Cognitive workload awareness

For determining cognitive workload awareness in driving context, an apparatus is disclosed. In some embodiments, the apparatus includes an activity monitor module that monitors driving activities of a driver. The apparatus includes a workload analysis module that determines a cognitive workload of the driver associated with the driving activities of the driver. Further, the apparatus includes a question-and-answer (QnA) dispatch module that determines an available QnA cognitive workload of the driver for performing QnA tasks based on the cognitive workload while ensuring a safe cognitive workload boundary associated with the driving activities of the driver.

FIELD

The subject matter disclosed herein relates to a system, an apparatus, and a method for implementing cognitive workload awareness in driving context.

BACKGROUND

Typically, drivers participate in daily driving activities with non-trivial time spent focused on their driving. In some cases, average drivers may devote about 58 minutes to driving per day, which may consume a non-trivial portion of a driver's active daily time, such as, e.g., 1 out of 16 hours in a 24 hour period of a day. Generally, driving hours are non-productive, and even though drivers should not be distracted while driving, it does not mean that drivers cannot do anything while driving. For instance, a driver can chat with a passenger, a driver can listen to music and control car audio, and a driver can make hands-free phone calls. Recently, in some cases, a driver can make voice commands to a built-in car voice recognition system or voice-activated mobile services.

BRIEF SUMMARY

An apparatus for implementing cognitive workload awareness in driving context is disclosed herein. In one embodiment, the apparatus includes an activity monitor module that monitors driving activities of a driver. The apparatus includes a workload analysis module that determines a cognitive workload of the driver associated with the driving activities of the driver. The apparatus includes a question-and-answer (QnA) dispatch module that determines an available QnA cognitive workload of the driver for performing QnA tasks based on the cognitive workload while ensuring a safe cognitive workload boundary associated with the driving activities of the driver.

A method for implementing cognitive workload awareness is disclosed. In one embodiment, the method includes monitoring driving activities of a driver while the driver is operating a vehicle. The method includes determining a current cognitive workload and a safe cognitive workload of the driver associated with the driving activities of the driver. The method includes determining an available QnA cognitive workload of the driver for performing QnA tasks based on the current cognitive workload while ensuring a safe cognitive workload boundary associated with the safe cognitive workload of the driver. The method includes selecting a QnA task out of a pool of QnA tasks so that an additional cognitive workload that the selected QnA task will add to the current cognitive workload does not exceed the available QnA cognitive workload. The method includes fetching the QnA task for driver response if a summation of the current cognitive workload and the additional cognitive workload that the fetched QnA task will add to the current cognitive workload is less than the safe cognitive workload so as to ensure the safe cognitive workload boundary. In some cases, the fetched QnA task for driver response may have a corresponding cognitive workload of less than a difference between the safe cognitive workload and the cognitive workload.

A computer program product that performs the functions of the method is disclosed. In one embodiment, the computer program product includes computer readable storage media having program instructions embodied therewith, the program instructions are readable/executable by a computer to cause the computer to monitor driving activities of a driver while the driver is operating a vehicle. The program instructions cause the computer to determine a cognitive workload and a safe cognitive workload of the driver associated with the driving activities of the driver. The program instructions cause the computer to determine an available QnA cognitive workload of the driver for performing QnA tasks based on the cognitive workload while ensuring a safe cognitive workload boundary associated with the safe cognitive workload of the driver. Further, the program instructions cause the computer to fetch a QnA task for driver response if a summation of the cognitive workload and an additional cognitive workload that the fetched QnA task will add to the cognitive workload is less than the safe cognitive workload so as to ensure the safe cognitive workload boundary. In some cases, the fetched QnA task for driver response may have a corresponding cognitive workload of less than a difference between the safe cognitive workload and the cognitive workload.

A device for implementing cognitive workload awareness is disclosed. In one embodiment, the device includes a processor and memory having stored thereon instructions that, when executed by the processor, cause the processor to monitor driving activities of a driver while the driver is operating a vehicle. The instructions cause the processor to determine a cognitive workload and a safe cognitive workload of the driver associated with the driving activities of the driver. The instructions cause the processor to determine an available QnA cognitive workload of the driver for performing QnA tasks based on the cognitive workload while ensuring a safe cognitive workload boundary associated with the safe cognitive workload of the driver. The instructions cause the processor to fetch a QnA task for driver response if a summation of the cognitive workload and an additional cognitive workload that the fetched QnA task will add to the cognitive workload is less than the safe cognitive workload so as to ensure the safe cognitive workload boundary. In some cases, the fetched QnA task for driver response may have a corresponding cognitive workload of less than a difference between the safe cognitive workload and the cognitive workload.

DETAILED DESCRIPTION

FIG. 1is a block diagram illustrating one embodiment of an apparatus100that utilizes a computing device102for implementing cognitive workload awareness in driving context with various methods and techniques associated therewith. In some cases, the apparatus100may be used for question-and-answer (QnA) style micro-task scheduling for drivers with cognitive workload awareness in driving context. Further, various schemes and/or techniques described herein may refer to service architecture that is configured for cognitive workload-aware QnA dispatch in driving context.

In reference toFIG. 1, the apparatus100may be implemented as a system having the computing device102purposed for cognitive workload awareness, to thereby transform the computing device102into a special purpose machine dedicated to cognitive workload awareness in driving context, as described herein. Therefore, the computing device102may include standard element(s) and/or component(s), including at least one processor(s)104, memory106(e.g., non-transitory computer-readable storage medium), peripherals, power, and various other computing elements and/or components that are not specifically shown inFIG. 1. Further, as shown inFIG. 1, the apparatus100may be associated with a display device130(e.g., a monitor or other display) that may be used to provide a graphical user interface (GUI)132. In some implementations, the GUI132may be used to receive input from a user (e.g., user input) associated with cognitive workload awareness in driving context. In other implementations, one or more other user interfaces (UI)120(e.g., one or more other user input devices or similar) may be used to receive input from one or more other users (e.g., other user input) associated with cognitive workload awareness in driving context. The apparatus100may also be associated with one or more databases150that may be configured to store data and information associated with users including messages and various communication data associated with the users.

Accordingly, the apparatus100may thus include the computing device102and instructions recorded on the computer-readable medium106(or one or more databases150) and executable by the at least one processor104. The apparatus100may be utilized to monitor various activities of users. Further, the apparatus100may include the display device130for providing output to a user, and the display device130may include the GUI132for receiving input from the user. Still further, the one or more other UIs120may be used for providing output to other users and receiving input from the other users.

The computing device102may include a network interface108adapted for communication with various external resources and/or external networks. For instance, the external resources may include one or more QnA service providers180, one or more cloud service providers182, and various other external data resources184. In another instance, the external networks may include various types of communication networks186. The network interface108may be embodied as a wireless communication module, such as a mobile cellular module, a wireless broadband module, a wireless satellite module, and/or various other types of wireless communication module including radio frequency (RF), microwave frequency (MWF), and/or infrared frequency (IRF) components adapted for communication with the various types of communication networks186. The network interface108may be further adapted to interface with a DSL (e.g., Digital Subscriber Line) modem, a PSTN (Public Switched Telephone Network) modem, an Ethernet device, and/or various other types of wired and/or wireless network communication devices adapted for communication with the various types of communication networks186.

The computing device102may include one or more modules, such as, e.g., an activity monitor module110. In some scenarios, the activity monitor module110may be used to monitor driving activities of a driver. Further, the activity monitor module110may monitor the driving activities of the driver while the driver is operating a vehicle, such as, e.g., any type of motor vehicle including various land-based automobiles, cars, trucks, etc., various marine-based vehicles, ships, boats, etc., and/or various air-based vehicles, aircraft, airplanes, helicopters, etc.

The driving activities of the driver may include various routine mental, physical, and/or temporal tasks associated with driving and operating a vehicle in various environments, such as, e.g., mental focus on driving conditions, physical movements of operating components, trip scheduling, etc. In some instances, the driving activities of the driver may include talking with one or more passengers while the driver is operating a vehicle. In other instances, the driving activities of the driver may include talking with one or more persons on a communication device, such as, e.g., a mobile phone. In some other instances, the driving activities of the driver may include one or more of listening to music, controlling car audio, conducting hands-free communication via a wireless network, and/or various other types of driving activities and/or driving tasks that may be associated with a driver driving and/or operating a vehicle.

The computing device102may include a workload analysis module112. In some scenarios, the workload analysis module112may be used to determine a cognitive workload of the driver associated with the driving activities of the driver. The cognitive workload of the driver may also be referred to as a current cognitive workload of the driver. The workload analysis module112may determine a safe cognitive workload of the driver associated with the driving activities of the driver. The cognitive workload of the driver may include a baseline cognitive workload of the driver and one or more variable cognitive workloads of the driver associated with the driving activities of the driver. The baseline cognitive workload may be associated with default driving activities of the driver including driving a vehicle under routine mental, physical, and temporal operating conditions. The one or more variable cognitive workloads may include one or more intensive task workloads associated with metropolitan driving activities, congested driving activities, extreme weather driving activities, and/or night driving activities. Further, in various implementations, the one or more variable cognitive workloads may also include one or more of listening to music, controlling car audio, talking with one or more passengers onboard, and/or conducting hands-free communication via a wireless network.

In some implementations, the workload analysis module112may determine an interface modality of the driver including, e.g., hands-free communication between the apparatus and the driver. The hands-free communication between the apparatus and driver may include audible speech from the driver and/or voice recognition by the apparatus via one or more user interface components and a microphone. The hands-free communication between the apparatus and driver may further include audible listening by the driver and computerized speech from the apparatus via one or more user interface components and a speaker. The apparatus may include the computing device102.

Generally, whether a driver participates in certain tasks or not while driving may depend on the driver's cognitive workload and interface modality. In some cases, cognitive workload constraints may refer as to whether a certain task adds extra cognitive workloads to result in a total workload high enough to affect a driver's driving activity, in which cases, the driver may not be able to participate in the task. In other cases, interface modality constraints may refer to various tasks that may not use modalities continuously necessary to driving, such as, e.g., visual attention, hands, feet, etc. Acceptable modalities may be analogous to those of a hands-free phone call or similar, which may involve, e.g., speaking and/or hearing. Therefore, in some driving context scenarios, the schemes and/or techniques described herein in reference to cognitive workload-aware QnA dispatching in driving context may be achieved. Some QnA tasks may include paid QnA tasks, and other similar or related QnA tasks are described in greater detail herein.

The computing device102may include a question-and-answer (QnA) dispatch module114. In some scenarios, the QnA Dispatch module114may be used to determine an available QnA cognitive workload of the driver for performing various QnA tasks based on the cognitive workload while ensuring a safe cognitive workload boundary associated with the driving activities of the driver. In some other scenarios, the QnA dispatch module114may be used to select a QnA task from (or out of) a pool of QnA tasks so that an additional cognitive workload that the selected QnA task will add to the cognitive workload does not exceed the available QnA cognitive workload. If a summation of the cognitive workload and the additional cognitive workload that the fetched QnA task will add to the cognitive workload is less than the safe cognitive workload, then the QnA dispatch module114may fetch the QnA task for driver response so as to ensure the safe cognitive workload boundary. Otherwise, if the summation of the cognitive workload and the additional cognitive workload that the fetched QnA task will add to the cognitive workload is equal to or greater than the safe cognitive workload, then the QnA dispatch module114may not fetch a QnA task for driver response so as to ensure the safe cognitive workload boundary. The fetched QnA task for driver response may include a corresponding cognitive workload of less than a difference between the safe cognitive workload and the cognitive workload.

In reference to cognitive activity, cognitive workload generally refers total mental effort utilized for working memory. Cognitive workload involves problem solving that may be differentiated into intrinsic, extraneous, and germane. For instance, intrinsic cognitive workload may refer to mental effort associated with specific topics. Extraneous cognitive workload may refer to a mental process by which information and/or tasks are presented. Germane cognitive workload may refer to mental attributed toward generating permanent stores of knowledge or schemas. The means by which to measure perceived mental effort may be referred to as cognitive workload. Various task-invoked responses provide reliable measurement of cognitive workload that may be associated with working memory. Cognitive workload has various effects on task related activities, and experiences of cognitive workload may be similar or somewhat different for everyone.

The QnA tasks may include targeted questions while the driver is operating a vehicle. The QnA tasks may include dynamically fetched QnA tasks associated with one or more of surveys, polls, and/or context aware questions. The QnA tasks may include customer satisfaction surveys. The QnA tasks may include public polling interviews. The QnA tasks may include crowd-source questions including one or more of single-sentence interpretation, ground-truth tagging to auditory data, and/or unknown music classification associated with genre and/or mood. The QnA tasks may include context aware questions including one or more of traffic status questions, accident report questions, and/or point-of-view (PoV) questions associated with location and/or time. Further, in some cases, one or more different languages (e.g., English, Spanish, French, German, Russian, etc.) may be used for and/or applied to the QnA tasks and/or questions. Thus, in some cases, the QnA task and/or questions may be language oriented questions, and as such, the questions may be asked in one language and then answered in a different language.

The user interfaces (UIs)120may include various types of user interfaces120A,120B, . . . ,120N that provide diagnostic data and information for implementing various features of cognitive workload awareness in driving context. For instance, the user interfaces (UIs)120may include an external interface120A that is configured to receive external data and information from external environmental sensors, cloud service providers182, and/or external data resources184including environmental data and information (e.g., data and information related to traffic, weather, global positioning system (GPS) data and information, etc.). The user interfaces (UIs)120may include a vehicle interface120B that is configured to receive vehicle related data and information from the wired and/or wireless communication networks186including diagnostic data and information from onboard systems and diagnostic sensors for the vehicle (e.g., data and information relate to engine, brakes, cooling system, speed, driving styles, etc.).

Further, the user interfaces (UIs)120may include a user device interface120C that is configured to receive user input data and information (e.g., user input data and information related to user preferences, user control settings, etc.) from one or more user devices (e.g., mobile phone, personal computer, notebook, laptop, etc.). The user interfaces (UIs)120may include a speech interface120D that is configured to receive audible speech, vocal, and/or voice input data and information from one or more users via one or more user devices (e.g., one or more microphones, or similar). The user interfaces (UIs)120may include an auditory interface120E that is configured to provide audible output data and information to one or more auditory devices (e.g., one or more speakers, or similar). The user interfaces (UIs)120may include any type of user interface120N that is configured to receive (as input) or provide (as output) various data and information for implementing cognitive workload awareness in driving context.

In reference toFIG. 1, the apparatus100is shown using various functional blocks or modules that represent discrete functionality. However, it should be understood that such illustration is provided for clarity and convenience, and therefore, it should be appreciated that the various functionalities may overlap or be combined within a described block(s) or module(s), and/or may be implemented by one or more additional block(s) or module(s) that are not specifically illustrated inFIG. 1. Further, it should be understood that various standard and/or conventional functionality that may be useful to the apparatus100ofFIG. 1may be included as well even though such standard and/or conventional elements are not illustrated explicitly, for the sake of clarity and convenience.

FIGS. 2A-2Bare various diagrams illustrating one embodiment of driver activity status and various components related to collecting and classifying driving context data and information. In particular,FIG. 2Aillustrates a diagram of driver activity status200A, andFIG. 2Billustrates various components related to collecting and/or classifying driving context data and information. Some considerations may include how to dispatch QnA tasks while ensuring a safe cognitive workload boundary. Other considerations may include how to collect and/or classify driving context data and information.

In reference toFIG. 2A, various driving activities of a driver are tracked, monitored, and identified, and a (total) cognitive workload210of the driver is determined based on the driving activities of the driver. Further, an available QnA cognitive workload212of the driver is determined for performing QnA tasks224based on the (total) cognitive workload210while ensuring a safe cognitive workload boundary for safe driving214associated with the driving activities of the driver. In some cases, cognitive workload of the driver may shift toward micro-tasking while driving, which may thus refer to cognitive workload-aware task distribution to drivers.

The (total) cognitive workload210of the driver may include a baseline cognitive workload220of the driver and one or more variable cognitive workloads222A,222B, . . . ,222E of the driver associated with the driving activities of the driver. In some scenarios, the baseline cognitive workload220are associated with various default driving activities of the driver including driving a vehicle under routine mental, physical, and/or temporal operating conditions. The one or more variable cognitive workloads222A,222B, . . . ,222E may include one or more intensive task workloads associated with night driving activity222A, extreme weather driving activity222B, congested driving activity222C, and/or metropolitan driving activity222D. Further, the variable cognitive workloads may include various other types of driving activities222E including, e.g., talking with other passengers in the vehicle while the driver is operating the vehicle. Various other types of variable cognitive workloads may include one or more of listening to music, controlling car audio, and conducting hands-free communication via a wireless network. In some implementations, the baseline cognitive workload220and each of the variable cognitive workloads222A,222B, . . . ,222E may be referred to as task workloads that are profiled and any related activity is measured. Generally, as driving conditions become more and more difficult, the driver's attention should focus on driving, and the driver's cognitive workload should shift toward driving activities. Otherwise, if driving conditions become less difficult, the driver's attention could possibly focus less on driving, and the driver's cognitive workload could shift toward answering QnA tasks and/or questions.

In reference to the available QnA cognitive workload212of the driver, the QnA tasks224may include dynamically fetched QnA tasks associated with one or more of surveys, polls, and/or context aware questions. In some cases, the QnA tasks224may include customer satisfaction surveys and/or public polling interviews. In some cases, the QnA tasks224may include crowd-source questions related to, e.g., one or more of single-sentence interpretation, various ground-truth tagging to auditory data, and unknown music classification associated with genre and/or mood. Further, in some cases, the QnA tasks224may include context aware questions including one or more of traffic status questions, accident report questions, and/or point-of-view (PoV) questions associated with location, time, and/or some combination thereof, such as, e.g., location in context of time.

In reference toFIG. 2B, the various driving activities of a driver and the various components related thereto may be collected and/or classified based on the driving context data and information. For instance, the baseline cognitive workload220may be associated with driver and/or passenger activity in driving context from personal mobile devices230, such as, e.g., smart phones, various wearable communication devices, audio devices, navigation devices, etc. In some instances, the variable workload for night driving activity222A may be associated with vehicle data from various onboard diagnostic sensors232, such as, e.g., speed, driving styles, etc. In some instances, the variable workload for one or more of extreme weather driving activity222B, congested driving activity222C, and/or metropolitan driving activity222D may be associated with vehicle data from the various onboard diagnostic sensors232and external data from cloud service providers234, such as, e.g., traffic data providers, weather data providers, location data providers, temporal data providers, etc. In other instances, the variable workload for other activities222E (e.g., talking or speaking to passengers, or similar) may be associated with driver and/or passenger activity in driving context from personal mobile devices230.

FIGS. 3A-3Care process flow diagrams illustrating one embodiment of a method300for implementing cognitive workload awareness in driving context.

In particular,FIG. 3Ais a process flow diagram illustrating a first portion300A of method300for implementing various schemes and techniques associated with cognitive workload awareness in driving context.

The method300and/or processes related thereto may begin with block310, and in block312, the method300retrieves user activity data from one or more data sources, such as, e.g., external sources and networks180,182,184,186ofFIG. 1. In some cases, the user activity data may be received from one or more user interface devices, such as, e.g., the user interfaces120A,120B, . . . ,120N ofFIG. 1.

In block314, the method300may classify various components of the user activity data of the driver with a user activity status of the driver, e.g., as described herein in reference toFIGS. 2A-2B.

In decision block316, the method300may decide whether the user activity data of the driver is associated with driving activity of the driver. If no, then the method300may terminate in block317. Otherwise, if yes, then in block318, the method300may associate the cognitive workload of the driver with a total workload Wtotal of the driver that references a driving workload Wdriving of the driver related to driving activities of the driver while the driver is operating a vehicle.
Wtotal=Wdriving

In block320, the method320may retrieve various driving condition data from at least one of the data sources180,182,184,186and/or from at least one of the user interfaces120A,120B, . . . ,120N.

In block322, the method300may further classify various components of the driving condition data that is associated with the driving activities of the driver.

FIG. 3Bis a process flow diagram illustrating a second portion300B of method300for implementing cognitive workload awareness in driving context.

In decision block330, the method300may decide whether the driving activity data of the driver is associated with night driving activity of the driver. If no, then the method300may proceed to decision block334. Otherwise, if yes, then in block332, the method300may associate the total cognitive workload Wtotal of the driver by adding a night driving cognitive workload Wnight of the driver to the total cognitive workload Wtotal of the driver.
Wtotal=Wtotal+Wnight

In decision block334, the method300may decide whether the driving activity data of the driver is associated with congested driving activity of the driver. If no, then the method300may proceed to decision block338. Otherwise, if yes, then in block336, the method300may associate the total cognitive workload Wtotal of the driver by adding a congested driving cognitive workload Wcongested of the driver to the total cognitive workload Wtotal of the driver.
Wtotal=Wtotal+Wcongested

In decision block338, the method300may decide whether the driving activity data of the driver is associated with talking while driving activity of the driver. If no, then the method300may proceed to block342. Otherwise, if yes, then in block340, the method300may associate the total cognitive workload Wtotal of the driver by adding a talking while driving cognitive workload Wtalking of the driver to the total cognitive workload Wtotal of the driver.
Wtotal=Wtotal+Wtalking

In block342, the method300may decide whether any additional driving activity and/or condition data of the driver should be associated with the driving activity of the driver. Therefore, in some optional instances, the method300may associate the total cognitive workload Wtotal of the driver by any additional driving cognitive workload Wadditional of the driver to the total cognitive workload Wtotal of the driver.
Wtotal=Wtotal+Wadditional

FIG. 3Cis a process flow diagram illustrating a third portion300C of method300for implementing cognitive workload awareness in driving context.

In block350, the method300may determine or calculate a safe cognitive workload Wsafe of the driver and/or a safe cognitive workload boundary for the driver associated with the safe cognitive workload Wsafe of the driver. Further, in block350, the method300may determine or calculate an available QnA cognitive workload Wavailable of the driver by subtracting the total cognitive workload Wtotal of the driver from the safe cognitive workload Wsafe of the driver.
Wavailable=Wsafe−Wtotal

In block352, the method300may retrieve or fetch a QnA task or question from a QnA source, such as, e.g., the QnA service provider180ofFIG. 1. The QnA task or question for driver response may have a question cognitive workload Wquestion that is at least less than the available QnA cognitive workload Wavailable of the driver.
Wquestion<Wavailable

In some cases, the fetched QnA task for driver response has a corresponding cognitive workload of less than a difference between the safe cognitive workload Wsafe and the total cognitive workload W total.

In block354, if the question cognitive workload Wquestion is less than the available QnA cognitive workload Wavailable, then the method300may ask the driver a question associated with the QnA task via an auditory interface (e.g., auditory interface120E ofFIG. 1, which may include audible output from a speaker). In this instance, the method300may fetch a QnA task or question for driver response from a QnA source (e.g., the QnA service provider180ofFIG. 1) that may ensure the safe cognitive workload boundary for safe driving214, as described in reference toFIG. 2A.

In block356, the method300may receive and/or obtain an answer from the driver for the asked question associated with the QnA task via a speech interface (e.g., speech interface120D ofFIG. 1, which may include audible input from a microphone, or similar). In this instance, the method300may receive and/or obtain the answer to the QnA task or question for driver response as provided from the QnA source (e.g., the QnA service provider180ofFIG. 1) so as to ensure the safe cognitive workload boundary for safe driving214, as described in reference toFIG. 2A. In some implementations, the question asked and the driver's answer may be stored in memory and/or transferred to the QnA source that provided the QnA task or question.

In decision block358, the method300may decide whether to ask another question and/or retrieve another QnA task from the QnA source (e.g., the QnA service provider180ofFIG. 1). If no, then the method300terminates in block317. Otherwise, if yes, then the method300may return to the start block310inFIG. 3A.

FIGS. 4A-4Bare process flow diagrams illustrating various embodiments of some other methods400A,400B, respectively, for implementing various schemes and techniques associated with cognitive workload awareness in driving context.

In particular,FIG. 4Aprovides a process flow diagram illustrating one embodiment of a method400A for implementing cognitive workload awareness in driving context. For instance, in block410, the method400A may monitor driving activities of a driver. In block412, the method400A may determine a cognitive workload of the driver associated with the driving activities of the driver. In block414, the method400A may determine an available QnA cognitive workload of the driver for performing QnA tasks based on the cognitive workload while ensuring a safe cognitive workload boundary associated with the driving activities of the driver.

Further,FIG. 4Bprovides another process flow diagram illustrating one embodiment of another method400B for implementing cognitive workload awareness in driving context. For instance, in block430, the method400B may monitor various driving activities of a driver while the driver is operating a vehicle. In block432, the method400B may determine a cognitive workload and a safe cognitive workload of the driver associated with the driving activities of the driver. In block434, the method400B may determine an available QnA cognitive workload of the driver for performing QnA tasks based on the cognitive workload while ensuring a safe cognitive workload boundary associated with the safe cognitive workload of the driver. In some cases, in block434, the method400B may select a QnA task out of a pool of QnA tasks so that an additional cognitive workload that the selected QnA task will add to the cognitive workload does not exceed the available QnA cognitive workload. As described herein, the cognitive workload of the driver may also be referred to as a current cognitive workload of the driver.

In block436, the method400B may sum the cognitive workload and the safe cognitive workload of the driver. In decision block438, the method400B may decide whether the sum is less than the safe cognitive workload. If the summation of the cognitive workload and the additional cognitive workload that the fetched QnA task will add to the cognitive workload is at least less than the safe cognitive workload, then in block440, the method400B may fetch the QnA task for driver response so as to ensure the safe cognitive workload boundary. Otherwise, if the summation of the cognitive workload and the additional cognitive workload that the fetched QnA task will add to the cognitive workload is not less than the safe cognitive workload, then in block442, the method400B may not fetch a QnA task for driver response so as to ensure the safe cognitive workload boundary. In some instances, the method400B may ensure that the fetched QnA task for driver response includes a corresponding cognitive workload of less than a difference between the safe cognitive workload and the cognitive workload.

It should be understood that even though methods400A,400B may indicate a particular order of operation execution, in some instances, various certain portions of the operations may be executed in a different order, and on different systems. In some other instances, additional operations, blocks, and/or steps may be added to and/or omitted from methods400A,400B. Further, the methods400A,400B may be implemented in hardware and/or software. If implemented in hardware, methods400A,400B may be implemented with various computing components, such as, e.g. described herein above in reference toFIG. 1. If implemented in software, the methods400A,400B may be implemented as a program or software instruction process that may be configured for implementing cognitive workload awareness in driving context as described herein. Further, if implemented in software, instructions related to implementing the methods400A,400B may be stored in memory and/or a database. For instance, a computer or various other computing devices having a processor and memory may be configured to perform methods400A,400B.

In various implementations, methods and system provided herein may refer to dynamically assigning micro question-and-answer (QnA) tasks to drivers with cognitive workload awareness. For instance, in some cases, a driver's current cognitive workload may be dynamically estimated based on data and information related to current driving activity, driving time, driving condition, activities with passengers, etc. This data and information may be retrieved from data sources including, e.g., the driver's or passengers' mobile devices, vehicular sensors, cloud-based information providers, etc. Micro question-and-answer (QnA) tasks may be selected and assigned considering the driver's available cognitive workload and pre-profiled cognitive workload of the tasks.

FIG. 5shows a block diagram illustrating one embodiment of a computing device500suitable for implementing various methods and techniques associated with cognitive workload awareness in driving context, including components of an apparatus, such as, e.g., the computing device102and/or the UIs120. In some implementations, the computing device102includes a communication network component512that is capable of communicating over one or more wired and/or wireless communication links530with the external network resources180,182,184and the external networks186. Therefore, it should be appreciated that the computing device102may be implemented as the computing device500for network computing and communication in a manner as follows.

In accordance with embodiments of the disclosure, the computer system500includes a bus502and/or other communication mechanism for communicating data and information, which interconnects subsystems and components, including processing component504(e.g., processor, micro-controller, digital signal processor (DSP), etc.), system memory component506(e.g., RAM), static storage component508(e.g., ROM), memory component510(e.g., magnetic), network interface component512(e.g., modem or Ethernet card), one or more display components514(e.g., LCD), input component516(e.g., keyboard, push-buttons), cursor control component518(e.g., slider), and one or more image capture components520(e.g., one or more digital cameras). In one implementation, the memory component510may comprise one or more databases having one or more flash drive components, disk drive components, or similar.

In accordance with various embodiments of the disclosure, the computer system500may perform specific operations by the processor504executing one or more sequences of one or more instructions contained in system memory component506. Such instructions may be read into the system memory component506from another computer readable medium, such as static storage component508or memory component510. In other embodiments, hard-wired circuitry may be used in place of or in combination with software instructions to implement the disclosure.

Logic may be encoded in a computer readable medium, which may refer to any medium that participates in providing instructions to processor504for execution. Such a medium may take many forms, including but not limited to, non-volatile media and volatile media. In some implementations, non-volatile media includes optical or magnetic disks, such as memory component510, and volatile media includes dynamic memory, such as system memory component506. In some implementations, data and information related to execution instructions may be transmitted to computer system500via a transmission media, such as in the form of acoustic or light waves, including those generated during radio frequency (RF) waves, infrared (IR) data communications, and/or microwave (MW) communication. In various implementations, transmission media may include coaxial cables, copper wire, and/or fiber optics, including wires that comprise bus502.

In some embodiments of the disclosure, execution of instruction sequences to practice the disclosure may be performed by the computer system500. In some other embodiments of the disclosure, a plurality of computer systems500may be coupled by communication link530(e.g., LAN, WLAN, PTSN, and/or various other wired or wireless networks, including telecommunications, mobile, and cellular phone networks) and may thus perform various instruction sequences to thereby practice various implementations of the disclosure in coordination with one another.

In various embodiments of the disclosure, the computer system500may transmit and receive messages, data, information, and instructions, including one or more programs (i.e., application code) through a communication link530and a communication interface512. Further, received program code may be executed by the processor504as received and/or stored in the memory component510or some other non-volatile storage component for execution.