicle safety assistance function, such as triggering a vehicle driving warning or triggering vehicle deceleration.

[0031] The obstacle that disappears from the detection result may be in a place where the sensor cannot detect it, such as being temporarily blocked by other objects, or it may be that the sensor has a detection error, resulting in an unreliable detection result, or it may have actually left the vicinity of the vehicle. For the situation where an obstacle detected at a previous time disappears from the detection result of the sensor, if it is directly determined that the obstacle has left the vicinity of the vehicle, it ignores the possibility that the obstacle may still interfere with the vehicle's driving. When the obstacle is detected again, the vehicle cannot be controlled in time to avoid the obstacle, or a warning cannot be given in time. For the situation where an obstacle detected at a previous time disappears from the detection result of the sensor, if the vehicle safety assistance function is directly triggered, it ignores the possibility that the obstacle may no longer be near the vehicle, resulting in a false trigger of the vehicle safety assistance function, which also has an adverse impact on the safety of the vehicle's driving.

[0032] In view of this, the embodiments of the present application are committed to providing a driving planning method, device, vehicle-mounted device and vehicle. For a target obstacle that disappears at the current time, based on the attribute information of the target obstacle before it disappears, the intersection situation between the target obstacle and the planned driving path of the vehicle in the future is predicted, and based on the obtained prediction result, the vehicle driving plan is carried out. Compared with directly determining that the target obstacle has left the vicinity of the vehicle or directly triggering the vehicle safety assistance function, the embodiments of the present application fully consider the uncertainty of the position of the target obstacle (it may be around the vehicle or may have left the vicinity of the vehicle). By predicting the interference of the target obstacle on the vehicle's driving in the future, the ability of the vehicle driving plan to avoid the target obstacle is improved, and the safety of the vehicle's driving is improved.

[0033] It should be noted that the driving planning method provided in the embodiments of the present application can be executed on a vehicle-mounted device or on a device that establishes a communication connection with the vehicle. The device that establishes a communication connection with the vehicle is, for example, a terminal used by a user in the vehicle (such as a mobile phone) or a device that provides remote services for the vehicle.

[0034] Exemplary method

[0035] Figure 1 is the flow of a driving planning method provided in the embodiments of the present application Figure 1 As Figure 1 shown, the driving planning method provided in this embodiment includes the following steps S101 to S104:

[0036] S101, obtain the environmental perception data of the vehicle and the planned driving path of the vehicle.

[0037] Among them, at least one sensor is deployed on the vehicle, and the at least one sensor is used for environmental perception. For example, a millimeter-wave radar sensor, a lidar sensor, and / or a camera are deployed on the vehicle.

[0038] Among them, the environmental perception data of the vehicle is collected by the sensors deployed on the vehicle. The environmental perception data is, for example, point cloud data collected by a millimeter-wave radar sensor and / or a lidar sensor, and image data and / or video data collected by a camera.

[0039] Among them, the planned driving path of the vehicle includes the driving path of the vehicle at a future time.

[0040] In one example, the planned driving path includes a navigation path from a navigation starting point to a navigation ending point.

[0041] In another example, the planned driving path includes the planned trajectory of the vehicle on at least one road. The at least one road may include: the current road where the vehicle is located, and / or other roads in front of the current road in the driving direction of the vehicle.

[0042] In this embodiment, the environmental perception data collected by the sensors on the vehicle can be obtained. The planned driving path input by the user can be obtained, or the planned driving path can be generated through a path planning algorithm, and the planned driving path output by the path planning algorithm can be obtained. The process of generating the planned driving path through the path planning algorithm can be executed on the same device as this embodiment, or on different devices, or the planned driving path can be obtained from a database, and the database is used to store the path data of the vehicle.

[0043] S102, according to the environmental perception data, determine the target obstacles that disappear at the current time and the attribute information of the target obstacles before they disappear.

[0044] Among them, the target obstacle can be a pedestrian, another vehicle, an animal, a road facility, etc.

[0045] In this embodiment, the environmental perception data may include the acquisition data of the sensors on the vehicle at a past time and the acquisition data of the sensors on the vehicle at the current time; the acquisition data of the sensors at the past time can be used for obstacle detection to obtain the obstacles detected by the sensors at the past time and the attribute information of the obstacles; the acquisition data of the sensors at the current time can be used for obstacle detection to obtain the obstacles detected by the sensors at the current time; compare the obstacles detected by the sensors at the current time with the obstacles detected by the sensors at the past time, determine the obstacles detected by the sensors at the past time but not detected at the current time, and determine the target obstacles that disappear at the current time according to the obstacles detected by the sensors at the past time but not detected at the current time. Obtain the attribute information of the target obstacle from the attribute information of the obstacles detected by the sensors at the past time, that is, the attribute information of the target obstacle before it disappears.

[0046] Among them, the past time may include the previous time of the current time.

[0047] In one example, the sensors collect data frame by frame, the past time is the past time frame, the current time is the current time frame, and the past time frame includes the previous time frame of the current time frame.

[0048] In one example, if multiple sensors are deployed on the vehicle, the environmental perception data may include the acquisition data of all the sensors on the vehicle at a past time and the acquisition data of all the sensors on the vehicle at the current time, and the target obstacle is the obstacle that disappears in the detection results of all the sensors at the current time. For the obstacle that disappears in the detection results of all the sensors, it is more difficult to obtain the obstacle position of the obstacle at the current time, and this embodiment can be used to improve the driving safety of the vehicle in this case.

[0049] Regarding the determination of the target obstacle, the following examples are provided:

[0050] In one example, determining the target obstacle that disappears at the current time according to the obstacle detected by the sensors at the past time but not detected at the current time may include: determining the target obstacle as the obstacle detected by the sensors at the past time but not detected at the current time.

[0051] In another example, determining a target obstacle that has disappeared at the current time based on obstacles detected by the sensor in the past time but not detected at the current time may include: among the obstacles detected by the sensor in the past time but not detected at the current time, searching for an obstacle whose distance between the position of the obstacle in the past time and the boundary of the detection range of the sensor is greater than a distance threshold, and this obstacle is the target obstacle. Among them, the obstacle was far from the boundary of the detection range of the sensor in the past time but disappeared at the current time. It is very likely that it was temporarily blocked by other objects or there was a detection error in the sensor. Such obstacles have a greater impact on the vehicle's driving. Predicting the crossing situation between such obstacles and the planned driving path of the vehicle in the future time can greatly improve driving safety.

[0052] S103. According to the attribute information, predict the crossing situation between the target obstacle and the planned driving path of the vehicle in the future time to obtain a prediction result.

[0053] Among them, the attribute information of the target obstacle is related to the position change of the target obstacle.

[0054] Among them, the future time may include time points and / or time periods after the current time.

[0055] In one example, the future time can be set in advance.

[0056] In another example, the future time is all or part of the time period corresponding to the planned driving path.

[0057] In another example, the future time can be determined according to the driving speed of the vehicle. For example, when the driving speed of the vehicle is relatively fast, a longer time period can be used for the future time to provide the crossing situation between the target obstacle and the planned driving path of the vehicle within a longer time period, and reserve a longer reaction time for the vehicle to drive. Thus, the flexibility and rationality of the future time are improved.

[0058] Among them, the prediction result indicates that the target obstacle crosses the planned driving path in the future time, or the prediction result indicates that the target obstacle does not cross the planned driving trajectory in the future time.

[0059] Optionally, the prediction result also indicates the crossing time. The crossing time may include: the time when the target obstacle crosses the planned driving path, and / or, the duration from the current time to the time when the target obstacle crosses the planned driving path. Thus, more information is provided for the vehicle driving plan, the obstacle avoidance ability of the vehicle driving plan is improved, and the driving safety of the vehicle is improved.

[0060] In this embodiment, based on the attribute information of the target obstacle, the obstacle position of the target obstacle at a future time can be predicted to obtain the predicted position of the target obstacle. According to the intersection situation between the predicted position of the target obstacle and the planned driving path of the vehicle, a prediction result is obtained. Alternatively, based on the attribute information of the target obstacle, the movement range of the target obstacle at a future time can be predicted to obtain the predicted movement range of the target obstacle. According to the intersection situation between the predicted movement range of the target obstacle and the planned driving path of the vehicle, a prediction result is obtained.

[0061] S104. Perform vehicle driving planning according to the prediction result.

[0062] Among them, vehicle driving planning refers to function planning related to vehicle driving.

[0063] In one example, vehicle driving planning includes vehicle driving path planning. Vehicle driving path planning can be performed according to the prediction result to obtain an updated planned driving path, and the target obstacle can be avoided in the updated planned driving path, improving the driving safety of the vehicle.

[0064] In another example, vehicle driving planning includes vehicle driving strategy planning. Vehicle driving strategy planning can be performed according to the prediction result to obtain a planned driving strategy, and the driving of the vehicle can be controlled according to the planned driving strategy. The planned driving strategy can include one or more of the following: planned driving speed, planned driving speed, planned driving safety operation. The planned driving safety operation can include one or more of the following: warning operation, braking operation, airbag opening operation, chassis ground clearance adjustment operation (so that the vehicle can drive over some obstacles), rearview mirror adjustment operation (to help the driver observe the obstacle).

[0065] In the embodiment of the present application, for a currently disappeared target obstacle, based on the attribute information of the target obstacle, the intersection situation between the target obstacle and the planned driving path of the vehicle at a future time is predicted, and vehicle driving planning is performed based on the prediction result. The uncertainty of the position of the target obstacle is fully considered, and by predicting the interference of the target obstacle to vehicle driving at a future time, the avoidance ability of vehicle driving planning for the target obstacle is improved, and the driving safety of the vehicle is improved.

[0066] Figure 2 This is the flow of a driving planning method provided by the embodiment of the present application. Figure 2 As Figure 2 shown, the driving planning method provided in this embodiment includes the following steps S201 to S208:

[0067] S201. Obtain the environmental perception data of the vehicle and the planned driving path of the vehicle.

[0068] S202. Based on the environmental perception data, determine the target obstacle that disappears at the current time and the attribute information of the target obstacle before disappearance. The attribute information includes the motion attribute and the obstacle position.

[0069] Among them, the implementation principles and technical effects of S201 - S202 refer to the foregoing embodiments and will not be elaborated herein.

[0070] Among them, the motion attribute refers to the motion attribute of the target obstacle before disappearance. The motion attribute can indicate the obstacle state of the target obstacle before disappearance. The obstacle state includes the stationary state and / or the non - stationary state. The obstacle position refers to the obstacle position of the target obstacle before disappearance.

[0071] S203. Whether the motion attribute indicates that the target obstacle is in a stationary state.

[0072] If the motion attribute indicates that the target obstacle is in a stationary state (was in a stationary state before disappearance), then execute S204 and S205. If the motion attribute indicates that the target obstacle is in a non - stationary state (was in a non - stationary state before disappearance), then execute S206 and S207.

[0073] It should be noted that S203 - S207 are optional steps. For example, in the case of predicting the intersection situation between a target obstacle in a stationary state and the planned driving path of the vehicle at a future time, this embodiment only needs to include S201, S202, S204, and S205 to solve the problem. Another example is that in the case of predicting the intersection situation between a target obstacle in a non - stationary state and the planned driving path of the vehicle at a future time, this embodiment only needs to include S201, S202, S206, and S207 to solve the problem.

[0074] S204. Determine whether the obstacle position is on the planned driving path.

[0075] S205. Determine the prediction result according to whether the obstacle position is on the planned driving path.

[0076] In this embodiment, for a target obstacle in a stationary state, the obstacle position of the target obstacle will not change. By determining whether the obstacle position of the target obstacle before disappearance is on the planned driving path, it can be predicted whether there is an intersection between the target obstacle and the planned driving path at a future time. If the obstacle position of the target obstacle before disappearance is on the planned driving path, it can be determined that the prediction result indicates that there is an intersection between the target obstacle and the planned driving path at a future time; otherwise, it can be determined that the prediction result indicates that there is no intersection between the target obstacle and the planned driving path at a future time.

[0077] Optionally, in a case where the prediction result indicates that the target obstacle intersects with the planned driving path at a future time, the prediction result further indicates the path point time corresponding to the obstacle position of the target obstacle on the planned driving path and / or the duration between the current time and the path point time.

[0078] In a possible implementation manner, the attribute information of the target obstacle before disappearing further includes the size information of the target obstacle before disappearing. Determining whether the obstacle position of the target obstacle is located on the planned driving path includes: determining, according to the size information of the target obstacle before disappearing, whether the target obstacle is an avoidance obstacle that the vehicle cannot drive through; if the target obstacle is an avoidance obstacle, then determining whether the obstacle position is located on the planned driving path. Thus, before determining whether the obstacle position is located on the planned driving path, it is determined whether the target obstacle is an avoidance obstacle, avoiding unnecessary determination of the obstacle position and improving the accuracy of predicting whether the target obstacle intersects with the planned driving path at a future time.

[0079] Wherein, the size information of the target obstacle before disappearing may include the height information, width information, and / or length information of the target obstacle before disappearing. Based on the size information of the target obstacle before disappearing, it can be determined whether the vehicle can drive through the target obstacle. If the vehicle cannot drive through the target obstacle, then the target obstacle is determined to be an avoidance obstacle.

[0080] S206. Predict the movement range of the target obstacle at a future time according to the obstacle position to obtain the predicted movement range of the target obstacle.

[0081] S207. Determine the prediction result according to whether the predicted movement range intersects with the planned driving path.

[0082] In this embodiment, for a target obstacle in a non - stationary state, the obstacle position changes. The movement range of the target obstacle at a future time can be predicted according to the obstacle position of the target obstacle before disappearing to obtain the predicted movement range of the target obstacle; by determining whether the predicted movement range of the target obstacle intersects with the planned driving path, it is predicted whether the target obstacle intersects with the planned driving path at a future time to obtain the prediction result. Wherein, if the predicted movement range intersects with the planned driving path, it can be determined that the prediction result indicates that the target obstacle intersects with the planned driving path at a future time, otherwise it can be determined that the prediction result indicates that the target obstacle does not intersect with the planned driving path at a future time.

[0083] Optionally, when the prediction result indicates that the target obstacle intersects with the planned driving path at a future time, the prediction result further indicates the following time information: the path point time corresponding to the intersection point of the predicted movement range and the planned driving path on the planned driving path, and / or, the duration between the current time and the path point time.

[0084] In one example, the attribute information of the target obstacle before disappearing further includes the obstacle type of the target obstacle. The predicted movement range of the target obstacle at a future time can be obtained by combining the obstacle position of the target obstacle before disappearing and the obstacle type of the target obstacle. Among them, the obstacle type of the target obstacle can to some extent reflect the movement characteristics of the target obstacle. Combining the obstacle position and the obstacle type for predicting the movement range can effectively improve the accuracy of the movement range prediction.

[0085] In one possible implementation, predicting the movement range of the target obstacle at a future time according to the obstacle position to obtain the predicted movement range of the target obstacle may include: when the obstacle type of the target obstacle is a pedestrian, taking the obstacle position as the center of a circle and the walking distance of the pedestrian at a future time as the radius, to determine the predicted movement range of the target obstacle.

[0086] In this implementation, the walking direction of the pedestrian is relatively variable. When the obstacle type of the target obstacle is a pedestrian, taking the obstacle position of the target obstacle before disappearing as the center of a circle and the walking distance of the pedestrian at a future time as the radius, a circular predicted movement range is formed. This predicted movement range covers all possible directions that the target obstacle may walk at a future time. Based on this predicted movement range, predicting the intersection situation between the target obstacle and the planned driving trajectory of the vehicle at a future time can improve the comprehensiveness of the prediction of the intersection situation and effectively protect the safety of pedestrians.

[0087] Among them, the walking distance of the pedestrian at a future time can be determined according to the duration of the future time. For example, if the duration of the future time is 3 seconds, based on the normal walking speed of the pedestrian, the walking distance of the pedestrian at a future time can be determined to be 4 meters, that is, the radius is determined to be 4 meters.

[0088] In another example, the motion attribute of the target obstacle before disappearance also indicates the driving speed of the target obstacle before disappearance, and the attribute information of the target obstacle before disappearance further includes the obstacle type of the target obstacle. The motion range of the target obstacle in the future time can be predicted by combining the driving speed of the target obstacle before disappearance, the obstacle position of the target obstacle before disappearance, and the obstacle type of the target obstacle, so as to obtain the predicted motion range of the target obstacle. Among them, the driving speed of the target obstacle before disappearance and the obstacle type of the target obstacle can to a certain extent reflect the motion characteristics of the target obstacle. By combining the driving speed, obstacle position, and obstacle type for motion range prediction, the accuracy of motion range prediction can be effectively improved.

[0089] In a possible implementation manner, predicting the motion range of the target obstacle in the future time according to the obstacle position to obtain the predicted motion range of the target obstacle includes: when the obstacle type of the target obstacle is a motor vehicle or a non-motor vehicle, determining the uniform motion trajectory of the target obstacle in the future time according to the driving speed of the target obstacle before disappearance; determining the predicted motion range of the target obstacle as the trajectory range corresponding to the uniform motion trajectory.

[0090] In this implementation manner, motor vehicles or non-motor vehicles usually drive forward within the lane lines, with a single driving direction and relatively stable speed under normal circumstances. When the obstacle type of the target obstacle is a motor vehicle or a non-motor vehicle, under the assumption that the target obstacle moves at a uniform speed at the driving speed before disappearance, the driving trajectory of the target obstacle is predicted to obtain the uniform motion trajectory of the target obstacle in the future time, and the predicted motion range of the target obstacle is determined as the trajectory range corresponding to this uniform motion trajectory. Thus, the motion range of the target obstacle is predicted in a targeted manner, improving the accuracy of predicting the motion range of the target obstacle.

[0091] S208. Perform vehicle driving planning according to the prediction result.

[0092] Among them, the implementation principle and technical effect of S208 refer to the foregoing embodiments and will not be elaborated herein.

[0093] In the embodiments of the present application, for the currently disappearing target obstacle, different methods are respectively adopted to predict the intersection situation between the target obstacle and the planned driving path of the vehicle in the future time according to whether the target obstacle is in a stationary state or a non-stationary state, improving the prediction accuracy, further improving the avoidance ability of the vehicle driving planning for the target obstacle, and improving the safety of vehicle driving.

[0094] Figure 3 This is the flow of a driving planning method provided by the embodiments of the present application Figure 3 . AsFigure 3 As shown in the figure, the driving planning method provided in this embodiment includes the following steps S301 to S305:

[0095] S301, obtain the environmental perception data of the vehicle and the planned driving path of the vehicle.

[0096] S302, according to the environmental perception data, determine the target obstacle that disappears at the current time and the attribute information of the target obstacle before disappearance.

[0097] S303, according to the attribute information, predict the crossing situation of the target obstacle with the planned driving path of the vehicle at a future time, and obtain a prediction result.

[0098] Among them, the implementation principles and technical effects of S301 to S303 refer to the foregoing embodiments and will not be elaborated here.

[0099] S304, update the obstacle tracking information according to the prediction result.

[0100] Among them, the obstacle tracking information includes the tracking information of the obstacles that need to be concerned about during vehicle driving.

[0101] Optionally, the tracking information may include one or more of the following: obstacle position, obstacle type, size information, whether there is an intersection between the obstacle and the planned driving path of the vehicle at a future time, and the intersection time of the obstacle and the planned driving path.

[0102] Optionally, the obstacle tracking information is an obstacle list, which contains the tracking information of the obstacles detected by the sensor, and the obstacle list can be updated according to the detection results of the sensor. Since the target obstacle disappears from the detection results of the sensor at the current time, if the tracking information of the target obstacle is directly deleted from the obstacle list, it may cause the vehicle driving plan to ignore the target obstacle that may intersect with the planned driving path. By adopting the method of this embodiment and updating the obstacle list according to the prediction result obtained by predicting the crossing situation of the target obstacle and the planned driving path, the accuracy of the obstacle list can be effectively improved.

[0103] In this embodiment, after obtaining the prediction result, if the prediction result indicates that the target obstacle intersects with the planned driving path at a future time, the tracking information of the target obstacle can be updated to the obstacle tracking information so that the vehicle driving can pay attention to the target obstacle in time.

[0104] In a possible implementation, according to the prediction result, the obstacle tracking information is updated, including: if the prediction result indicates that the target obstacle intersects with the planned driving path in the future, the obstacle tracking information is updated according to the intersection situation between the target obstacle and the planned driving path; if the prediction result indicates that the target obstacle does not intersect with the planned driving path in the future, the tracking information of the target obstacle is deleted from the obstacle tracking information.

[0105] In this implementation, if the prediction result indicates that the target obstacle intersects with the planned driving path in the future, one or more of the obstacle position of the target obstacle, the obstacle type of the target obstacle, the size information of the target obstacle, whether there is an intersection between the target obstacle and the planned driving path in the future, and the intersection time between the target obstacle and the planned driving path can be updated to the obstacle tracking information, so that the vehicle driving planning process can pay attention to the target obstacle and this information of the target obstacle, improve the avoidance ability of the vehicle driving planning for the target obstacle, and improve the safety of vehicle driving. If the prediction result indicates that the target obstacle does not intersect with the planned driving path in the future, it can be considered that the target obstacle has no impact on vehicle driving, and the tracking information of the target obstacle can be deleted from the obstacle tracking information to reduce the redundancy of the obstacle tracking information and reduce the data processing burden of vehicle driving planning.

[0106] S305, perform vehicle driving planning according to the obstacle tracking information.

[0107] Among them, the vehicle driving planning can refer to the foregoing embodiments and will not be elaborated here.

[0108] In this embodiment, vehicle driving planning is performed according to the updated obstacle tracking information. For example, the obstacle tracking information is input into the relevant algorithm of driving planning, and vehicle driving planning is performed based on the obstacle tracking information in the relevant algorithm to obtain a vehicle driving planning scheme.

[0109] In the embodiment of the present application, the intersection situation between the target obstacle that disappears at the current time and the planned driving trajectory of the vehicle is predicted to obtain a prediction result; based on the prediction result, the obstacle tracking information is updated. Compared with directly deleting the tracking information of the target obstacle from the obstacle tracking information by considering that the target obstacle is not around the vehicle, updating the obstacle tracking information based on the prediction result can effectively improve the accuracy of the obstacle tracking information, and further improve the avoidance ability of the vehicle driving planning for obstacles and the safety of vehicle driving.

[0110] Exemplary device

[0111] Corresponding to the above driving planning method, the embodiment of the present application also provides a driving planning device.Figure 4 This is a schematic structural diagram of a driving planning device provided by an embodiment of the present application. As Figure 4 shown, the driving planning device 40 provided by the embodiment of the present application includes: an acquisition unit 41, a determination unit 42, a prediction unit 43, and a planning unit 44:

[0112] The acquisition unit 41 is configured to acquire the environmental perception data of the vehicle and the planned driving path of the vehicle;

[0113] The determination unit 42 is configured to determine, according to the environmental perception data, the target obstacle that disappears at the current time and the attribute information of the target obstacle before disappearance;

[0114] The prediction unit 43 is configured to predict, according to the attribute information, the intersection situation between the target obstacle and the planned driving path of the vehicle at a future time, and obtain a prediction result;

[0115] The planning unit 44 is configured to perform vehicle driving planning according to the prediction result.

[0116] In some embodiments, the attribute information includes a motion attribute and an obstacle position. Specifically, the prediction unit 43 is configured to: when the motion attribute indicates that the target obstacle is in a stationary state, determine whether the obstacle position is on the planned driving path; and determine the prediction result according to whether the obstacle position is on the planned driving path.

[0117] In some implementation manners, the attribute information further includes size information. Specifically, the prediction unit 43 is configured to: determine, according to the size information, whether the target obstacle is an avoidance obstacle that the vehicle cannot drive through; and if the target obstacle is an avoidance obstacle, determine whether the obstacle position is on the planned driving path.

[0118] In some implementation manners, the attribute information includes a motion attribute and an obstacle position. Specifically, the prediction unit 43 is configured to: when the motion attribute indicates that the target obstacle is in a non-stationary state, predict the motion range of the target obstacle at a future time according to the obstacle position, and obtain the predicted motion range of the target obstacle; and determine the prediction result according to whether the predicted motion range intersects with the planned driving path.

[0119] In some implementation manners, the attribute information further includes an obstacle type. Specifically, the prediction unit 43 is configured to: when the obstacle type is a pedestrian, determine the predicted motion range with the obstacle position as the center and the walking distance of the pedestrian at a future time as the radius.

[0120] In some implementations, the motion attribute further indicates the driving speed of the target obstacle, and the attribute information further includes the obstacle type. The prediction unit 43 is specifically configured to: when the obstacle type is a motor vehicle or a non-motor vehicle, determine the uniform motion trajectory of the target obstacle at a future time according to the driving speed; and determine the predicted motion range as the trajectory range corresponding to the uniform motion trajectory.

[0121] In some implementations, the planning unit 44 is specifically configured to: update the obstacle tracking information according to the prediction result; and perform vehicle driving planning according to the obstacle tracking information.

[0122] In some implementations, the planning unit 44 is specifically configured to: if the prediction result indicates that the target obstacle intersects with the planned driving path at a future time, update the obstacle tracking information according to the intersection situation between the target obstacle and the planned driving path; if the prediction result indicates that the target obstacle does not intersect with the planned driving path at a future time, delete the tracking information of the target obstacle from the obstacle tracking information.

[0123] The driving planning device provided in this embodiment belongs to the same inventive concept as the driving planning method provided in the above embodiments of the present application, and can execute the driving planning method provided in any of the above embodiments of the present application, and has the corresponding functional modules and beneficial effects for executing the driving planning method. For the technical details not described in detail in this embodiment, reference may be made to the specific processing content of the driving planning method provided in the above embodiments of the present application, which will not be elaborated here.

[0124] The functions implemented by the above acquisition unit 41, determination unit 42, prediction unit 43, and planning unit 44 may be implemented by the same or different processors, which is not limited in the embodiments of the present application.

[0125] It should be understood that the units in the above device can be implemented in the form of a processor invoking software. For example, the device includes a processor, the processor is connected to a memory, instructions are stored in the memory, and the processor invokes the instructions stored in the memory to implement any of the above methods or the functions of each unit of the device. The processor can be a general-purpose processor, such as a CPU or a microprocessor, etc., and the memory can be a memory inside the device or a memory outside the device. Alternatively, the units in the device can be implemented in the form of a hardware circuit, and the functions of some or all of the units can be implemented through the design of the hardware circuit. The hardware circuit can be understood as one or more processors. For example, in one implementation, the hardware circuit is an ASIC, and the functions of some or all of the above units are implemented through the design of the logical relationship of the components in the circuit. Again, for example, in another implementation, the hardware circuit can be implemented through a PLD. Taking an FPGA as an example, it can include a large number of logic gate circuits, and the connection relationship between the logic gate circuits is configured through a configuration file, so as to implement the functions of some or all of the above units. All the units of the above device can be all implemented in the form of a processor invoking software, or all implemented in the form of a hardware circuit, or some implemented in the form of a processor invoking software, and the remaining part implemented in the form of a hardware circuit.

[0126] In the embodiments of the present application, a processor is a circuit with the ability to process signals. In one implementation, the processor can be a circuit with the ability to read and execute instructions, such as a CPU, a microprocessor, a GPU, or a DSP, etc. In another implementation, the processor can implement certain functions through the logical relationship of a hardware circuit, and the logical relationship of the hardware circuit is fixed or can be reconfigured. For example, the processor is a hardware circuit implemented by an ASIC or a PLD, such as an FPGA. In a reconfigurable hardware circuit, the process of the processor loading a configuration document to implement the configuration of the hardware circuit can be understood as the process of the processor loading instructions to implement the functions of some or all of the above units. In addition, it can also be a hardware circuit designed for artificial intelligence, which can be understood as a kind of ASIC, such as an NPU, a TPU, a DPU, etc.

[0127] It can be seen that each unit in the above device can be one or more processors (or processing circuits) configured to implement the above method. For example: CPU, GPU, NPU, TPU, DPU, microprocessor, DSP, ASIC, FPGA, or a combination of at least two of these processor forms.

[0128] In addition, all or part of the units in the above device can be integrated together or can be implemented independently. In one implementation, these units are integrated together and implemented in the form of an SOC. The SOC may include at least one processor for implementing any of the above methods or the functions of the units of the device. The types of the at least one processor may be different. For example, it includes a CPU and an FPGA, a CPU and an artificial intelligence processor, a CPU and a GPU, etc.

[0129] Exemplary system

[0130] An embodiment of the present application provides a vehicle-mounted device. Refer to Figure 5 As shown, the vehicle-mounted device includes a memory 500 and a processor 510. Among them, the memory 500 is connected to the processor 510 and is used to store programs. The processor 510 is used to implement the driving planning method disclosed in any of the above embodiments by running the programs stored in the memory 500.

[0131] Specifically, the above system may further include: a bus, a communication interface 520, an input device 530, and an output device 540.

[0132] The processor 510, the memory 500, the communication interface 520, the input device 530, and the output device 540 are interconnected through the bus. Among them:

[0133] The bus may include a path for transmitting information between various components of the computer system.

[0134] The processor 510 may be a general-purpose processor, such as a general-purpose central processing unit (CPU), a microprocessor, etc., or may be an application-specific integrated circuit (ASIC), or one or more integrated circuits for controlling the execution of the program of the present invention. It may also be a digital signal processor (DSP), an application-specific integrated circuit (ASIC), a field-programmable gate array (FPGA) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components.

[0135] The processor 510 may include a main processor and may also include a baseband chip, a modem, etc.

[0136] The program for implementing the technical solution of the present invention is stored in the memory 500, and the operating system and other key services may also be stored. Specifically, the program may include program code, and the program code includes computer operation instructions. More specifically, the memory 500 may include a read-only memory (ROM), other types of static storage devices that can store static information and instructions, a random access memory (RAM), other types of dynamic storage devices that can store information and instructions, a disk memory, a flash memory, etc.

[0137] The input device 530 may include devices for receiving data and information input by the user, such as a keyboard, a mouse, a camera, a scanner, a light pen, a voice input device, a touch screen, a pedometer, or a gravity sensor, etc.

[0138] The output device 540 may include devices for allowing information to be output to the user, such as a display screen, a printer, a speaker, etc.

[0139] The communication interface 520 may include devices of any transceiver type for communicating with other devices or communication networks, such as Ethernet, a radio access network (RAN), a wireless local area network (WLAN), etc.

[0140] The processor 510 executes the program stored in the memory 500 and calls other devices, and can be used to implement each step of any one of the driving planning methods provided in the above embodiments of the present application.

[0141] An embodiment of the present application also proposes a chip, which includes a processor and a data interface. The processor reads and runs the program stored on the memory through the data interface to execute the driving planning method introduced in any of the above embodiments. The specific processing process and its beneficial effects can be referred to the embodiments of the driving planning method described above.

[0142] An embodiment of the present application also proposes a vehicle, which is equipped with the above vehicle-mounted device and is used to execute the steps of the above driving planning method.

[0143] Exemplary computer program product and storage medium

[0144] In addition to the above methods and devices, an embodiment of the present application may also be a computer program product, which includes computer program instructions. When the computer program instructions are run by a processor, the processor is caused to execute the steps in the driving planning method according to various embodiments of the present application described in any of the above embodiments of this specification.

[0145] The computer program product may be written in any combination of one or more programming languages for programming code to perform the operations of the embodiments of the present application. The programming languages include object-oriented programming languages such as Java, C++, etc., and also include conventional procedural programming languages such as the "C" language or similar programming languages. The programming code may be executed entirely on the user computing device, partially on the user device, executed as a stand-alone software package, partially on the user computing device and partially on a remote computing device, or entirely on a remote computing device or server.

[0146] In addition, an embodiment of the present application may also be a storage medium having a computer program stored thereon, and the computer program is executed by a processor to perform the steps in the driving planning method according to various embodiments of the present application described in any of the above embodiments of the present specification, and specifically may implement the steps of the driving planning method as described above.

[0147] For the foregoing method embodiments, for the sake of simple description, they are all expressed as a series of action combinations. However, those skilled in the art should know that the present application is not limited by the described order of actions, because according to the present application, certain steps may be performed in other orders or simultaneously. Secondly, those skilled in the art should also know that the embodiments described in the specification are all preferred embodiments, and the actions and modules involved are not necessarily essential to the present application.

[0148] It should be noted that the embodiments in this specification are all described in a progressive manner. Each embodiment focuses on the differences from other embodiments. The same or similar parts among the embodiments may be referred to each other. For the device embodiments, since they are basically similar to the method embodiments, they are described relatively simply, and the relevant parts may refer to the partial description of the method embodiments.

[0149] The steps in the methods of the embodiments of the present application may be adjusted, combined, and deleted according to actual needs, and the technical features recorded in each embodiment may be replaced or combined.

[0150] The units of the devices in the embodiments of the present application may be combined, divided, and deleted according to actual needs.

[0151] In several embodiments provided by the present application, it should be understood that the disclosed terminals, devices, and methods can be implemented in other ways. For example, the terminal embodiments described above are merely illustrative. For example, the division of modules or sub-modules is only a logical function division. In actual implementation, there may be other division methods. For example, multiple sub-modules or modules can be combined or integrated into another module, or some features can be ignored or not executed. Another point is that the displayed or discussed coupling, direct coupling, or communication connection to each other can be through some interfaces. The indirect coupling or communication connection of devices or modules can be in electrical, mechanical, or other forms.

[0152] The modules or sub-modules described as separate components may or may not be physically separated. The components as modules or sub-modules may or may not be physical modules or sub-modules, that is, they can be located in one place, or they can be distributed to multiple network modules or sub-modules. Some or all of the modules or sub-modules can be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0153] In addition, each functional module or sub-module in various embodiments of the present application can be integrated in a processing module, or each module or sub-module can exist physically alone, or two or more modules or sub-modules can be integrated in one module. The above-mentioned integrated modules or sub-modules can be implemented in the form of hardware or in the form of software functional modules or sub-modules.

[0154] Those skilled in the art can further realize that the units and algorithm steps of each example described in combination with the embodiments disclosed in this article can be implemented by electronic hardware, computer software, or a combination of the two. To clearly illustrate the interchangeability of hardware and software, the composition and steps of each example have been generally described according to functions in the above description. Whether these functions are executed in a hardware or software manner depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementation should not be considered to exceed the scope of the present application.

[0155] The steps of the methods or algorithms described in combination with the embodiments disclosed in this article can be directly implemented by hardware, software units executed by a processor, or a combination of the two. The software units can be placed in a random access memory (RAM), memory, read-only memory (ROM), electrically programmable ROM, electrically erasable programmable ROM, registers, hard disk, removable disk, CD-ROM, or any other form of storage medium well-known in the technical field.

[0156] Finally, it should also be noted that in this text, relational terms such as first and second are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any actual relationship or order between these entities or operations. Moreover, the term "comprising", "including" or any other variant thereof is intended to cover non-exclusive inclusion, such that a process, method, article or device comprising a series of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such process, method, article or device. Without further limitation, an element defined by the statement "comprising an..." does not exclude the presence of additional identical elements in the process, method, article or device comprising the element.

[0157] The foregoing description of the disclosed embodiments enables those skilled in the art to implement or use the present application. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of the present application. Therefore, the present application will not be limited to the embodiments shown herein, but is to be accorded the widest scope consistent with the principles and novel features disclosed herein.

Claims

1. A driving planning method, characterized in that, Including: Obtaining the environmental perception data of the vehicle and the planned driving path of the vehicle; Determining, according to the environmental perception data, a target obstacle that disappears at the current time and attribute information of the target obstacle before disappearance; Predicting, according to the attribute information, the crossing situation between the target obstacle and the planned driving path at a future time to obtain a prediction result; Performing vehicle driving planning according to the prediction result.

2. The driving planning method according to claim 1, characterized in that The attribute information includes a motion attribute and an obstacle position. The predicting, according to the attribute information, the crossing situation between the target obstacle and the planned driving path at a future time to obtain a prediction result includes: When the motion attribute indicates that the target obstacle is in a stationary state, determining whether the obstacle position is on the planned driving path; Determining the prediction result according to whether the obstacle position is on the planned driving path.

3. The driving planning method according to claim 2, characterized in that: The attribute information further includes size information. The determining whether the obstacle position of the target obstacle is on the planned driving path includes: Determining, according to the size information, whether the target obstacle is an avoidance obstacle that the vehicle cannot drive through; If the target obstacle is the avoidance obstacle, determining whether the obstacle position is on the planned driving path.

4. The driving planning method according to claim 1, characterized in that The attribute information includes a motion attribute and an obstacle position. The predicting, according to the attribute information, the crossing situation between the target obstacle and the planned driving path at a future time to obtain a prediction result includes: When the motion attribute indicates that the target obstacle is in a non-stationary state, predicting, according to the obstacle position, the motion range of the target obstacle at the future time to obtain the predicted motion range of the target obstacle; Determining the prediction result according to whether the predicted motion range intersects with the planned driving path.

5. The driving planning method according to claim 4, wherein The attribute information further includes an obstacle type. The predicting, according to the obstacle position, the motion range of the target obstacle at the future time to obtain the predicted motion range of the target obstacle includes: When the obstacle type is a pedestrian, determining the predicted motion range with the obstacle position as the center and the walking distance of the pedestrian at the future time as the radius.

6. The driving planning method according to claim 4, wherein The motion attribute further indicates the driving speed of the target obstacle. The attribute information further includes an obstacle type. The predicting, according to the obstacle position, the motion range of the target obstacle at the future time to obtain the predicted motion range of the target obstacle includes: When the obstacle type is a motor vehicle or a non-motor vehicle, determining the uniform motion trajectory of the target obstacle at a future time according to the driving speed; Determining the predicted motion range as the trajectory range corresponding to the uniform motion trajectory.

7. The driving planning method according to any one of claims 1 to 6, characterized in that, The performing vehicle driving planning according to the prediction result includes: Updating the obstacle tracking information according to the prediction result; Performing vehicle driving planning according to the obstacle tracking information.

8. The driving planning method according to claim 7, characterized in that: Updating the obstacle tracking information according to the prediction result includes: If the prediction result indicates that the target obstacle intersects with the planned driving path at the future time, updating the obstacle tracking information according to the intersection situation between the target obstacle and the planned driving path; If the prediction result indicates that the target obstacle does not intersect with the planned driving path at the future time, deleting the tracking information of the target obstacle from the obstacle tracking information.

9. A travel planning device, characterized in that, It includes: An acquisition unit for acquiring the environmental perception data of the vehicle and the planned driving path of the vehicle; A determination unit for determining, according to the environmental perception data, the target obstacle that disappears at the current time and the attribute information of the target obstacle before disappearance; A prediction unit for predicting, according to the attribute information, the intersection situation between the target obstacle and the planned driving path at a future time to obtain a prediction result; A planning unit for performing vehicle driving planning according to the prediction result.

10. A vehicle-mounted device, characterized in that: It includes a memory and a processor; The memory is connected to the processor and is used for storing programs; The processor is used for implementing the driving planning method according to any one of claims 1 to 8 by running the programs in the memory.

11. A vehicle, characterized in that, The vehicle is equipped with the in-vehicle device according to claim 10.

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