IBM Patent | Augmented reality projection of predicted high-risk movements
Patent: Augmented reality projection of predicted high-risk movements
Patent PDF: 20240391482
Publication Number: 20240391482
Publication Date: 2024-11-28
Assignee: International Business Machines Corporation
Abstract
A computer-implemented method, a computer system and a computer program product display a projection of driving risk to a vehicle from activity in the surrounding area. The method includes acquiring a vehicle path from the vehicle. The method also includes capturing driving conditions from the surrounding area using a sensor and recognizing an object in the surrounding area that is transmitting relevant data. The method further includes identifying a high-risk object in the surrounding area by calculating a risk score for the object relative to the vehicle based on the vehicle path and intended movements of the object and classifying the object as the high-risk object when the risk score is above a risk threshold for the vehicle. Lastly, the method includes generating an augmented reality display of the surrounding area using an augmented reality device, wherein the augmented reality display of the surrounding area indicates the high-risk object.
Claims
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Description
BACKGROUND
Embodiments relate generally to image projection within a virtual or augmented reality environment, and more specifically to displaying predicted high-risk activity in the surroundings of an autonomous or semi-autonomous vehicle to a driver viewing the augmented reality environment.
Augmented reality (AR) technology may provide a real-time view of a physical, real-world environment with objects that may be augmented with computer-generated virtual elements, e.g., sound, video, graphics and/or positioning data. For example, mobile computing devices may provide augmented reality applications that allow users to see an augmented view of a surrounding real-world environment through a camera of the mobile computing device, where an application may overlay the camera view of the surrounding environment with data, such as telemetry information about vehicles in the proximate area of a user. In addition to mobile computing devices, a user may wear a headset or other apparatus that may be used to view the enhanced environment.
SUMMARY
An embodiment is directed to a computer-implemented method for displaying a projection of driving risk to a vehicle from activity in a surrounding area. The method may include acquiring a vehicle path from the vehicle. The method may also include capturing driving conditions from the surrounding area using a sensor and recognizing an object in the surrounding area, where the driving conditions include relevant data transmitted by the object. The method may further include identifying a high-risk object in the surrounding area by determining intended movements of the object from the relevant data, calculating a risk score for the object relative to the vehicle based on the vehicle path and the intended movements of the object, and classifying the object as the high-risk object when the risk score is above a risk threshold for the vehicle. Lastly, the method may include generating an augmented reality display of the surrounding area using an augmented reality device, wherein the augmented reality display of the surrounding area indicates the high-risk object.
In another embodiment, the method may include transmitting a notification about the high-risk object to the vehicle.
In a further embodiment, the obtaining the vehicle path from the vehicle may include obtaining telemetry data from the vehicle and determining the vehicle path from the telemetry data.
In yet another embodiment, the identifying the high-risk object may include determining that a recognized object is not transmitting the relevant data and classifying the recognized object as the high-risk object.
In still another embodiment, the identifying the high-risk object may include creating a digital twin instance for the vehicle, generating a digital twin simulation output by simulating the vehicle path using the digital twin instance and simulating the intended movements of the object, and updating the risk score based on the digital twin simulation output.
In another embodiment, the determining the risk score for the object may use a machine learning model that calculates a probability of an incident between the vehicle and the object based on prior detected movements of the object and the driving conditions.
In a further embodiment, the risk threshold for the vehicle may be determined using a machine learning model that predicts vehicle risk based on a vehicle profile and the driving conditions.
In a further embodiment, the obtaining the vehicle path from the vehicle may include obtaining telemetry data from the vehicle and determining the vehicle path from the telemetry data.
In addition to a computer-implemented method, additional embodiments are directed to a computer system and a computer program product for displaying a projection of driving risk to a vehicle from activity in a surrounding area.
This Summary is provided to introduce a selection of concepts in a simplified form that are further described below in the Detailed Description. This Summary is not intended to identify key features or essential features of the claimed subject matter, nor is it intended to be used as an aid in determining the scope of the claimed subject matter.
BRIEF DESCRIPTION OF THE DRAWINGS
FIG. 1 depicts a block diagram of an example computer system in which various embodiments may be implemented.
FIG. 2 depicts a flow chart diagram for a process that displays a projection of driving risk to a vehicle from activity in a surrounding area according to an embodiment.
DETAILED DESCRIPTION
As the transportation system may transition to more autonomous and semi-autonomous vehicles, both vehicles and drivers may be more reliant on devices and sensors that connect to communications networks. Information may be collected about the surrounding environment and sent to the vehicle directly for processing or to a human driver to make decisions about different actions that could be taken. In preserving the safety of vehicles and passengers, it may be important to identify high-risk activity in the surrounding area, e.g., children playing in or near a roadway, objects such as trees or light poles or other vehicles that may collide with vehicles and cause damage, or perhaps dangerous road conditions or weather events, that may force a driver to take remedial action. Incident prevention systems typically include video or other information that may be gathered to display driving conditions to a vehicle or driver but generally are not able to provide context to the vehicle or driver about the risk that any detected conditions or events may pose to a vehicle if a specific path, which may be predicted by telemetry data of the vehicle, is followed.
It may therefore be useful to provide a method or system to project driving risk to a vehicle from activity in the surrounding area using an augmented reality device. Augmented reality (AR) applications, which may become more common in today's technology ecosystem, may offer a real-time view of a physical, real-world environment whose elements are “augmented” by computer-generated sensory input such as sound, video, graphics and positioning data. A display of the surrounding environment around a vehicle may be enhanced by augmented data pertinent to the surroundings, e.g., graphical or textual information, and a headset, also known as AR “glass,” may be provided to a driver to see the augmented display. An augmented reality application may use images in the surrounding environment from a camera or any type of sensor, which may also determine position based on global positioning satellite (GPS) data, triangulation of a device's location, or other positioning methods. The application may then overlay the camera view of the surrounding environment with indicators such as highlighting obstructions or text indicating distance to the obstruction. Virtual reality (VR) environments may also be used as an alternative, with the difference that instead of displaying the actual surroundings as the background, an entirely artificial environment may be rendered by a virtual reality application with similar enhancements made to the objects in the field of view. Such a method or system may detect objects or other activity in the surroundings of a vehicle and determine if the activity is high-risk, e.g., if the activity is likely to affect the vehicle. For instance, there may be a group of pedestrians on a street corner that could attempt to cross the street as the vehicle passes and the method or system may detect the presence of the pedestrians and calculate a risk to both the pedestrian and the vehicle. If the risk is high enough, the driver may be warned to alter course or be alerted to the presence of the pedestrian in an augmented reality display, at which point the driver may take evasive action. Such a method or system may improve existing vehicle incident avoidance systems by providing more detailed data directly to a driver that may predict incident effects based on vehicle movements and driver actions, such that the driver may take manual action to avoid high-risk activity. As a result, driver and passenger safety may be improved and the efficiency of incident avoidance systems may be enhanced.
Referring to FIG. 1, computing environment 100 contains an example of an environment for the execution of at least some of the computer code involved in performing the inventive methods, such as vehicle risk projection module 150. In addition to vehicle risk projection module 150, computing environment 100 includes, for example, computer 101, wide area network (WAN) 102, end user device (EUD) 103, remote server 104, public cloud 105, and private cloud 106. In this embodiment, computer 101 includes processor set 110 (including processing circuitry 120 and cache 121), communication fabric 111, volatile memory 112, persistent storage 113 (including operating system 122 and vehicle risk projection module 150, as identified above), peripheral device set 114 (including user interface (UI), device set 123, storage 124, and Internet of Things (IoT) sensor set 125), and network module 115. Remote server 104 includes remote database 130. Public cloud 105 includes gateway 140, cloud orchestration module 141, host physical machine set 142, virtual machine set 143, and container set 144.
Computer 101 may take the form of a desktop computer, laptop computer, tablet computer, smart phone, smart watch or other wearable computer, mainframe computer, quantum computer or any other form of computer or mobile device now known or to be developed in the future that is capable of running a program, accessing a network or querying a database, such as remote database 130. As is well understood in the art of computer technology, and depending upon the technology, performance of a computer-implemented method may be distributed among multiple computers and/or between multiple locations. On the other hand, in this presentation of computing environment 100, detailed discussion is focused on a single computer, specifically computer 101, to keep the presentation as simple as possible. Computer 101 may be located in a cloud, even though it is not shown in a cloud in FIG. 1. On the other hand, computer 101 is not required to be in a cloud except to any extent as may be affirmatively indicated.
Processor set 110 includes one, or more, computer processors of any type now known or to be developed in the future. Processing circuitry 120 may be distributed over multiple packages, for example, multiple, coordinated integrated circuit chips. Processing circuitry 120 may implement multiple processor threads and/or multiple processor cores. Cache 121 is memory that is located in the processor chip package(s) and is typically used for data or code that should be available for rapid access by the threads or cores running on processor set 110. Cache memories are typically organized into multiple levels depending upon relative proximity to the processing circuitry. Alternatively, some, or all, of the cache for the processor set may be located “off chip.” In some computing environments, processor set 110 may be designed for working with qubits and performing quantum computing.
Computer readable program instructions are typically loaded onto computer 101 to cause a series of operational steps to be performed by processor set 110 of computer 101 and thereby effect a computer-implemented method, such that the instructions thus executed will instantiate the methods specified in flowcharts and/or narrative descriptions of computer-implemented methods included in this document (collectively referred to as “the inventive methods”). These computer readable program instructions are stored in various types of computer readable storage media, such as cache 121 and the other storage media discussed below. The program instructions, and associated data, are accessed by processor set 110 to control and direct performance of the inventive methods. In computing environment 100, at least some of the instructions for performing the inventive methods may be stored in vehicle risk projection module 150 in persistent storage 113.
Communication fabric 111 is the signal conduction paths that allow the various components of computer 101 to communicate with each other. Typically, this fabric is made of switches and electrically conductive paths, such as the switches and electrically conductive paths that make up busses, bridges, physical input/output ports and the like. Other types of signal communication paths may be used, such as fiber optic communication paths and/or wireless communication paths.
Volatile memory 112 is any type of volatile memory now known or to be developed in the future. Examples include dynamic type random access memory (RAM) or static type RAM. Typically, the volatile memory 112 is characterized by random access, but this is not required unless affirmatively indicated. In computer 101, the volatile memory 112 is located in a single package and is internal to computer 101, but, alternatively or additionally, the volatile memory may be distributed over multiple packages and/or located externally with respect to computer 101.
Persistent storage 113 is any form of non-volatile storage for computers that is now known or to be developed in the future. The non-volatility of this storage means that the stored data is maintained regardless of whether power is being supplied to computer 101 and/or directly to persistent storage 113. Persistent storage 113 may be a read only memory (ROM), but typically at least a portion of the persistent storage allows writing of data, deletion of data and re-writing of data. Some familiar forms of persistent storage include magnetic disks and solid-state storage devices. Operating system 122 may take several forms, such as various known proprietary operating systems or open-source Portable Operating System Interface-type operating systems that employ a kernel. The code included in vehicle risk projection module 150 typically includes at least some of the computer code involved in performing the inventive methods.
Peripheral device set 114 includes the set of peripheral devices of computer 101. Data communication connections between the peripheral devices and the other components of computer 101 may be implemented in various ways, such as Bluetooth connections, Near-Field Communication (NFC) connections, connections made by cables (such as universal serial bus (USB) type cables), insertion type connections (for example, secure digital (SD) card), connections made through local area communication networks and even connections made through wide area networks such as the internet. In various embodiments, UI device set 123 may include components such as a display screen, speaker, microphone, wearable devices (such as goggles and smart watches), keyboard, mouse, printer, touchpad, game controllers, and haptic devices. Storage 124 is external storage, such as an external hard drive, or insertable storage, such as an SD card. Storage 124 may be persistent and/or volatile. In some embodiments, storage 124 may take the form of a quantum computing storage device for storing data in the form of qubits. In embodiments where computer 101 is required to have a large amount of storage (for example, where computer 101 locally stores and manages a large database) then this storage may be provided by peripheral storage devices designed for storing very large amounts of data, such as a storage area network (SAN) that is shared by multiple, geographically distributed computers. IoT sensor set 125 is made up of sensors that can be used in Internet of Things applications. For example, one sensor may be a thermometer and another sensor may be a motion detector.
Network module 115 is the collection of computer software, hardware, and firmware that allows computer 101 to communicate with other computers through WAN 102. Network module 115 may include hardware, such as modems or Wi-Fi signal transceivers, software for packetizing and/or de-packetizing data for communication network transmission, and/or web browser software for communicating data over the internet. In some embodiments, network control functions and network forwarding functions of network module 115 are performed on the same physical hardware device. In other embodiments (for example, embodiments that utilize software-defined networking (SDN)), the control functions and the forwarding functions of network module 115 are performed on physically separate devices, such that the control functions manage several different network hardware devices. Computer readable program instructions for performing the inventive methods can typically be downloaded to computer 101 from an external computer or external storage device through a network adapter card or network interface included in network module 115.
WAN 102 is any wide area network (for example, the internet) capable of communicating computer data over non-local distances by any technology for communicating computer data, now known or to be developed in the future. In some embodiments, the WAN 102 may be replaced and/or supplemented by local area networks (LANs) designed to communicate data between devices located in a local area, such as a Wi-Fi network. The WAN and/or LANs typically include computer hardware such as copper transmission cables, optical transmission fibers, wireless transmission, routers, firewalls, switches, gateway computers and edge servers.
End User Device (EUD) 103 is any computer system that is used and controlled by an end user (for example, a customer of an enterprise that operates computer 101) and may take any of the forms discussed above in connection with computer 101. EUD 103 typically receives helpful and useful data from the operations of computer 101. For example, in a hypothetical case where computer 101 is designed to provide a recommendation to an end user, this recommendation would typically be communicated from network module 115 of computer 101 through WAN 102 to EUD 103. In this way, EUD 103 can display, or otherwise present, the recommendation to an end user. In some embodiments, EUD 103 may be a client device, such as thin client, heavy client, mainframe computer, desktop computer and so on.
Remote server 104 is any computer system that serves at least some data and/or functionality to computer 101. Remote server 104 may be controlled and used by the same entity that operates computer 101. Remote server 104 represents the machine(s) that collect and store helpful and useful data for use by other computers, such as computer 101. For example, in a hypothetical case where computer 101 is designed and programmed to provide a recommendation based on historical data, then this historical data may be provided to computer 101 from remote database 130 of remote server 104.
Public cloud 105 is any computer system available for use by multiple entities that provides on-demand availability of computer system resources and/or other computer capabilities, especially data storage (cloud storage) and computing power, without direct active management by the user. Cloud computing typically leverages sharing of resources to achieve coherence and economies of scale. The direct and active management of the computing resources of public cloud 105 is performed by the computer hardware and/or software of cloud orchestration module 141. The computing resources provided by public cloud 105 are typically implemented by virtual computing environments that run on various computers making up the computers of host physical machine set 142, which is the universe of physical computers in and/or available to public cloud 105. The virtual computing environments (VCEs) typically take the form of virtual machines from virtual machine set 143 and/or containers from container set 144. It is understood that these VCEs may be stored as images and may be transferred among and between the various physical machine hosts, either as images or after instantiation of the VCE. Cloud orchestration module 141 manages the transfer and storage of images, deploys new instantiations of VCEs and manages active instantiations of VCE deployments. Gateway 140 is the collection of computer software, hardware, and firmware that allows public cloud 105 to communicate through WAN 102.
Some further explanation of VCEs will now be provided. VCEs can be stored as “images.” A new active instance of the VCE can be instantiated from the image. Two familiar types of VCEs are virtual machines and containers. A container is a VCE that uses operating-system-level virtualization. This refers to an operating system feature in which the kernel allows the existence of multiple isolated user-space instances, called containers. These isolated user-space instances typically behave as real computers from the point of view of programs running in them. A computer program running on an ordinary operating system can utilize all resources of that computer, such as connected devices, files and folders, network shares, CPU power, and quantifiable hardware capabilities. However, programs running inside a container can only use the contents of the container and devices assigned to the container, a feature which is known as containerization.
Private cloud 106 is similar to public cloud 105, except that the computing resources are only available for use by a single enterprise. While private cloud 106 is depicted as being in communication with WAN 102, in other embodiments a private cloud may be disconnected from the internet entirely and only accessible through a local/private network. A hybrid cloud is a composition of multiple clouds of different types (for example, private, community or public cloud types), often respectively implemented by different vendors. Each of the multiple clouds remains a separate and discrete entity, but the larger hybrid cloud architecture is bound together by standardized or proprietary technology that enables orchestration, management, and/or data/application portability between the multiple constituent clouds. In this embodiment, public cloud 105 and private cloud 106 are both part of a larger hybrid cloud.
Computer environment 100 may be used to project driving risk to a vehicle from activity in the surrounding area. Specifically, vehicle risk projection module 150 may acquire a vehicle path from a vehicle, or alternatively obtain telemetry data from the vehicle and predict the vehicle path based on the telemetry data. The module 150 may then capture driving conditions from the surrounding area in the form of video, audio or text data using appropriate sensors, including recognizing objects in the surrounding area that may be transmitting relevant data. These sensors may be cameras or microphones, mounted in fixed locations in and around a roadway or embedded in mobile devices such as smartphones or tablets, but may also include data from Internet of Things (IoT) devices that may be in the surrounding area and telemetry and other data from other vehicles that may be in the surroundings. It should be noted that driving conditions in this context refers to specific activity in the surrounding area that may possibly be the cause of incidents involving the vehicle, such as objects, obstructions, weather conditions, human activity such as pedestrians or other activity that may result in vehicle risk, or any relevant aspect of the surrounding area. Objects in the surrounding area may then be identified as high-risk objects using both the projected path of the vehicle and the driving conditions. A prediction may be made about risk to the vehicle from a recognized object in the surrounding area by determining intended movements of the recognized object and then calculating a risk score for the object relative to the vehicle. The risk score may indicate the expected level of vehicle risk, where an object may be classified as a high-risk object when the risk score may be above a risk threshold for the vehicle. The information about objects in the surrounding area may be displayed in an augmented reality projection of the surrounding area that may be viewed by a driver or other person in the vehicle, where high-risk objects may be indicated, perhaps with the risk score or other details, such that evasive action may be taken to avoid a possible incident between the object and the vehicle. It should be noted that the display is not required to be static and may be updated as changes are made to the characteristics of the vehicle path, e.g., speed or direction are changes, and new telemetry data may be received by the vehicle risk projection module 150.
Referring to FIG. 2, an operational flowchart illustrating a process 200 that displays a projection of driving risk to a vehicle from activity in the surrounding area is depicted according to at least one embodiment. At 202, a vehicle path may be captured from a vehicle. This step may include the registering of the vehicle with the vehicle risk projection module 150 and may include using simple telemetry from the vehicle, such as speed or direction, or perhaps real-time angular rotation of the steering wheel or changes in steering wheel angular movement (as this information may be the best indicator of the vehicle path) to make a prediction of the vehicle path based on the telemetry data.
Information that may be captured at this step with respect to a vehicle may also include information specific to a driver. It should be noted that all collection of information from a driver or any video, audio or text that may personally identify a human driver or is sensitive in any other way requires the informed consent of all people whose information may be collected and analyzed by vehicle risk projection module 150. Consent may be obtained in real time or through a prior waiver or other process that informs a subject that their information may be captured by a device or other process and that the information may be used to predict the path of the vehicle and provide augmented reality projections of the effects of potential incidents between the vehicle and the surrounding environment, as will be described in detail below. The information owner is free to decide at any time to revoke consent for use of sensitive information as these settings are permanently retained to keep the vehicle risk projection module 150 updated with the latest information and also allow the owner of the information complete control over their informed consent to use sensitive information in the process described herein. The consent described here may also refer to allowing some, or any, data relating to the information owner from being sent to a local server, cloud server or any other location. The owner has complete control on the transmission of information that may be sensitive or personally identify the owner of the information. It should be noted that in addition to consent with respect to personally identifying information, consent may also be requested and received from any user with respect to devices that may be owned by the user, such as augmented reality (AR) equipment that may be used as described below. As with the information described above, this consent is also revocable at any time should conditions change for any reason.
In an embodiment, a supervised machine learning model may be trained to predict the path of a vehicle based on telemetry data from the vehicle or other sources. One or more of the following machine learning algorithms may be used: logistic regression, naïve Bayes, support vector machines, deep neural networks, random forest, decision tree, gradient-boosted tree, multilayer perceptron. In an embodiment, an ensemble machine learning technique may be employed that uses multiple machine learning algorithms together to assure better classification when compared with the classification of a single machine learning algorithm. In this embodiment, training data for the model may include historical information about a vehicle, including dimensions and shape of a vehicle or other fixed specifications of a vehicle, but may also extend to prediction of driver movements using learned behaviors that may be specific to the driver. The training data may be collected from a single vehicle or multiple vehicles over a longer period of time. The results may be stored in a database so that the data is most current, and the output would always be up to date.
At 204, driving conditions may be captured from the surrounding area using cameras and sensors that may be fixed or mounted in a stationary location or may be fitted to a vehicle. One example may be the installation of forward-facing, rear, side repeater, or internal cameras that may detect objects and people around the vehicle, as well as monitor driver behavior. Other examples include radar that may be installed on each side of the vehicle for obstacle detection, braking and steering assistance, autonomous driving functions, blind spot monitoring or other driving conditions, built-in sensors that may gather data on steering angle, wheel speed, brake pedal pressure, throttle position and other vehicle functions that help evaluate driver behavior, road conditions and the like. These cameras and sensors may determine driving conditions, e.g., object presence, relative location, and type along with vehicle performance data like speed or braking. It should be noted here that driving conditions as described at this step refers to the data that may be collected by the sensors and analyzed by the module 150.
The driving conditions captured at this step may be used to create a 3D model of the surrounding that may be used as an augmented or virtual reality projection of the surrounding area, as will be described and used below. Many methods may be used at this step, e.g., Convolutional Neural Networks (CNN), which may utilize camera data to generate an accurate 3D model of the surrounding area through computer vision and object recognition techniques, where objects may be recreated to accurately reflect orientation and distance from the vehicle, Mobile LiDAR, where a 3D laser scanner mounted on the vehicle may scan the surrounding area to generate a high-resolution map of the surrounding environment based on object identification and object distance estimation, or Recurrent Neural Networks (RNN), which may process sensor data over time to generate a continuous stream of object observations and allow the system to adapt and update as conditions change. In addition to detecting people and objects or vehicles in the surrounding area, driving conditions as described herein may also include information about weather conditions, either through sensor data that may describe the conditions or through video information from cameras that may provide a physical representation of the conditions, such as rain, snow or ice. Such weather information may assist in the prediction of the effect of objects and other factors in the surrounding area on the potential for an incident and may be recreated in the augmented reality environment. For instance, wet roads may cause a possible lack in traction and issues for the driver pertaining to steering, turning, braking and possibly exacerbate potential damage in an incident, which may then be projected in the augmented reality environment. In cold weather environments, snow, ice, or slush may further exacerbate the situation of steering and handling on the road, which may also be projected to the driver in the augmented reality environment through the 3D model, along with potential warnings about the surrounding area.
It should be noted that the vehicle and other objects or vehicles in the surrounding area may communicate with one another using an appropriate network. As such, the captured driving conditions are not required to be transmitted from a single vehicle but may also be sourced from other vehicles, or possibly other recognized objects. Communication may be through the network or from the other objects or vehicles directly, where temporal ad-hoc networks may be formed. For example, Vehicle-to-everything (V2X) is communication between a vehicle and any entity that may affect, or may be affected by, the vehicle. It is a vehicular communication system that incorporates other more specific types of communication as V2I (vehicle-to-infrastructure), where vehicles may communicate with infrastructure such as traffic lights, road signs, other vehicles, or the like, V2N (vehicle-to-network), V2V (vehicle-to-vehicle), where vehicle sensors and communication devices may communicate with surrounding vehicles in real time, V2P (vehicle-to-pedestrian), V2D (vehicle-to-device), using potential network technologies such as Vehicle-to-vehicle (V2V). This list should not be seen as limiting as there may be further technologies and locations for sensors or cameras to source both the telemetry data that may be used by the module 150 to learn about the vehicle and the surrounding area, including recognizing and communicating with objects in the surrounding area. The vehicle risk projection module 150 may also use a traffic simulation model to analyze current and future traffic flows on major roads, intersections, and highways to identify areas of congestion or collisions.
At 206, high-risk objects may be identified in the surrounding area based on a prediction of the probability that the object, or the activity in which the object may be used, poses a risk to the vehicle to the point that a driver may need to take evasive action. This risk prediction may be accomplished by determining intended movements of the object, calculating a risk score for the object relative to the vehicle, and classifying an object as a high-risk object when the risk score is above a risk threshold for the vehicle. In recognizing objects in the surrounding area, the vehicle risk projection module 150 may also monitor relevant data, such as telemetry for the object or other information regarding the intent of the object, that may be transmitted by the recognized object. This relevant data may be used to determine the intended movements of the object or in the calculation of risk score, and the absence of relevant data from the object may cause the module 150 to immediately classify the object as a high-risk object to be highlighted in the augmented reality display to the driver.
In an embodiment, a supervised machine learning model may be trained to predict the intended movements of a recognized object based on historical information about the movements of the object or about similar activity related to a recognized object or based on specific data that may be transmitted by the object, including telemetry or other data that may be relevant. One or more of the following machine learning algorithms may be used: logistic regression, naïve Bayes, support vector machines, deep neural networks, random forest, decision tree, gradient-boosted tree, multilayer perceptron. In an embodiment, an ensemble machine learning technique may be employed that uses multiple machine learning algorithms together to assure better classification when compared with the classification of a single machine learning algorithm. In this embodiment, training data for the model may include historical information about the object, including a profile or other specifications of the object that may be relevant. The training data may be collected from a single object or multiple objects, where different types of historical incidents in any sort of surroundings or context may indicate the movements of one or more objects. The results may be stored in a database so that the data is most current, and the output would always be up to date.
It should be noted that another supervised machine learning model, which may be separate from the above or combined, may be trained to calculate the risk score for the object relative to the vehicle, or predict the level of risk that a recognized object may pose to the vehicle based on historical information about the vehicle and objects of a similar type or through an analysis of the predicted vehicle path and the intended movements of the recognized object. One or more of the following machine learning algorithms may be used: logistic regression, naïve Bayes, support vector machines, deep neural networks, random forest, decision tree, gradient-boosted tree, multilayer perceptron. In an embodiment, an ensemble machine learning technique may be employed that uses multiple machine learning algorithms together to assure better classification when compared with the classification of a single machine learning algorithm. In this embodiment, training data for the model may also include historical information about the object, including a profile or other specifications of the object that may be relevant. The training data may be collected from a single object or multiple objects. The results may be stored in a database so that the data is most current, and the output would always be up to date. It should be noted that this machine learning model may also be used to compare the risk score to a risk threshold for the vehicle and also determine the risk threshold for the vehicle based on information about the vehicle and information about the surrounding area, including the driving conditions.
In a further embodiment, identifying a high-risk object in the surrounding area, including determining the intended movements of a recognized object, or calculating a risk score for the object, may be accomplished through a digital twin simulation of a potential incident that may achieve maximum precision in the determination of risk to the vehicle and also to capture multiple datapoints. A digital twin is a virtual model designed to accurately reflect a physical object. The object being studied, e.g., the vehicle or the object, may be outfitted with various sensors related to vital areas of functionality which produce data about different aspects of the physical object's performance or physical attributes. This data may then be relayed to a processing system and applied to the digital copy. Once informed with such data, the virtual model can be used to run simulations of incidents where the object and vehicle collide, and then study performance issues and generate possible improvements, including ways to avoid the collision or minimize the effect, all with the goal of generating valuable insights, all of which may then be applied back to the original physical object.
Although simulations and digital twins both utilize digital models to replicate various processes, a digital twin is actually a virtual environment and while a simulation typically studies one particular process, a digital twin can itself run any number of useful simulations in order to study multiple processes. Digital twins are designed around a two-way flow of information that first occurs when object sensors provide relevant data to the system processor and then happens again when insights created by the processor are shared back with the original source object. By having better and constantly updated data related to a wide range of areas, along with the added computing power that accompanies a virtual environment, digital twins are able to study more issues from far more vantage points than standard simulations and have greater ultimate potential to improve products and processes. Examples of the types of digital twins include component twins, which are the basic unit of digital twin or the smallest example of a functioning component, parts twins, which pertain to components of slightly less importance, asset twins, which study the interaction between components that work together, system (or unit) twins, which enable you to see how different assets come together to form an entire functioning system, and process twins, which are the macro level of magnification and reveal how systems work together to create an entire production facility, which may help determine the precise timing schemes that ultimately influence overall effectiveness.
At 208, an augmented reality (AR) display of the driving conditions may be generated. Included in the display may be the prediction of risk to the vehicle, e.g., the risk score of the object relative to the vehicle, where identified high-risk objects may be indicated or highlighted in the augmented reality display. The predicted path of the vehicle and the intended movements of the recognized object may also be displayed in the AR environment and the vehicle risk projection module 150 may also make a prediction of driving decisions that may be made to avoid a potential incident or lower the risk to the vehicle of the detected activity, including text or graphics indicating a path that may be safe, i.e., avoids the activity, and also a notification to the driver of the high-risk object, including the risk level, which may include a text message displayed on the augmented reality screen or a transmitted message such as an email or SMS message or a voice prompt to the driver as a warning. One of ordinary skill in the art will recognize the many forms of notification that are possible in this context. The augmented reality device that may display the AR environment may include a display that is mounted inside the vehicle that may be viewed by the driver or passengers of the vehicle or may be a headset that is worn by the driver or passengers. The resulting display may be an actual view of the surroundings from a camera or other device or may also be computer-generated with the enhancements or other information mentioned above overlaid on the view. The virtual model of the vehicle may also be overlaid on the display, such that the vehicle may be seen simultaneously to the surrounding driving conditions.
The vehicle risk projection module 150 may further continuously monitor the driving conditions and vehicle path, including telemetry data from one or more vehicles, and update the calculation of risk score and predictions of intended movements of the object and the risk to the vehicle as the inputs change. Updating the risk score may also change whether a recognized object is classified as a high-risk object and therefore the augmented reality display may add or remove an indication of the object in the display. Also, it should be noted that the display may proactively provide information to the driver about evasive action that may be taken by the driver to avoid a potential incident or collision that may be inferred to be imminent from the risk prediction. This information may include text in the display that may be seen by a driver or passenger or may take the form of a visible or audible warning notification to the driver or passenger, including a voice prompt that may be transmitted in addition to the augmented reality display.
Various aspects of the present disclosure are described by narrative text, flowcharts, block diagrams of computer systems and/or block diagrams of the machine logic included in computer program product (CPP) embodiments. With respect to any flowcharts, depending upon the technology involved, the operations can be performed in a different order than what is shown in a given flowchart. For example, again depending upon the technology involved, two operations shown in successive flowchart blocks may be performed in reverse order, as a single integrated step, concurrently, or in a manner at least partially overlapping in time.
A computer program product embodiment (“CPP embodiment” or “CPP”) is a term used in the present disclosure to describe any set of one, or more, storage media (also called “mediums”) collectively included in a set of one, or more, storage devices that collectively include machine readable code corresponding to instructions and/or data for performing computer operations specified in a given CPP claim. A “storage device” is any tangible device that can retain and store instructions for use by a computer processor. Without limitation, the computer readable storage medium may be an electronic storage medium, a magnetic storage medium, an optical storage medium, an electromagnetic storage medium, a semiconductor storage medium, a mechanical storage medium, or any suitable combination of the foregoing. Some known types of storage devices that include these mediums include: diskette, hard disk, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or Flash memory), static random access memory (SRAM), compact disc read-only memory (CD-ROM), digital versatile disk (DVD), memory stick, floppy disk, mechanically encoded device (such as punch cards or pits/lands formed in a major surface of a disc) or any suitable combination of the foregoing. A computer readable storage medium, as that term is used in the present disclosure, is not to be construed as storage in the form of transitory signals per se, such as radio waves or other freely propagating electromagnetic waves, electromagnetic waves propagating through a waveguide, light pulses passing through a fiber optic cable, electrical signals communicated through a wire, and/or other transmission media. As will be understood by those of skill in the art, data is typically moved at some occasional points in time during normal operations of a storage device, such as during access, de-fragmentation or garbage collection, but this does not render the storage device as transitory because the data is not transitory while it is stored.
The descriptions of the various embodiments of the present invention have been presented for purposes of illustration but are not intended to be exhaustive or limited to the embodiments disclosed. Many modifications and variations will be apparent to those of ordinary skill in the art without departing from the scope and spirit of the described embodiments. The terminology used herein was chosen to best explain the principles of the embodiments, the practical application or technical improvement over technologies found in the marketplace, or to enable others of ordinary skill in the art to understand the embodiments disclosed herein.
