Patent Publication Number: US-2022212679-A1

Title: Systems And Methods For Generating A Context-Dependent Experience For A Driver

Description:
TECHNICAL FIELD 
     The present disclosure relates to vehicle-based systems and methods, and more particularly to systems and methods for generating a context-dependent experience for a driver of a vehicle, e.g., to relax or otherwise enhance the driver&#39;s mental state. 
     BACKGROUND 
     It is known that driving is stressful activity for many, if not most, people. Driving requires full concentration and a level of calm, so it is important for a driver to control or manage their stress levels, for the safety of the driver and others on the road. However, many factors can raise a driver&#39;s stress level, for example driving in heavy traffic, being stuck in traffic, stopped at a traffic light (sometimes for multiple cycles of the light), or driving at high speeds, e.g., highway driving. In addition, the stress of everyday life, such as work or personal problems, can further contribute to the driver&#39;s stress level. The negative effects of stress on a driver are well-known, including causing the driver to lose concentration or make unpredictable or aggressive maneuvers, for example. 
     One common technique for combating stress is meditation. The meditation industry is rapidly growing with mindfulness applications and new technologically smart products. However, such applications and products are typically not adapted for a vehicle driving context. Existing guided meditation applications often instruct the user to close their eyes, and are typically designing for use in static spaces (e.g., a living room or bedroom). In addition, conventional meditation guides typically involve the playback of continuous audio clips. This does not adapt well to the dynamics of driving a vehicle, where the driver must be aware of surroundings and remain focused while maneuvering and operating the vehicle. 
     At the other end of the spectrum, drivers can often become bored, or too relaxed, which can also lead to a loss of concentration or attention. For example, an unchallenging driving scenario with a lack of mental stimulation, e.g., driving alone on a rural highway, may cause the driver to lose focus or become sleepy, or look to their smartphone for entertainment or other engagement, leading to a very dangerous driving situation. 
     Thus, there is a need for systems and methods for generating a relaxing, focusing, or otherwise mentally enhancing experience for a driver, in particular for systems and methods that adjust to the current situational context. 
     SUMMARY 
     Systems and methods are provided for generating a context-dependent experience for a vehicle driver, e.g., to relax or otherwise enhance the driver&#39;s mental state. A system may include various data sources that generate or access driving context data regarding a driving situation, an output device that outputs human-perceivable content (e.g., speakers that play selected audio clips), and a system controller. The driving context data may include (a) vehicle operation data regarding the operation of the vehicle, e.g., generated by vehicle-based sensors, and (b) environmental data regarding an environment external to the vehicle, e.g., weather data, traffic data, or traffic light data. Such environmental data may be obtained at the vehicle from remote server(s) via wireless communication links. The system controller may be configured to collect the driving context data from the various data sources, and identify a content triggering event based on (a) the collected driver context data and (b) a set of content triggering rules. For example, the system controller may determine the vehicle is stopped at a red light for a particular amount of time (e.g., predicted based on known information). In response to identifying the content triggering event, the system controller may select a human-perceivable content element, and control the output device to output the selected content element to the driver. Content elements may be embodied as any type of human-perceivable content, e.g., audio output, displayed images, lighting control, haptic feedback (e.g., seat massage), HVAC system control, seat warmer control, etc. As one particular example, the system controller may select an audio clip from a set of available audio clips (e.g., based on the length and/or content of the available audio clips), and play the selected audio clip through the vehicle sound system. 
     Whereas current meditation applications typically involve the playback of continuous audio clips, embodiments of the present invention may play a variety of selected audio clips at selected times and/or order or playback, depending on the context of the particular driving situation, e.g., based on the current location or operation of the vehicle, environmental factors (e.g., weather, traffic data, traffic light data, etc.), and/or the current mental state of the driver (e.g., as determined by driver-monitoring sensor data or input from the driver). The concept of selective, context-dependent use of audio clips rather than playing one continuous audio clip provides flexibility to tailor and personalize the experience for the driver. 
     In addition, by utilizing vehicle-based sensor data and tailoring the driver experience to the actual driving context (and not simply for generic use), systems and methods disclosed herein provide (a) a safe experience (e.g., the driver experience may be suspended during driving maneuvers that require high driver focus), (b) a contextually relevant experience, and (c) a multi-sensory, holistic experience (e.g., the system may combine audio output with vehicle-based sensory modalities such as air flow, lighting control, heated seat, seat massage or other haptic seat feedback, etc.). 
     One aspect provides a method, implemented by a system controller, for generating a context-dependent experience for a driver of a vehicle. The method includes receiving driving context data regarding a driving situation at the system controller; accessing a set of content triggering rules defining at least one content triggering event; identifying a content triggering event based on (a) the received driver context data and (b) the set of content triggering rules; selecting a human-perceivable content element (from a set of human-perceivable driver experience actions) to output to the driver in response to identifying the content triggering event; and controlling an output device to output the selected human-perceivable content element to the driver. 
     In one embodiment, receiving driving context data at the system controller comprises receiving vehicle operation data related to a current operation of the vehicle, the vehicle operation data comprising at least one of (a) sensor-based vehicle data generated by at least one sensor monitoring at least one operational aspect of the vehicle, or (b) vehicle location data generated by a Global Positioning System (GPS) system. 
     In one embodiment, receiving driving context data at the system controller comprises (a) receiving vehicle operation data related to a current operation of the vehicle and (b) receiving environmental data regarding an environment external to the vehicle. 
     In one embodiment, receiving environmental data regarding an environment external to the vehicle comprises receiving environmental data from a remote server via wireless communications. In one embodiment, the received vehicle operation data comprises GPS data indicating a location of the vehicle, and the received environmental data includes comprises location-specific environment data associated with the location of the vehicle or a defined related location, wherein the location-specific environment data includes at least one of (a) weather data, (b) traffic data, (c) traffic light data, or (d) road classification data. 
     In one embodiment, the step of identifying a content triggering event based on (a) the received driver context data and (b) the set of content triggering rules comprises determining a predicted stop time and comparing the predicted stop time to at least one threshold time. In one embodiment, determining a predicted stop time comprises receiving traffic data or traffic light data from a remote server via wireless communications, and determining a predicted stop time based on the received traffic data or traffic light data. 
     In one embodiment, receiving driving context data at the system controller comprises accessing historical driver navigation data for the driver, and selecting a human-perceivable content element to output to the driver in response to identifying the content triggering event comprises: determining a current location of the vehicle; predicting a navigation route of the vehicle based on the current location of the vehicle and the historical driver navigation data for the driver; and selecting a human-perceivable content element to output to the driver based at least on the predicted navigation route of the vehicle. 
     In one embodiment, the method includes accessing historical driver navigation data for the driver; determining a current location of the vehicle; predicting a navigation route of the vehicle based on the current location of the vehicle and the historical driver navigation data for the driver; and selecting the set of human-perceivable content elements, from a larger set of human-perceivable content elements, based at least on the predicted navigation route of the vehicle. 
     In one embodiment, selecting a human-perceivable content element to output to the driver in response to identifying the content triggering event includes determining, by the system controller, a stressfulness stage for the driver, and selecting a human-perceivable content element based at least on the determined stressfulness stage. 
     In one embodiment, determining the stressfulness stage for the driver includes receiving vehicle operation data related to a current operation of the vehicle, receiving driver data regarding a mental state of the driver, and determining a stressfulness stage for the driver based on the received vehicle operation data and the received driver data regarding the mental state of the driver. 
     In one embodiment, receiving driver data comprises (a) receiving sensor-based driver data generated by at least one sensor configured to monitor the driver, or (b) accessing a driver profile including driver-specific data. 
     In one embodiment, the set of human-perceivable content elements comprises a set of audio clips having different lengths, and selecting a human-perceivable content elements to output to the driver in response to identifying the content triggering event includes determining a predicted stop time, and selecting, from the set of audio clips, an audio clip having a length consistent with the predicted stop time (e.g., equal to or less than the predicted stop time). 
     In one embodiment, controlling the output device to output the selected human-perceivable content element to the driver comprises controlling at least one speaker to play a selected audio clip. 
     In one embodiment, controlling the output device to output the selected human-perceivable content element to the driver includes controlling a display screen or other lighting, controlling a heater or haptic feedback device provided in a seat, and/or controlling an HVAC system of the vehicle. 
     Another aspect of the invention provides a system for generating a context-dependent experience for a driver of a vehicle. The system includes at least one driving context data source provided in the vehicle and configured to generate or access driving context data regarding a driving situation, an output device configured to output human-perceivable content, and a system controller. The system controller is configured to receive the driving context data from the at least one driving context data source; access a set of content triggering rules defining at least one content triggering event; identify a content triggering event based on (a) the received driver context data and (b) the set of content triggering rules; in response to identifying the content triggering event, select a human-perceivable content element to output to the driver, from the set of human-perceivable content elements; and control the output device to output the selected human-perceivable content element to the driver. 
     In one embodiment, driving context data sources configured to generate or access driving context data regarding a driving situation include at least one of (a) at least one sensor configured to generate sensor data regarding at least one operational aspect of the vehicle or (b) a GPS system configured to generate vehicle location data. 
     In one embodiment, driving context data sources configured to generate or access driving context data regarding a driving situation include (a) at least one vehicle operation data source configured to generate vehicle operation data related to a current operation of the vehicle and (b) at least one environmental data source configured to generate or obtain environmental data regarding an environment external to the vehicle. 
     In one embodiment, the environmental data source comprises a wireless communications interface provided in the vehicle and configured to receive environmental data from a remote server via wireless communications. 
     In one embodiment, the vehicle operation data generated by the vehicle operation data source comprises GPS data indicating a location of the vehicle, and the environmental data received from the remote server includes comprises location-specific environment data associated with the location of the vehicle or a defined related location, wherein the location-specific environment data includes at least one of (a) weather data, (b) traffic data, (c) traffic light data, or (d) road classification data. 
     In one embodiment, the system controller is configured to select a human-perceivable content element to output to the driver in response to identifying the content triggering event by a selection process including (a) determining a stressfulness stage for the driver, and (b) selecting a human-perceivable content element based at least on the determined stressfulness stage. 
     In one embodiment, the system controller is configured to determine the stressfulness stage for the driver by a stage analysis process including receiving vehicle operation data related to a current operation of the vehicle, receiving driver data regarding a mental state of the driver, and determining a stressfulness stage for the driver based on the received vehicle operation data and the received driver data regarding the mental state of the driver. 
     In one embodiment, the set of human-perceivable content elements comprises a set of audio clips having different lengths, and the system controller is configured to select a human-perceivable content element to output to the driver in response to identifying the content triggering event by a selection process including determining a predicted stop time and selecting an audio clip, from the set of audio clips, having a length consistent with the predicted stop time. 
     In one embodiment, the output device configured to output human-perceivable content comprises at least one speaker provided in the vehicle, the selected human-perceivable content element comprises a selected audio clip, and the system controller is configured to control the at least one speaker to play the selected audio clip. 
     In one embodiment, the output device configured to output human-perceivable content includes a display screen or other lighting, a heater or haptic feedback device provided in a seat, and/or a vehicle HVAC system. 
    
    
     
       BRIEF DESCRIPTION OF THE FIGURES 
       Example aspects of the present disclosure are described below in conjunction with the figures, in which: 
         FIG. 1  illustrates an example driver experience system for generating a context-dependent experience for a vehicle driver, e.g., to relax or manage the driver&#39;s stress level, according to example embodiments; 
         FIG. 2  illustrates a process flow of an example context-dependent driving experience session (mindfulness session) provided and managed by the driver experience system shown in  FIG. 1 , according to example embodiments; 
         FIG. 3  illustrates a process flow of an example context-dependent driving experience session (mindfulness session), showing the triggering and output of example content elements, according to one example embodiment; 
         FIGS. 4A-4B  illustrate an example driver experience system for generating a context-dependent experience for a vehicle driver, according to one example embodiment; 
         FIG. 5  shows an example mindfulness session flow illustrating the playback of a warm-up/welcome audio clip and a driving context-triggered audio clip, according to one example embodiment; 
         FIGS. 6A-6B  show a more detailed version of the mindfulness session flow of  FIG. 5 , according to one example embodiment; 
         FIG. 7  illustrates a time-based progression of an example mindfulness session flow, illustrating the use of a time-based content trigger in the absence of a detected driving context-based trigger, according to one example embodiment; 
         FIGS. 8A-8B  illustrate an example mindfulness session flow illustrating an implementation of the context-based and time-based content triggers introduced in  FIG. 7 , according to one example embodiment; 
         FIG. 9  illustrates an example stop length prediction system, according to one example embodiment; and 
         FIG. 10  illustrates an example protocol for content selection decisioning when encountering a red light during a mindfulness session, according to one example embodiment. 
     
    
    
     It should be understood that the reference number for any illustrated element that appears in multiple different figures has the same meaning across the multiple figures, and the mention or discussion herein of any illustrated element in the context of any particular figure also applies to each other figure, if any, in which that same illustrated element is shown. 
     DETAILED DESCRIPTION 
     As noted above, embodiments of the present invention provide driver experience systems and methods for generating a context-dependent experience for a driver during a driving session, e.g., playing selected audio clips in a context-dependent manner, to relax or otherwise enhance the driver&#39;s mental state. Various data sources may collect driving context data regarding a driving situation and/or driver data regarding the driver. Driving context data may include (a) vehicle operation data regarding the operation of the vehicle, e.g., generated by vehicle-based sensors, and (b) environmental data regarding an environment external to the vehicle, e.g., weather data, traffic data, or traffic light data. Such environmental data may be obtained at the vehicle from remote server(s) via wireless communication links. Driver data may include (a) stored data regarding the driver, e.g., a driver profile, historical navigational data of the driver, and/or the historical behavioral responses of the driver to context-dependent experiences presented by the system, and/or (b) driver state data regarding the driver&#39;s mental state, e.g., as determined by driver-monitoring devices or based on input from the driver. 
     A system controller may be configured to collect the driving context data and/or driver data from the various data sources, and identify content triggering events during the driving session based on (a) the relevant driving situation indicate by the driver context data and (b) a set of content triggering rules. For example, the system controller may determine the vehicle is stopped at a red light for a particular amount of time (e.g., predicted based on known information). In response to identifying the content triggering event, the system controller may select a human-perceivable content element, and control the output device to output the selected content element to the driver. For example, the system controller may select an audio clip from a set of available audio clips (e.g., based on the length and/or content of the available audio clips), and play the selected audio clip through the vehicle sound system. In some embodiments, the system controller may select particular content elements to output to the driver during the driving session based on selected driving context data and/or driver data, as discussed below. 
     The “driver experience” systems and methods are also referred to herein as a “mindful driving” systems and methods, as the generated experience (e.g., delivered audio content) may focus on core elements of mindfulness (e.g., breathing, being fully aware, being non-reactive/accepting, etc.) and with emphasis on a driving experience (e.g., the driver noticing what they see, feeling the movement of the vehicle, hearing sounds from the engine, accepting heavy traffic, etc.). 
       FIG. 1  illustrates an example driver experience system  100  for generating a context-dependent experience for a vehicle driver, e.g., to relax or manage the driver&#39;s stress level, according to example embodiments. Driver experience system  100  may include a system controller  104  configured to receive or generate various types of context data  102 , and select content elements to output to the driver, in a context-dependent manner, via one or more output devices  106 . In the illustrated example, context data  102  includes driver data  110 , vehicle operation data  112 , and environmental data  114 . 
     Driver data  112  may include any known or detected information regarding a driver that may be relevant to the system controller  104  for context-based selection and output of content during a driving session. As shown, example types of driver data  110  may include stored driver profiles for one or more drivers, driver history information, and information regarding the driver&#39;s physical or emotional state. 
     Vehicle operation data  112  may include any data regarding the current, past, or future (e.g., predicted) operation of the vehicle that may be relevant to the system controller  104  for context-based selection and output of content during a driving session. As shown, example types of vehicle operation data  112  may include GPS data (or other vehicle location data) and data from various sensors or systems of the vehicle, e.g., data indicating vehicle speed and acceleration, braking data, transmission data (e.g., indicating the currently selected gear), and a currently active driver assistance mode or status (e.g., in a vehicle with self-driving functionality). 
     Environmental data  114  may include any data regarding an environment external to the vehicle that may be relevant to the system controller  104  for context-based selection and output of content during a driving session. As shown, example types of environmental data  114  may include traffic data, traffic light data, weather data, and road classification data. Each type of environmental data  114  may be generated by sensors or systems provided at the vehicle, or obtained at the vehicle from remote server(s) via wireless communication links, e.g., using a cellular communication system. 
     As shown in  FIG. 1 , system  100  may analyze various data, e.g., vehicle operation data  112  and environmental data  114 , to determine a driving context for the current driving situation, as indicated at  120 . 
     The system controller  104  may select and implement a mindfulness session  130  having a defined session protocol and a selected session theme. The session protocol may define rules used by the session manager to determine when and how to output content elements (e.g., audio clips) to the driver, e.g., based on the relevant driving context. The session theme may define a set of content elements (e.g., audio clips) related to a particular theme, for example, “starting the day,” “letting go of work,” “unwinding from family stresses,” or “staying alert and focused.” The session theme to be implemented at any particular time may be selected in any suitable manner, e.g., automatically by the system controller  104  (e.g., based on the current driving context and/or driver information) or by the driver (e.g., by a speech-based command or via a touchscreen or other user interface provided by the vehicle or a user mobile device, e.g., the driver&#39;s smartphone). The system controller  104  may analyze the current driving context  120  in view of the rules defined by the active mindfulness session  130  to identify content triggering event, also referred to herein as “contextual triggers.” 
     For each identified content triggering event, the system controller  104  may select one or multiple content elements  132  (e.g., audio clip, HVAC control, etc.) from an available set of content elements to output to the driver. The system controller  104  may use relevant driver data  110  and/or selected driving context data  120  for selecting the appropriate content element(s)  132 . In some embodiments, the system controller  104  may determine a current stressfulness stage for the driver, e.g., based on (a) relevant vehicle operation data indicating a level of driver workload or required driver concentration, (b) relevant environmental data (e.g., weather, traffic load, road conditions, etc.) and (c) driver data regarding a current mental state of the driver. The system controller  104  may then select particular content elements for output based on the determined stressfulness stage. The system controller  104  may also consider the length of available content elements (e.g., the length of different audio clips) when making content element selections. For example, the system controller  104  may be configured to determine (e.g., predict) a duration that the vehicle will remain in stopped (e.g., while waiting at a red light), and select an audio clip to play based at least on the respective lengths of the available audio clips, e.g., such that the audio clip completes before the stopped vehicle resumes driving. 
     After selecting one or more particular content element, the system controller  104  may signal an output controller  140  to control one or more content output devices  106  to output the selected content. As shown in  FIG. 1 , example types of content output devices  106  include speakers, a display (e.g., a console, mapping, or infotainment display), lighting, HVAC system components, seat heaters, a seat massager, and a scent emitter. In some embodiments or instances, a particular content elements may include multiple modality components intended to be output simultaneously to provide the desired experience. Thus, the output controller  140  may control multiple different output devices  106  to output multiple content modalities simultaneously, e.g., by playing an audio clip while blowing cool air via the HVAC system. 
       FIG. 2  illustrates a process flow  200  of an example context-dependent driving experience session (mindfulness session) provided and managed by the driver experience system  100  shown in  FIG. 1 , according to example embodiments. At  210 , the driving experience session begins. The session may begin automatically, e.g., upon starting the vehicle, placing the vehicle into driving gear, upon a detection of vehicle movement, or upon detection of another defined session initiation event or condition, e.g., detection of a stressfulness level above a defined threshold, or detection of a particular driving situation (e.g., a vehicle stoppage having a predicted duration of more than 2 minutes). As another alternative, the session may begin in response to a defined user action (e.g., by the driver or a passenger), for example the user selecting a driving experience session via a touchscreen or other interactive display or interface provided in the vehicle. Alternatively, the session may begin in response to a defined user action (e.g., by the driver or a passenger), for example the user selecting a driving experience session via a touchscreen or other interactive display or interface provided in the vehicle. 
     As  220 , the driver experience system controller may output a mindful warm-up or introduction, e.g., including predefined audio clip(s) and/or other predefined content elements. In some embodiments, contextual triggering of content may be suspended during the warm-up/introduction phase  220 . After the warm-up/introduction phase  220 , the session advances to a context-based content output phase  230 . At phase  230 , the system controller collects various context data and performs contextual triggering at  240  to identify content triggering events. The system controller may then select content elements to output, based on the current context data, and output such content elements via one or more content output modalities  250 . For audio content, the system controller may select particular audio clips for output, based on the current context data and/or based on the length of the available audio clips. 
     The driving experience session ends at  270 , e.g., at the end of a defined time (e.g., 10 minutes), automatically upon a defined triggering event, or in response to a defined user input. For example, the session may end automatically in response to a “reaching destination” trigger determined by the system controller (e.g., based on known route and current location data), or upon turning off the vehicle, placing the vehicle into a parking gear, or upon detection of a stressfulness level below a defined threshold. As another example, the session may be ended by a defined user action (e.g., by the driver or a passenger), for example the user selecting a displayed “end mindfulness session” interface, or other user action configured to terminate the session, e.g., a user turning on the radio or interacting with an in-vehicle mapping system. 
       FIG. 3  illustrates a process flow  300  of an example context-dependent driving experience session (mindfulness session), showing the triggering and output of example content elements, according to one example embodiment. In this example, the driver experience system is configured to output content in the form of (a) audio content and (b) control of other vehicle-based out devices (actuators), e.g., the vehicle air conditioning system. The lower portion of  FIG. 3  shows example instances of sensor data collection  310 , output of selected audio clips  320 , and output of non-audio content (actuator control)  330 , with respect to time. The upper portion of  FIG. 3  shows example content elements output to the driver during the driving experience session. 
       FIGS. 4A-4B  illustrate an example driver experience system  400  for generating a context-dependent experience (mindfulness session) for a vehicle driver, e.g., to relax or manage the driver&#39;s stress level, according to example embodiments. Driver experience system  400  shown in  FIG. 4A  may be viewed as a more specific representation of the driver experience system  100  shown in  FIG. 1 . 
     Driver experience system  400  may include a system controller  402  configured to receive or generate various types of context data from various context data sources, and select content elements to output to the driver, in a context-dependent manner, via one or more output devices  410 . In the illustrated example, context data sources may include (a) driver data sources  404  configured to provide driver data to system controller  402 , (b) vehicle operation data sources  406  configured to provide vehicle operation data to system controller  402 , and (c) environmental data sources  408  configured to provide environmental data to system controller  402 . 
     Any one or more vehicle data sources  406 , driver data sources  404 , and/or environmental data sources  408  may be stored or located remotely, e.g., on one or more remote server  412 , and accessible by controller  401  via a communications network  414 . Communications network  414  may include the internet, one or more local area network (LAN), wireless LAN (WLAN), wide area network (WAN), mobile networks (e.g., GSM, CDMA, 3G, 4G, LTE, 5G, etc.), and/or any other type of public or private network including wired and/or wireless communication links. System controller  402  may access such remote data via any suitable wireless communication link(s) with network  414 , e.g., mobile data links (e.g., GSM, CDMA, 3G, 4G, LTE, 5G, etc.), WLAN links, or any other type of wireless links. For example, as discussed below, system  400  may include a wireless network interface  452  for receiving wireless communications of environmental data via network  414 . 
     In some embodiments, vehicle operation data provided by vehicle operation data sources  406  and environmental data provided by environmental data sources  408  may collectively define a driving context  480 . As discussed below in more detail, the driving context  480  may be used by (a) a trigger manager  474  for identifying content triggering events and/or (b) a stage manager  473  for determining a current stressfulness stage, which may be used for selecting appropriate content elements to output to the driver. 
     Driver data sources  404  may include various data sources configured to generate, store, or access driver data for communication to system controller  402 . Example driver data sources  404  include (a) driver profiles  438  for one or more drivers (b) historical navigation data  440  for one or more drivers, (c) historical mindfulness session data  442  for one or more drivers, and (d) driver calendar data  443 . Driver data  438 ,  440 ,  442 ,  443  may be stored in memory accessible to system controller  402 , e.g., in vehicle-based memory or in remote storage accessible to system controller  402  via communications network  414 . 
     A driver profile  438  for a respective driver may indicate known physical and/or mental attributes of the driver, a driving experience/skill level, and settings or preferences for outputs generated by the driver experience system, for example, preferred or non-preferred types of content (e.g., preferred or non-preferred types of audio content or specific audio clips) or modalities of content delivery or specific content elements. The driver profile  438  may also include driver identification information for automatically identifying the driver, e.g., allowing system controller  402  to provide a driving experience tailored to the current driver. The driver identification information may include, for example, key information integrated in the driver&#39;s key and automatically readable by the vehicle, e.g., upon a user action performed on a key fob (e.g., to unlock the door), upon entry into the vehicle, or upon insertion of the key into the ignition. As another example, the driver identification information may include voice data allowing voice-based recognition of the driver. As another example, the driver identification information may include image(s) of the driver, for camera-based facial recognition of the driver. 
     A driver profile  438  may be input by a user, e.g., via data entry at a vehicle-based interface (e.g., touchscreen), or via a mobile application or web-based application configured to communicate with system controller  402 . 
     Historical navigation data  440  for a driver may include historical data regarding driving routes taken by the driver. System controller  402  may be configured for predictive analysis of such historical navigation data, e.g., allowing the system controller  402  to determine a frequent route (e.g., between home and work, or between home and the airport) and select particular mindfulness sessions and/or content elements based on such predictive knowledge. In addition, system controller  402  may correlate a driver&#39;s historical navigation data may be correlated with dates, times, or days of the week for improved predictive analysis. 
     Historical mindfulness session data  442  for a driver may include historical data regarding prior mindfulness sessions delivered to the driver, the driver&#39;s response to such mindfulness sessions and/or individual content elements, a level of mindfulness expertise of the driver, and the driver&#39;s personal mindfulness settings, e.g., learned from previous sessions via artificial intelligence (user observation plus learning). 
     As discussed below, selected driver profile data  438 , historical navigation data  440 , historical mindfulness session data  442 , a driver calendar data  443  may collectively define a driver history/experience context  482  used by a stage manager  473  for determining a current stressfulness stage, which may be used for selecting appropriate content elements to output to the driver. 
     Driver calendar data  443  may include a calendar or other scheduling information for the driver, e.g., indicating the driver&#39;s work schedule and/or other events. System controller  402  may use such driver calendar data  443  as an input for determining the current driving route and/or the driver&#39;s current mental state, and/or for automatic selection of a particular session theme. For example, system controller  402  may assess the driver&#39;s mental state based on the type of activity the driver has been engaged in (e.g., work, shopping, school, doctor&#39;s appointment, etc.), as well as the level of activity/busyness indicated by the calendar. 
     In some embodiments, driver data sources  404  may also include one or more driver monitoring sensors  444  configured to monitor physiological attributes of the driver, for example the driver&#39;s respiratory rate, heart rate, speech attributes (e.g., volume, frequency tone, etc.), body movements, etc. As shown, driver monitoring sensors  444  may include at least one driver-facing camera  446 , a microphone  448 , and/or physical interface sensor  450 . A physical interface sensors  450  may include any type of sensor that physically interfaces with the driver to detect one or more physiological attributes of the driver, for example (a) a smartwatch configured to monitor the driver&#39;s heart rate, blood pressure, respiratory rate, etc., and wirelessly communicate such data to system controller  402 , or (b) a sensor integrated in the steering wheel, driver&#39;s seat, or other vehicle component and configured to detect or monitor one or more physiological attributes of the driver. 
     In some embodiments, system controller  402  may use such data from driver monitoring sensor(s)  444  for determining a driver mental state  481 . As discussed below, a stage manager  473  may use the driver mental state  481  (e.g., in combination with the driving context  480  and/or driver mental state  481 ) for determining a current stressfulness stage, which may be used for selecting appropriate content elements to output to the driver. For example, if system controller  402  determines the driver mental state  481  is relaxed, system controller  402  may select an engaging session theme (or engaging content) appropriate for a relaxed driver. Alternatively, if the determined driver mental state  481  is highly stressed, system controller  402  may select a calming session theme (or calming content) intended to calm the highly stressed driver. In some embodiments, the system controller  402  may dynamically adapt a session during execution, e.g., based on the real-time driving context data  480  and/or driver mental state data  481 . 
     Vehicle operation data sources  406  may include various data sources configured to generate, store, or access vehicle operation data for communication to system controller  402 . Example vehicle operation data sources  404  include at least one (a) user interface  416  allowing user selection of various operational settings, (b) radio/display/infotainment system  418 , (c) GPS system  420 , (d) telemetrics system  422 , (e) speed sensor  424 , (f) acceleration sensor  426  (e.g., an accelerometer or accelerator pedal position sensor), (g) braking sensor  428 , (h) transmission system  430  (e.g., indicating active gear information), (i) driver assistance system  432  (e.g., in a vehicle with self-driving functionality), (j) temperature sensor  434 , (k) microphone, and/or any other types of sensors or systems for monitoring any operational aspect of the vehicle. 
     Any one, some, or all of the vehicle data sources  406  may be connected with a Controller Area Network bus (CAN bus) connected and configured to deliver vehicle-related data to system controller  402 . 
     Environmental data sources  408  may include various data sources configured to generate, store, or access environmental data for communication to system controller  402 . Environmental data may include any data regarding an environment external to the vehicle that may be usable by system controller  402  for context-based selection and output of content during a driving session. Example types of environmental data  114  may include traffic data, traffic light data, weather data, and road classification data. 
     As shown, example environmental data sources  408  include (a) a wireless network interface  452  for obtaining remotely-generated or remotely-stored environmental data and/or (b) environmental sensors  454  provided at the vehicle. Wireless network interface  452  may be configured for wireless communications with remote server(s)  412  or other remote devices via communications network  414 , e.g., via a cellular communication system (e.g., GSM, CDMA, 3G, 4G, LTE, 5G, etc.), wireless LAN, or any other type of wireless communications. Wireless network interface  452  may includes any suitable wireless communications hardware, software, and/or firmware for accessing remote environmental data. 
     In some embodiments, wireless network interface  452  may be configured to interface with APIs  460  for accessing various data from one or more remote server  412 , for example traffic data  462 , traffic light data  464 , weather data  468 , and road classification and/or condition data  470 . System controller  402  may use the current and/or predicted location of the vehicle, e.g., based on data from GPS system  420  and/or driver navigation history data  440 , to request and obtain location-specific environmental data, e.g., location-specific weather, traffic, traffic light, and/or road data. 
     As discussed above, in some embodiments, environmental data provided by environmental data sources  408  and vehicle operational data provided by vehicle operational data sources  406  may collectively define a driving context  480 . The driving context  480  may be used by (a) a trigger manager  474  for identifying content triggering events and/or (b) a stage manager  473  for determining a current stressfulness stage, which may be used for selecting appropriate content elements to output to the driver. 
     Mindfulness output devices  410  may include any suitable devices for outputting human-perceptible content related to a mindfulness session. Example output devices  410  include speakers  493 , a display  494  (e.g., touchscreen or other display visible to the driver), interior vehicle lighting  495 , haptic devices  496  (e.g., integrated to the steering wheel), an HVAC system  497 , seat heaters or seat massagers  498 , and scent emitters  499 . 
     In general, system controller  402  is configured to implement a mindfulness session (e.g., selected from a variety of different mindfulness sessions) for a driving session. The mindfulness session may be defined by a session protocol and/or rules  487  for determining when to output content elements (e.g., audio clips), and which content elements (e.g., audio clips) to output in each situation. 
     System controller  402  is configured to receive the various types of context data from driver data sources  404 , vehicle operation data sources  406 , and environmental data sources  408 , and select content elements to output to the driver, in a context-dependent manner, via one or more output devices  410 . As shown, system controller  402  may include various interconnected control units, including a session manager  471 , a driver detection system  472 , a stressfulness stage manager  473 , a trigger manager  474 , a stop length predictor  475 , a content manager  476 , timers  478 , an output controller  491 , and a driver feedback manager  492  for providing various functionality of driver experience system  400 . Any one or more of the various control units of system controller  402  may receive data from various data sources (e.g., driver data sources  404 , vehicle data sources  406 , environmental data sources  408 , etc.), process such data, and communicate signals with other control unit(s) to provide the various functionality of system controller  402  disclosed herein. 
     Each of the various components of system controller  402  may include any hardware, software, and/or firmware components for performing the respective functionality of each component. For example, each component of system controller  402  may comprise software and/or firmware stored in non-transitory memory  486  and executable by at least one processor  484  (e.g., microprocessor, microcontroller, FPGA, etc.). As shown, memory  486  may also store session protocols and rules  487 , content triggering rules  488 , and mindfulness content  489 , e.g., including audio clips  490  and/or other types of content elements. 
     Session manager  471  is configured as the central control system of system controller  402 . Session manager  471  may be configured to (a) receive content triggering events from content triggering manager  474 , (b) select context-dependent content elements to output to the driver (e.g., based on input from stressfulness stage manager  473 , stop length predictor  475 , and/or timers  478 ), and control relevant output devices  410  to output the selected content elements to the driver. 
     Driver detection system  472  may be configured to automatically identify the current driver, e.g., based on an automatic detection of driver-specific information stored in driver profile  438 , to allow system controller  402  to provide a driving experience tailored to the particular driver. For example, driver detection system  472  may identify the driver based on driver ID information obtained from the driver&#39;s key or key fob. For example, driver detection system  472  may identify the driver based on images from camera  446  and stored driver images in driver profile(s)  438 . 
     Upon identifying the current driver, system controller  402  may access the relevant driver profile  438 , e.g., to access driver-specific driver data  482 , e.g., driver history, experience/skill level, or settings or preferences for mindfulness outputs, which may be used as input by stressfulness stage manager  473 . In addition, system controller  402  may access stored physiological or mental data regarding the identified driver, which may be used (e.g., as baseline data) for determining the driver&#39;s current mental state based on data from driver monitoring sensors  444 . 
     Stressfulness stage manager  473  is configured to determine a current stressfulness stage, e.g., based on (a) the current driving context  480 , (b) the current driver mental state  481 , and/or (c) the driver history and experience  482 . The current stressfulness stage represents an estimated level of stress experienced by the driver. 
     Content triggering manager  474  is configured to identify content triggering events at various times based on (a) the current driving context  480  and (b) the session protocol or rules  487  for the active mindfulness session, and notify the session manager  471  of each identified content triggering event. 
     Stop length predictor  475  may be configured to predict when a vehicle will stop and/or how long the vehicle will remain stopped, based on any suitable input data predictive of the length of a vehicle stop, for example (a) vehicle-related data collected by any vehicle data source(s)  406 , e.g., delivered to system controller  402  as CAN signals via the vehicle&#39;s CAN bus, (b) traffic data  462  and/or traffic light data  464  corresponding with the detected GPS location of the vehicle, (c) historical driver data  440 , (d) driver data collected by driver monitoring sensor(s)  444  (e.g., detected movements of the driver&#39;s head) and/or any other suitable data. Example types of CAN signals used as input for the stop length predictor  475  include a time series of brake pressure, steering wheel angle, acceleration, and velocity (speed) of the vehicle. Example traffic data  462  used as input for the stop length predictor  475  includes data indicating the presence and location of vehicle(s) in front of the vehicle being analyzed. 
     In one embodiment, stop length predictor  475  may utilize a recurrent neural network (RNN), e.g., a long short-term memory (LSTM), to predict a stop duration based on various input data. In some embodiments, system controller  402  may use predicted stop duration data from stop length predictor  475  to (a) determine whether a vehicle stop is long enough to trigger a content output and/or (b) selectively identify particular content elements suitable for the predicted stop length, e.g., content elements having a duration shorter than the predicted stop length, or shorter than the predicted stop length less a predefined buffer time (e.g., 5 seconds). 
     Content manager  476  is configured to load selected content elements  489  (e.g., audio clips  490 ) from memory  486 , and cooperate with the output controller  491  to control output devices  410  to output selected content elements. 
     Timers  478  may monitor the duration of any events or sessions relevant to system controller  402 . For example, a session timer may record the ongoing duration of the active mindfulness session, and the duration of various stages within the session. As another example, a stopped vehicle timer may monitor the time the vehicle is stopped, which may be used by content triggering manager  474  for identifying certain content triggering events, and/or other components of system controller  402 . 
     Driver feedback manager  492  is configured to observe the driver during a mindfulness session (e.g., using driver monitoring sensors  444 ) and store driver behavior or other feedback as historical driver mindfulness data  442 . 
     In operation, session manager  471  may be configured to select and manage a mindfulness session for a driver based on input from stage manager  473 , content triggering manager  474 , stop length predictor  475 , content manager  476 , and timers  478 . During the session, content triggering manager  474  may identify content triggering events at various times based on the currently determined driving context  480  and the relevant session protocol or rules  487 , and notify the session manager  471  of each content triggering event. 
     For each identified content triggering event, the session manager  471  may select an appropriate content element  489  (e.g., audio clip  490 ) to output to the driver, based on (a) the driving context  480 , (b) the current stressfulness stage as determined by stage manager  473 , and (c) if relevant, a stop duration predicted by stop length predictor  475 . 
     The system controller  104  may then select particular content elements (e.g., audio clips) for output based on the determined stressfulness stage. The system controller  104  may also consider the length of available content elements (e.g., the length of different audio clips) when making content element selections. For example, the system controller  104  may be configured to determine (e.g., predict) a duration that the vehicle will remain in stopped (e.g., while waiting at a red light), and select an audio clip to play based at least on the respective lengths of the available audio clips, e.g., such that the audio clip completes before the stopped vehicle resumes driving. 
     Upon selecting a content element  489  to be output, content manager  476  may access and load the content element  489  from memory  486 , and instruct output controller  491  to control selected output device(s)  410  to output the selected content element  489 . 
     As discussed above, stressfulness stage manager  473  may monitor the current stressfulness stage for the driver, e.g., based on (a) the current driving context  480  (which may incorporate relevant vehicle operation data  406  and environmental data  408 ), (b) the current driver mental state  481  (e.g., as determined based on driver monitoring sensors  444 ), and/or (c) driver history and experience/skill data  482 . In one embodiment, stressfulness stage manager  473  ( a ) determines a base stressfulness stage, and then (b) adjusts the base stressfulness stage based on any relevant adjustment factors. The stressfulness stage may represent a level of workload for the driver (e.g., mental, physical, etc.) and/or a level of driving engagement. 
     Tables 1 and 2 below illustrate one example implementation of an embodiment that defines six different stages, ranging from Stage 0 (e.g., when the driver is not engaged in driving) to Stage 5 (e.g., full driver attention is needed to control the vehicle). 
     In this embodiment, stressfulness stage manager  473  first uses Table 1 to determine a base stressfulness stage, based on driving context data  480 . Then, stressfulness stage manager  473  uses Table 2 to determine adjustments (if any) to the base stressfulness stage, based on environmental data (relevant traffic and weather conditions), driver data (driver experience/skill level and historical navigational data), and sensor-based driver monitoring data (regarding the driver&#39;s respiratory rate). 
     
       
         
           
               
             
               
                 TABLE 1 
               
             
            
               
                   
               
               
                 Base stressfulness stage classification based on driving context 
               
            
           
           
               
               
               
               
               
               
            
               
                 Stage 0 
                 Stage 1 
                 Stage 2 
                 Stage 3 
                 Stage 4 
                 Stage 5 
               
               
                   
               
               
                 Not 
                 Simple driving 
                 Very simple or 
                 Normal level of 
                 High level of 
                 Driving - highly 
               
               
                 driving 
                 conditions &amp; not 
                 assisted driving 
                 driving workload 
                 driving 
                 stressful 
               
               
                   
                 moving 
                 &amp; moving 
                   
                 workload 
                   
               
               
                 Parked 
                 Highway driving → 
                 Highway driving 
                 Rural area driving 
                 Urban driving 
                 Uncontrolled 
               
               
                   
                 ADAS on 
                 (manual) 
                 (suburbia) 
                   
                 intersection 
               
               
                 Self- 
                 Stopped at a red 
                 Stopped at a 
                 Countryside driving 
                 Driving in a 
                 Emergency 
               
               
                 Driving 
                 light 
                 STOP sign 
                   
                 parking lot 
                 breaking 
               
               
                 mode 
                   
                   
                   
                   
                   
               
               
                   
                   
                   
                 lane change - 
                 Merging onto 
                 Backing up 
               
               
                   
                   
                   
                 highway 
                 highway 
                   
               
               
                   
                   
                   
                 Lane merging 
                 Lane change, city 
                 Parking vehicle 
               
               
                   
                   
                   
                 Leaving highway 
                 School area 
                   
               
               
                   
                   
                   
                   
                 Pedestrian Xing 
                   
               
               
                   
                   
                   
                   
                 Roundabout 
               
               
                   
               
            
           
         
       
     
     
       
         
           
               
             
               
                 TABLE 2 
               
             
            
               
                   
               
               
                 Stressfulness stage adjustment factors 
               
            
           
           
               
               
               
               
            
               
                 −1 Stage 
                 +1 Stage 
                 +2 Stage 
                 Explanation 
               
               
                   
               
            
           
           
               
            
               
                 Driving context specific 
               
            
           
           
               
               
               
               
            
               
                 ADAS ON 
                   
                   
                 If driver assistance functions are on, the workload is 
               
               
                   
                   
                   
                 automatically reduced as the vehicle supports the driver 
               
               
                   
                   
                   
                 with the driving task 
               
               
                 Light to no 
                   
                   
                 If little or no traffic, the workload decreases 
               
               
                 traffic 
                   
                   
                   
               
               
                   
                 Heavy traffic 
                   
                 In heavy traffic, workload increases (e.g., independent from 
               
               
                   
                   
                   
                 the road categorization) 
               
               
                   
                 Bad weather 
                   
                 In bad weather, workload increases 
               
               
                   
                 conditions 
                   
                   
               
            
           
           
               
            
               
                 Driver specific - based on user profile, historical navigational data 
               
            
           
           
               
               
               
               
            
               
                 Route familiarity 
                   
                   
                 If the driver is very familiar with the route (PNAV) the 
               
               
                   
                   
                   
                 workload decreases 
               
               
                 Expert driver 
                 Insecure driver 
                   
                 Driving experience (e.g., from user profile, learned over 
               
               
                   
                   
                   
                 time) 
               
            
           
           
               
            
               
                 Driver specific - based on sensor-based driver monitoring 
               
            
           
           
               
               
               
               
            
               
                 Respiratory rate 
                   
                   
                   
               
               
                 &lt;90% of 
                   
                   
                   
               
               
                 baseline rate 
                   
                   
                   
               
               
                   
                 Respiratory rate 
                   
                   
               
               
                   
                 above 110% 
                   
                   
               
               
                   
                 baseline rate 
                   
                   
               
               
                   
                   
                 Respiratory rate 
                   
               
               
                   
                   
                 above 125% 
                   
               
               
                   
                   
                 baseline rate 
               
               
                   
               
            
           
         
       
     
       FIG. 5  shows an example process flow  500  of a driving experience session, illustrating the playback of a warm-up/welcome audio clip and a driving context-triggered audio clip, according to one example embodiment. At  504 , the session manager  471  may start the driving experience session, e.g., automatically in response to a defined action (e.g., starting the vehicle, placing the vehicle into “drive” gear, detection of vehicle movement, etc.) or in response to a defined user input (e.g., via a user interface provided by the vehicle or on the driver&#39;s smartphone or other mobile device communicatively connected to system controller  402 ), e.g., as discussed above regarding  FIG. 2 . 
     At  506  (request data) and  508  (receive data), the session manager  471  may obtain driver history/experience context data  482  regarding the driver, which may include, for example, driver profile data  438 , historical navigation data  440 , historical mindfulness session data  442 , and/or driver calendar data  443 , as discussed above. The session manager  471  may obtain the driver history/experience context data  482  from any suitable source, e.g., (a) a mobile device (e.g., smartphone), key fob, or other device associated with the driver, (b) memory  486  of system controller  402 , or (c) a remote server  412  via network  414 . 
     At  510 , the session manager  471  may select a particular session protocol and/or session theme. The session manager  471  may select the session protocol and/or session theme automatically (e.g., based on the current driving context and/or driver information) or by the driver (e.g., by a speech-based command or via a touchscreen or other user interface provided by the vehicle or the driver&#39;s smartphone). The selected session theme may define at least one “welcome” content element  489  (e.g., a welcome audio clip  490 ). 
     At  512 , the session manager  471  may signal the content manager to access the “welcome” content element  489  for the selected session  130 , and instruct the output controller  491  to control the relevant output device(s)  410  to output the “welcome” content element  489  (e.g., signaling the vehicle speakers to play a welcome audio clip  490 ). The “welcome” content element  489  is output via the relevant output device  410  at  514 . 
     After outputting the “welcome” content element, according to the session protocol the session manager  471  may await a signal from trigger manager  474  to trigger an output of the next content element. At  516 , trigger manager  474  may detect a content triggering event, e.g., based on any suitable contextual information as discussed above (e.g., driving context data  480 , driver state data  481 , etc.), and notify the session manager  471 . At  518 , the session manager  471  may select at least one content element  489  to output, e.g., based on the session protocol, session theme, the particular content triggering event, or other suitable information. 
     At  520 , the session manager  471  may signal the content manager to access the selected content element(s)  489  (e.g., audio clip, seat massage, and/or air conditioner control), and instruct the output controller  491  to control the relevant output device(s)  410  to output the selected content element(s)  489  to the driver. The selected content element(s)  489  are then output via the relevant output device(s)  410  at  522 . After outputting the content element(s) at  522 , according to the session protocol the session manager  471  may await another signal from trigger manager  474  to trigger an output of the next content element. 
       FIGS. 6A-6B  shows an example process flow  600  of a driving experience session (mindfulness session), according to one example embodiment. Example process flow  600  is a more detailed view than process flow  500 , showing the involvement of additional system components, e.g., stressfulness stage manager  473 , content manager  476 , a timer  478 , etc. 
     At  602 , the session manager  471  may start the driving experience session, e.g., automatically in response to a defined action or in response to a defined user input, e.g., as discussed above. Session manager  471  may signal a timer  478  to start counting. The timer  478  may monitor the duration of the driving experience session, and trigger an end of the session after a defined session duration, as discussed below at step  650 . At  604 , the session manager  471  may request a driver profile  438  for the driver (and/or other driver history/experience context data  482 ) from a source storing the driver profile  438 , e.g., (a) a mobile device (e.g., smartphone), key fob, or other device associated with the driver, (b) system controller  402 , or (c) a remote server  412  via network  414 . At  606 , the source storing the driver profile  438  may retrieve the driver profile  438 , and return the requested driver profile  438  to the session manager  471  at  608 . 
     Example process flow  600  shows selected procedures for selecting and outputting content to the driver, for three stages of the driving experience session: (1) a start-of-session stage  670 , (b) a mid-session stage  672 , and (c) an end-of-session stage  676 . The process flow in each session stage is implemented by respective content selection logic, e.g., executed by session manager  471  and other various components of system controller  402 . 
     Starting with the start-of-session stage  670 , at  610  the session manager  471  may request a current stressfulness stage from a stressfulness stage manager  473 , which may calculate or determine a current stressfulness stage for the driver at  612 , and return the requested stressfulness stage to the session manager  471  at  614 . At  616 , the session manager  471  may determine a current session status, in this case a start-of-session stage, and identify a content element category (i.e., a group of available content elements) for the start-of-session stage. At  618 , the session manager  471  may select one or more particular content element  489  to be output to the driver, based on various input data, including (a) the determined current stressfulness stage, the driver profile  438 , the determined content element category, additional contextual information (e.g., driving context data  480  and/or driver state data  481 ), and/or the length (output duration) of each respective content element  489 . The session manager  471  may then notify a content manager  476  of the selected content element(s)  489  to be output. At  620 , the content manager  476  may access the selected content element(s)  489 , and forward the selected content element(s)  489  to the session manager  471 . The content manager  476  may also store a record of the selected content element(s)  489 , at  624 . 
     At  626 , the session manager  471  may output the selected content element(s)  489 , e.g., by instructing an output controller  491  to control one or more output devices  410  to output the selected content element(s)  489  to the driver. While the content element(s)  489  are output, a driver feedback manager  492  may observe the driver&#39;s behavior at  630 , e.g., using suitable driver monitoring sensors  444 , and store the observed driver feedback at  632 . 
     At the end of the content element output, at  634 , the session manager  471  may start a timer that may be used for time-based triggering of a next content element output. For example, the session protocol may specify a time-based content triggering event after any 3 minute period without content output (e.g., 3 minutes without the occurrence of a context-based triggering event). 
     Next, during the mid-session stage  672 , the session manager  471  may initiate content output in response to trigging events detected by trigger manager  474 . As shown, trigger manager  474  may detect a content triggering event at  640  based on contextual data, e.g., driving context data  480  and/or driver state data  481 , received from any local and/or remote data sources. At  644 , session manager  471  may select one or more particular content element  489  to be output to the driver, based on various input data, including (a) the type or details of the content triggering event (including the contextual data underlying the triggering event), (b) the current stressfulness stage, and (c) the length (duration) of available content elements  489 . At  646 , the session manager  471  may output the selected content element(s)  489 , e.g., by instructing the output controller  491  to control output device(s)  410  to output the selected content element(s)  489  to the driver. 
     The trigger manager  474  and session manager  471  may continue to detect context-based triggering events and select and output suitable content elements throughout the duration of the mid-session stage  672 . For each content output instance, the driver feedback manager  492  may observe the driver&#39;s behavior at  630 , e.g., using suitable driver monitoring sensors  444 , and store the observed driver feedback. 
     At the end of the timed session duration, the timer  478  may notify the session manager  471 , as indicated at  650 , which may initiate the end-of-session stage  674 . At  652 , the session manager  471  may select suitable end-of-session content element(s)  489 , and output the selected content element(s)  489  at  654 , e.g., by instructing the output controller  491  to control output device(s)  410  to output the selected content element(s)  489  to the driver. At the end of the session, indicated at  660 , the driver feedback manager  492  may evaluate the driver&#39;s behavioral feedback throughout the session at  664 , and update the driver&#39;s mindfulness historical data  442  accordingly. 
     In some embodiments, each instance of a driving experience session may follow a defined time-based protocol including rules for determining the timing and types of content output during the session. 
       FIG. 7  illustrates an example time-based session protocol  700  executed by session manager  471  to implement a driving experience session, according to one example embodiment. The session protocol starts at  702 , e.g., automatically or in response to a defined user input, e.g., as discussed above at step  504  or  602 . After a defined time period, T(Welcome), at  704  session manager  471  selects and outputs a “Welcome” content element (or elements) to the driver. The “Welcome” content element(s) may be designed to welcome the driver to the driving experience session. 
     After completion of the “Welcome” content element(s), followed by a next time period, T(Intro), at  706  session manager  471  selects and outputs an “Intro” content element (or elements) to the driver. The “Intro” content element(s) may introduce the driver to the theme, purpose, and/or details of the driving experience session. 
     After completion of the “Intro” content element(s), followed by a next time period, T(Start_Breathing), at  708  session manager  471  selects and outputs a “Start Breathing” content element (or elements) to the driver. The “Start Breathing” content element(s) may be a first content element, e.g., audio clip, for a relaxation session for the driver. 
     After completion of the “Start Breaking” content element(s), the session protocol enters a trigger-based content phase, in which a series of “Session Arc” content outputs are initiated based on (a) detected context-based triggering events (also referred to as external triggers) and (b) time-based triggers. In this example, the protocol defines a time period T(Min) representing a minimum time delay after completion of each content element, during which context-based triggering is disabled. The protocol also defines a time period T(Max) representing a maximum time between content element that triggers; thus, after completion of a particular content element, if T(Max) is reached without the occurrence of any context-based triggering event, session manager  471  outputs a predefined (e.g., generic) content element. 
     The protocol may also define a buffer period T(Buffer) representing an addition buffer that can be added to T(Max) (thereby delaying the predefined/generic content element output) when session manager  471  identifies a “predictive trigger” during the period T(Max). A predictive trigger is a prediction that a context-based triggering event will occur during the T(Buffer) period, e.g., based on traffic light timing information or other data available to session manager  471  (e.g., by determining a percentage likelihood of an event that exceeds a defined threshold value). 
     Returning to session protocol  700 , a “Coming Back” content element may be output at the end of the session, e.g., at the end of a predefined duration of the session, or in response to the system detecting the driver is reaching her destination. 
       FIGS. 8A-8B  illustrate an example process flow  800  of a driving experience session (mindfulness session) implementing the context-based and time-based content triggering of the session protocol shown in  FIG. 7 , according to one example embodiment. At  802 , the driving experience session starts, and session manager  471  starts a session timer  478 . After completion of a time period T(Welcome), indicated at  804 , session manager  471  outputs Welcome content element(s). After completion of the Welcome content, session manager  471  starts a timer T(Intro) at  808 . If a context-based triggering event (external trigger) is detected before the end of T(Intro), as indicated at  810 , the external trigger is ignored, as discussed above regarding  FIG. 7 . 
     At the completion of T(Intro), session manager  471  outputs Intro content element(s) at  812 . After completion of the Intro content, session manager  471  starts a timer T(Start_Breathing) at  814 . At the end of timer T(StartBreathing), session manager  471  outputs Start_Breathing output element(s) at  816 . If an external trigger is detected during the output of Start_Breathing output element(s), as indicated at  818 , the external trigger is ignored. 
     At the completion of the Start_Breathing output element(s), session manager  471  starts a timer T(Min) followed by T(Max), indicated at  820 ,  822 . If an external trigger is detected during T(Min), as indicated at  824 , the external trigger is ignored. However, if an external trigger is detected during T(Max), as indicated at  826 , the external trigger is signaled to session manager  471 , which selects and outputs Context-Sensitive content element(s), e.g., based on driving context data  481  and a current stressfulness stage, at  828 . 
     At the completion of the Context-Sensitive content element(s) at  828 , session manager  471  again starts a timer T(Min) followed by T(Max), indicated at  830 ,  832 . If T(Max)  832  is reached without detection of an external trigger, as shown in  FIG. 8 , a time-based trigger is signaled to session manager  471 , which selects and outputs Generic content element(s) at  834 . 
     At the completion of the Generic content element(s) at  834 , session manager  471  again starts a timer T(Min) followed by T(Max), indicated at  840 ,  842 . If a “predictive trigger” is detected during T(Max)  832 , as indicated at  844 , session manager  471  may extend T(Max) by a buffer period T(Buffer), indicated at  846 . Then, if the trigger manager  474  detects the predicted context-based trigger during the buffer period T(Buffer), as indicated at  848 , the trigger manager  474  signals the session manager  471 , which then selects and outputs Context-Sensitive content element(s), e.g., based on driving context data  481  and a current stressfulness stage, at  850 . 
     At the completion of the Context-Sensitive content element(s) at  850 , session manager  471  again starts a timer T(Min) followed by T(Max), indicated at  852 ,  854 . In this example, a session ending event occurs during T(Max). For example, as indicated at  860 A, a defined session duration may expire, triggering session manager  471  to select and output “ComingBack” content element(s) as indicated at  862 . Alternatively, as indicated at  860 B, a context-based trigger may trigger the end of the session. For example, trigger manager  474  may detect, based on GPS data, that the driver will arrive at the determined destination at a defined time, e.g., in 2 minutes from the current time. 
     In addition to the process discussed above, as indicated at  470  the session may be terminated at any time by the driver, e.g., upon receiving a defined user input from the driver. 
     As discussed above, in some embodiments system controller  402  may use traffic light data  464 , e.g., obtained from a remote server  412 . Traffic light data is also referred to herein as TLO (traffic light online) data. In some implementations, the time the driver is waiting on a traffic light to turn green may be a good time to output particular types of content, e.g., engaging mindfulness exercises, such as deep breathing exercises or even small physical exercises, for example. In some embodiments, the TLO data source (e.g., remote server) communicates the TLO information to system controller  402  shortly before (e.g., a couple seconds before) the vehicle reaches the traffic light. The communicated TLO data may include the currently active traffic light status (e.g., light color/arrow) and the next predicted status. 
       FIG. 9  illustrates an example stop length prediction algorithm  900 , which may be implemented by stop length predictor  475 , according to one example embodiment. Algorithm  900  analyzes CAN signals that monitor the driver&#39;s behavior, for example, brake pressure, accelerator pedal position, steering wheel angle, and vehicle velocity. When the vehicle stops (or during a period prior to the stop) the algorithm feeds the CAN signals into a time-series prediction model (for example an LSTM, categorical clustering, or other machine learning model), which is trained to predict the length of the vehicle stop. The algorithm may be configured to derive the driver&#39;s intention regarding the stop with high accuracy, based on the driving behavior data (CAN signals) when slowing down for the stop, which data statistically indicates the driver&#39;s intent. 
     As shown in algorithm  900 , when the vehicle comes to a stop (or is about to come to a stop) the LSTM predicts the length of the stop using CAN signal data from a defined time period (e.g., 2.5 seconds or 5 seconds) prior to the vehicle stop. If the stop length predictor  475  predicts a long stop (e.g., above a defined threshold duration), stop length predictor  475  may trigger an output of one or more selected content element(s), e.g., by signaling session manager  471 . The content may be output until completion of the selected content element(s), or until trigger manager  474  system detects one of the following triggering events that terminates the content output: 
     (a) the vehicle brake has been released or the vehicle has started accelerating, 
     (b) the vehicle ahead has started to move, or 
     (c) the traffic light is predicted to turn green within a defined time, e.g., based on traffic light data  464 . 
     It should be understood that algorithm  900  is an example only, and stop length predictor  475  may utilize any other rules or algorithms for predicting a vehicle stop length. 
       FIG. 10  illustrates an example process flow  1000  of a portion of a driving experience session (mindfulness session) when the driver encounters a red light, according to one example embodiment. In this example embodiment, trigger manager  474  implements a TLO protocol including the following rules:
         1. A TLO content element is only triggered when the car reached the Traffic Light and has stopped.   2. Only content elements are selected that are shorter than the Traffic light prediction time. If there are no such content elements, the content trigger is ignored.   3. The TLO signals can manipulate the time-based trigger and add a T(Buffer) to the T(Max) period (if the traffic light will be reached within this time).   4. A TLO content element is only triggered when there is no active turn signal (blinker, directional, etc.) set, such that the system assumes the driver is continuing straight, and thus not mentally burdened by a turning maneuver.       

     Turning to process flow  1000 , the session starts at  1002  and proceeds through a starting sequence at  1004 , and enters a mid-session phase  1006 . During a particular T(Max) period indicated at  1010 , trigger manager  474  detects the vehicle is approaching a traffic light, and obtains TLO data for the traffic light from a remote server  412 , indicated at  1012 , and forwards the TLO data to the session manager  471 . The session manager  471  analyzes the TLO data at  1014 . In this example, if session manager  471  both (a) determines the current or next status of the traffic light is “red light” (e.g., with a confidence measure that exceeds a corresponding threshold confidence value) and (b) determines a stopping confidence measure that exceeds a corresponding threshold confidence value, the session manager  471  adds a buffer time T(Buffer) to the T(Max) time, to prevent the time-based triggering of a generic content element in order to allow a TLO-based content triggering. The stopping confidence measure may represent a statistical likelihood or other predictive measure that the vehicle will (i) stop at the red light before the completion of a buffer time T(Buffer) that can be appended to T(Max) and (ii) remain at the red light for a defined minimum time period. 
     In the illustrated example, the session manager  471  determines the above in the positive, thus adding the buffer time T(Buffer), and the vehicle stops at the red light at  1020 . When the vehicle stops, the session manager  471  determines at  1022  whether to output a traffic stop-specific content element. In this embodiment, the session manager  471  determines whether (a) the light is red, (b) the vehicle turn signal is inactive, and (c) at least one content element having a duration less than a predicted vehicle stopping time (e.g., determined according to concepts discussed above regarding  FIG. 9 ) is available, all with a statistical confidence measure that exceeds a threshold value. If session manager  471  determines the above in the positive, session manager  471  may select and output a traffic stop-specific content element at  1024 . 
     In this example, the traffic stop-specific content element completes while the vehicle remains stopped, and the protocol returns to a T(Min) timer as indicated at  1030 . As shown, the vehicle then begins moving (above 5 mph) at  1032 . In some embodiments, if the vehicle then begins moving (e.g., above 5 mph or other defined threshold speed) during the output of the traffic stop-specific content element, the content output is interrupted. 
     In addition to the various types of content trigger discussed above, some embodiments provide weather-based content triggering, e.g., rain-based content triggering. The system controller  402  may determine the weather conditions in multiple different ways. For example, the system controller  402  may send API calls to an online weather service provider  412  to obtain the weather data for the vehicle&#39;s current GPS position. Or, system controller  402  may use on-board vehicle sensors, e.g., to detect rain or other weather conditions. In one embodiment, system controller  402  may use a combination of a rain sensor and a signal indicating the windshield wiper status, combined with a defined time delay (e.g., to avoid a false positive detection when the driver is cleaning the windshield) to detect a rain condition. 
     In one embodiment, a rain-based content triggering protocol may include the following rules:
         1. System controller  402  subscribes to 2 MQTT topics: Rain Sensor and Wiper status, each of which generates and sends a signal to session manager  471  upon each rain or wiper status change.   2. The rain sensor sends a value between 0 to 100. The wiper sends an On/Off signal.   3. The session manager  471  caches the received signal values.   4. When the rain sensor sends a value above 50 and the Wiper sends and On signal, session manager  471  starts a timer for 10 seconds.   5. After 10 seconds to session manager  471  checks the cached values of the rain sensor and the wiper status.   6. If the wiper is ON after 10 seconds and the rain sensor is above 50, to session manager  471  outputs a wiper-specific content element.   7. If the rain sensor value drops below 50 OR the wiper turns off, session manager  471  does not output a content element.   8. When the 10 second timer runs out the protocol timer must be in a T(Max) period to receive and process the wiper-based content trigger.   9. If the timer is in a T(Min) period or another content element is being outputted, the trigger is ignored.   10. If the trigger is ignored, session manager  471  waits to receive another rain sensor or wiper status value, upon which (depending on the received values) the session manager  471  will restart the timer for 10 seconds and the cycle restarts.