Method and device for detecting abnormal operating state of an autonomous driving vehicle

Abstract

The present disclosure provides a method, device, electronic device, computer-readable storage medium and computer program product for detecting the abnormal operating state of an autonomous driving vehicle, and relates to the field of intelligent transportation technology, and in particular to the field of autonomous driving technology. The method includes: obtaining the operating state of the autonomous driving vehicle and the traffic scene information around the autonomous driving vehicle; based on the operating state of the autonomous driving vehicle and the traffic scene information around the autonomous driving vehicle, determining the impact information of the operating state of the autonomous driving vehicle on the current road's traffic efficiency; and, in response to determining that the operating state of the autonomous driving vehicle causes the current road's traffic efficiency to decrease, determining that the autonomous driving vehicle is in an abnormal operating state, wherein the abnormal operating state includes an abnormal parking state or an abnormal traffic state. The technical solution disclosed in the present disclosure can accurately and efficiently detect the abnormal operating state of the autonomous driving vehicle, which is conducive to improving the road's traffic efficiency.

Description

Technical Field

[0001] The present disclosure relates to the field of intelligent transportation technology, in particular to the field of autonomous driving technology, and specifically to a method, device, electronic device, computer-readable storage medium, and computer program product for detecting abnormal operating conditions of an autonomous driving vehicle. Background Art

[0002] Autonomous driving vehicles rely on the collaborative efforts of artificial intelligence, visual computing, radar, monitoring devices, and global positioning systems. They can be driven without the need for actual human control of the vehicle on site. They are one of the main development directions of future intelligent transportation.

[0003] Efficient traffic efficiency is an important sign of the maturity of autonomous driving technology and an objective condition that directly affects the riding experience of autonomous driving vehicles. How to reduce congestion and improve road traffic efficiency is an important direction of concern for technical personnel in this field.

[0004] The technical means described in this section are not necessarily technical means that have been previously conceived or adopted. Unless otherwise specified, it should not be assumed that any technical means described in this section are considered to be prior art simply because they are included in this section. Similarly, unless otherwise specified, the issues mentioned in this section should not be considered to have been recognized in any prior art. Summary of the invention

[0005] The present disclosure provides a method, an apparatus, an electronic device, a computer-readable storage medium, and a computer program product for detecting an abnormal operating state of an autonomous driving vehicle.

[0006] According to one aspect of the present disclosure, a method for detecting an abnormal operating state of an autonomous driving vehicle is provided, the method comprising:

[0007] Acquiring the operating status of the autonomous driving vehicle and traffic scene information around the autonomous driving vehicle;

[0008] Determining, based on the operating state of the autonomous driving vehicle and traffic scene information around the autonomous driving vehicle, information on the impact of the operating state of the autonomous driving vehicle on the traffic efficiency of the current road; and

[0009] In response to determining that the operating state of the autonomous driving vehicle causes a decrease in the traffic efficiency of the current road, it is determined that the autonomous driving vehicle is in an abnormal operating state, wherein the abnormal operating state includes an abnormal parking state or an abnormal traffic state.

[0010] According to one aspect of the present disclosure, there is provided a device for detecting an abnormal operating state of an autonomous driving vehicle, the device comprising:

[0011] an acquisition unit, configured to acquire the operating status of the autonomous driving vehicle and traffic scene information around the autonomous driving vehicle;

[0012] a first determining unit configured to determine, based on the operating state of the autonomous driving vehicle and traffic scene information around the autonomous driving vehicle, information about the impact of the operating state of the autonomous driving vehicle on the traffic efficiency of a current road; and

[0013] The second determination unit is configured to determine that the autonomous driving vehicle is in an abnormal operating state in response to determining that the operating state of the autonomous driving vehicle causes a decrease in the traffic efficiency of the current road, wherein the abnormal operating state includes an abnormal parking state or an abnormal traffic state.

[0014] According to one aspect of the present disclosure, an electronic device is provided, comprising: at least one processor; and a memory communicatively connected to the at least one processor; wherein the memory stores instructions executable by the at least one processor, and the instructions are executed by the at least one processor so that the at least one processor can execute the method described in the aforementioned aspects.

[0015] According to one aspect of the present disclosure, a computer-readable storage medium storing computer instructions is provided, wherein the computer instructions are configured to enable a computer to execute the method described in the aforementioned aspect.

[0016] According to an aspect of the present disclosure, a computer program product is provided, including a computer program, wherein the computer program implements the method described in the above aspect when executed by a processor.

[0017] According to one or more embodiments of the present disclosure, the abnormal operating state of an autonomous driving vehicle can be accurately and efficiently detected, which is beneficial to improving the traffic efficiency of the road and further improving the user experience of the autonomous driving vehicle.

[0018] It should be understood that the content described in this section is not intended to identify the key or important features of the embodiments of the present disclosure, nor is it intended to limit the scope of the present disclosure. Other features of the present disclosure will become easily understood through the following description. BRIEF DESCRIPTION OF THE DRAWINGS

[0019] The accompanying drawings exemplarily illustrate the embodiments and constitute a part of the specification, and together with the text description of the specification, are used to explain the exemplary implementation of the embodiments. The embodiments shown are for illustrative purposes only and do not limit the scope of the claims. In all drawings, the same reference numerals refer to similar but not necessarily identical elements.

[0020] Figure 1A schematic diagram showing an exemplary system in which the various methods described herein may be implemented according to some embodiments of the present disclosure;

[0021] Figure 2A A flowchart of a method for detecting an abnormal operating state of an autonomous driving vehicle according to some embodiments of the present disclosure is shown;

[0022] Figure 2B A flowchart of a method for detecting an abnormal operating state of an autonomous driving vehicle according to some embodiments of the present disclosure is shown;

[0023] Figure 3 A structural block diagram of an apparatus for detecting an abnormal operating state of an autonomous driving vehicle according to some embodiments of the present disclosure is shown; and

[0024] Figure 4 A structural block diagram of an exemplary electronic device that can be used to implement the embodiments of the present disclosure is shown. DETAILED DESCRIPTION

[0025] The following is a description of exemplary embodiments of the present disclosure in conjunction with the accompanying drawings, including various details of the embodiments of the present disclosure to facilitate understanding, which should be considered as merely exemplary. Therefore, it should be recognized by those of ordinary skill in the art that various changes and modifications may be made to the embodiments described herein without departing from the scope of the present disclosure. Similarly, for the sake of clarity and conciseness, the description of well-known functions and structures is omitted in the following description.

[0026] In the present disclosure, unless otherwise specified, the use of the terms "first", "second", etc. to describe various elements is not intended to limit the positional relationship, temporal relationship, or importance relationship of these elements, and such terms are only used to distinguish one element from another element. In some examples, the first element and the second element may refer to the same instance of the element, and in some cases, based on the description of the context, they may also refer to different instances.

[0027] The terms used in the description of various examples in this disclosure are only for the purpose of describing specific examples and are not intended to be limiting. Unless the context clearly indicates otherwise, if the number of elements is not specifically limited, the element can be one or more. In addition, the term "and / or" used in this disclosure covers any one of the listed items and all possible combinations.

[0028] With the rapid development of autonomous driving technology, efficient traffic efficiency is an important sign of the maturity of autonomous driving technology and an objective condition that directly affects the riding experience of autonomous driving vehicles. How to reduce congestion and improve road traffic efficiency is an important direction of concern for technicians in this field.

[0029] Autonomous vehicles may be in abnormal operation at some time due to software, hardware or network reasons. Abnormal operation here refers to abnormal parking state or abnormal traffic state. Abnormal parking state can be understood as the state where the autonomous vehicle is stagnant when it is allowed to pass, and abnormal traffic state can be understood as the state where the autonomous vehicle rushes to pass when it is not allowed to pass or when it is not recommended to pass because it may affect the efficiency of road traffic. Abnormal operation of autonomous vehicles will lead to increased traffic congestion, which will seriously affect the efficiency of traffic.

[0030] Accurately detecting the abnormal operating state of an autonomous driving vehicle can provide an important reference for the research and development of autonomous driving vehicles and the development, simulation and optimization of operating routes, so it is of great significance. In related technologies, manual methods are mainly used to screen the abnormal operating state of autonomous driving vehicles. The reviewers need to be familiar with traffic rules and need to objectively judge whether the vehicle is in an abnormal operating state based on the traffic scene at the time. The manual screening method is inefficient and has high labor costs. Moreover, it is affected by the experience level and subjective feelings of the reviewers, and the accuracy of the screening results is difficult to guarantee.

[0031] Based on this, the embodiments of the present disclosure provide a method, device, electronic device, computer-readable storage medium and computer program product for detecting the abnormal operating state of an autonomous driving vehicle, so as to accurately and efficiently detect the abnormal operating state of an autonomous driving vehicle, thereby facilitating improving the traffic efficiency of the road.

[0032] The embodiments of the present disclosure will be described in detail below with reference to the accompanying drawings.

[0033] Figure 1 FIG. 1 is a schematic diagram of an exemplary system 100 in which various methods and apparatuses described herein may be implemented according to an embodiment of the present disclosure. Figure 1 , the system 100 includes a motor vehicle 110 , a server 120 , and one or more communication networks 130 coupling the motor vehicle 110 to the server 120 .

[0034] In an embodiment of the present disclosure, the motor vehicle 110 may include a computing device according to an embodiment of the present disclosure and / or be configured to perform a method according to an embodiment of the present disclosure.

[0035] The server 120 may run one or more services or software applications that enable the communication control method of the vehicle disclosed herein to be implemented. In some embodiments, the server 120 may also provide other services or software applications, which may include non-virtual environments and virtual environments. Figure 1In the configuration shown, the server 120 may include one or more components that implement the functions performed by the server 120. These components may include software components, hardware components, or a combination thereof that can be executed by one or more processors. A user of the motor vehicle 110 may in turn utilize one or more client applications to interact with the server 120 to utilize the services provided by these components. It should be understood that a variety of different system configurations are possible, which may differ from the system 100. Therefore, Figure 1 is one example of a system for implementing the various methods described herein and is not intended to be limiting.

[0036] Server 120 may include one or more general purpose computers, dedicated server computers (e.g., PC (personal computer) servers, UNIX servers, mid-range servers), blade servers, mainframe computers, server clusters, or any other suitable arrangement and / or combination. Server 120 may include one or more virtual machines running virtual operating systems, or other computing architectures involving virtualization (e.g., one or more flexible pools of logical storage devices that may be virtualized to maintain a server's virtual storage device). In various embodiments, server 120 may run one or more services or software applications that provide the functionality described below.

[0037] The computing units in the server 120 may run one or more operating systems including any of the above operating systems and any commercially available server operating systems. The server 120 may also run any of a variety of additional server applications and / or middle-tier applications, including HTTP servers, FTP servers, CGI servers, JAVA servers, database servers, etc.

[0038] In some embodiments, server 120 may include one or more applications to analyze and consolidate data feeds and / or event updates received from motor vehicle 110. Server 120 may also include one or more applications to display data feeds and / or real-time events via one or more display devices of motor vehicle 110.

[0039] The network 130 may be any type of network known to those skilled in the art that may support data communications using any of a variety of available protocols, including but not limited to TCP / IP, SNA, IPX, etc. By way of example only, one or more networks 130 may be a satellite communications network, a local area network (LAN), an Ethernet-based network, a token ring, a wide area network (WAN), the Internet, a virtual network, a virtual private network (VPN), an intranet, an extranet, a blockchain network, a public switched telephone network (PSTN), an infrared network, a wireless network (including, for example, Bluetooth, WiFi), and / or any combination of these and other networks.

[0040] The system 100 may also include one or more databases 150. In some embodiments, these databases may be used to store data and other information. For example, one or more of the databases 150 may be used to store information such as audio files and video files. The data repository 150 may reside in various locations. For example, the data repository used by the server 120 may be local to the server 120, or may be remote from the server 120 and may communicate with the server 120 via a network-based or dedicated connection. The data repository 150 may be of different types. In some embodiments, the data repository used by the server 120 may be a database, such as a relational database. One or more of these databases may store, update, and retrieve data from the database in response to a command.

[0041] In some embodiments, one or more of the databases 150 may also be used by an application to store application data. The databases used by the application may be different types of databases, such as a key-value store, an object store, or a conventional store backed by a file system.

[0042] The motor vehicle 110 may include a sensor 111 for sensing the surrounding environment. The sensor 111 may include one or more of the following sensors: a visual camera, an infrared camera, an ultrasonic sensor, a millimeter wave radar, and a laser radar (LiDAR). Different sensors may provide different detection accuracy and range. The camera may be installed at the front, rear, or other position of the vehicle. The visual camera may capture the situation inside and outside the vehicle in real time and present it to the driver and / or passengers. In addition, by analyzing the images captured by the visual camera, information such as traffic light indications, intersection conditions, and the operating status of other vehicles may be obtained. The infrared camera may capture objects under night vision conditions. Ultrasonic sensors may be installed around the vehicle to measure the distance of objects outside the vehicle from the vehicle using the characteristics of strong ultrasonic directionality. The millimeter wave radar may be installed at the front, rear, or other position of the vehicle to measure the distance of objects outside the vehicle from the vehicle using the characteristics of electromagnetic waves. The laser radar may be installed at the front, rear, or other position of the vehicle to detect the edge and shape information of the object, thereby identifying and tracking the object. Due to the Doppler effect, the radar device may also measure the speed change of the vehicle and the moving object.

[0043] The motor vehicle 110 may also include a communication device 112. The communication device 112 may include a satellite positioning module capable of receiving satellite positioning signals (e.g., Beidou, GPS, GLONASS, and GALILEO) from satellites 141 and generating coordinates based on these signals. The communication device 112 may also include a module for communicating with a mobile communication base station 142, and the mobile communication network may implement any suitable communication technology, such as GSM / GPRS, CDMA, LTE, and other current or evolving wireless communication technologies (e.g., 5G technology). The communication device 112 may also have a vehicle-to-vehicle or vehicle-to-everything (V2X) module, which is configured to implement, for example, vehicle-to-vehicle (V2V) communication with other vehicles 143 and vehicle-to-infrastructure (V2I) communication with infrastructure 144. In addition, the communication device 112 may also have a module configured to communicate with a user terminal 145 (including but not limited to a smartphone, a tablet computer, or a wearable device such as a watch) by, for example, using a wireless LAN or Bluetooth of the IEEE802.11 standard. Using the communication device 112, the motor vehicle 110 may also access the server 120 via the network 130.

[0044] The motor vehicle 110 may also include a control device 113. The control device 113 may include a processor that communicates with various types of computer-readable storage devices or media, such as a central processing unit (CPU) or a graphics processing unit (GPU), or other dedicated processors. The control device 113 may include an automatic driving system for automatically controlling various actuators in the vehicle. The automatic driving system is configured to control the powertrain, steering system, and braking system of the motor vehicle 110 (not shown) via multiple actuators in response to inputs from multiple sensors 111 or other input devices to control acceleration, steering, and braking, respectively, without human intervention or limited human intervention. Some processing functions of the control device 113 can be implemented through cloud computing. For example, some processing can be performed using an on-board processor, while other processing can be performed using computing resources in the cloud. The control device 113 can be configured to perform a method according to the present disclosure. In addition, the control device 113 can be implemented as an example of a computing device on the motor vehicle side (client) according to the present disclosure.

[0045] Figure 1 The system 100 may be configured and operated in various ways to enable the application of various methods and apparatuses described in the present disclosure.

[0046] Some embodiments of the present disclosure provide a method for detecting an abnormal operating state of an autonomous driving vehicle. The specific type of the autonomous driving vehicle is not limited, for example, it can be a uniformly operated autonomous driving bus, autonomous driving taxi, autonomous driving shared car, or autonomous driving food delivery vehicle, etc.

[0047] like Figure 2A As shown, some embodiments of the present disclosure provide a method 200 for detecting abnormal operating conditions of an autonomous driving vehicle, including the following steps S201 to S203.

[0048] In step S201, the operating status of the autonomous driving vehicle and the traffic scene information around the autonomous driving vehicle are obtained.

[0049] In step S202, based on the operating status of the autonomous driving vehicle and the traffic scene information around the autonomous driving vehicle, the impact of the operating status of the autonomous driving vehicle on the traffic efficiency of the current road is determined, for example, whether it will cause the traffic efficiency of the current road to decrease.

[0050] In step S203, in response to determining that the operating state of the autonomous driving vehicle causes the traffic efficiency of the current road to decrease, it is determined that the autonomous driving vehicle is in an abnormal operating state, wherein the abnormal operating state includes an abnormal parking state or an abnormal traffic state.

[0051] The technical solution of the disclosed embodiment can use electronic devices such as cluster computers as the execution subject of the above method, and the electronic devices communicate with the server of the autonomous driving vehicle and / or the autonomous driving system, so as to collect and obtain the operating status of the autonomous driving vehicle and the traffic scene information around the autonomous driving vehicle. Under the constraints of traffic regulations and a unified standard framework, the processor of the electronic device can determine the impact of the operating status of the autonomous driving vehicle on the current road traffic efficiency based on the operating status of the autonomous driving vehicle and the traffic scene information around the autonomous driving vehicle, and then determine whether the autonomous driving vehicle is in an abnormal operating state.

[0052] In the disclosed embodiments, the abnormal parking state refers to the state in which the autonomous driving vehicle is stagnant when it is allowed to pass, and the abnormal passing state refers to the state in which the autonomous driving vehicle rushes to pass when it is not allowed to pass or is not recommended to pass because it affects the passing efficiency of the road. Both the abnormal parking state and the abnormal passing state will lead to the aggravation of traffic congestion, thereby seriously affecting the passing efficiency.

[0053] Compared with related technologies, the detection of abnormal operating status of autonomous vehicles in the embodiments of the present disclosure is performed by electronic equipment, and its detection results are more objective and accurate, and the detection efficiency is higher. The detection results can provide important references for the research and development of autonomous vehicles and the development, simulation and optimization of operating routes, thereby helping to improve the traffic efficiency of roads, and are particularly suitable for the product development of large-scale autonomous vehicle operation systems.

[0054] In the disclosed embodiment, the operating state of the autonomous driving vehicle includes at least one of the following: parking, starting, driving, turning, changing lanes, and braking, that is, the autonomous driving vehicle can achieve the autonomous driving state by relying on the collaboration of artificial intelligence, visual computing, radar, monitoring devices, and global positioning systems when no one is driving. In actual operation, the autonomous driving vehicle may be in an abnormal operating state at some time due to software, hardware, or network reasons. Therefore, it is necessary to collect information on the abnormal operating state of the autonomous driving vehicle and conduct research and review on it in order to further develop and improve the vehicle and related products.

[0055] In some embodiments of the present disclosure, the traffic scene information around the autonomous driving vehicle includes at least one of the following: the positioning information of the autonomous driving vehicle in the map, the lane information of the autonomous driving vehicle, the obstacle information around the autonomous driving vehicle, and the traffic light information around the autonomous driving vehicle. The positioning information of the autonomous driving vehicle in the map may include, for example, the latitude and longitude information of the autonomous driving vehicle, the road information, the high-precision map information of the range area, etc., wherein the map information may be accurate to the lane, signal light, shoulder, or fence, etc.

[0056] The computer system of the autonomous driving vehicle can judge the actual traffic scene based on the stored information, the command information issued by the server, and the sensing information of its own equipment such as radar, camera, sensor, etc., and control the autonomous driving vehicle to achieve autonomous driving based on the actual traffic scene, such as controlling the start, stop, acceleration, deceleration, steering, etc. of the autonomous driving vehicle. Therefore, the autonomous driving vehicle can provide the above-mentioned traffic scene information to the electronic device executing the aforementioned method program. Providing accurate and comprehensive traffic scene information helps to improve the accuracy of detecting abnormal operating conditions of autonomous driving vehicles.

[0057] The obstacle information around the autonomous vehicle may include, for example, the type of obstacle, the distance between the obstacle and the vehicle, the size of the obstacle, etc., which may be sensed or collected by the radar, camera, sensor, etc. of the autonomous vehicle, and the computer system of the autonomous vehicle may calculate and determine the obstacle information. The traffic light information around the autonomous vehicle may include, for example, the indication information of the traffic light, which may be collected by the camera of the autonomous vehicle, and then the computer system of the autonomous vehicle may perform image recognition on the collected image to determine the indication information of the traffic light, such as the color of the light head.

[0058] In some embodiments of the present disclosure, the operating status of the autonomous driving vehicle and the traffic scene information around the autonomous driving vehicle are obtained, including: obtaining the operating status of the autonomous driving vehicle and the traffic scene information around the autonomous driving vehicle in each time slice of multiple continuous time slices one by one.

[0059] The above information can be obtained once and the abnormal operation status of the autonomous driving vehicle can be detected after the autonomous driving vehicle has completed this operation or a period of operation. Acquiring multiple continuous time slices one by one can make the information acquisition more comprehensive and avoid information omissions. For example, if the autonomous driving vehicle has been running for 2 hours, 10 seconds can be used as the unit of time slice to obtain the operation status of the autonomous driving vehicle and the traffic scene information around the autonomous driving vehicle in each time slice of the multiple time slices within the 2 hours.

[0060] In some embodiments of the present disclosure, determining the impact of the operating state of the autonomous driving vehicle on the traffic efficiency of the current road includes:

[0061] Based on the traffic scene information around the autonomous driving vehicle, a series of algorithms are used to obtain understanding information about the traffic scene around the autonomous driving vehicle; and

[0062] Based on the operating status of the autonomous driving vehicle, the planned trajectory information and the understanding of the traffic scene around the autonomous driving vehicle, determine the impact of the operating status of the autonomous driving vehicle on the traffic efficiency of the current road, for example, determine whether the operating status of the autonomous driving vehicle will cause the traffic efficiency of the current road to decrease.

[0063] The understanding information of the traffic scene refers to the judgment information of the traffic scene obtained after calculation. Based on a certain traffic scene, the vehicle is allowed to pass or not allowed to pass.

[0064] It may include at least one of the following: whether the autonomous driving vehicle is in a docking state (the state in which an autonomous driving vehicle performs a handover with another autonomous driving vehicle at a designated docking point is the docking state), whether the vehicle in front of the lane where the autonomous driving vehicle is located is in a queuing state, whether the vehicle in front of the lane where the autonomous driving vehicle is located is in an abnormal parking state (for example, the lane allows passage, but the vehicle in front cannot start due to some objective factors, then the vehicle in front is in an abnormal parking state), whether the road in front of the lane where the autonomous driving vehicle is located is in a prohibited passage indication state (for example, due to construction or accidents, the road in front of the lane is equipped with pile barrels, signs, or vehicles turn on double flashes and other indication information), whether there are pedestrians on the road in front of the lane where the autonomous driving vehicle is located, whether the autonomous driving vehicle meets the safe lane change conditions (for example, the distance to the rear vehicle in the adjacent lane is not less than the safe lane change distance, which is regarded as meeting the safe lane change conditions), whether the autonomous driving vehicle meets the safe turning conditions (for example, the turning radius provided for the vehicle is not less than the safe turning radius, which is regarded as meeting the safe turning conditions), and whether the road in front of the lane where the autonomous driving vehicle is located meets the borrowed lane passage conditions (borrowed lane passage refers to avoiding obstacles when the passage space is relatively narrow, and generally it is necessary to ensure that the width is sufficient for vehicles to pass). Among the above-mentioned understanding information of the traffic scene around the autonomous driving vehicle, some understanding information, such as whether the autonomous driving vehicle meets the conditions for safe lane change, whether the autonomous driving vehicle meets the conditions for safe turning, whether the road ahead of the lane where the autonomous driving vehicle is located meets the conditions for passing through the lane, etc., need to be judged in combination with local traffic regulations.

[0065] In some embodiments of the present disclosure, determining the impact of the operating state of the autonomous driving vehicle on the traffic efficiency of the current road includes:

[0066] Determining whether the autonomous driving vehicle is in a state where passage is allowed, the state where passage is allowed includes at least one of a state where straight passage is allowed, a state where lane change is allowed, or a state where turn passage is allowed; and

[0067] In response to determining that the autonomous driving vehicle is in a state where passage is allowed, and the autonomous driving vehicle is in a parking state and the parking duration reaches a duration threshold, it is determined that the operating state of the autonomous driving vehicle causes the passage efficiency of the current road to decrease.

[0068] In the state where passage is allowed, the autonomous driving vehicle did not choose a suitable way to pass, but was in a parking state and the parking duration reached a time threshold (the time threshold is set to 8 seconds, for example). This situation will affect the passage of following vehicles, resulting in a decrease in passage efficiency and may very likely cause traffic jams. Therefore, this situation will be determined as the autonomous driving vehicle being in an abnormal parking state.

[0069] For example, when the traffic light ahead is green, the autonomous vehicle stops at the intersection. For example, when the lane ahead is under construction and the adjacent lane is open, the autonomous vehicle stops instead of changing lanes. This situation will be determined as an abnormal parking state.

[0070] An autonomous driving vehicle may also be in a parked state in some special scenarios (for example, connecting with other autonomous driving vehicles, reaching the end of the operation, having an accident or collision, or being under human control). The autonomous driving vehicles in these scenarios cannot be identified as being in an abnormal parking state. The judgment of the abnormal parking state in the embodiments of the present disclosure should exclude these special scenarios.

[0071] In some embodiments, determining information about the impact of the operating state of the autonomous driving vehicle on the traffic efficiency of the current road also includes: in response to determining that the autonomous driving vehicle is not in a state where traffic is allowed and the autonomous driving vehicle is in a driving state, determining that the operating state of the autonomous driving vehicle causes the traffic efficiency of the current road to decrease.

[0072] When traffic is not allowed, the autonomous driving vehicle does not stop and wait, but instead drives ahead or cuts in. This situation will not only lead to aggravated traffic congestion and seriously reduce traffic efficiency, but may also cause safety accidents. This situation will be determined as the autonomous driving vehicle being in an abnormal traffic state.

[0073] For example, the traffic light ahead is red, and the autonomous vehicle runs through the red light due to hardware failure. For example, all lanes of the road are blocked, and the autonomous vehicle drives into the emergency lane without stopping and waiting. These can all be considered abnormal traffic conditions.

[0074] In some embodiments of the present disclosure, determining the impact of the operating state of the autonomous driving vehicle on the traffic efficiency of the current road includes:

[0075] Determine the efficiency of the lane in which the automated vehicle is located and the efficiency of lanes adjacent to it; and

[0076] In response to determining that the traffic efficiency of an adjacent lane is less than the traffic efficiency of the lane in which the autonomous driving vehicle is located, and the autonomous driving vehicle is in a state of changing lanes to the adjacent lane, it is determined that the operating state of the autonomous driving vehicle causes the traffic efficiency of the current road to decrease.

[0077] In the state of allowing passage, the autonomous driving vehicle chooses to change lanes to a lane with lower traffic efficiency, which will further reduce the traffic efficiency of the lane, which may lead to aggravated traffic congestion. This situation should also be the focus of the improvement and optimization of the software and hardware of the autonomous driving vehicle.

[0078] The above methods can efficiently and relatively accurately detect the abnormal operating status of autonomous driving vehicles, and can provide important reference for the research and development of autonomous driving vehicles and the development, simulation and optimization of operating routes, which is conducive to improving and alleviating traffic congestion and improving traffic efficiency.

[0079] In the disclosed embodiment, the specific algorithm used to determine whether the autonomous driving vehicle is in an abnormal operating state is not limited. For example, a decision tree algorithm can be used. The decision tree algorithm is a method of approximating the value of a discrete function. It is a typical classification method. First, the data is processed, and readable rules and decision trees are generated using an inductive algorithm. Then, the decision tree is used to analyze the new data. In essence, the decision tree is a process of classifying data through a series of rules.

[0080] like Figure 2B The figure shows a flow chart of a method 210 for detecting abnormal operating status of an autonomous driving vehicle provided by some embodiments of the present disclosure in a specific application scenario of queuing. The method 210 may include the following steps S211 to S218.

[0081] In step S211, the operating status of the autonomous driving vehicle (such as parking) and the traffic scene information around the autonomous driving vehicle (such as the positioning information of the autonomous driving vehicle in the map, the lane information of the autonomous driving vehicle, and the obstacle in front of the autonomous driving vehicle is the vehicle in front) are obtained.

[0082] In step S212, based on the traffic scene information around the autonomous driving vehicle, understanding information of the traffic scene around the autonomous driving vehicle is obtained (such as the vehicles ahead in the lane are in a queue).

[0083] In step S213, based on the operating status of the autonomous driving vehicle (parked), the planned trajectory information (planning to continue moving in this lane) and the understanding of the traffic scene around the autonomous driving vehicle (the vehicle in front of the lane is in a queue), it is determined that the autonomous driving vehicle is in a state where passage is allowed.

[0084] In step S214, in response to determining that the duration of the autonomous driving vehicle being parked in a traffic-allowed state reaches a time threshold (e.g., 8 seconds), it is determined that the operating state of the autonomous driving vehicle causes the traffic efficiency of the current road to decrease.

[0085] like Figure 3 As shown, the embodiment of the present disclosure further provides a device 300 for detecting an abnormal operating state of an autonomous driving vehicle, comprising:

[0086] An acquisition unit 301 is configured to acquire the operating status of the autonomous driving vehicle and traffic scene information around the autonomous driving vehicle;

[0087] A first determining unit 302 is configured to determine information on the impact of the operating state of the autonomous driving vehicle on the traffic efficiency of the current road based on the operating state of the autonomous driving vehicle and traffic scene information around the autonomous driving vehicle; and

[0088] The second determination unit 303 is configured to determine that the autonomous driving vehicle is in an abnormal operating state in response to determining that the operating state of the autonomous driving vehicle causes a decrease in the traffic efficiency of the current road, wherein the abnormal operating state includes an abnormal parking state or an abnormal traffic state.

[0089] In some embodiments, the first determination unit 302 may include a first sub-determination unit and a second sub-determination unit. The first sub-determination unit is configured to determine whether the autonomous driving vehicle is in a state where passage is allowed, and the state where passage is allowed includes at least one of a state where straight passage is allowed, a state where lane change is allowed, or a state where turning is allowed. The second sub-determination unit is configured to determine that the operating state of the autonomous driving vehicle causes the passage efficiency of the current road to decrease in response to determining that the autonomous driving vehicle is in a state where passage is allowed, and the autonomous driving vehicle is in a parking state and the parking duration reaches a duration threshold.

[0090] In some embodiments, the first determination unit 302 may further include a third sub-determination unit, which is configured to determine that the operating state of the autonomous driving vehicle causes the traffic efficiency of the current road to decrease in response to determining that the autonomous driving vehicle is not in a state where traffic is allowed and the autonomous driving vehicle is in a driving state.

[0091] In some embodiments, the first determination unit 302 may further include a fourth sub-determination unit and a fifth sub-determination unit. The fourth sub-determination unit is configured to determine the traffic efficiency of the lane where the autonomous driving vehicle is located and the traffic efficiency of the lane adjacent to it. The fifth sub-determination unit is configured to determine that the operating state of the autonomous driving vehicle causes the traffic efficiency of the current road to decrease in response to determining that the traffic efficiency of the adjacent lane is less than the traffic efficiency of the lane where the autonomous driving vehicle is located, and the autonomous driving vehicle is in a state of changing lanes to the adjacent lane.

[0092] By using the device of the disclosed embodiment, the abnormal operating state of the autonomous driving vehicle can be accurately and efficiently detected, thereby providing an important reference basis for the research and development of autonomous driving vehicles and the development, simulation and optimization of operating routes, which is conducive to improving the traffic efficiency of autonomous driving vehicles and is particularly suitable for the development of large-scale autonomous driving vehicle operation systems.

[0093] According to an embodiment of the present disclosure, an electronic device is also provided, comprising: at least one processor; and a memory communicatively connected to the at least one processor; wherein the memory stores instructions executable by the at least one processor, and the instructions are executed by the at least one processor so that the at least one processor can execute the method of the aforementioned embodiment.

[0094] refer to Figure 4 , a block diagram of an electronic device 400 that can be used as a server or client of the present disclosure will now be described, which is an example of a hardware device that can be applied to various aspects of the present disclosure. The electronic device is intended to represent various forms of digital electronic computer devices, such as laptop computers, desktop computers, workbenches, personal digital assistants, servers, blade servers, mainframe computers, and other suitable computers. The electronic device can also represent various forms of mobile devices, such as personal digital processing, cellular phones, smart phones, wearable devices, and other similar computing devices. The components shown herein, their connections and relationships, and their functions are merely examples and are not intended to limit the implementation of the present disclosure described and / or required herein.

[0095] like Figure 4 As shown, the electronic device 400 includes a computing unit 401, which can perform various appropriate actions and processes according to a computer program stored in a read-only memory (ROM) 402 or a computer program loaded from a storage unit 408 into a random access memory (RAM) 403. In the RAM 403, various programs and data required for the operation of the electronic device 400 can also be stored. The computing unit 401, the ROM 402, and the RAM 403 are connected to each other via a bus 404. An input / output (I / O) interface 405 is also connected to the bus 404.

[0096] Multiple components in the electronic device 400 are connected to the I / O interface 405, including: an input unit 406, an output unit 407, a storage unit 408, and a communication unit 409. The input unit 406 can be any type of device that can input information to the electronic device 400. The input unit 406 can receive input digital or character information and generate key signal input related to user settings and / or function control of the electronic device, and can include but is not limited to a mouse, a keyboard, a touch screen, a track pad, a track ball, a joystick, a microphone, and / or a remote controller. The output unit 407 can be any type of device that can present information, and can include but is not limited to a display, a speaker, a video / audio output terminal, a vibrator, and / or a printer. The storage unit 408 can include but is not limited to a disk, an optical disk. The communication unit 409 allows the electronic device 400 to exchange information / data with other devices through a computer network such as the Internet and / or various telecommunication networks, and can include but is not limited to a modem, a network card, an infrared communication device, a wireless communication transceiver, and / or a chipset, such as a Bluetooth™ device, an 802.11 device, a WiFi device, a WiMax device, a cellular communication device, and / or the like.

[0097] The computing unit 401 may be a variety of general and / or special processing components with processing and computing capabilities. Some examples of the computing unit 401 include, but are not limited to, a central processing unit (CPU), a graphics processing unit (GPU), various dedicated artificial intelligence (AI) computing chips, various computing units running machine learning model algorithms, digital signal processors (DSPs), and any appropriate processors, controllers, microcontrollers, etc. The computing unit 401 performs the various methods and processes described above. For example, some embodiment methods may be implemented as a computer software program, which is tangibly contained in a machine-readable medium, such as a storage unit 408. In some embodiments, part or all of the computer program may be loaded and / or installed on the electronic device 400 via ROM 402 and / or communication unit 409. When the computer program is loaded into RAM 403 and executed by the computing unit 401, one or more steps of the embodiment method described above may be performed. Alternatively, in other embodiments, the computing unit 401 may be configured to perform the embodiment method described above in any other appropriate manner (e.g., by means of firmware).

[0098] The embodiments of the present disclosure also provide a computer-readable storage medium storing computer instructions, wherein the computer instructions are configured to enable a computer to execute the steps of any of the aforementioned method embodiments.

[0099] In addition, an embodiment of the present disclosure further provides a computer program product, including a computer program, wherein when the computer program is executed by a processor, the steps of any of the methods in the aforementioned embodiments are implemented.

[0100] Various implementations of the systems and techniques described above herein can be implemented in digital electronic circuit systems, integrated circuit systems, field programmable gate arrays (FPGAs), application specific integrated circuits (ASICs), application specific standard products (ASSPs), systems on chips (SOCs), load programmable logic devices (CPLDs), computer hardware, firmware, software, and / or combinations thereof. These various implementations can include: being implemented in one or more computer programs that can be executed and / or interpreted on a programmable system including at least one programmable processor, which can be a special purpose or general purpose programmable processor that can receive data and instructions from a storage system, at least one input device, and at least one output device, and transmit data and instructions to the storage system, the at least one input device, and the at least one output device.

[0101] The program code for implementing the method of the present disclosure may be written in any combination of one or more programming languages. These program codes may be provided to a processor or controller of a general-purpose computer, a special-purpose computer, or other programmable data processing device, so that the program code, when executed by the processor or controller, enables the functions / operations specified in the flow chart and / or block diagram to be implemented. The program code may be executed entirely on the machine, partially on the machine, partially on the machine as a stand-alone software package and partially on a remote machine, or entirely on a remote machine or server.

[0102] In the context of the present disclosure, a machine-readable medium may be a tangible medium that may contain or store a program for use by or in conjunction with an instruction execution system, device, or equipment. A machine-readable medium may be a machine-readable signal medium or a machine-readable storage medium. A machine-readable medium may include, but is not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, device, or equipment, or any suitable combination of the foregoing. A more specific example of a machine-readable storage medium may include an electrical connection based on one or more lines, a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing.

[0103] To provide interaction with a user, the systems and techniques described herein can be implemented on a computer having: a display device (e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor) for displaying information to the user; and a keyboard and pointing device (e.g., a mouse or trackball) through which the user can provide input to the computer. Other types of devices can also be used to provide interaction with the user; for example, the feedback provided to the user can be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user can be received in any form (including acoustic input, voice input, or tactile input).

[0104] The systems and techniques described herein may be implemented in a computing system that includes back-end components (e.g., as a data server), or a computing system that includes middleware components (e.g., an application server), or a computing system that includes front-end components (e.g., a user computer with a graphical user interface or a web browser through which a user can interact with implementations of the systems and techniques described herein), or a computing system that includes any combination of such back-end components, middleware components, or front-end components. The components of the system may be interconnected by any form or medium of digital data communication (e.g., a communication network). Examples of communication networks include: a local area network (LAN), a wide area network (WAN), and the Internet.

[0105] A computer system may include a client and a server. The client and the server are generally remote from each other and usually interact through a communication network. The relationship of client and server is generated by computer programs running on respective computers and having a client-server relationship with each other. The server may be a cloud server, a server of a distributed system, or a server combined with a blockchain.

[0106] It should be understood that the various forms of processes shown above can be used to reorder, add or delete steps. For example, the steps described in this disclosure can be performed in parallel, sequentially or in different orders, as long as the desired results of the technical solutions disclosed in this disclosure can be achieved, and this document does not limit this.

[0107] Although the embodiments or examples of the present disclosure have been described with reference to the accompanying drawings, it should be understood that the above-mentioned methods, systems and devices are merely exemplary embodiments or examples, and the scope of the present invention is not limited by these embodiments or examples, but only by the claims after authorization and their equivalent scope. Various elements in the embodiments or examples can be omitted or replaced by their equivalent elements. In addition, each step can be performed in an order different from that described in the present disclosure. Further, the various elements in the embodiments or examples can be combined in various ways. It is important that with the evolution of technology, many elements described herein can be replaced by equivalent elements that appear after the present disclosure.

Claims

1. A method for detecting an abnormal operating state of an autonomous driving vehicle, the method comprising: Acquiring the operating status of the autonomous driving vehicle and traffic scene information around the autonomous driving vehicle; Based on the operating state of the autonomous driving vehicle and traffic scene information around the autonomous driving vehicle, determining the impact information of the operating state of the autonomous driving vehicle on the traffic efficiency of the current road, wherein the determining the impact information of the operating state of the autonomous driving vehicle on the traffic efficiency of the current road includes: Determining whether the autonomous driving vehicle is in a state where passage is allowed, the state where passage is allowed includes at least one of a state where straight passage is allowed, a state where lane change is allowed, or a state where turning is allowed; In response to determining that the autonomous driving vehicle is in a state where passage is allowed, and the autonomous driving vehicle is in a parked state and the parking duration reaches a duration threshold, determining that the operating state of the autonomous driving vehicle causes a decrease in the passage efficiency of the current road; In response to determining that the autonomous driving vehicle is not in the passage-permitting state and the autonomous driving vehicle is in a driving state, determining that the operating state of the autonomous driving vehicle causes a decrease in the passage efficiency of the current road; and In response to determining that the operating state of the autonomous driving vehicle causes a decrease in the traffic efficiency of the current road, it is determined that the autonomous driving vehicle is in an abnormal operating state, wherein the abnormal operating state includes an abnormal parking state or an abnormal traffic state.

2. The method according to claim 1, wherein: The operating state of the autonomous driving vehicle includes at least one of the following: parking, starting, driving, turning, changing lanes and braking.

3. The method according to claim 1, wherein: The traffic scene information around the autonomous driving vehicle includes at least one of the following: positioning information of the autonomous driving vehicle in a map, lane information of the autonomous driving vehicle, obstacle information around the autonomous driving vehicle, and traffic light information around the autonomous driving vehicle.

4. The method according to claim 1, wherein: Determining the impact of the operating state of the autonomous driving vehicle on the traffic efficiency of the current road, including: Based on the traffic scene information around the autonomous driving vehicle, obtaining understanding information of the traffic scene around the autonomous driving vehicle; and Determine the impact of the operating state of the autonomous driving vehicle on the traffic efficiency of the current road based on the operating state of the autonomous driving vehicle, the planned trajectory information, and the understanding information of the traffic scene around the autonomous driving vehicle; Among them, the understanding information includes at least one of the following: whether the autonomous driving vehicle is in a docking state, whether the vehicle in front of the lane where the autonomous driving vehicle is located is in a queuing state, whether the vehicle in front of the lane where the autonomous driving vehicle is located is in an abnormal parking state, whether the road in front of the lane where the autonomous driving vehicle is located is in a prohibited passage indication state, whether there are pedestrians passing on the road in front of the lane where the autonomous driving vehicle is located, whether the autonomous driving vehicle meets the safe lane change conditions, whether the autonomous driving vehicle meets the safe turning conditions, and whether the road in front of the lane where the autonomous driving vehicle is located meets the conditions for passing through the detour.

5. The method according to claim 1, wherein: Determining the impact of the operating state of the autonomous driving vehicle on the traffic efficiency of the current road, including: Determining the traffic efficiency of the lane in which the autonomous driving vehicle is located and the traffic efficiency of the lanes adjacent to the lane; and In response to determining that the traffic efficiency of the adjacent lane is less than the traffic efficiency of the lane in which the autonomous driving vehicle is located, and the autonomous driving vehicle is in a state of changing lanes to the adjacent lane, it is determined that the operating state of the autonomous driving vehicle causes the traffic efficiency of the current road to decrease.

6. The method according to any one of claims 1 to 5, wherein: Acquiring the operating status of the autonomous driving vehicle and traffic scene information around the autonomous driving vehicle, including: The operating status of the autonomous driving vehicle and the traffic scene information around the autonomous driving vehicle in each of the multiple continuous time slices are obtained one by one.

7. A device for detecting an abnormal operating state of an autonomous driving vehicle, the device comprising: an acquisition unit, configured to acquire the operating status of the autonomous driving vehicle and traffic scene information around the autonomous driving vehicle; A first determining unit is configured to determine, based on the operating state of the autonomous driving vehicle and traffic scene information around the autonomous driving vehicle, information about the impact of the operating state of the autonomous driving vehicle on the traffic efficiency of the current road, wherein the first determining unit includes: A first sub-determination unit is configured to determine whether the autonomous driving vehicle is in a state where passage is allowed, wherein the state where passage is allowed includes at least one of a state where straight passage is allowed, a state where lane change is allowed, or a state where turning is allowed; a second sub-determination unit, configured to, in response to determining that the autonomous driving vehicle is in a state where passage is allowed, and the autonomous driving vehicle is in a parking state and the parking duration reaches a duration threshold, determine that the operating state of the autonomous driving vehicle causes a decrease in the passage efficiency of the current road; a third sub-determination unit configured to, in response to determining that the autonomous driving vehicle is not in the passage-permitted state and the autonomous driving vehicle is in a driving state, determine that the running state of the autonomous driving vehicle causes a decrease in the passage efficiency of the current road; and The second determination unit is configured to determine that the autonomous driving vehicle is in an abnormal operating state in response to determining that the operating state of the autonomous driving vehicle causes a decrease in the traffic efficiency of the current road, wherein the abnormal operating state includes an abnormal parking state or an abnormal traffic state.

8. The device according to claim 7, wherein: The first determining unit includes: a fourth sub-determination unit, configured to determine the traffic efficiency of the lane in which the autonomous driving vehicle is located and the traffic efficiency of the lanes adjacent to the lane; and The fifth sub-determination unit is configured to determine that the operating state of the autonomous driving vehicle has caused the traffic efficiency of the current road to decrease in response to determining that the traffic efficiency of the adjacent lane is less than the traffic efficiency of the lane in which the autonomous driving vehicle is located, and the autonomous driving vehicle is in a state of changing lanes to the adjacent lane.

9. An electronic device, comprising: at least one processor; as well as a memory communicatively connected to the at least one processor; wherein, The memory stores instructions that can be executed by the at least one processor, and the instructions are executed by the at least one processor to enable the at least one processor to perform the method according to any one of claims 1 to 6.

10. A computer-readable storage medium storing computer instructions, wherein: The computer instructions are configured to cause a computer to execute the method according to any one of claims 1 to 6.

11. A computer program product comprising a computer program, wherein: The computer program implements the method according to any one of claims 1 to 6 when executed by a processor.

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