Patent Publication Number: US-2023152115-A1

Title: Vehicle based external environment augmentation for operator alertness

Description:
BACKGROUND 
     The present invention relates generally to a method, system, and computer program product for augmented reality generation. More particularly, the present invention relates to a method, system, and computer program product for vehicle based external environment augmentation for operator alertness. 
     Augmented reality is an interactive experience of a real-world environment in which objects in a real or physical environment are enhanced by computer-generated information using one or more sensory modalities such as visual, auditory, haptic, and olfactory, generating a virtual environment. In some implementations, the virtual environment is overlaid on top of the real environment (e.g. by projecting a computer-generated image into a portion of a user’s vision or using a computer-generated image through which the real environment remains visible), while in other implementations the virtual environment replaces the real environment (e.g. by projecting a computer-generated image into a user’s entire visual field using goggles or providing sound through headphones that do not let in external sound). A user is typically able to interact in real time with objects in both the real and virtual environments. Generation of an augmented reality environment is also referred to as environment augmentation. 
     A vehicle transports people or objects from one physical location to another. An autonomous vehicle (AV) is a vehicle that is capable of sensing its environment and moving safely with little or no human input. Thus, a vehicle is operable by a human user, a machine (as in an AV), or a combination of a human user and a machine (e.g., a human pilot utilizing an autopilot for some or all portions of a flight). Some non-limiting examples of a vehicle are cars, trucks, tractors, aircraft, and boats. A vehicle user is a vehicle operator (either human or machine, or a combination), or a passenger in a vehicle. 
     SUMMARY 
     The illustrative embodiments provide a method, system, and computer program product. An embodiment includes a method that divides, into a set of segments using a route optimization engine, a route from a first physical location to a second physical location. An embodiment scores, using a user response analysis model, a response to a physical environment associated with a segment in the set of segments, the scoring resulting in a score. An embodiment augments, using a content generation model and the score, the physical environment, the augmenting combining the physical environment and a generated environment. 
     An embodiment includes a computer usable program product. The computer usable program product includes one or more computer-readable storage devices, and program instructions stored on at least one of the one or more storage devices. 
     An embodiment includes a computer system. The computer system includes one or more processors, one or more computer-readable memories, and one or more computer-readable storage devices, and program instructions stored on at least one of the one or more storage devices for execution by at least one of the one or more processors via at least one of the one or more memories. 
    
    
     
       BRIEF DESCRIPTION OF THE DRAWINGS 
       Certain novel features believed characteristic of the invention are set forth in the appended claims. The invention itself, however, as well as a preferred mode of use, further objectives and advantages thereof, will best be understood by reference to the following detailed description of the illustrative embodiments when read in conjunction with the accompanying drawings, wherein: 
         FIG.  1    depicts a block diagram of a network of data processing systems in which illustrative embodiments may be implemented; 
         FIG.  2    depicts a block diagram of a data processing system in which illustrative embodiments may be implemented; 
         FIG.  3    depicts a block diagram of an example configuration for vehicle based external environment augmentation for operator alertness in accordance with an illustrative embodiment; 
         FIG.  4    depicts a block diagram of an example configuration for vehicle based external environment augmentation for operator alertness in accordance with an illustrative embodiment; 
         FIG.  5    depicts a block diagram of an example configuration for vehicle based external environment augmentation for operator alertness in accordance with an illustrative embodiment; 
         FIG.  6    depicts an example of vehicle based external environment augmentation for operator alertness in accordance with an illustrative embodiment; 
         FIG.  7    depicts a flowchart of an example process for vehicle based external environment augmentation for operator alertness in accordance with an illustrative embodiment; 
         FIG.  8    depicts a cloud computing environment according to an embodiment of the present invention; and 
         FIG.  9    depicts abstraction model layers according to an embodiment of the present invention. 
     
    
    
     DETAILED DESCRIPTION 
     The illustrative embodiments recognize that maintaining alertness in a vehicle operator is a known problem. The operator must be alert enough to respond quickly when necessary (e.g. to avoid an obstacle on a road). However, traversing long distances when the landscape, vehicle route, and weather change very gradually, and consequently the operator has little to do, sap an operator’s alertness. For example, driving through a flat landscape on a straight road with little other traffic might be so boring that a driver might fail to react in time when a car in front brakes suddenly, or the driver might even fall asleep. A similar problem can occur when an airplane pilot’s main task is to monitor an autopilot. In addition, although a non-human AV operator does not fall asleep, if an AV is learning from its environment, the AV can become overfit or underfit to the current environment and fail to react appropriately to a change in the environment below a particular size or rate of change. 
     The illustrative embodiments also recognize that techniques are presently available to detect a human operator’s lack of alertness and suggest taking a rest. However, rest is not always a viable option. For example, a driver may be on a road without an available rest area, or an airplane pilot might not be near a suitable landing area. In addition, taking a rest adds delay to a journey. Consequently, the illustrative embodiments recognize that there is an unmet need to maintain vehicle operator alertness without adding delay or a detour to a journey. 
     The illustrative embodiments recognize that the presently available tools or solutions do not address these needs or provide adequate solutions for these needs. The illustrative embodiments used to describe the invention generally address and solve the above-described problems and other problems related to vehicle based external environment augmentation for operator alertness. 
     An embodiment can be implemented as a software application. The application implementing an embodiment can be configured as a modification of an existing vehicle operator monitoring system or vehicle operating system, as a separate application that operates in conjunction with an existing vehicle operator monitoring system or vehicle operating system, a standalone application, or some combination thereof. 
     Particularly, some illustrative embodiments provide a method that divides a route into a set of segments, scores a response to a physical environment associated with a segment in the set of segments, and augments, using a content generation model and the score, the physical environment by combining the physical environment and a generated environment. 
     An embodiment receives data of a vehicle user. The data includes starting and ending physical locations of a journey for which the user is using the vehicle. One embodiment receives starting and ending physical locations via a user interface from the user. Another embodiment receives starting and ending physical locations indirectly, for example from route information entered into a mapping application, from route plan data provided to a third party (e.g. a flight plan for an aircraft or a float plan for a boat), or from a user’s online activities monitored on an opt-in basis. Optionally, the user data also includes profile data of a user, such as user-supplied data regarding preferences in audio content or physical environment, or data regarding the user’s previous interactions with an embodiment. 
     An embodiment also receives data of a physical environment of the vehicle. Some non-limiting examples of a physical environment of the vehicle are a road on which a vehicle is travelling, terrain along the vehicle’s route, and an atmospheric condition along the vehicle’s route. The data of a physical environment includes data of a present condition of the environment, and optionally data of a future condition (e.g. a weather forecast for an area along the route for a time at which the vehicle will be in the area). In embodiments, the data of a physical environment includes image data (e.g. obtained using a still image or video camera), audio data (e.g. obtained using a microphone), or both. 
     An embodiment also receives data of one or more preset environments of the vehicle. A preset environment is a previously generated environment or a component of a previously generated environment. A generated environment is a virtual environment displayable using a component of an augmented reality system. 
     An embodiment uses a route optimization engine and available physical environment data to optimize a vehicle’s route between supplied endpoints and to divide a vehicle’s route from one physical location to another into one or more segments. A route optimization engines uses a presently known technique to optimize a vehicle’s route between supplied endpoints. In one embodiment, all the route segments are equal in size. Another embodiment uses the physical environment data to set a segment boundary where some aspect of the physical environment of the route changes, or is expected to change, by more than a threshold amount. For example, an embodiment might place a segment boundary where a route changes from a six-lane highway to a rural road, where treeless terrain changes to terrain with more than a threshold percentage of trees, where flat terrain changes to terrain with hills, or where an area forecast to have clear skies changes to an area forecast to have rain. 
     An embodiment uses a user response analysis model to score a user’s response to a physical environment associated with a segment in the set of segments. In one embodiment, the scored user response corresponds to an alertness level of a user. Techniques for determining a user’s alertness, or conversely a user’s drowsiness, are presently known. Some example techniques include monitoring a driver’s steering wheel inputs to determine a pattern associated with alertness or drowsiness, using a camera to monitor a vehicle’s position within a lane of a road (drifting out of lane, or a pattern of drifting, can indicate drowsiness), using a camera to detect a pattern associated with alertness or drowsiness in a driver’s face or eye movements (e.g. head nodding or eyes closing), using one or more sensors to detect a pattern associated with alertness or drowsiness in measured parameters such as brain activity, heart rate, skin conductance, muscle activity, and the like, and using a microphone monitoring sounds within a vehicle to detect a pattern associated with alertness or drowsiness (e.g. singing along with music might indicate alertness, while snoring indicates that a user is sleeping). In another embodiment, the scored user response corresponds to a mood or sentiment of a user, for example a score within a range from happy to sad or calm to angry. Techniques for scoring user mood are also presently known. In one embodiment, the scored user response is binary (e.g. alert or not alert). In another embodiment, the scored user response is a number within a range (e.g. 6 within a 0-10 range, or 0.6 within a 0-1 range). 
     Based on the score of a user’s response to a physical environment, an embodiment generates an environment intended to maintain or improve the user’s score. To generate the environment, an embodiment uses a content generation model to generate new content or adjust a preset environment or data of a physical environment. In one embodiment, the content generation model is implemented using a trained Generative Adversarial Network (GAN). A GAN is a presently known model that includes wo sub-models: a generator model trained to generate new examples of input data, and a discriminator model that tries to classify generated examples as either real (from real input data) or fake (generated by the generator). The two sub-models are trained together until the discriminator is fooled about half the time, meaning the generator is generating plausible examples. 
     In particular, an embodiment uses an image content generation model to generate or adjust an input image, such as an image component of a preset environment or image data of a physical environment of a vehicle, generating an image depicting a generated environment. In one embodiment, the generated environment includes a depiction of a route of travel. For example, the generated environment might include an enhancement of a road’s boundary or lane markings, which could be useful to assist a driver in low visibility conditions such as fog. As another example, the generated environment might include a schematic depiction of a route of flight of an aircraft, to assist a pilot in piloting along that route of flight. In another embodiment, the generated environment includes a depiction of terrain associated with a route of travel. For example, the generated environment might include a depiction of trees, mountains, a beach, or another terrain feature, intended to assist a user in maintaining alertness by providing interesting scenery. In another embodiment, the generated environment includes a depiction of an atmospheric condition associated with a route of travel. For example, the generated environment might include a depiction of clouds intended to alter an effect of too much sun, or a depiction of a clear sky intended to alter an effect of an overcast. 
     An embodiment selects and displays an image depicting the generated environment on a display visible to a vehicle user. One embodiment displays the generated environment as an overlay on top of the real environment of the vehicle, thereby augmenting the real environment by combining the generated and real environments within a user’s field of view. Another embodiment replaces the real environment with the generated environment, for example by projecting a computer-generated image into a user’s entire visual field using virtual reality goggles. In one embodiment, real environment replacement is only provided to a vehicle passenger, not a vehicle operator required to monitor the real environment during operation. 
     An embodiment uses an audio content generation model to adjust audio data, such as an audio component of a preset environment or audio data of a physical environment of a vehicle, generating audio data of a generated environment. For example, the generated environment might include birdsong or music. 
     An embodiment uses an audio output device audible to a vehicle operator to provide an audio component of the generated environment to a vehicle user. One embodiment augments the real environment by combining the generated and real environments within a user’s hearing, for example by using an output device that lets in external sound, such as speaker. Another embodiment replaces the real environment with the generated environment, for example by providing sound through headphones that do not let in external sound. In one embodiment, real environment replacement is only provided to a vehicle passenger, not a vehicle operator required to monitor the real environment during operation. 
     An embodiment generates data that is not image or audio data. Some non-limiting examples of data that is not image or audio data are a vibration pattern (e.g. within a steering wheel or a vehicle seat) and input data to a vehicle’s environmental system (e.g. to adjust vehicle heating or air conditioning to attain a particular temperature, or to generate a particular smell). An embodiment uses an output device to provide the generated data to a vehicle user, thus combining the generated and real environments for a user. 
     An embodiment uses a user response analysis model to score a user’s response to an augmented environment associated with a segment in a manner described herein. The embodiment uses the user’s response to further adapt a model used for environment generation to a particular user. For example, one user’s alertness might improve when flat, treeless terrain is augmented with images of trees and hills. As a result, the model learns that images of trees and hills improve this user’s alertness, and will generate such images for this user in the future. As another example, User A’s alertness might improve when a particular song is provided, while User B’s alertness might decrease when that same song is provided. As a result, the model learns to use the song for User A but not for User B. Techniques for adapting a model based on a response to a previous model output are presently known. An embodiment stores user data, including a user’s response to a generated environment, in a user profile. 
     An embodiment stores a generated environment in a library of present environments, for use as preset environment data. 
     The manner of vehicle based external environment augmentation for operator alertness described herein is unavailable in the presently available methods in the technological field of endeavor pertaining to vehicle operations. A method of an embodiment described herein, when implemented to execute on a device or data processing system, comprises substantial advancement of the functionality of that device or data processing system in dividing a route into a set of segments, scoring a response to a physical environment associated with a segment in the set of segments, and augmenting, using a content generation model and the score, the physical environment by combining the physical environment and a generated environment. 
     The illustrative embodiments are described with respect to certain types of routes, vehicles, environments, images, audio data, responses, scores, thresholds, adjustments, sensors, measurements, devices, data processing systems, environments, components, and applications only as examples. Any specific manifestations of these and other similar artifacts are not intended to be limiting to the invention. Any suitable manifestation of these and other similar artifacts can be selected within the scope of the illustrative embodiments. 
     Furthermore, the illustrative embodiments may be implemented with respect to any type of data, data source, or access to a data source over a data network. Any type of data storage device may provide the data to an embodiment of the invention, either locally at a data processing system or over a data network, within the scope of the invention. Where an embodiment is described using a mobile device, any type of data storage device suitable for use with the mobile device may provide the data to such embodiment, either locally at the mobile device or over a data network, within the scope of the illustrative embodiments. 
     The illustrative embodiments are described using specific code, designs, architectures, protocols, layouts, schematics, and tools only as examples and are not limiting to the illustrative embodiments. Furthermore, the illustrative embodiments are described in some instances using particular software, tools, and data processing environments only as an example for the clarity of the description. The illustrative embodiments may be used in conjunction with other comparable or similarly purposed structures, systems, applications, or architectures. For example, other comparable mobile devices, structures, systems, applications, or architectures therefor, may be used in conjunction with such embodiment of the invention within the scope of the invention. An illustrative embodiment may be implemented in hardware, software, or a combination thereof. 
     The examples in this disclosure are used only for the clarity of the description and are not limiting to the illustrative embodiments. Additional data, operations, actions, tasks, activities, and manipulations will be conceivable from this disclosure and the same are contemplated within the scope of the illustrative embodiments. 
     Any advantages listed herein are only examples and are not intended to be limiting to the illustrative embodiments. Additional or different advantages may be realized by specific illustrative embodiments. Furthermore, a particular illustrative embodiment may have some, all, or none of the advantages listed above. 
     It is to be understood that although this disclosure includes a detailed description on cloud computing, implementation of the teachings recited herein are not limited to a cloud computing environment. Rather, embodiments of the present invention are capable of being implemented in conjunction with any other type of computing environment now known or later developed. 
     Cloud computing is a model of service delivery for enabling convenient, on-demand network access to a shared pool of configurable computing resources (e.g., networks, network bandwidth, servers, processing, memory, storage, applications, virtual machines, and services) that can be rapidly provisioned and released with minimal management effort or interaction with a provider of the service. This cloud model may include at least five characteristics, at least three service models, and at least four deployment models. 
     Characteristics are as follows: 
     On-demand self-service: a cloud consumer can unilaterally provision computing capabilities, such as server time and network storage, as needed automatically without requiring human interaction with the service’s provider.   Broad network access: capabilities are available over a network and accessed through standard mechanisms that promote use by heterogeneous thin or thick client platforms (e.g., mobile phones, laptops, and PDAs).   Resource pooling: the provider’s computing resources are pooled to serve multiple consumers using a multi-tenant model, with different physical and virtual resources dynamically assigned and reassigned according to demand. There is a sense of location independence in that the consumer generally has no control or knowledge over the exact location of the provided resources but may be able to specify location at a higher level of abstraction (e.g., country, state, or datacenter).   Rapid elasticity: capabilities can be rapidly and elastically provisioned, in some cases automatically, to quickly scale out and rapidly released to quickly scale in. To the consumer, the capabilities available for provisioning often appear to be unlimited and can be purchased in any quantity at any time.   Measured service: cloud systems automatically control and optimize resource use by leveraging a metering capability at some level of abstraction appropriate to the type of service (e.g., storage, processing, bandwidth, and active user accounts). Resource usage can be monitored, controlled, and reported, providing transparency for both the provider and consumer of the utilized service.   

     Service Models are as follows: 
     Software as a Service (SaaS): the capability provided to the consumer is to use the provider’s applications running on a cloud infrastructure. The applications are accessible from various client devices through a thin client interface such as a web browser (e.g., web-based e-mail). The consumer does not manage or control the underlying cloud infrastructure including network, servers, operating systems, storage, or even individual application capabilities, with the possible exception of limited user-specific application configuration settings.   Platform as a Service (PaaS): the capability provided to the consumer is to deploy onto the cloud infrastructure consumer-created or acquired applications created using programming languages and tools supported by the provider. The consumer does not manage or control the underlying cloud infrastructure including networks, servers, operating systems, or storage, but has control over the deployed applications and possibly application hosting environment configurations.   Infrastructure as a Service (IaaS): the capability provided to the consumer is to provision processing, storage, networks, and other fundamental computing resources where the consumer is able to deploy and run arbitrary software, which can include operating systems and applications. The consumer does not manage or control the underlying cloud infrastructure but has control over operating systems, storage, deployed applications, and possibly limited control of select networking components (e.g., host firewalls).   

     Deployment Models are as follows: 
     Private cloud: the cloud infrastructure is operated solely for an organization. It may be managed by the organization or a third party and may exist on-premises or off-premises.   Community cloud: the cloud infrastructure is shared by several organizations and supports a specific community that has shared concerns (e.g., mission, security requirements, policy, and compliance considerations). It may be managed by the organizations or a third party and may exist on-premises or off-premises.   Public cloud: the cloud infrastructure is made available to the general public or a large industry group and is owned by an organization selling cloud services.   Hybrid cloud: the cloud infrastructure is a composition of two or more clouds (private, community, or public) that remain unique entities but are bound together by standardized or proprietary technology that enables data and application portability (e.g., cloud bursting for load-balancing between clouds).   

     A cloud computing environment is service oriented with a focus on statelessness, low coupling, modularity, and semantic interoperability. At the heart of cloud computing is an infrastructure that includes a network of interconnected nodes. 
     With reference to the figures and in particular with reference to  FIGS.  1  and  2   , these figures are example diagrams of data processing environments in which illustrative embodiments may be implemented.  FIGS.  1  and  2    are only examples and are not intended to assert or imply any limitation with regard to the environments in which different embodiments may be implemented. A particular implementation may make many modifications to the depicted environments based on the following description. 
       FIG.  1    depicts a block diagram of a network of data processing systems in which illustrative embodiments may be implemented. Data processing environment  100  is a network of computers in which the illustrative embodiments may be implemented. Data processing environment  100  includes network  102 . Network  102  is the medium used to provide communications links between various devices and computers connected together within data processing environment  100 . Network  102  may include connections, such as wire, wireless communication links, or fiber optic cables. 
     Clients or servers are only example roles of certain data processing systems connected to network  102  and are not intended to exclude other configurations or roles for these data processing systems. Server  104  and server  106  couple to network  102  along with storage unit  108 . Software applications may execute on any computer in data processing environment  100 . Clients  110 ,  112 , and  114  are also coupled to network  102 . A data processing system, such as server  104  or  106 , or client  110 ,  112 , or  114  may contain data and may have software applications or software tools executing thereon. 
     Only as an example, and without implying any limitation to such architecture,  FIG.  1    depicts certain components that are usable in an example implementation of an embodiment. For example, servers  104  and  106 , and clients  110 ,  112 ,  114 , are depicted as servers and clients only as example and not to imply a limitation to a client-server architecture. As another example, an embodiment can be distributed across several data processing systems and a data network as shown, whereas another embodiment can be implemented on a single data processing system within the scope of the illustrative embodiments. Data processing systems  104 ,  106 ,  110 ,  112 , and  114  also represent example nodes in a cluster, partitions, and other configurations suitable for implementing an embodiment. 
     Device  132  is an example of a device described herein. For example, device  132  can take the form of a smartphone, a tablet computer, a laptop computer, client  110  in a stationary or a portable form, a wearable computing device, or any other suitable device. Any software application described as executing in another data processing system in  FIG.  1    can be configured to execute in device  132  in a similar manner. Any data or information stored or produced in another data processing system in  FIG.  1    can be configured to be stored or produced in device  132  in a similar manner. Device  132  includes camera  134 , microphone  136 , and operator monitoring sensor  138 , for use in obtaining data of a physical environment and a vehicle user. Device  132  includes display  139  and speaker  140 , for use in providing an augmented environment to a user. Device  132  can be mounted within a vehicle. 
     Vehicle  142  is an example of a device described herein. For example, vehicle  142  can include a smartphone, a tablet computer, a laptop computer, client  110  in a stationary or a portable form, or any other suitable device. Any software application described as executing in another data processing system in  FIG.  1    can be configured to execute in vehicle  142  in a similar manner. Any data or information stored or produced in another data processing system in  FIG.  1    can be configured to be stored or produced in vehicle  142  in a similar manner. Vehicle  142  includes camera  144 , microphone  146 , and operator monitoring sensor  148 , for use in obtaining data of a physical environment and a user of vehicle  142 . Vehicle  142  includes display  149  and speaker  150 , for use in providing an augmented environment to a user of vehicle  142 . 
     Application  105  implements an embodiment described herein. Application  105  executes in any of servers  104  and  106 , clients  110 ,  112 , and  114 , device  132 , and vehicle  142 . 
     Servers  104  and  106 , storage unit  108 , and clients  110 ,  112 , and  114 , and device  132  may couple to network  102  using wired connections, wireless communication protocols, or other suitable data connectivity. Clients  110 ,  112 , and  114  may be, for example, personal computers or network computers. 
     In the depicted example, server  104  may provide data, such as boot files, operating system images, and applications to clients  110 ,  112 , and  114 . Clients  110 ,  112 , and  114  may be clients to server  104  in this example. Clients  110 ,  112 ,  114 , or some combination thereof, may include their own data, boot files, operating system images, and applications. Data processing environment  100  may include additional servers, clients, and other devices that are not shown. 
     In the depicted example, data processing environment  100  may be the Internet. Network  102  may represent a collection of networks and gateways that use the Transmission Control Protocol/Internet Protocol (TCP/IP) and other protocols to communicate with one another. At the heart of the Internet is a backbone of data communication links between major nodes or host computers, including thousands of commercial, governmental, educational, and other computer systems that route data and messages. Of course, data processing environment  100  also may be implemented as a number of different types of networks, such as for example, an intranet, a local area network (LAN), or a wide area network (WAN).  FIG.  1    is intended as an example, and not as an architectural limitation for the different illustrative embodiments. 
     Among other uses, data processing environment  100  may be used for implementing a client-server environment in which the illustrative embodiments may be implemented. A client-server environment enables software applications and data to be distributed across a network such that an application functions by using the interactivity between a client data processing system and a server data processing system. Data processing environment  100  may also employ a service oriented architecture where interoperable software components distributed across a network may be packaged together as coherent business applications. Data processing environment  100  may also take the form of a cloud, and employ a cloud computing model of service delivery for enabling convenient, on-demand network access to a shared pool of configurable computing resources (e.g. networks, network bandwidth, servers, processing, memory, storage, applications, virtual machines, and services) that can be rapidly provisioned and released with minimal management effort or interaction with a provider of the service. 
     With reference to  FIG.  2   , this figure depicts a block diagram of a data processing system in which illustrative embodiments may be implemented. Data processing system  200  is an example of a computer, such as servers  104  and  106 , or clients  110 ,  112 , and  114  in  FIG.  1   , or another type of device in which computer usable program code or instructions implementing the processes may be located for the illustrative embodiments. 
     Data processing system  200  is also representative of a data processing system or a configuration therein, such as data processing system  132  in  FIG.  1    in which computer usable program code or instructions implementing the processes of the illustrative embodiments may be located. Data processing system  200  is described as a computer only as an example, without being limited thereto. Implementations in the form of other devices, such as device  132  in  FIG.  1   , may modify data processing system  200 , such as by adding a touch interface, and even eliminate certain depicted components from data processing system  200  without departing from the general description of the operations and functions of data processing system  200  described herein. 
     In the depicted example, data processing system  200  employs a hub architecture including North Bridge and memory controller hub (NB/MCH)  202  and South Bridge and input/output (I/O) controller hub (SB/ICH)  204 . Processing unit  206 , main memory  208 , and graphics processor  210  are coupled to North Bridge and memory controller hub (NB/MCH)  202 . Processing unit  206  may contain one or more processors and may be implemented using one or more heterogeneous processor systems. Processing unit  206  may be a multi-core processor. Graphics processor  210  may be coupled to NB/MCH  202  through an accelerated graphics port (AGP) in certain implementations. 
     In the depicted example, local area network (LAN) adapter  212  is coupled to South Bridge and I/O controller hub (SB/ICH)  204 . Audio adapter  216 , keyboard and mouse adapter  220 , modem  222 , read only memory (ROM)  224 , universal serial bus (USB) and other ports  232 , and PCI/PCIe devices  234  are coupled to South Bridge and I/O controller hub  204  through bus  238 . Hard disk drive (HDD) or solid-state drive (SSD)  226  and CD-ROM  230  are coupled to South Bridge and I/O controller hub  204  through bus  240 . PCI/PCIe devices  234  may include, for example, Ethernet adapters, add-in cards, and PC cards for notebook computers. PCI uses a card bus controller, while PCIe does not. ROM  224  may be, for example, a flash binary input/output system (BIOS). Hard disk drive  226  and CD-ROM  230  may use, for example, an integrated drive electronics (IDE), serial advanced technology attachment (SATA) interface, or variants such as external-SATA (eSATA) and micro- SATA (mSATA). A super I/O (SIO) device  236  may be coupled to South Bridge and I/O controller hub (SB/ICH)  204  through bus  238 . 
     Memories, such as main memory  208 , ROM  224 , or flash memory (not shown), are some examples of computer usable storage devices. Hard disk drive or solid state drive  226 , CD-ROM  230 , and other similarly usable devices are some examples of computer usable storage devices including a computer usable storage medium. 
     An operating system runs on processing unit  206 . The operating system coordinates and provides control of various components within data processing system  200  in  FIG.  2   . The operating system may be a commercially available operating system for any type of computing platform, including but not limited to server systems, personal computers, and mobile devices. An object oriented or other type of programming system may operate in conjunction with the operating system and provide calls to the operating system from programs or applications executing on data processing system  200 . 
     Instructions for the operating system, the object-oriented programming system, and applications or programs, such as application  105  in  FIG.  1   , are located on storage devices, such as in the form of code  226 A on hard disk drive  226 , and may be loaded into at least one of one or more memories, such as main memory  208 , for execution by processing unit  206 . The processes of the illustrative embodiments may be performed by processing unit  206  using computer implemented instructions, which may be located in a memory, such as, for example, main memory  208 , read only memory  224 , or in one or more peripheral devices. 
     Furthermore, in one case, code  226 A may be downloaded over network  201 A from remote system  201 B, where similar code  201 C is stored on a storage device  201 D. in another case, code  226 A may be downloaded over network  201 A to remote system  201 B, where downloaded code  201 C is stored on a storage device  201 D. 
     The hardware in  FIGS.  1 - 2    may vary depending on the implementation. Other internal hardware or peripheral devices, such as flash memory, equivalent non-volatile memory, or optical disk drives and the like, may be used in addition to or in place of the hardware depicted in  FIGS.  1 - 2   . In addition, the processes of the illustrative embodiments may be applied to a multiprocessor data processing system. 
     In some illustrative examples, data processing system  200  may be a personal digital assistant (PDA), which is generally configured with flash memory to provide non-volatile memory for storing operating system files and/or user-generated data. A bus system may comprise one or more buses, such as a system bus, an I/O bus, and a PCI bus. Of course, the bus system may be implemented using any type of communications fabric or architecture that provides for a transfer of data between different components or devices attached to the fabric or architecture. 
     A communications unit may include one or more devices used to transmit and receive data, such as a modem or a network adapter. A memory may be, for example, main memory  208  or a cache, such as the cache found in North Bridge and memory controller hub  202 . A processing unit may include one or more processors or CPUs. 
     The depicted examples in  FIGS.  1 - 2    and above-described examples are not meant to imply architectural limitations. For example, data processing system  200  also may be a tablet computer, laptop computer, or telephone device in addition to taking the form of a mobile or wearable device. 
     Where a computer or data processing system is described as a virtual machine, a virtual device, or a virtual component, the virtual machine, virtual device, or the virtual component operates in the manner of data processing system  200  using virtualized manifestation of some or all components depicted in data processing system  200 . For example, in a virtual machine, virtual device, or virtual component, processing unit  206  is manifested as a virtualized instance of all or some number of hardware processing units  206  available in a host data processing system, main memory  208  is manifested as a virtualized instance of all or some portion of main memory  208  that may be available in the host data processing system, and disk  226  is manifested as a virtualized instance of all or some portion of disk  226  that may be available in the host data processing system. The host data processing system in such cases is represented by data processing system  200 . 
     With reference to  FIG.  3   , this figure depicts a block diagram of an example configuration for vehicle based external environment augmentation for operator alertness in accordance with an illustrative embodiment. Application  300  is an example of application  105  in  FIG.  1    and executes in any of servers  104  and  106 , clients  110 ,  112 , and  114 , device  132 , and vehicle  142  in  FIG.  1   . 
     Application  300  receives data of a vehicle user. The data includes starting and ending physical locations of a journey for which the user is using the vehicle. One implementation of application  300  receives starting and ending physical locations via a user interface from the user. Another implementation of application  300  receives starting and ending physical locations indirectly, for example from route information entered into a mapping application, from route plan data provided to a third party (e.g. a flight plan for an aircraft or a float plan for a boat), or from a user’s online activities monitored on an opt-in basis. Optionally, the user data also includes profile data of a user, such as user-supplied data regarding preferences in audio content or physical environment, or data regarding the user’s previous interactions with an embodiment. 
     Application  300  also receives data of a physical environment of the vehicle. Some non-limiting examples of a physical environment of the vehicle are a road on which a vehicle is travelling, terrain along the vehicle’s route, and an atmospheric condition along the vehicle’s route. The data of a physical environment includes data of a present condition of the environment, and optionally data of a future condition (e.g. a weather forecast for an area along the route for a time at which the vehicle will be in the area). In implementations of application  300 , the data of a physical environment includes image data (e.g. obtained using a still image or video camera), audio data (e.g. obtained using a microphone), or both. Application  300  also receives data of one or more preset environments of the vehicle. 
     Route segmentation module  310  uses a route optimization engine and available physical environment data to optimize a vehicle’s route between supplied endpoints and to divide a vehicle’s route from one physical location to another into one or more segments. A route optimization engines uses a presently known technique to optimize a vehicle’s route between supplied endpoints. In one implementation of module  310 , all the route segments are equal in size. Another implementation of module  310  uses the physical environment data to set a segment boundary where some aspect of the physical environment of the route changes, or is expected to change, by more than a threshold amount. For example, module  310  might place a segment boundary where a route changes from a six-lane highway to a rural road, where treeless terrain changes to terrain with more than a threshold percentage of trees, where flat terrain changes to terrain with hills, or where an area forecast to have clear skies changes to an area forecast to have rain. 
     Segment augmentation module  320  uses a user response analysis model to score a user’s response to a physical environment associated with a segment in the set of segments. In one implementation of module  320 , the scored user response corresponds to an alertness level of a user. Techniques for determining a user’s alertness, or conversely a user’s drowsiness, are presently known. In another implementation of module  320 , the scored user response corresponds to a mood or sentiment of a user, for example a score within a range from happy to sad or calm to angry. Techniques for scoring user mood are also presently known. In one implementation of module  320 , the scored user response is binary (e.g. alert or not alert). In another implementation of module  320 , the scored user response is a number within a range (e.g. 6 within a 0-10 range, or 0.6 within a 0-1 range). 
     Based on the score of a user’s response to a physical environment, module  320  generates an environment intended to maintain or improve the user’s score. To generate the environment, module  320  uses a content generation model to generate new content or adjust a preset environment or data of a physical environment. Module  320  selects and outputs the generated environment to a vehicle user. 
     Feedback module  330  uses a user response analysis model to score a user’s response to an augmented environment associated with a segment in a manner described herein. Module  330  uses the user’s response to further adapt a model used for environment generation to a particular user. User profile module  340  stores user data, including a user’s response to a generated environment. 
     With reference to  FIG.  4   , this figure depicts a block diagram of an example configuration for vehicle based external environment augmentation for operator alertness in accordance with an illustrative embodiment. In particular,  FIG.  4    provides more detail of segment augmentation module  320  in  FIG.  3   . 
     Augmentation generation module  410  uses a content generation model to generate new content or adjust a preset environment or data of a physical environment. In one implementation of module  410 , the content generation model is implemented using a trained GAN. In particular, module  410  uses an image content generation model to generate or adjust an input image, such as an image component of a preset environment or image data of a physical environment of a vehicle, generating an image depicting a generated environment. 
     Module  410  also uses an audio content generation model to adjust audio data, such as an audio component of a preset environment or audio data of a physical environment of a vehicle, generating audio data of a generated environment. Module  410  also generates data that is not image or audio data 
     Augmentation selection module  420  selects, from an input, generated, or adjusted image, an image depicting a generated environment for display. Augmentation selection module  420  also selects audio data of a generated environment for output to a user. Augmentation selection module  420  also selects data that is not image or audio data for output to a user. 
     Augmentation presentation module  430  displays an image depicting the generated environment on a display visible to a vehicle user. One implementation of module  430  displays the generated environment as an overlay on top of the real environment of the vehicle, thereby augmenting the real environment by combining the generated and real environments within a user’s field of view. Another implementation of module  430  replaces the real environment with the generated environment, for example by projecting a computer-generated image into a user’s entire visual field using virtual reality goggles. In one implementation of module  430 , real environment replacement is only provided to a vehicle passenger, not a vehicle operator required to monitor the real environment during operation. 
     Augmentation presentation module  430  uses an audio output device audible to a vehicle operator to provide an audio component of a generated environment to a vehicle user. One implementation of module  430  augments the real environment by combining the generated and real environments within a user’s hearing, for example by using an output device that lets in external sound, such as speaker. Another implementation of module  430  replaces the real environment with the generated environment, for example by providing sound through headphones that do not let in external sound. In one implementation of module  430 , real environment replacement is only provided to a vehicle passenger, not a vehicle operator required to monitor the real environment during operation. 
     Augmentation presentation module  430  uses an output device to provide the generated data to a vehicle user, thus combining the generated and real environments for a user. 
     With reference to  FIG.  5   , this figure depicts a block diagram of an example configuration for vehicle based external environment augmentation for operator alertness in accordance with an illustrative embodiment. In particular,  FIG.  5    provides more detail of augmentation generation module  410  in  FIG.  4   . 
     Route augmentation module  510  uses an image content generation model to generate or adjust an input image, such as an image component of a preset environment or image data of a physical environment of a vehicle, generating an image depicting a route of travel of a vehicle. Terrain augmentation module  520  uses an image content generation model to generate or adjust an input image, such as an image component of a preset environment or image data of a physical environment of a vehicle, generating an image depicting terrain associated with a route of travel. Atmospheric augmentation module  530  uses an image content generation model to generate or adjust an input image, such as an image component of a preset environment or image data of a physical environment of a vehicle, generating an image depicting an atmospheric condition associated with a route of travel. Non-visual augmentation module  540  uses an audio content generation model to adjust audio data, such as an audio component of a preset environment or audio data of a physical environment of a vehicle, generating audio data of a generated environment of the vehicle. Non-visual augmentation module  540  also generates data that is not image or audio data. 
     With reference to  FIG.  6   , this figure depicts an example of vehicle based external environment augmentation for operator alertness in accordance with an illustrative embodiment. The example can be executing using application  300  in  FIG.  3   . 
       FIG.  6    depicts vehicle  602  proceeding along route  604 , which application  300  has segmented into segments  610 ,  620 , and  630 . 
     In segment  610 , application  300  receives input regarding actual environment  612 , and scores a user of vehicle  602 ’s response to actual environment  612 . Because the user has above a threshold level of alertness, no environment augmentation is needed. This result is depicted in status  514 . 
     In segment  620 , application  300  receives input regarding actual environment  622 , and scores a user of vehicle  602 ’s response to actual environment  622 . As depicted in status  624 , the user has below a threshold level of alertness. Consequently, application  300  produces augmentation  626 , an adjustment of environment  622  adding trees and hills, adjusting an atmospheric condition from partly cloudy to sunny, and altering the music from song B to song A. Application scores a user of vehicle  602 ’s response to augmentation  626 , in which the user has above a threshold level of alertness and no environment augmentation is needed. This result is depicted in status  628 . 
     In segment  630 , application  300  receives input regarding actual environment  632 , and scores a user of vehicle  602 ’s response to actual environment  632 . As depicted in status  634 , the user has below a threshold level of alertness. Consequently, application  300  produces augmentation  636 , an adjustment of environment  632  augmenting route  604  (to compensate for the snowy conditions), adding trees and hills, adjusting an atmospheric condition from snowy to partly cloudy, and altering the music from song B to song A. Application scores a user of vehicle  602 ’s response to augmentation  636 , in which the user has above a threshold level of alertness and no environment augmentation is needed. This result is depicted in status  638 . 
     With reference to  FIG.  7   , this figure depicts a flowchart of an example process for vehicle based external environment augmentation for operator alertness in accordance with an illustrative embodiment. Process  700  can be implemented in application  300  in  FIG.  3   . 
     In block  702 , the application uses a route optimization engine to divide a route into a set of segments. In block  704 , the application scores a response to a physical environment associated with a segment in the set of segments. In block  706 , the application uses a content generation model and the scored response to generate an image depicting a generated environment associated with the segment. In block  708 , the application depicts the generated environment on a display visible to a vehicle operator, creating an augmented environment combining the physical environment with the generated environment. Then the application ends. 
     Referring now to  FIG.  8   , illustrative cloud computing environment  50  is depicted. As shown, cloud computing environment  50  includes one or more cloud computing nodes  10  with which local computing devices used by cloud consumers, such as, for example, personal digital assistant (PDA) or cellular telephone  54 A, desktop computer  54 B, laptop computer  54 C, and/or automobile computer system  54 N may communicate. Nodes  10  may communicate with one another. They may be grouped (not shown) physically or virtually, in one or more networks, such as Private, Community, Public, or Hybrid clouds as described hereinabove, or a combination thereof. This allows cloud computing environment  50  to offer infrastructure, platforms and/or software as services for which a cloud consumer does not need to maintain resources on a local computing device. It is understood that the types of computing devices  54 A-N depicted are intended to be illustrative only and that computing nodes  10  and cloud computing environment  50  can communicate with any type of computerized device over any type of network and/or network addressable connection (e.g., using a web browser). 
     Referring now to  FIG.  9   , a set of functional abstraction layers provided by cloud computing environment  50  ( FIG.  8   ) is shown. It should be understood in advance that the components, layers, and functions depicted are intended to be illustrative only and embodiments of the invention are not limited thereto. As depicted, the following layers and corresponding functions are provided: 
     Hardware and software layer  60  includes hardware and software components. Examples of hardware components include: mainframes  61 ; RISC (Reduced Instruction Set Computer) architecture based servers  62 ; servers  63 ; blade servers  64 ; storage devices  65 ; and networks and networking components  66 . In some embodiments, software components include network application server software  67  and database software  68 .   Virtualization layer  70  provides an abstraction layer from which the following examples of virtual entities may be provided: virtual servers  71 ; virtual storage  72 ; virtual networks  73 , including virtual private networks; virtual applications and operating systems  74 ; and virtual clients  75 .   In one example, management layer  80  may provide the functions described below. Resource provisioning  81  provides dynamic procurement of computing resources and other resources that are utilized to perform tasks within the cloud computing environment. Metering and Pricing  82  provide cost tracking as resources are utilized within the cloud computing environment, and billing or invoicing for consumption of these resources. In one example, these resources may include application software licenses. Security provides identity verification for cloud consumers and tasks, as well as protection for data and other resources. User portal  83  provides access to the cloud computing environment for consumers and system administrators. Service level management  84  provides cloud computing resource allocation and management such that required service levels are met. Service Level Agreement (SLA) planning and fulfillment  85  provide pre-arrangement for, and procurement of, cloud computing resources for which a future requirement is anticipated in accordance with an SLA.   Workloads layer  90  provides examples of functionality for which the cloud computing environment may be utilized. Examples of workloads and functions which may be provided from this layer include: mapping and navigation  91 ; software development and lifecycle management  92 ; virtual classroom education delivery  93 ; data analytics processing  94 ; transaction processing  95 ; and application selection based on cumulative vulnerability risk assessment  96 .   

     Thus, a computer implemented method, system or apparatus, and computer program product are provided in the illustrative embodiments for vehicle based external environment augmentation for operator alertness and other related features, functions, or operations. Where an embodiment or a portion thereof is described with respect to a type of device, the computer implemented method, system or apparatus, the computer program product, or a portion thereof, are adapted or configured for use with a suitable and comparable manifestation of that type of device. 
     Where an embodiment is described as implemented in an application, the delivery of the application in a Software as a Service (SaaS) model is contemplated within the scope of the illustrative embodiments. In a SaaS model, the capability of the application implementing an embodiment is provided to a user by executing the application in a cloud infrastructure. The user can access the application using a variety of client devices through a thin client interface such as a web browser (e.g., web-based e-mail), or other light-weight client-applications. The user does not manage or control the underlying cloud infrastructure including the network, servers, operating systems, or the storage of the cloud infrastructure. In some cases, the user may not even manage or control the capabilities of the SaaS application. In some other cases, the SaaS implementation of the application may permit a possible exception of limited user-specific application configuration settings. 
     The present invention may be a system, a method, and/or a computer program product at any possible technical detail level of integration. The computer program product may include a computer readable storage medium (or media) having computer readable program instructions thereon for causing a processor to carry out aspects of the present invention. 
     The computer readable storage medium can be a tangible device that can retain and store instructions for use by an instruction execution device. The computer readable storage medium may be, for example, but is not limited to, an electronic storage device, a magnetic storage device, an optical storage device, an electromagnetic storage device, a semiconductor storage device, or any suitable combination of the foregoing. A non-exhaustive list of more specific examples of the computer readable storage medium includes the following: a portable computer diskette, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or Flash memory), a static random access memory (SRAM), a portable compact disc read-only memory (CD-ROM), a digital versatile disk (DVD), a memory stick, a floppy disk, a mechanically encoded device such as punch-cards or raised structures in a groove having instructions recorded thereon, and any suitable combination of the foregoing. A computer readable storage medium, as used herein, is not to be construed as being transitory signals per se, such as radio waves or other freely propagating electromagnetic waves, electromagnetic waves propagating through a waveguide or other transmission media (e.g., light pulses passing through a fiber-optic cable), or electrical signals transmitted through a wire. 
     Computer readable program instructions described herein can be downloaded to respective computing/processing devices from a computer readable storage medium or to an external computer or external storage device via a network, for example, the Internet, a local area network, a wide area network and/or a wireless network. The network may comprise copper transmission cables, optical transmission fibers, wireless transmission, routers, firewalls, switches, gateway computers and/or edge servers. A network adapter card or network interface in each computing/processing device receives computer readable program instructions from the network and forwards the computer readable program instructions for storage in a computer readable storage medium within the respective computing/processing device. 
     Computer readable program instructions for carrying out operations of the present invention may be assembler instructions, instruction-set-architecture (ISA) instructions, machine instructions, machine dependent instructions, microcode, firmware instructions, state-setting data, configuration data for integrated circuitry, or either source code or object code written in any combination of one or more programming languages, including an object oriented programming language such as Smalltalk, C++, or the like, and procedural programming languages, such as the “C” programming language or similar programming languages. The computer readable program instructions may execute entirely on the user’s computer, partly on the user’s computer, as a stand-alone software package, partly on the user’s computer and partly on a remote computer or entirely on the remote computer or server. In the latter scenario, the remote computer may be connected to the user’s computer through any type of network, including a local area network (LAN) or a wide area network (WAN), or the connection may be made to an external computer (for example, through the Internet using an Internet Service Provider). In some embodiments, electronic circuitry including, for example, programmable logic circuitry, field-programmable gate arrays (FPGA), or programmable logic arrays (PLA) may execute the computer readable program instructions by utilizing state information of the computer readable program instructions to personalize the electronic circuitry, in order to perform aspects of the present invention. 
     Aspects of the present invention are described herein with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the invention. It will be understood that each block of the flowchart illustrations and/or block diagrams, and combinations of blocks in the flowchart illustrations and/or block diagrams, can be implemented by computer readable program instructions. 
     These computer readable program instructions may be provided to a processor of a computer, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions/acts specified in the flowchart and/or block diagram block or blocks. These computer readable program instructions may also be stored in a computer readable storage medium that can direct a computer, a programmable data processing apparatus, and/or other devices to function in a particular manner, such that the computer readable storage medium having instructions stored therein comprises an article of manufacture including instructions which implement aspects of the function/act specified in the flowchart and/or block diagram block or blocks. 
     The computer readable program instructions may also be loaded onto a computer, other programmable data processing apparatus, or other device to cause a series of operational steps to be performed on the computer, other programmable apparatus or other device to produce a computer implemented process, such that the instructions which execute on the computer, other programmable apparatus, or other device implement the functions/acts specified in the flowchart and/or block diagram block or blocks. 
     The flowchart and block diagrams in the Figures illustrate the architecture, functionality, and operation of possible implementations of systems, methods, and computer program products according to various embodiments of the present invention. In this regard, each block in the flowchart or block diagrams may represent a module, segment, or portion of instructions, which comprises one or more executable instructions for implementing the specified logical function(s). In some alternative implementations, the functions noted in the blocks may occur out of the order noted in the Figures. For example, two blocks shown in succession may, in fact, be accomplished as one step, executed concurrently, substantially concurrently, in a partially or wholly temporally overlapping manner, or the blocks may sometimes be executed in the reverse order, depending upon the functionality involved. It will also be noted that each block of the block diagrams and/or flowchart illustration, and combinations of blocks in the block diagrams and/or flowchart illustration, can be implemented by special purpose hardware-based systems that perform the specified functions or acts or carry out combinations of special purpose hardware and computer instructions.