Monitoring method and system for automatic driving system

Abstract

The invention discloses a monitoring method and system for an automatic driving system. The method comprises the following steps: comparing a first position and a second position of a vehicle through a positioning monitoring module to determine positioning alarm information; the area proportion of a perceptual travelable area and an actual travelable area is determined through a perceptual monitoring module, and perceptual alarm information is determined based on detection of a road line; determining the failure times of the trajectory updating operation of the automatic driving system through a planning monitoring module, and determining planning alarm information; an error between an actual control position and a target control position is calculated through a control monitoring module, and control alarm information is determined; recording execution state information of the automatic driving system in each execution stage through a task monitoring module when the automatic driving system processes the target task, and determining task alarm information; and detecting the communication state information of the front-end interaction interface and the rear end through the interaction monitoring module, and determining interaction alarm information. According to the scheme, omnibearing monitoring and problem positioning of the automatic driving system can be realized.

Description

Technical Field

[0001] The present invention relates to the field of autonomous driving technology, and in particular to a monitoring method and system for an autonomous driving system. Background Art

[0002] The autonomous driving system uses multi-sensor technology and complex algorithms to perform environmental perception, path planning and vehicle control, thereby achieving automatic control of the vehicle. If an error occurs in the autonomous driving system, it will directly lead to errors in the control of the vehicle, or even control failure. Therefore, monitoring the autonomous driving system is crucial. However, in the existing technology, generally only a single module or a single link of the autonomous driving system can be monitored. Especially when an abnormality occurs in the autonomous driving system, it is impossible to perform fault diagnosis and recovery in a timely and effective manner based on the monitoring results. Summary of the invention

[0003] The present invention provides a monitoring method and system for an autonomous driving system, which can realize all-round monitoring of the autonomous driving system and can quickly and accurately locate the abnormal link, thereby improving the safety and stability of the autonomous driving system.

[0004] In a first aspect, the present invention provides a monitoring method for an automatic driving system, comprising:

[0005] A position deviation result is obtained by comparing the first position and the second position of the target vehicle at the next moment through the positioning monitoring module, and a positioning alarm message is determined based on the position deviation result; wherein the first position is the position of the target vehicle at the next moment predicted based on the historical position information of the target vehicle; the second position is the actual position of the target vehicle at the next moment obtained based on the positioning of the automatic driving system; and the automatic driving system is configured on the target vehicle;

[0006] The area ratio relationship between the perceived drivable area and the actual drivable area is determined by the perception monitoring module; if the area ratio relationship exceeds a preset ratio threshold, the roadside line is detected by the perception monitoring module to obtain a roadside line detection result, and the perception alarm information is determined based on the roadside line detection result; wherein the perceived drivable area is a road area where the target vehicle is passable as determined by the automatic driving system; the actual drivable area is a road area where the target vehicle is actually passable corresponding to the perceived drivable area in the high-precision map;

[0007] Determine the number of failures of the automatic driving system to perform a trajectory update operation within a preset time period through a planning monitoring module, and determine planning alarm information based on the relationship between the number of failures and a preset number threshold; the trajectory update operation is used to update the trajectory points of the target vehicle during driving;

[0008] The lateral and longitudinal errors between the actual control position and the target control position of the target vehicle are calculated by the control monitoring module, and the control alarm information is determined based on the relationship between the lateral and longitudinal errors and the error threshold; wherein the actual control position is the position actually reached after the automatic driving system controls the target vehicle according to the vehicle control parameters; and the target control position is the position expected to be reached after the automatic driving system controls the target vehicle according to the vehicle control parameters;

[0009] Recording, by means of a task monitoring module, execution status information of the autonomous driving system at each execution stage when processing a target task, and determining task alarm information based on the execution status information;

[0010] The communication status information between the front-end interactive interface in the autonomous driving system and the back-end of the autonomous driving system is detected through the interactive monitoring module, and the interactive alarm information is determined based on the communication status information.

[0011] In a second aspect, the present invention further provides a monitoring system for an autonomous driving system, comprising:

[0012] A positioning monitoring module, used for comparing a first position and a second position of a target vehicle at a next moment to obtain a position deviation result, and determining positioning alarm information based on the position deviation result; wherein the first position is the position of the target vehicle at the next moment predicted based on historical position information of the target vehicle; the second position is the actual position of the target vehicle at the next moment obtained based on positioning by an automatic driving system; and the automatic driving system is configured on the target vehicle;

[0013] A perception monitoring module is used to determine the area ratio relationship between the perceived drivable area and the actual drivable area; if the area ratio relationship exceeds a preset ratio threshold, the roadside line is detected to obtain a roadside line detection result, and the perception alarm information is determined based on the roadside line detection result; wherein the perceived drivable area is a road area where the target vehicle is passable as determined by the automatic driving system; the actual drivable area is a road area where the target vehicle is actually passable corresponding to the perceived drivable area in the high-precision map;

[0014] A planning monitoring module, used to determine the number of failures of the automatic driving system to perform a trajectory update operation within a preset time period, and determine planning alarm information based on the relationship between the number of failures and a preset number threshold; the trajectory update operation is used to update the trajectory points of the target vehicle during driving;

[0015] A control monitoring module, used to calculate the lateral and longitudinal errors between the actual control position of the target vehicle and the target control position, and determine the control alarm information based on the relationship between the lateral and longitudinal errors and the error threshold; wherein the actual control position is the position actually reached by the automatic driving system after controlling the target vehicle according to the vehicle control parameters; and the target control position is the position expected to be reached by the automatic driving system after controlling the target vehicle according to the vehicle control parameters;

[0016] A task monitoring module, used to record the execution status information of the automatic driving system at each execution stage when processing the target task, and determine the task alarm information based on the execution status information;

[0017] The interaction monitoring module is used to detect the communication status information between the front-end interaction interface in the autonomous driving system and the back-end of the autonomous driving system, and determine the interaction alarm information based on the communication status information.

[0018] The technical solution provided by the present invention is to compare the first position and the second position of the target vehicle at the next moment to obtain a position deviation result through a positioning monitoring module, and determine the positioning alarm information based on the position deviation result; determine the area ratio relationship between the perceived drivable area and the actual drivable area through a perception monitoring module; if the area ratio relationship exceeds a preset ratio threshold, the roadside line is detected by the perception monitoring module to obtain a roadside line detection result, and the perception alarm information is determined based on the roadside line detection result; determine the number of failures of the automatic driving system to perform trajectory update operations within a preset time period through a planning monitoring module, and determine the planning alarm information based on the relationship between the number of failures and the preset number threshold; calculate the lateral and longitudinal errors between the actual control position and the target control position of the target vehicle through a control monitoring module, and determine the control alarm information based on the relationship between the lateral and longitudinal errors and the error threshold; record the execution status information of the automatic driving system at each execution stage when processing the target task through a task monitoring module, and determine the task alarm information based on the execution status information; detect the communication status information between the front-end interactive interface in the automatic driving system and the back-end of the automatic driving system through an interaction monitoring module, and determine the interaction alarm information based on the communication status information. The solution of the present invention can monitor the positioning, perception, planning, control, tasks and interaction of the autonomous driving system separately. Through multi-level and multi-dimensional all-round monitoring, it can timely discover the anomalies in the autonomous driving system and can quickly and accurately locate the link where the anomaly occurs, thereby improving the safety and stability of the autonomous driving system.

[0019] The above invention content is only an overview of the technical solution of the present invention. In order to more clearly understand the technical means of the present invention, it can be implemented according to the contents of the specification. In order to make the above and other purposes, features and advantages of the present invention more obvious and easy to understand, the specific implementation methods of the present invention are listed below. BRIEF DESCRIPTION OF THE DRAWINGS

[0020] The above and other features, advantages and aspects of the embodiments of the present invention will become more apparent with reference to the following detailed description in conjunction with the accompanying drawings. Throughout the accompanying drawings, the same or similar reference numerals represent the same or similar elements. It should be understood that the drawings are schematic and the originals and elements are not necessarily drawn to scale.

[0021] Figure 1 is a flow chart of a monitoring method for an automatic driving system provided by an embodiment of the present invention;

[0022] Figure 2 is a flow chart of another monitoring method for an automatic driving system provided by an embodiment of the present invention;

[0023] Figure 3 It is a structural schematic diagram of a monitoring system for an automatic driving system provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0024] Embodiments of the present invention will be described in more detail below with reference to the accompanying drawings. Although certain embodiments of the present invention are shown in the accompanying drawings, it should be understood that the present invention can be implemented in various forms and should not be construed as being limited to the embodiments described herein, which are instead provided for a more thorough and complete understanding of the present invention. It should be understood that the drawings and embodiments of the present invention are only for exemplary purposes and are not intended to limit the scope of protection of the present invention.

[0025] It should be understood that the various steps described in the method implementation of the present invention can be performed in different orders and / or in parallel. In addition, the method implementation may include additional steps and / or omit the steps shown. The scope of the present invention is not limited in this respect. The term "including" and its variations used herein are open inclusions, that is, "including but not limited to". The term "based on" means "at least partially based on". The term "one embodiment" means "at least one embodiment"; the term "another embodiment" means "at least one other embodiment"; the term "some embodiments" means "at least some embodiments". The relevant definitions of other terms will be given in the following description.

[0026] It should be noted that the concepts of "first", "second", etc. mentioned in the present invention are only used to distinguish different modules or units, and are not used to limit the order or interdependence of the functions performed by these modules or units. The modifications of "one" and "multiple" mentioned in the present invention are illustrative and not restrictive. Those skilled in the art should understand that unless otherwise clearly indicated in the context, it should be understood as "one or more". The names of the messages or information exchanged between multiple modules in the embodiments of the present invention are for illustrative purposes only, and are not used to limit the scope of these messages or information.

[0027] Figure 1 The present invention provides a flow chart of a monitoring method for an autonomous driving system. The present invention is applicable to monitoring an autonomous driving system configured on a vehicle. The method can be executed by a monitoring system for an autonomous driving system. The monitoring system for an autonomous driving system can be implemented in the form of software and / or hardware. The monitoring system for an autonomous driving system can be deployed on a vehicle and run based on an independent thread. Figure 1 As shown, the monitoring method for the automatic driving system in the embodiment of the present invention may include the following process:

[0028] S101. Compare the first position and the second position of the target vehicle at the next moment by the positioning monitoring module to obtain a position deviation result, and determine the positioning alarm information based on the position deviation result; wherein the first position is the position of the target vehicle at the next moment predicted based on the historical position information of the target vehicle; the second position is the actual position of the target vehicle at the next moment based on the positioning of the automatic driving system; the automatic driving system is configured on the target vehicle.

[0029] The target vehicle is a vehicle that needs to be monitored by the automatic driving system. The automatic driving system is configured on the target vehicle, and the automatic driving system can be used to control the target vehicle to perform automatic driving.

[0030] The historical location information of the target vehicle is obtained by the automatic driving system. The historical location information specifically refers to the location information that has been determined before the next moment. For example, the historical location information may include the location information at the current moment and the location information at the previous moment. The historical location information of the vehicle can essentially reflect the corresponding movement of the vehicle between different moments.

[0031] The first position is the position of the target vehicle at the next moment predicted based on the historical position information of the target vehicle; that is, the first position essentially reflects the position information that the target vehicle can theoretically move to at the next moment after continuing to travel at the speed corresponding to the current relevant historical position information. Correspondingly, the second position is the position information of the target vehicle at the next moment based on the positioning of the automatic driving system. The position deviation result can be obtained by subtracting the first position from the second position.

[0032] Specifically, in actual scenarios, the positioning capability of the autonomous driving system is crucial for the target vehicle, but the positioning capability of the autonomous driving system may also have problems. Therefore, the first position and the second position of the target vehicle at the next moment can be compared through the positioning monitoring module to obtain the position deviation result. Since the first position reflects the predicted position of the target vehicle at the next moment, and the second position is the position actually located by the autonomous driving system at the next moment, then based on the comparison of the two, it is possible to determine whether the positioning capability of the autonomous driving system is accurate. And based on the position deviation result, the positioning alarm information is determined, and the positioning alarm information is used to reflect the positioning anomaly of the autonomous driving system.

[0033] S102. Determine the area ratio relationship between the perceived drivable area and the actual drivable area through the perception monitoring module; if the area ratio relationship exceeds the preset ratio threshold, detect the roadside line through the perception monitoring module to obtain the roadside line detection result, and determine the perception alarm information based on the roadside line detection result; wherein, the perceived drivable area is the road area where the target vehicle is passable as determined by the automatic driving system; the actual drivable area is the road area where the target vehicle is actually passable corresponding to the perceived drivable area in the high-precision map.

[0034] Among them, the autonomous driving system can rely on the fusion of multiple sensors and technologies to perceive the environment around the target vehicle. The perceived drivable area is the road area where the target vehicle can pass as determined by the autonomous driving system. Correspondingly, the actual drivable area is the road area where the target vehicle can actually pass corresponding to the perceived drivable area in the high-precision map. The high-precision map is a high-precision map that has been pre-configured in the autonomous driving system. The accuracy of the high-precision map can reach the centimeter level. The high-precision map has detailed lane line information, traffic sign information, and road feature information, etc., so as to provide accurate and reliable map information for the autonomous driving system. That is, the perceived drivable area is essentially the drivable area determined by the autonomous driving system after perception, while the actual drivable area is the area that is truly drivable in the actual geographical environment. Roadside lines refer to various boundary lines in the road, for example, lane lines in the road, etc.

[0035] In actual scenarios, the autonomous driving system will perceive the road area in front of the target vehicle, thereby providing the target vehicle with the ability to understand the surrounding environment, which facilitates the autonomous driving system to carry out relevant path planning and decision-making. Then, the perception monitoring module can be used to detect the perception ability of the autonomous driving system to determine whether there is any abnormality in the perception ability of the autonomous driving system.

[0036] Specifically, the perceived drivable area determined by the autonomous driving system can be projected into the high-precision map through the perception monitoring module to determine the area ratio relationship between the perceived drivable area and the actual drivable area. The obtained area ratio relationship can be compared with the preset ratio threshold. Among them, the preset ratio threshold is generally set to 1 by default. If the area ratio relationship exceeds the preset ratio threshold, that is, the area ratio of the perceived drivable area to the actual drivable area is greater than 1, it indicates that the perceived drivable area determined by the autonomous driving system is larger than the area of ​​the actual drivable area, which means that the current perception ability of the autonomous driving system is obviously problematic. Therefore, the roadside can be further detected through the perception monitoring module. Detecting the roadside refers to detecting the roadside in the perception result of the autonomous driving system and the corresponding roadside in the high-precision map to determine the deviation between the two, thereby obtaining the roadside detection result, and determining the perception alarm information based on the roadside detection result. The perception alarm information is used to reflect the perception abnormality of the autonomous driving system.

[0037] S103. Determine the number of failed trajectory update operations of the autonomous driving system within a preset time period through a planning monitoring module, and determine planning alarm information based on the relationship between the number of failures and a preset number threshold; the trajectory update operation is used to update the trajectory points of the target vehicle during driving.

[0038] The preset time period can be set based on actual needs. For example, the preset time period can be set to 50ms. The automatic driving system performs a trajectory update operation to update the trajectory points of the target vehicle during driving. The trajectory points can indicate the location points that the target vehicle needs to reach at various time points during driving.

[0039] The preset number threshold is also set based on actual needs. The preset number threshold can reflect the acceptable range of failures of the trajectory update operation. For example, the preset number threshold can be set to 3 times. If the number of failures is within this range, it may not cause problems to the vehicle's driving, which is acceptable; if the number of failures exceeds this range, it will cause huge problems to the vehicle's driving, for example, it may cause the vehicle to stop moving, which is unacceptable.

[0040] Specifically, the autonomous driving system can plan and update the driving trajectory of the target vehicle, that is, update the trajectory points of the target vehicle during driving. However, the autonomous driving system may fail to successfully update the trajectory points, that is, the trajectory update operation fails. Therefore, the planning monitoring module can be used to determine the number of failures of the autonomous driving system to perform trajectory update operations within a preset time period, and the planning alarm information can be determined based on the relationship between the number of failures and the preset number threshold, so as to determine whether the autonomous driving system has serious problems in updating the vehicle's trajectory. The planning alarm information is used to reflect the existence of planning anomalies in the autonomous driving system.

[0041] S104. Calculate the lateral and longitudinal errors between the actual control position and the target control position of the target vehicle through the control monitoring module, and determine the control alarm information based on the relationship between the lateral and longitudinal errors and the error thresholds; wherein the actual control position is the position actually reached by the automatic driving system after controlling the target vehicle according to the vehicle control parameters; the target control position is the position expected to be reached by the automatic driving system after controlling the target vehicle according to the vehicle control parameters.

[0042] Among them, the vehicle control parameters can be used to adjust the movement behavior of the vehicle. Vehicle control parameters include parameters such as speed, acceleration, angular velocity, angular acceleration, steering angle and steering rate. The actual control position is the position actually reached after the automatic driving system controls the target vehicle according to the vehicle control parameters; the target control position is the position expected to be reached after the automatic driving system controls the target vehicle according to the vehicle control parameters. It should be noted that the actual control position and the target control position are essentially two positions corresponding to the same vehicle control parameters after control. The actual control position is the position that can actually be reached, and the target control position is the position expected to be reached.

[0043] The lateral and longitudinal errors include lateral errors and longitudinal errors. The lateral and longitudinal errors reflect the difference between the actual control position and the target control position.

[0044] Specifically, after the automatic driving system determines the specific vehicle control parameters for controlling the target vehicle, the vehicle's motion behavior can be controlled based on these vehicle control parameters. However, in the process of controlling the vehicle, there may be control errors, resulting in the vehicle being unable to reach the desired position. Therefore, the lateral and longitudinal errors between the actual control position and the target control position of the target vehicle can be calculated by the control monitoring module to reflect the controllability of the target vehicle, and then the control alarm information can be determined based on the relationship between the lateral and longitudinal errors and the error threshold; the control alarm information reflects that the automatic driving system has abnormal control over the vehicle. For example, if the lateral and longitudinal errors exceed the error threshold, it means that the controllability of the vehicle is abnormal, and the control alarm information can be prompted for subsequent adjustments.

[0045] S105. Record the execution status information of the autonomous driving system at each execution stage when processing the target task through the task monitoring module, and determine the task alarm information based on the execution status information.

[0046] The target task is the task that the autonomous driving system needs to process and promote. For example, the target task can be a perception task, a positioning task, a planning task, etc. The execution stage includes the start of the task, the ongoing task, and the end of the task. The execution status information includes normal execution and abnormal execution.

[0047] Specifically, when the autonomous driving system processes the target task, problems or anomalies may occur at each execution stage of the target task. Therefore, the task monitoring module can record the execution status information of the autonomous driving system at each execution stage when processing the target task, and determine the task alarm information based on the execution status information. The task alarm information reflects the abnormality of the task execution of the autonomous driving system.

[0048] Optionally, the task alarm information may also record corresponding specific abnormal information. The autonomous driving system may take corresponding countermeasures based on the task alarm information. For example, the task may be restarted, the task alarm information may be displayed on the interactive interface of the target vehicle, etc.

[0049] S106. Detect the communication status information between the front-end interactive interface in the autonomous driving system and the back-end of the autonomous driving system through the interactive monitoring module, and determine the interactive alarm information based on the communication status information.

[0050] The front-end interactive interface can not only provide users with various parameter information and status information, but also support users to perform different operations. For example, the front-end interactive interface can display the task execution status, the health of the autonomous driving system, map information, the vehicle's position, vehicle chassis information, prompt information and various alarm information to users, so as to facilitate users to understand and control; at the same time, users can also perform related operations on the front-end interface.

[0051] The communication status information refers to the communication status during the request and response process between the front-end interactive interface and the back-end of the autonomous driving system. For example, the front-end interactive interface and the back-end of the autonomous driving system can communicate through streaming.

[0052] Specifically, the communication status information between the front-end interactive interface and the back-end of the autonomous driving system can be detected by the interaction monitoring module, and the interaction alarm information can be determined based on the communication status information. The interaction alarm information is used to reflect the interaction anomaly of the autonomous driving system. For example, if it is detected that the front-end and back-end cannot communicate normally within the SSE (Server-SentEvents) cycle, it is considered that the autonomous driving system has an interaction anomaly.

[0053] The technical solution provided by the present invention is to compare the first position and the second position of the target vehicle at the next moment to obtain a position deviation result through a positioning monitoring module, and determine the positioning alarm information based on the position deviation result; determine the area ratio relationship between the perceived drivable area and the actual drivable area through a perception monitoring module; if the area ratio relationship exceeds a preset ratio threshold, the roadside line is detected by the perception monitoring module to obtain a roadside line detection result, and the perception alarm information is determined based on the roadside line detection result; determine the number of failures of the automatic driving system to perform trajectory update operations within a preset time period through a planning monitoring module, and determine the planning alarm information based on the relationship between the number of failures and the preset number threshold; calculate the lateral and longitudinal errors between the actual control position and the target control position of the target vehicle through a control monitoring module, and determine the control alarm information based on the relationship between the lateral and longitudinal errors and the error threshold; record the execution status information of the automatic driving system at each execution stage when processing the target task through a task monitoring module, and determine the task alarm information based on the execution status information; detect the communication status information between the front-end interactive interface in the automatic driving system and the back-end of the automatic driving system through an interaction monitoring module, and determine the interaction alarm information based on the communication status information. The solution of the present invention can monitor the positioning, perception, planning, control, tasks and interaction of the autonomous driving system separately. Through multi-level and multi-dimensional all-round monitoring, it can timely discover the anomalies in the autonomous driving system and can quickly and accurately locate the link where the anomaly occurs, thereby improving the safety and stability of the autonomous driving system.

[0054] Figure 2 This is a flow chart of another monitoring method for an automatic driving system provided by an embodiment of the present invention. The technical solution of this embodiment further optimizes the process of determining the positioning alarm information based on the position deviation result by the positioning monitoring module in the above embodiment on the basis of the technical solution of the above embodiment. This embodiment can be combined with various optional solutions in one or more of the above embodiments. Figure 2 As shown, the monitoring method for the automatic driving system in the embodiment of the present invention may include the following process:

[0055] S201. Compare the first position and the second position of the target vehicle at the next moment through the positioning monitoring module to obtain a position deviation result; wherein the first position is the position of the target vehicle at the next moment predicted based on the historical position information of the target vehicle; the second position is the actual position of the target vehicle at the next moment based on the positioning of the automatic driving system; the automatic driving system is configured on the target vehicle.

[0056] As an optional but non-limiting implementation, the historical position information includes the position information of the target vehicle at the current moment and the position information at the previous moment; accordingly, the process of determining the first position includes the following steps A1-A2:

[0057] Step A1: Obtain the current position information and the previous position information of the target vehicle located by the automatic driving system through the positioning monitoring module.

[0058] Among them, the location information is the vehicle positioning data obtained by the automatic driving system.

[0059] Specifically, the positioning monitoring module can obtain the target vehicle's current location information and the previous location information by monitoring the message data in the automatic driving system.

[0060] It should be noted that in the embodiment of the present invention, the various parameters or data determined by the autonomous driving system can be transmitted in the form of topic messages. Then, each monitoring module in the monitoring method can monitor these topic information to obtain the required parameter information or data information from the autonomous driving system. Other places in this embodiment will not be described in detail.

[0061] Step A2: Input the current position information and the previous position information into the trajectory prediction model through the positioning monitoring model, and output the first position; wherein the trajectory prediction model can predict the position information that the target vehicle will reach at the next moment based on the position information of the target vehicle at the current moment.

[0062] Among them, the trajectory prediction model can be obtained by using the LSTM (Long Short-Term Memory) neural network model as the initial network model training. Specifically, during the training process, the historical position information of the target vehicle can be used as training data, and each historical position information is input into the neural network model for training. The following formula is used during the training process:

[0063] h t =f(W h ·[h t-1 ,x t ]+bh );

[0064] Among them, h t represents the hidden state at time step t; x t Represents the historical location information of the input; W h represents weight; b h Represents and bias terms.

[0065] The model outputs the predicted location It can be calculated by the following formula:

[0066]

[0067] in, represents the predicted position; W0 represents the initial weight; b 0h represents the initial bias term.

[0068] After the LSTM neural network model is trained, the trained neural network model can be used as a trajectory prediction model. Then, the current position information and the previous position information are input into the trajectory prediction model through the positioning monitoring module, and the first position can be output.

[0069] S202: If the position deviation result exceeds the preset deviation threshold, the point cloud data of the target vehicle at the current moment and the point cloud data at the next moment are obtained through the positioning monitoring module.

[0070] Among them, the point cloud data can be determined based on the laser radar configured on the target vehicle.

[0071] Specifically, if the position deviation result exceeds the preset deviation threshold, it means that at the next moment, the actual position of the vehicle located by the autonomous driving system is far different from the predicted position, and the positioning ability of the autonomous driving system may be problematic. Therefore, it is necessary to continue to make more detailed and accurate judgments on the target vehicle. Specifically, the point cloud data of the target vehicle at the current moment and the point cloud data at the next moment can be obtained through the positioning monitoring module. These two frames of point cloud data essentially reflect the actual movement of the target vehicle from the current moment to the next moment in the actual scene. Therefore, it is possible to consider further judging whether the positioning ability of the automatic navigation system is abnormal based on the actual point cloud data.

[0072] S203, start the point cloud matching algorithm to determine the corresponding matching point group in the point cloud data at the current moment and the point cloud data at the next moment; wherein the matching point group includes a first measured point and a second measured point, the first measured point belongs to the point cloud data at the current moment, and the second measured point belongs to the point cloud data at the next moment.

[0073] Among them, the matching point group essentially reflects the corresponding groups of corresponding points in two adjacent frames of point cloud data. The matching point group includes the first measured point and the second measured point. The first measured point belongs to the point cloud data at the current moment, and the second measured point belongs to the point cloud data at the next moment.

[0074] Specifically, after obtaining the point cloud data of the target vehicle at the current moment and the point cloud data at the next moment, the point cloud matching algorithm ICP (Iterative Closest Point) can be started to determine the corresponding matching point groups in the point cloud data at the current moment and the point cloud data at the next moment.

[0075] S204: Determine a translation vector between a first measured point and a second measured point in the matching point group, and convert the translation vector into a translation distance.

[0076] The translation vector is used to reflect the moving distance and moving direction between two frames of point cloud data. In other words, the translation vector essentially reflects the real movement of the target vehicle from the current moment to the next moment. The translation distance is used to reflect the actual moving distance of the target vehicle from the current moment to the next moment.

[0077] Specifically, the translation vector between the first measured point and the second measured point can be calculated by the following formula:

[0078] T = argmin T ∑ i ||p i -q i || 2 ;

[0079] Where T is the translation vector; p i is the first measured point; q i The second measured point.

[0080] After obtaining the translation vector, the translation vector can be modulo calculated to obtain the translation distance.

[0081] S205: Determine positioning alarm information based on the translation distance and a preset distance threshold.

[0082] The preset distance threshold is used to reflect the acceptable range of the vehicle position deviation. Specifically, based on the relationship between the translation distance and the preset distance threshold, the positioning alarm information can be determined.

[0083] As an optional but non-limiting implementation, the distance threshold includes a first distance threshold and a second distance threshold, wherein the first distance threshold is less than the second distance threshold. Accordingly, the positioning alarm information is determined based on the translation distance and the preset distance threshold, including: if the translation distance is greater than the first distance threshold but not greater than the second distance threshold, the alarm mechanism is triggered through the positioning monitoring module, and a prompt message is displayed on the interactive interface of the target vehicle; if the translation distance is greater than the second distance threshold, an emergency stop instruction is sent to the automatic driving system through the positioning monitoring module, and the automatic driving system is prompted that a positioning abnormality occurs.

[0084] Specifically, the distance threshold can be set based on actual needs, and the distance threshold can be set to two distance thresholds, one large and one small, where the smaller value is used as the first distance threshold and the larger value is used as the second distance threshold. If the translation distance is greater than the first distance threshold but not greater than the second distance threshold, indicating that the position of the target vehicle has deviated, but the degree of deviation is not too serious, the alarm mechanism can be triggered through the positioning monitoring module, and a prompt message can be displayed on the interactive interface of the target vehicle; if the translation distance is greater than the second distance threshold, indicating that the position of the target vehicle has deviated significantly, and the degree of deviation is relatively serious, an emergency stop command is sent to the automatic driving system through the positioning monitoring module, and a prompt is prompted that the automatic driving system has a positioning abnormality.

[0085] S206. Determine the area ratio relationship between the perceived drivable area and the actual drivable area through the perception monitoring module; if the area ratio relationship exceeds the preset ratio threshold, detect the roadside line through the perception monitoring module to obtain the roadside line detection result, and determine the perception alarm information based on the roadside line detection result; wherein, the perceived drivable area is the road area where the target vehicle is passable as determined by the automatic driving system; the actual drivable area is the road area where the target vehicle is actually passable corresponding to the perceived drivable area in the high-precision map.

[0086] As an optional but non-limiting implementation, detecting the roadside line by the perception monitoring module to obtain the roadside line detection result may include the following steps B1-B5:

[0087] Step B1: Fit the perception results output by the autonomous driving system through the perception monitoring module to obtain the first roadside.

[0088] The first roadside line is a roadside line in the perception result of the automatic driving system. Specifically, the first roadside line is obtained by fitting the perception result output by the automatic driving system through the perception monitoring module.

[0089] Step B2: Determine a second route along the road corresponding to the first route along the road based on the high-precision map.

[0090] The second roadside is a roadside corresponding to the first roadside in the high-precision map. Specifically, based on the location information obtained by positioning the vehicle and the high-precision map, the second roadside corresponding to the first roadside can be determined.

[0091] Step B3: performing interval sampling on the first roadside line to determine each fitting sampling point, and determining the actual sampling point corresponding to each fitting sampling point on the second roadside line.

[0092] When performing interval sampling, the sampling interval can be set differently based on actual needs, for example, the sampling interval can be 2m. Specifically, interval sampling can be performed on the first roadside according to the sampling interval to determine each fitting sampling point. Then, each actual sampling point corresponding to each fitting sampling point is determined on the second roadside.

[0093] Step B4: Determine the distance deviation value between each fitting sampling point and the corresponding actual sampling point.

[0094] The distance deviation value is the distance between the fitting sampling point and the corresponding actual sampling point in a bird's eye view, that is, the distance between the two after being projected on the same plane. In an embodiment of the present invention, the distance deviation value can be calculated based on the following formula:

[0095]

[0096] Among them, x i is the horizontal coordinate of the fitting sampling point, y i is the ordinate of the fitting sampling point; is the horizontal coordinate of the actual sampling point, The ordinate of the actual sampling point; d i For the fitting sampling point (x i ,y i ) and the actual sampling point The distance deviation between them.

[0097] In the same principle, the distance deviation value between each fitting sampling point and the corresponding actual sampling point can be determined based on the above formula.

[0098] Step B5: Determine the roadside detection result based on the numerical distribution between each distance deviation value.

[0099] The numerical distribution is used to reflect the dispersion of the calculated distance deviation values.

[0100] Specifically, since there is a certain order between each sampling point, the distance deviation values ​​obtained also have a certain order. For each distance deviation value, if only one distance deviation value does not meet the requirements, this may be caused by inaccurate calculation data; but if some consecutive distance deviation values ​​do not meet the requirements, it can be said that the automatic driving system has an abnormality in the detection of the roadside. Therefore, the roadside detection result can be determined based on the numerical distribution between each distance deviation value.

[0101] As an optional but non-limiting implementation method, the roadside line detection result is determined based on the numerical distribution between each distance deviation value, including: if a preset number of consecutive distance deviation values ​​in each distance deviation value are all smaller than the distance deviation threshold, then the roadside line detection result is that there is an abnormality in the roadside line detection.

[0102] Specifically, if a preset number of consecutive distance deviation values ​​among the distance deviation values ​​are all smaller than the distance deviation threshold, it means that the first roadside line and the second roadside line obtained by the perception fitting of the automatic driving system are already quite different. In this case, the roadside line detection result is that there is an abnormality in the roadside line detection.

[0103] S207. Determine the number of failed trajectory update operations of the automatic driving system within a preset time period through a planning monitoring module, and determine planning alarm information based on the relationship between the number of failures and a preset number threshold; the trajectory update operation is used to update the trajectory points of the target vehicle during driving.

[0104] As an optional but non-limiting implementation, determining the number of failures of the trajectory update operation of the autonomous driving system within a preset time period by the planning monitoring module may include the following steps C1-C3:

[0105] Step C1: Determine each trajectory point planned by the autonomous driving system within a preset time period through the planning monitoring module.

[0106] The trajectory point can indicate the position point that the target vehicle needs to reach at each time point during the driving process. The automatic driving system performs a trajectory update operation to update the trajectory point of the target vehicle during the driving process, so that the target vehicle uses the trajectory point as the aiming point at each time point and moves to the corresponding trajectory point.

[0107] Specifically, the topic message of the autonomous driving system can be monitored through the planning monitoring module to determine the various trajectory points planned by the autonomous driving system within a preset time period.

[0108] Step C2: determining the moving distance between adjacent track points in each track point; the moving distance is used to reflect the moving situation between two adjacent track points.

[0109] Specifically, each trajectory point is planned for the autonomous driving system, and the moving distance between all two adjacent trajectory points in each trajectory point can be determined.

[0110] Step C3: Count the number of moving distances that are less than the preset distance value to obtain a statistical number; and use the statistical number as the number of failures.

[0111] The preset distance value can be set based on actual needs, or the preset distance value can be set to 0 by default. Specifically, if the moving distance between two adjacent trajectory points is less than the preset distance value, it means that the two adjacent trajectory points have not been updated. Therefore, the number of moving distances less than the preset distance value can be counted to obtain a statistical number, and the statistical number is used as the number of failures.

[0112] As an optional but non-limiting implementation method, the collision time between the target vehicle and the reference obstacle is calculated in real time through the planning monitoring module; the reference obstacle is the obstacle around the target vehicle, and the collision time is determined based on the first distance and the first speed; wherein the first distance is the distance between the target vehicle and the reference obstacle; the first speed is the relative speed between the target vehicle and the reference obstacle. If the collision time is lower than the safety time threshold, an alarm is issued through the planning monitoring module; if the number of alarms issued by the planning monitoring module exceeds the set number threshold, an emergency stop command is sent to the automatic driving system through the planning monitoring module, and the planning monitoring module is prompted to be abnormal.

[0113] The reference obstacle is an obstacle around the target vehicle, for example, the reference obstacle can be a road bollard, a guardrail, etc. The collision time can be used to reflect the potential collision risk of the target vehicle, and the safety of the vehicle trajectory can be quickly evaluated with the help of the collision time. The collision time can be determined based on the first distance and the first speed. The first distance is the distance between the target vehicle and the reference obstacle; the first speed is the relative speed between the target vehicle and the reference obstacle.

[0114] Specifically, after calculating the collision time, if the collision time is lower than the safety time threshold, an alarm will be issued through the planning monitoring module; if the number of alarm prompts issued by the planning monitoring module exceeds the set number threshold, an emergency stop command will be sent to the automatic driving system through the planning monitoring module, and the planning monitoring module will be prompted to have an abnormality.

[0115] S208. Calculate the lateral and longitudinal errors between the actual control position and the target control position of the target vehicle through the control monitoring module, and determine the control alarm information based on the relationship between the lateral and longitudinal errors and the error thresholds; wherein the actual control position is the position actually reached by the automatic driving system after controlling the target vehicle according to the vehicle control parameters; the target control position is the position expected to be reached by the automatic driving system after controlling the target vehicle according to the vehicle control parameters.

[0116] As an optional but non-limiting implementation, the lateral and longitudinal errors include lateral errors and longitudinal errors; accordingly, the lateral and longitudinal errors between the actual control position and the target control position of the target vehicle are calculated by the control monitoring module, which can be calculated by the following formula:

[0117] e x =|T e,x -T a,x |;

[0118] e y =|T e,y -T a,y |;

[0119] Among them, e x is the lateral error; e y is the longitudinal error; T e,x is the horizontal coordinate corresponding to the target control position; T e,y is the ordinate corresponding to the target control position; T a,x is the horizontal coordinate corresponding to the actual control position; T a,y is the vertical coordinate corresponding to the actual control position.

[0120] S209, recording, by means of a task monitoring module, execution status information of the autonomous driving system at each execution stage when processing the target task, and determining task alarm information based on the execution status information;

[0121] S210. Detect the communication status information between the front-end interactive interface in the autonomous driving system and the back-end of the autonomous driving system through the interactive monitoring module, and determine the interactive alarm information based on the communication status information.

[0122] The technical solution provided by the present invention is to compare the first position and the second position of the target vehicle at the next moment to obtain a position deviation result through a positioning monitoring module, and determine the positioning alarm information based on the position deviation result; determine the area ratio relationship between the perceived drivable area and the actual drivable area through a perception monitoring module; if the area ratio relationship exceeds a preset ratio threshold, the roadside line is detected by the perception monitoring module to obtain a roadside line detection result, and the perception alarm information is determined based on the roadside line detection result; determine the number of failures of the automatic driving system to perform trajectory update operations within a preset time period through a planning monitoring module, and determine the planning alarm information based on the relationship between the number of failures and the preset number threshold; calculate the lateral and longitudinal errors between the actual control position and the target control position of the target vehicle through a control monitoring module, and determine the control alarm information based on the relationship between the lateral and longitudinal errors and the error threshold; record the execution status information of the automatic driving system at each execution stage when processing the target task through a task monitoring module, and determine the task alarm information based on the execution status information; detect the communication status information between the front-end interactive interface in the automatic driving system and the back-end of the automatic driving system through an interaction monitoring module, and determine the interaction alarm information based on the communication status information. The solution of the present invention can monitor the positioning, perception, planning, control, tasks and interaction of the autonomous driving system separately. Through multi-level and multi-dimensional all-round monitoring, it can timely discover the anomalies in the autonomous driving system and can quickly and accurately locate the link where the anomaly occurs, thereby improving the safety and stability of the autonomous driving system.

[0123] Figure 3 The present invention provides a schematic diagram of a monitoring system for an autonomous driving system. The present invention is applicable to monitoring an autonomous driving system configured on a vehicle. The monitoring system for an autonomous driving system can be implemented in the form of software and / or hardware. The monitoring system for an autonomous driving system can be deployed on a vehicle and run based on an independent thread. Figure 3 As shown, the monitoring system for the autonomous driving system in the embodiment of the present invention may include a positioning monitoring module 310, a perception monitoring module 320, a planning monitoring module 330, a control monitoring module 340, a task monitoring module 350 and an interaction monitoring module 360. Among them:

[0124] The positioning monitoring module 310 is used to compare the first position and the second position of the target vehicle at the next moment to obtain a position deviation result, and determine the positioning alarm information based on the position deviation result; wherein the first position is the position of the target vehicle at the next moment predicted based on the historical position information of the target vehicle; the second position is the actual position of the target vehicle at the next moment obtained based on the positioning of the automatic driving system; the automatic driving system is configured on the target vehicle;

[0125] The perception monitoring module 320 is used to determine the area ratio relationship between the perceived drivable area and the actual drivable area; if the area ratio relationship exceeds a preset ratio threshold, the roadside line is detected to obtain a roadside line detection result, and the perception alarm information is determined based on the roadside line detection result; wherein the perceived drivable area is a road area where the target vehicle is passable as determined by the automatic driving system; the actual drivable area is a road area where the target vehicle is actually passable corresponding to the perceived drivable area in the high-precision map;

[0126] The planning monitoring module 330 is used to determine the number of failures of the automatic driving system to perform a trajectory update operation within a preset time period, and determine planning alarm information based on the relationship between the number of failures and a preset number threshold; the trajectory update operation is used to update the trajectory points of the target vehicle during driving;

[0127] The control monitoring module 340 is used to calculate the lateral and longitudinal errors between the actual control position of the target vehicle and the target control position, and determine the control alarm information based on the relationship between the lateral and longitudinal errors and the error threshold; wherein the actual control position is the position actually reached by the automatic driving system after controlling the target vehicle according to the vehicle control parameters; and the target control position is the position expected to be reached by the automatic driving system after controlling the target vehicle according to the vehicle control parameters;

[0128] The task monitoring module 350 is used to record the execution status information of the automatic driving system at each execution stage when processing the target task, and determine the task alarm information based on the execution status information;

[0129] The interaction monitoring module 360 ​​is used to detect the communication status information between the front-end interaction interface in the autonomous driving system and the back-end of the autonomous driving system, and determine the interaction alarm information based on the communication status information.

[0130] As an optional but non-limiting implementation method, the historical position information includes the position information of the target vehicle at the current moment and the position information at the previous moment; accordingly, the process of determining the first position includes: obtaining the position information of the target vehicle at the current moment and the position information at the previous moment obtained by the automatic driving system through the positioning monitoring module; inputting the position information at the current moment and the position information at the previous moment into the trajectory prediction model through the positioning monitoring module, and outputting the first position; wherein the trajectory prediction model can predict the position information to be reached by the target vehicle at the next moment based on the position information of the target vehicle at the current moment.

[0131] As an optional but non-limiting implementation, the positioning monitoring module 310 includes a point cloud data acquisition unit, a matching point group determination unit, a translation distance determination unit, and a positioning alarm information determination unit. Among them: the point cloud data acquisition unit is used to obtain the point cloud data of the target vehicle at the current moment and the point cloud data at the next moment if the position deviation result exceeds the preset deviation threshold; the matching point group determination unit is used to start the point cloud matching algorithm to determine the corresponding matching point group in the point cloud data at the current moment and the point cloud data at the next moment; wherein the matching point group includes a first measured point and a second measured point, the first measured point belongs to the point cloud data at the current moment, and the second measured point belongs to the point cloud data at the next moment; the translation distance determination unit is used to determine the translation vector between the first measured point and the second measured point in the matching point group, and convert the translation vector into a translation distance; the positioning alarm information determination unit is used to determine the positioning alarm information based on the translation distance and the preset distance threshold.

[0132] As an optional but non-limiting implementation method, the distance threshold includes a first distance threshold and a second distance threshold, wherein the first distance threshold is smaller than the second distance threshold; accordingly, the positioning alarm information determination unit is specifically used to: if the translation distance is greater than the first distance threshold but not greater than the second distance threshold, trigger an alarm mechanism and display a prompt message on the interactive interface of the target vehicle; if the translation distance is greater than the second distance threshold, send an emergency stop command to the automatic driving system, and prompt the automatic driving system that a positioning abnormality has occurred.

[0133] As an optional but non-limiting implementation, the perception monitoring module 320 includes a first roadside determination unit, a second roadside determination unit, a sampling point determination unit, a distance deviation value determination unit, and a roadside detection result determination unit. Among them: the first roadside determination unit is used to fit the perception result output by the automatic driving system to obtain a first roadside; the second roadside determination unit is used to determine the second roadside corresponding to the first roadside based on the high-precision map; the sampling point determination unit is used to perform interval sampling on the first roadside to determine each fitting sampling point, and determine the actual sampling point corresponding to each fitting sampling point on the second roadside; the distance deviation value determination unit is used to determine the distance deviation value between each fitting sampling point and the corresponding actual sampling point; the roadside detection result determination unit is used to determine the roadside detection result based on the numerical distribution between each distance deviation value.

[0134] As an optional but non-limiting implementation method, the roadside line detection result determination unit is specifically used to: if a preset number of consecutive distance deviation values ​​in each of the distance deviation values ​​are all smaller than the distance deviation threshold, then the roadside line detection result is that there is an abnormality in the roadside line detection.

[0135] As an optional but non-limiting implementation, the planning monitoring module 330 includes a trajectory point determination unit, a moving distance determination unit, and a failure count unit. Among them: the trajectory point determination unit is used to determine each trajectory point planned by the automatic driving system within a preset time period; the moving distance determination unit is used to determine the moving distance between adjacent trajectory points in each of the trajectory points; the moving distance is used to reflect the movement between two adjacent trajectory points; the failure count unit is used to count the number of each moving distance that is less than the preset distance value to obtain a statistical number; and the statistical number is used as the failure number.

[0136] As an optional but non-limiting implementation method, the planning monitoring module 330 is also used to: calculate in real time the collision time between the target vehicle and a reference obstacle; the reference obstacle is an obstacle around the target vehicle, and the collision time is determined based on a first distance and a first speed; wherein the first distance is the distance between the target vehicle and the reference obstacle; the first speed is the relative speed between the target vehicle and the reference obstacle; if the collision time is lower than a safety time threshold, an alarm is issued; if the number of alarms exceeds a set number threshold, an emergency stop instruction is sent to the automatic driving system, and the automatic driving system is prompted that a planning abnormality occurs.

[0137] As an optional but non-limiting implementation, the lateral and longitudinal errors include lateral errors and longitudinal errors; accordingly, the control monitoring module 340 is used to calculate the lateral and longitudinal errors between the actual control position and the target control position of the target vehicle, specifically calculated by the following formula:

[0138] e x =|T e,x -T a,x |;

[0139] e y =|T e,y -T a,y |;

[0140] Among them, e x is the lateral error; e y is the longitudinal error; T e,x is the horizontal coordinate corresponding to the target control position; T e,y is the ordinate corresponding to the target control position; T a,x is the horizontal coordinate corresponding to the actual control position; T a,y is the vertical coordinate corresponding to the actual control position.

[0141] The technical solution provided by the present invention includes a positioning monitoring module, which is used to compare the first position and the second position of the target vehicle at the next moment to obtain a position deviation result, and determine the positioning alarm information based on the position deviation result; a perception monitoring module, which is used to determine the area ratio relationship between the perceived drivable area and the actual drivable area; if the area ratio relationship exceeds a preset ratio threshold, the roadside line is detected to obtain a roadside line detection result, and the perception alarm information is determined based on the roadside line detection result; a planning monitoring module, which is used to determine the number of failures of the automatic driving system to perform trajectory update operations within a preset time period, and determine the planning alarm information based on the relationship between the number of failures and the preset number threshold; a control monitoring module, which is used to calculate the lateral and longitudinal errors between the actual control position and the target control position of the target vehicle, and determine the control alarm information based on the relationship between the lateral and longitudinal errors and the error threshold; a task monitoring module, which is used to record the execution status information of the automatic driving system at each execution stage when processing the target task, and determine the task alarm information based on the execution status information; an interaction monitoring module, which is used to detect the communication status information between the front-end interaction interface and the back-end of the automatic driving system in the automatic driving system, and determine the interaction alarm information based on the communication status information. By adopting the scheme of the present invention, the positioning, perception, planning, control, tasks and interaction links of the autonomous driving system can be detected separately. Through multi-level and multi-dimensional all-round monitoring, anomalies in the autonomous driving system can be discovered in time, and the link where the anomaly occurs can be located quickly and accurately, thereby improving the safety and stability of the autonomous driving system.

[0142] The monitoring system for the autonomous driving system provided in the embodiment of the present invention can be used to execute the monitoring method for the autonomous driving system, and has the corresponding functional modules and beneficial effects for executing the monitoring method for the autonomous driving system.

[0143] It is worth noting that the various units and modules included in the above system are only divided according to functional logic, but are not limited to the above division, as long as the corresponding functions can be achieved; in addition, the specific names of the functional units are only for the convenience of distinguishing each other, and are not used to limit the protection scope of the embodiments of the present invention.

[0144] The flow chart and block diagram in the accompanying drawings illustrate the possible architecture, function and operation of the system, method and computer program product according to various embodiments of the present invention. In this regard, each box in the flow chart or block diagram can represent a module, a program segment or a part of a code, and the module, the program segment or a part of the code contains one or more executable instructions for realizing the specified logical function. It should also be noted that in some alternative implementations, the functions marked in the box can also occur in a sequence different from that marked in the accompanying drawings. For example, two boxes represented in succession can actually be executed substantially in parallel, and they can sometimes be executed in the opposite order, depending on the functions involved. It should also be noted that each box in the block diagram and / or flow chart, and the combination of the boxes in the block diagram and / or flow chart can be used to implement a special hardware-based system for executing the specified function or operation, or can be implemented with a combination of special hardware and computer instructions.

[0145] The units involved in the embodiments of the present invention may be implemented in software or in hardware. The name of the unit does not limit the unit itself in some cases. The functions described above may be performed at least in part by one or more hardware logic components. For example, exemplary types of hardware logic components that may be used include, without limitation, field programmable gate arrays (FPGAs), application specific integrated circuits (ASICs), application specific standard products (ASSPs), systems on chips (SOCs), complex programmable logic devices (CPLDs), and the like.

[0146] The above description is only a preferred embodiment of the present invention and an explanation of the technical principles used. Those skilled in the art should understand that the disclosure scope involved in the present invention is not limited to the technical solution formed by a specific combination of the above technical features, but should also cover other technical solutions formed by any combination of the above technical features or their equivalent features without departing from the above disclosed concept. For example, the above features are replaced with the technical features with similar functions disclosed in the present invention (but not limited to) by each other.

[0147] In addition, although each operation is described in a specific order, this should not be construed as requiring these operations to be performed in the specific order shown or in a sequential order. Under certain circumstances, multitasking and parallel processing may be advantageous. Similarly, although some specific implementation details are included in the above discussion, these should not be construed as limiting the scope of the present invention. Some features described in the context of a separate embodiment can also be implemented in a single embodiment in combination. On the contrary, the various features described in the context of a single embodiment can also be implemented in multiple embodiments individually or in any suitable sub-combination mode.

[0148] Although the subject matter has been described in language specific to structural features and / or methodological logical actions, it should be understood that the subject matter defined in the appended claims is not necessarily limited to the specific features or actions described above. On the contrary, the specific features and actions described above are merely example forms of implementing the claims.

Claims

1. A monitoring method for an automatic driving system, characterized in that: The method comprises: A position deviation result is obtained by comparing the first position and the second position of the target vehicle at the next moment through the positioning monitoring module, and a positioning alarm message is determined based on the position deviation result; wherein the first position is the position of the target vehicle at the next moment predicted based on the historical position information of the target vehicle; the second position is the actual position of the target vehicle at the next moment obtained based on the positioning of the automatic driving system; and the automatic driving system is configured on the target vehicle; The area ratio relationship between the perceived drivable area and the actual drivable area is determined by the perception monitoring module; if the area ratio relationship exceeds a preset ratio threshold, the roadside line is detected by the perception monitoring module to obtain a roadside line detection result, and the perception alarm information is determined based on the roadside line detection result; wherein the perceived drivable area is a road area where the target vehicle is passable as determined by the automatic driving system; the actual drivable area is a road area where the target vehicle is actually passable corresponding to the perceived drivable area in the high-precision map; Determine the number of failures of the automatic driving system to perform a trajectory update operation within a preset time period through a planning monitoring module, and determine planning alarm information based on the relationship between the number of failures and a preset number threshold; the trajectory update operation is used to update the trajectory points of the target vehicle during driving; The lateral and longitudinal errors between the actual control position and the target control position of the target vehicle are calculated by the control monitoring module, and the control alarm information is determined based on the relationship between the lateral and longitudinal errors and the error threshold; wherein the actual control position is the position actually reached after the automatic driving system controls the target vehicle according to the vehicle control parameters; and the target control position is the position expected to be reached after the automatic driving system controls the target vehicle according to the vehicle control parameters; Recording, by means of a task monitoring module, execution status information of the autonomous driving system at each execution stage when processing a target task, and determining task alarm information based on the execution status information; The communication status information between the front-end interactive interface in the autonomous driving system and the back-end of the autonomous driving system is detected through the interactive monitoring module, and the interactive alarm information is determined based on the communication status information.

2. The method according to claim 1, characterized in that The historical location information includes the location information of the target vehicle at the current moment and the location information at the previous moment; Accordingly, the process of determining the first position includes: The positioning monitoring module obtains the current position information and the previous position information of the target vehicle located by the automatic driving system; The position information at the current moment and the position information at the previous moment are input into the trajectory prediction model through the positioning monitoring module, and the first position is output; wherein the trajectory prediction model can predict the position information to be reached by the target vehicle at the next moment based on the position information of the target vehicle at the current moment.

3. The method according to claim 2, characterized in that The determining of positioning alarm information based on the position deviation result includes: If the position deviation result exceeds the preset deviation threshold, the point cloud data of the target vehicle at the current moment and the point cloud data at the next moment are obtained through the positioning monitoring module; Starting a point cloud matching algorithm to determine a corresponding matching point group in the point cloud data at the current moment and the point cloud data at the next moment; wherein the matching point group includes a first measured point and a second measured point, the first measured point belongs to the point cloud data at the current moment, and the second measured point belongs to the point cloud data at the next moment; Determine a translation vector between the first measured point and the second measured point in the matching point group, and convert the translation vector into a translation distance; The positioning alarm information is determined based on the translation distance and a preset distance threshold.

4. The method according to claim 3, characterized in that The distance threshold includes a first distance threshold and a second distance threshold, wherein the first distance threshold is smaller than the second distance threshold; Correspondingly, the determining the positioning alarm information based on the translation distance and a preset distance threshold includes: If the translation distance is greater than the first distance threshold but not greater than the second distance threshold, an alarm mechanism is triggered by the positioning monitoring module, and a prompt message is displayed on the interactive interface of the target vehicle; If the translation distance is greater than a second distance threshold, an emergency stop instruction is sent to the automatic driving system through the positioning monitoring module, and the automatic driving system is prompted that a positioning abnormality occurs.

5. The method according to claim 1, characterized in that: The detecting of the roadside line by the sensing monitoring module to obtain the roadside line detection result includes: Fitting the perception result output by the automatic driving system through the perception monitoring module to obtain a first roadside line; Determine a second roadside line corresponding to the first roadside line based on the high-precision map; Performing interval sampling on the first roadside line to determine each fitting sampling point, and determining the actual sampling point corresponding to each fitting sampling point on the second roadside line; Determine the distance deviation value between each fitting sampling point and the corresponding actual sampling point; The roadside line detection result is determined based on the numerical distribution between the distance deviation values.

6. The method according to claim 5, characterized in that The determining the roadside line detection result based on the numerical distribution of each of the distance deviation values ​​includes: If a preset number of consecutive distance deviation values ​​among the distance deviation values ​​are all smaller than the distance deviation threshold, then the roadside line detection result is that the roadside line detection is abnormal.

7. The method according to claim 1, characterized in that The determining, by the planning monitoring module, the number of failures of the trajectory update operation of the autonomous driving system within a preset time period includes: Determine each trajectory point planned by the autonomous driving system within a preset time period through the planning monitoring module; Determine the moving distance between adjacent track points in each of the track points; the moving distance is used to reflect the movement between two adjacent track points; The number of movement distances that are less than a preset distance value is counted to obtain a statistical number; and the statistical number is used as the number of failures.

8. The method according to claim 1, characterized in that The method further comprises: The planning and monitoring module calculates in real time the collision time between the target vehicle and the reference obstacle; the reference obstacle is an obstacle around the target vehicle, and the collision time is determined based on a first distance and a first speed; wherein the first distance is the distance between the target vehicle and the reference obstacle; and the first speed is the relative speed between the target vehicle and the reference obstacle; If the collision time is lower than the safety time threshold, an alarm is issued through the planning monitoring module; If the number of alarm prompts issued by the planning monitoring module exceeds the set threshold, an emergency stop command will be sent to the autonomous driving system through the planning monitoring module, and the autonomous driving system will be prompted that a planning abnormality has occurred.

9. The method according to claim 1, characterized in that: The transverse and longitudinal errors include transverse errors and longitudinal errors; Accordingly, the lateral and longitudinal errors between the actual control position and the target control position of the target vehicle are calculated by the control monitoring module, and are calculated by the following formula: e x =|T e,x -T a,x |; e y =|T e,y -T a,y |; Among them, e x is the lateral error; e y is the longitudinal error; T e,x is the horizontal coordinate corresponding to the target control position; T e,y is the ordinate corresponding to the target control position; T a,x is the horizontal coordinate corresponding to the actual control position; T a,y is the vertical coordinate corresponding to the actual control position.

10. A monitoring system for an automatic driving system, characterized in that: The system comprises: A positioning monitoring module, used for comparing a first position and a second position of a target vehicle at a next moment to obtain a position deviation result, and determining positioning alarm information based on the position deviation result; wherein the first position is the position of the target vehicle at the next moment predicted based on historical position information of the target vehicle; the second position is the actual position of the target vehicle at the next moment obtained based on positioning by an automatic driving system; and the automatic driving system is configured on the target vehicle; A perception monitoring module is used to determine the area ratio relationship between the perceived drivable area and the actual drivable area; if the area ratio relationship exceeds a preset ratio threshold, the roadside line is detected to obtain a roadside line detection result, and the perception alarm information is determined based on the roadside line detection result; wherein the perceived drivable area is a road area where the target vehicle is passable as determined by the automatic driving system; the actual drivable area is a road area where the target vehicle is actually passable corresponding to the perceived drivable area in the high-precision map; A planning monitoring module, used to determine the number of failures of the automatic driving system to perform a trajectory update operation within a preset time period, and determine planning alarm information based on the relationship between the number of failures and a preset number threshold; the trajectory update operation is used to update the trajectory points of the target vehicle during driving; A control monitoring module, used to calculate the lateral and longitudinal errors between the actual control position of the target vehicle and the target control position, and determine the control alarm information based on the relationship between the lateral and longitudinal errors and the error threshold; wherein the actual control position is the position actually reached by the automatic driving system after controlling the target vehicle according to the vehicle control parameters; and the target control position is the position expected to be reached by the automatic driving system after controlling the target vehicle according to the vehicle control parameters; A task monitoring module, used to record the execution status information of the automatic driving system at each execution stage when processing the target task, and determine the task alarm information based on the execution status information; The interaction monitoring module is used to detect the communication status information between the front-end interaction interface in the autonomous driving system and the back-end of the autonomous driving system, and determine the interaction alarm information based on the communication status information.

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