Vehicle HMI actuation

Abstract

The present disclosure provides "vehicle HMI actuation". A system includes a computer having a processor and a memory. The memory stores instructions executable by the processor to: determine a wake-up time for an occupant of a vehicle based on a destination and an object in an interior of the vehicle; and actuating a vehicle component to wake up the occupant.

Description

Technical Field

[0001] This disclosure relates to human-machine interface actuation in vehicles. Background Technology

[0002] The vehicle may include features for operating the vehicle and / or actuating vehicle components based on inputs other than operator inputs and / or inputs provided as an alternative to operator inputs. Furthermore, the vehicle may include a human-machine interface (HMI) to receive user inputs and / or provide outputs to users such as vehicle operators and / or other occupants. Summary of the Invention

[0003] The computer in the vehicle can receive data indicating the user's destination, data about the user (e.g., sleep status), and / or data about objects associated with the user. The computer can calculate an alarm time based on this data and, once the alarm time is reached, actuate vehicle components to wake the user.

[0004] Therefore, this disclosure includes a system comprising a computer having a processor and a memory storing instructions executable by the processor to: determine the wake-up time of an occupant of the vehicle based on a destination and objects inside the vehicle; and actuate vehicle components to wake the occupant.

[0005] The object can be detected by vehicle sensors.

[0006] The presence of the object inside the vehicle can be determined based on user input.

[0007] The wake-up time can be determined based on the object's classification.

[0008] The wake-up time can be determined based on conditions specified by user input.

[0009] The wake-up time can be determined based on user input of the expected difference between the wake-up time and the estimated time to reach the destination.

[0010] The wake-up time can be determined based on conditions specified by user data obtained from the mobile device.

[0011] Determining the wake-up time based on the destination may include predicting the time of arrival at the destination.

[0012] Determining the wake-up time based on the destination may include predicting the time needed to prepare for arrival at the destination.

[0013] Determining the wake-up time based on the destination may include determining the category of the destination.

[0014] The occupant may be one of a plurality of occupants in the vehicle, and the second wake-up time of the second occupant in the vehicle may be determined.

[0015] The second wake-up time of the second occupant may be based on a predicted second time of arrival at the second destination.

[0016] The wake-up time can be determined based on the number of occupants in the vehicle.

[0017] The wake-up time can also be determined based on one or more of the occupant's status, traffic data, and historical data.

[0018] The wake-up time can also be determined based on the occupant's sleep state.

[0019] One method includes: determining the wake-up time of an occupant of the vehicle based on the destination and objects inside the vehicle; and actuating vehicle components to wake the occupant.

[0020] The wake-up time can be determined based on the object's classification.

[0021] The wake-up time can be determined based on user input of the expected difference between the wake-up time and the estimated time to reach the destination.

[0022] The wake-up time can be determined based on conditions specified by user data obtained from the mobile device.

[0023] The occupant may be one of a plurality of occupants in the vehicle, and the second wake-up time of the second occupant in the vehicle may be determined. Attached Figure Description

[0024] Figure 1 This is a block diagram of an example vehicle system.

[0025] Figure 2 This is a top-down illustrative view of the vehicle, exposing the passenger compartment for explanation.

[0026] Figure 3 This is a flowchart of an example process for determining the actuation time of a component. Detailed Implementation

[0027] Referring to the accompanying drawings, in which like reference numerals indicate like parts throughout several views, vehicle system 100 includes a computer 104 for vehicle 102, the computer including a processor and a memory. The memory stores instructions executable by the processor, including instructions for: determining the wake-up time of vehicle occupants based on the destination and objects inside the vehicle; and actuating vehicle components to wake the occupants.

[0028] Exemplary system components

[0029] refer to Figure 1 Vehicle 102 includes vehicle system 100. Vehicle system 100 includes computer 104 with memory including instructions executable by computer 104 to perform processes and operations as described herein. Computer 104 may be communicatively coupled to sensors 106, displays 108, audio output devices 110, and communication modules 112 via vehicle communication network 114. Vehicle 102 includes various other components, such as steering systems, propulsion systems, and braking systems. Vehicle 102 may be a passenger or commercial vehicle, such as a sedan, truck, SUV, crossover, van, minivan, taxi, bus, ICE (internal combustion engine), BEV (battery electric vehicle), hybrid vehicle, PHEV (plug-in hybrid electric vehicle), etc.

[0030] As mentioned above, vehicle computer 104 includes a processor and memory. The memory includes one or more forms of computer-readable medium and stores instructions executable by the processor to perform various operations, including those disclosed herein. For example, computer 104 may be a general-purpose computer having a processor and memory as described above, and / or may include electronic control units (ECUs) or controllers for specific functions or sets of functions, and / or dedicated electronic circuitry including ASICs (Application-Specific Integrated Circuits) manufactured for specific operations (e.g., ASICs for processing and / or transmitting sensor data). In another example, computer 104 may include an FPGA (Field-Programmable Gate Array), which is manufactured as a user-configurable integrated circuit. Typically, hardware description languages ​​such as VHDL (Very High Speed ​​Integrated Circuit Hardware Description Language) are used in electronic design to describe digital and mixed-signal systems such as FPGAs and ASICs. For example, an ASIC is manufactured based on VHDL programming provided before manufacturing, while the logic components within an FPGA may be configured based on VHDL programming (e.g., stored in memory electrically connected to the FPGA circuitry). In some examples, computer 104 may include a combination of processor, ASIC, and / or FPGA circuitry. Computer 104 can be multiple computers coupled together.

[0031] The memory can be of any type (e.g., hard disk drive, solid-state drive, server, or any volatile or non-volatile media). The memory can store collected data transmitted from sensor 106. The memory can be a separate device from computer 104, and computer 104 can retrieve the information stored in the memory via network 114 in vehicle 102 (e.g., via CAN bus, wireless network, etc.). Alternatively or additionally, the memory can be part of computer 104 (e.g., as memory of computer 104).

[0032] Computer 104 may include programming to: operate one or more of the vehicle components, such as propulsion (e.g., controlling the speed of vehicle 102 by controlling one or more of an internal combustion engine, an electric motor, a hybrid engine, etc.), steering, braking, interior and / or exterior lights, display 108, audio output device 110, etc.; and determine whether and when computer 104 (not a human operator) controls such operation.

[0033] Computer 104 is typically arranged for communication over a vehicle communication network 114, which may include buses in vehicle 102, such as Controller Area Network (CAN), and / or other wired and / or wireless mechanisms. Alternatively or additionally, where computer 104 actually comprises multiple devices, vehicle communication network 114 may be used for communication between the devices represented herein as computer 104. Furthermore, as mentioned below, various controllers and / or sensors 106 may provide data to computer 104 via vehicle communication network 114.

[0034] Via vehicle network 114, computer 104 can transmit messages to and / or receive messages (e.g., CAN messages) from various devices and / or components in vehicle 102 (e.g., sensors 106, ECUs, audio output devices 110, etc.). Alternatively or additionally, where computer 104 actually comprises multiple devices, vehicle communication network 114 can be used for communication between devices represented herein as computer 104. Furthermore, as mentioned below, various controllers and / or sensors 106 can provide data to computer 104 via vehicle communication network 114.

[0035] Vehicle 102 typically includes a variety of sensors 106. Sensor 106 is a device that acquires one or more measurements of one or more physical phenomena. Some sensors 106 detect the internal state of vehicle 102, such as wheel speed, wheel orientation, and engine and transmission variables. Some sensors 106 detect the position or orientation of vehicle 102, such as GPS sensors. Some sensors 106 detect objects, such as radar sensors, scanning laser rangefinders, light detection and ranging LiDAR devices, and image processing sensors (such as cameras). Other sensors 106 detect sound, such as dynamic or capacitive microphones, piezoelectric transducers, ultrasonic sensors, acoustic emission sensors, etc.

[0036] Vehicle 102 may include one or more displays 108. Displays 108 display visual data, such as two-dimensional visual data, to users of vehicle 102. Displays 108 may be of any suitable type for displaying content clearly visible to the respective occupant, such as light-emitting diodes (LEDs), organic light-emitting diodes (OLEDs), liquid crystal displays (LCDs), plasma displays, digital light processing technology (DLPT), etc. Displays 108 may display visual data in monochrome or color via a screen, and the visual data may be updated at a certain frame rate, such as 60 frames per second. The displayed visual data may be a static image, in which most of the two-dimensional area does not change with each frame, or it may be a dynamic image, in which most of the two-dimensional area changes with each frame. The visual data to be displayed on display 108 may be generated by a display controller. The display controller is a computing device such as an ECU, which may receive data to be displayed on display 108 in a visual format from computer 104, other vehicle ECUs, or from external computing device 118 via server 116.

[0037] In addition to providing output, display 108 can also allow user input. For example, display 108 may be a suitable touchscreen display, allowing the user to provide input to computer 104 via display 108 (e.g., selecting a dataset to be output by display 108 via display 108). The user can provide input to computer 104 via display 108. The touchscreen can be any suitable type for receiving input from the user (e.g., resistive, capacitive, infrared, etc.).

[0038] Vehicle components may include an audio output device 110 and a display 108. The audio output device 110 can be any suitable device configured to output sound to a user of the vehicle 102. For example, the audio output device 110 can be a speaker, a personal device (such as headphones), etc. A speaker is an electroacoustic transducer that converts electrical signals into sound. A speaker can be any suitable type for producing a corresponding audible (e.g., dynamic) sound for a user. A personal device can be any suitable device for emitting sound to a single user, such as in-ear or over-ear headphones, a portable speaker, etc. The personal device can be connected to the vehicle network 114 via a wired connection (such as an audio jack) or a wireless connection (such as Bluetooth™).

[0039] refer to Figure 2 The image shows the passenger compartment 204 of vehicle 102. Passenger compartment 204 can accommodate one or more occupants of vehicle 102, such as... Figure 2 Users 200 and 202 are shown. Passenger compartment 204 includes one or more compartment positions (e.g., one or more compartment positions located in the front row of passenger compartment 204 and one or more compartment positions located in the second row behind the front row). Passenger compartment 204 may also include a third row (not shown) of compartment positions located at the rear of passenger compartment 204. In the example shown, user 200 is in the operator's position, and user 202 is in the right rear position of compartment 204.

[0040] Passenger compartment 204 may not include objects, may include one or more objects, such as objects 206 and 208 shown. As used herein, an object is a collection of physical matter that forms a single thing. In the current example, objects 206 and 208 are a tie and a briefcase, respectively, but can be any object that can be placed in compartment 204. The objects can be detected by vehicle sensors.

[0041] Computer 104 may classify objects 206, 208 based on data collected by sensor 106. Classifying objects 206, 208 means determining the type or category of objects 206, 208 according to any suitable technology. For example, various technologies may be used to analyze data from cameras or other sensors 106 to determine the type or category of objects 206, 208. The type or category of objects 206, 208 means the kind of object (e.g., briefcase, tie, pet carrier, folding bicycle, etc.). Image data used for object classification may be a single image or multiple images collected at different times. Image data may also be used by computer 104 to identify occupants of compartment 204 who possess objects 206, 208 (e.g., users who bring objects 206, 208 into the vehicle). Computer 104 may alternatively or additionally determine the presence of objects inside the vehicle based on user input. For example, users 200, 202 may specify to computer 104 via display 108 that they are carrying a briefcase.

[0042] The classifier may include a neural network trained in a suitable manner to classify objects 206, 208. When training the deep neural network, a training dataset including example objects 206, 208 may be used. The training dataset may include many (e.g., thousands) example images labeled to indicate the type of objects 206, 208 in the images. Once trained, the neural network can be implemented in the vehicle computer 104 and used to classify objects.

[0043] As mentioned above, computer 104 can be programmed to identify users 200 and 202 associated with objects 206 and 208, in addition to classifying them. Users 200 and 202 associated with objects can be determined based on whether they brought objects 206 and 208 into carriage 204, and it is expected that they will take objects with them when they leave carriage 204. Computer 104 can detect objects 206 and 208 and / or users 200 and 202 based on analysis of data such as image data. When computer 104 determines, based on data such as image data, that users 200 and 202 touched objects 206 and 208 and / or that users 200 and 202 were closer to objects 206 and 208 than other users 200 and 202, computer 104 can determine that users 200 and 202 are associated with objects 206 and 208. Computer 104 may alternatively or additionally determine object-user associations based on user input (e.g., input specifying objects 206 and 208 belonging to users 200 and 202). Alternatively or additionally, computer 104 may associate users 200 and 202 with objects 206 and 208 based on detecting a predetermined relationship (such as a specified distance) between users 200 and 202 and objects 206 and 208. For example, computer 104 may associate objects 206 and 208 with users 200 and 202 located closest to objects 206 and 208 within passenger compartment 204, and / or may associate objects 206 and 208 with users 200 and 202 who have placed objects 206 and 208 within passenger compartment.

[0044] Computer 104 may include a navigation system. The navigation system may be any suitable system for vehicle navigation. The navigation system provides a route to a destination and an estimated time of arrival. In addition to the final destination, the route may also include one or more intermediate destinations (i.e., waypoints). Waypoints may be, for example, “get on” or “get off” locations specified by users 200, 202, and may have corresponding estimated arrival times.

[0045] Users 200 and 202 may rest (e.g., sleep) in passenger compartment 204. In some examples, users 200 and 202 may only rest when they are in any compartment position other than the driver's position or when the vehicle is not in operational mode (e.g., parked), but in the future, it is envisioned that autonomous vehicle technology may develop to the point where users 200 in the operator's position may rest when vehicle 102 is in operational mode. Computer 104 may determine that users 200 and 202 are sleeping based on user input specifying that users 200 and 202 intend to sleep. Alternatively or additionally, computer 104 may determine that users 200 and 202 are sleeping based on data collected by sensor 106. The data may include image data (e.g., the user's eyes are closed), biometric data (e.g., heart rate data, respiratory data), etc.

[0046] Computer 104 can determine the wake-up time of vehicle occupants based on the destination and objects inside the vehicle, and actuate vehicle components to wake the occupants. The wake-up time is the time at which computer 104 actuates one or more components (e.g., audio output device 110) to wake one or more users 200, 202. The wake-up time can be a specified amount of time before another time (e.g., the time of arrival at the destination). Computer 104 can actuate display 108 to output light, actuate audio output device 110 to output audio, actuate other tactile devices, etc. Different users 200, 202 may have corresponding wake-up times. For example, the wake-up time of the first user 200 may be 4:30 PM, and the wake-up time of the second user 202 may be 5:00 PM. Computer 104 can actuate the target to wake up user 200, 202 or the component positioned to wake up user 200, 202 at the user's corresponding wake-up time. For example, computer 104 may only actuate audio output device 110 installed at the vehicle position closest to the first user 200 in the carriage 204, relative to other audio output devices 110.

[0047] Computer 104 can determine the wake-up time based on detecting that a user is sleeping (e.g., by detecting closed eyes, decreased heart rate, etc.) or by user input indicating that designated users 200 and 202 intend to sleep. When users 200 and 202 notify computer 104 that they intend to sleep, computer 104 can enter "monitoring mode" and begin collecting data (e.g., closed eyes, decreased heart rate, etc.) so that computer 104 can determine the time when users 200 and 202 fall asleep.

[0048] Computer 104 can determine the wake-up time based on the specifications of users 200 and 202 (e.g., users 200 and 202 can specify a wake-up time of 4:30 PM). That is, the wake-up time can be the expected difference between the wake-up time and the estimated time of arrival at the destination, as input by users 200 and 202. In the absence of any wake-up time specified by users 200 and 202, computer 104 can determine the wake-up time of a particular user 200 or 202 based on one or more factors. For example, computer 104 can start from a baseline wake-up time and adjust the baseline wake-up time based on one or more factors. These factors will be described in further detail below. Computer 104 can store lookup tables, etc., that provide time adjustments to the baseline wake-up time based on the corresponding factors. The baseline wake-up time of users 200 and 202 can be, for example, ten minutes before the time of arrival at the user's destination.

[0049] Computer 104 can assign corresponding adjustments to factors (e.g., by "subtracting" a value corresponding to the amount specified for the corresponding factor from the wake-up time). That is, computer 104 can first specify a baseline wake-up time. The baseline wake-up time can be specified during the development of computer 104. The baseline wake-up time can be, for example, ten minutes before a user's specified arrival time (e.g., the time of arrival at a destination, a time specified in a personal calendar, etc.). Computer 104 can then adjust the wake-up time by applying the specified adjustments to the baseline wake-up time.

[0050] Computer 104 can apply weights to factors used to determine wake-up time. In this context, a weight is a scalar value multiplied by a factor. That is, computer 104 can increase or decrease adjustments based on the weights applied to one or more factors used to determine the adjustment. For example, if the destination category factor has a weight of two, and no other factors being used are weighted, the weight of the adjustment based on the destination category will be doubled relative to the adjustments for other factors. Computer 104 can assign weights based on user input. For example, a user could provide the following input: the destination category “Work” is assigned a weight of two, while the destination category “Home” is assigned a weight of 0.5 (i.e., half). Alternatively or additionally, the computer can assign weights based on stored data. For example, weights can vary based on the time of day. As an example, occupant state factors indicating deep sleep (e.g., NREM states discussed below) may be given higher weight (e.g., II) during daytime hours (e.g., 7:00 a.m. to 8:00 p.m.) and lower weight (e.g., I) during nighttime hours (e.g., 8:01 p.m. to 6:59 a.m.).

[0051] An example expression that computer 104 can use to calculate priority scores is represented by Equation 1:

[0052] Equation 1:

[0053] Wake-up time = (Arrival time – 10) – F1 – (F2 / 2) – F3 – … FN

[0054] Where F refers to the adjustment based on the corresponding factors as specified in the lookup table (e.g., one minute for each small object associated with the user, fifteen minutes for walking to the destination after leaving the vehicle, etc.). F2 is given a weight of half and other factors are unweighted, or in other words, it receives a default weight of one.

[0055] Computer 104 can adjust the wake-up time based on the classification of objects 206 and 208. That is, computer 104 can adjust the wake-up time based on the classification of objects 206 and 208 associated with users 200 and 202. Computer 104 can store predetermined adjustments corresponding to different classifications to be applied to a baseline wake-up time. These adjustments can be stored in a lookup table and can be determined empirically based on how long it takes test users 200 and 202 to collect objects 206 and 208 of a specific classification before leaving vehicle 102. For example, after determining that user 200 is associated with object 206, computer 104 can adjust the wake-up time by adding two minutes to the difference between the wake-up time and the arrival time based on classifying object 206 as a tie, thus giving user 200 an appropriate amount of time to put on a tie before leaving vehicle 102. As another example, computer 104 can adjust the wake-up time by three minutes based on object 208 being classified as a briefcase.

[0056] In addition to adjusting the wake-up time based on factors determined by computer 104, computer 104 may also adjust the wake-up time based on conditions specified by user input. Conditions specified by user input refer to any factors that may or may not be determined by computer 104 but are specifically input into computer 104 by users 200 and 202. User-specified conditions may include specific factors that leave the time adjustment to be determined by computer 104 (e.g., based on a lookup table). User-specified conditions may also include specific adjustments to the wake-up time. For example, user 200 may specify to computer 104 that user 200 will be conducting a test at their destination and therefore needs an additional ten minutes to prepare before leaving vehicle 102. Computer 104 may then adjust the wake-up time by an additional ten minutes (e.g., if the wake-up time is 15 minutes before arrival, it is adjusted to 25 minutes before arrival).

[0057] Computer 104 can adjust the wake-up time based on conditions specified by user data obtained from mobile device 118. That is, computer 104 can set or adjust the wake-up time based on data stored in the user's mobile device 118, such as dates set in messages, calendar dates, etc. If users 200, 202 allow computer 104 access to mobile device 118, computer 104 can read any date and / or time through mobile device 118. Computer 104 can set a baseline wake-up time, for example, ten minutes before the date indicated by the data. Computer 104 can then adjust the wake-up time based on factors described herein. Computer 104 can retrieve datasets, such as lookup tables specifying adjustments to the wake-up time based on user data. For example, the dataset might specify dates associated with meetings during specified times of the day (e.g., lunch or other meal times, such as 11:30 AM to 1:30 PM) that result in a 30-minute adjustment to the wake-up time, while dates associated with school events result in a 30-minute adjustment to the wake-up time. Adjustments based on data can be specified to computer 104 by users 200, 202.

[0058] Computer 104 can adjust wake-up time based on occupant status. "Occupant status" refers to a condition specific to users 200 and 202 that may affect the amount of time required for users 200 and 202 to leave vehicle 102. Examples of occupant status data may include object-user association, occupant awake or asleep state, occupant destination, occupant activity at the destination, etc. Occupant status data can be determined from various sources, such as vehicle sensors 106, occupant mobility devices 118 (e.g., stored calendar data, etc.). Sleep state is a sleep depth point between NREM (non-rapid eye movement) and REM (rapid eye movement) in a sleep cycle. A user's sleep state can be, for example, any of NREM 1, NREM 2, NREM 3, NREM 4, and REM. Computer 104 can detect biometric data (e.g., respiration or heart rate) about users 200 and 202 from sensors. Computer 104 can determine the sleep state of users 200 and 202 based on biometric data, the time when users 200 and 202 started sleeping, and the average length of human sleep cycles. For example, computer 104 can determine that the user's sleep state is REM because user 200 started sleeping 60 minutes ago (where the average sleep cycle is between 80 and 120 minutes) and because the user's breathing and heart rate have slowed down over time.

[0059] Computer 104 can adjust wake-up times based on occupant states (such as the sleep states of users 200 and 202). Computer 104 can store lookup tables, if any, specifying adjustments to wake-up times based on user sleep states. These tables can specify adjustments to wake-up times such that users 200 and 202 are awakened when they are in NREM sleep rather than REM sleep. For example, if computer 104 determines that the sleep cycles of users 200 and 202 will cause them to enter REM sleep twenty minutes before the appointed time, computer 104 can adjust the wake-up time to be more than twenty minutes before the appointed time, causing users 200 and 202 to be awakened from NREM sleep.

[0060] Alternatively or additionally, in order to adjust the wake-up time based on occupant status, computer 104 may adjust the wake-up time based on factors such as current and / or historical traffic data. For example, when vehicle 102 travels a route once or multiple times, computer 104 may obtain data about the route. This data may be obtained by sensor 106 and / or received from server 116, and may include data about traffic density (e.g., the number of vehicles passing a point on the road per unit time), construction, events affecting traffic (e.g., sporting events), etc. Computer 104 may average the data about the route to produce an average condition of the route at a given time (e.g., average delay) and adjust the wake-up time based on the average condition of the route. The adjustment may be rule-based or stored in a lookup table. The data may include whether the traffic density along the route has exceeded a specified traffic threshold. The traffic threshold may be a value stored by computer 104. Computer 104 may determine when traffic-induced delays meet or exceed the traffic threshold, and then adjust the wake-up time accordingly. For example, if the planned route experiences traffic exceeding a traffic threshold during the planned travel time (e.g., causing a delay of ten minutes or more), computer 104 can adjust the wake-up time to correspond to any delay caused by traffic by shifting the wake-up time to a later time (e.g., if the traffic delay is determined to be ten minutes, the wake-up time can be adjusted to be ten minutes later). As another example, computer 104 can detect via sensor data that user 200 typically spends a certain amount of time leaving vehicle 102 at a specific destination (such as the user's workplace). Computer 104 can average the amount of time between the arrival time and the time users 200, 202 spend leaving vehicle 102, and adjust the wake-up time to the calculated average. For example, if the user typically spends five to ten minutes leaving the vehicle, the average departure time might be seven minutes, and this could then be the amount of adjustment to the wake-up time.

[0061] Computer 104 may adjust the wake-up time based on the destination. Determining the wake-up time based on the destination may include predicting the time of arrival at the destination. That is, as described above, the wake-up time may be determined as the difference between the wake-up time and the arrival time. The wake-up time may be a determined amount of time prior to the arrival time. Computer 104 determines the difference between the wake-up time and the time of arrival at the destination by adjusting a baseline wake-up time using specified adjustments corresponding to factors described herein (e.g., object-user association, sleep state, personal data, etc.).

[0062] Computer 104 can adjust the wake-up time based on the classification of the destination. That is, computer 104 can classify the destination and adjust the wake-up time by an amount determined based on the destination classification. The destination classification can specify the purpose of the destination or the category of activities at said destination (e.g., office, school, restaurant, etc.). Computer 104 can determine the destination classification based on identifiers associated with location coordinates, street addresses, and / or other identifying information (such as the name of the structure (e.g., "Central High School")) and / or user input specifying the destination classification. Adjustments based on the destination classification (if any) can be stored in a lookup table, etc. For example, if computer 104 classifies the destination as a sports field, the lookup table can specify adjusting the wake-up time by ten minutes to give users 200, 202 time to make any preparations (e.g., find a parking space) before leaving vehicle 102.

[0063] Computer 104 can adjust the wake-up time based on a prediction of the time needed to prepare for arrival at a destination. That is, computer 104 can predict the amount of time that users 200 and 202 will need between waking up at a specific destination and leaving vehicle 102, and adjust the wake-up time accordingly. Computer 104 can use, for example, a neural network to predict the amount of time required to prepare for arrival. The neural network can be trained, as described above, based on examples of test users 200 and 202 preparing to leave the vehicle at different destinations. The prediction may be partly based on the classification of the destination (e.g., a park may require more time than a workplace).

[0064] Computer 104 can adjust the wake-up time based on the number of occupants in the vehicle. If there is more than one user 200, 202 in vehicle 102, computer 104 can adjust the wake-up time to allow user 200, 202 more time between waking up and leaving vehicle 102. The adjustment can be specified to computer 104 via user input and / or via data determined based on empirical testing (e.g., during vehicle development) and stored in a lookup table or similar format. For example, if computer 104 detects the presence of more than one user 200, 202, it can adjust the wake-up time to 2 minutes per user 200, 202. Computer 104 can also adjust the wake-up time of users 200, 202 based on the detected age of other occupants (e.g., user input can provide the occupant's age, and the adjustment can take into account infants or the elderly, potentially allowing other users 200, 202 to assist them in leaving vehicle 102).

[0065] Table 1 shows a sample lookup table that provides a non-limiting example of how computer 104 can use (instead of other lookup tables such as those described herein or elsewhere) to adjust wake-up time user-object associations:

[0066]

[0067] Table 1

[0068] If the wake-up time meets a specified wake-up time threshold after adjustments are applied based on only a few factors, computer 104 may ignore the adjustment to the wake-up time. The wake-up time threshold is the maximum permissible adjustment to the wake-up time. If computer 104 has already applied an adjustment to the wake-up time such that it is adjusted to the threshold (e.g., twenty minutes), computer 104 may not make further adjustments. The wake-up time threshold may be specified by users 200, 202 and / or stored by computer 104. For example, computer 104 adjusts the wake-up time to thirty minutes before the arrival time of users 200, 202. Users 200, 202 are also associated with objects 206, 208 that typically require further adjustment of their wake-up time. However, if the wake-up time threshold is thirty minutes, computer 104 may ignore any other factors and not make any further adjustments to the wake-up time.

[0069] Example process

[0070] refer to Figure 1 and Figure 2 Description Figure 3 An example process 300 is shown for adjusting the wake-up time of users 200 and 202 so that computer 104 can actuate vehicle components at a desired time to wake up users 200 and 202. Process 300 can be executed according to program instructions executed by computer 104.

[0071] Process 300 begins at decision box 310, where computer 104 determines whether it has entered monitoring mode. Monitoring mode is an operating mode in which computer 104 collects data about the aforementioned factors to determine the wake-up times of users 200 and 202. As described above, computer 104 may enter monitoring mode based on user input indicating that the user intends to sleep. Alternatively or additionally, computer 104 may also enter monitoring mode based on detecting that the user has fallen asleep but has not provided input indicating that the user intends to sleep. If computer 104 is in monitoring mode, process 300 continues to box 315. Otherwise, the process continues to box 370.

[0072] Next, in decision box 315, the computer then determines whether a wake-up time has been specified; if process 300 reaches box 315, it means that computer 104 has determined to provide wake-up output because the user has set a wake-up time and / or because users 200 and 202 are sleeping. In box 315, computer 104 determines whether sleeping users 200 and 202 have requested to be woken up at a specific time (e.g., at 4:30 PM or fifteen minutes before that time). If users 200 and 202 have specified a wake-up time, process 300 continues to box 355. Otherwise, the process continues to box 320.

[0073] In decision box 320, after determining that users 200 and 202 who are sleeping have not specified a wake-up time, computer 104 determines whether they have the right to access data on mobile device 118 belonging to users 200 and 202. If computer 104 is allowed to access mobile device 118, the process continues to box 325; otherwise, the process continues to box 330.

[0074] In box 325, computer 104 retrieves from mobile device 118 any data indicating the reservations of users 200 and 202, their dates and / or times, and any data that will occur within a specified time window around the arrival time. A time window is typically defined as a range of minutes before and after the arrival time at the destination. For example, a time window could be thirty minutes, meaning it would cover from fifteen minutes before the arrival time to fifteen minutes after. The time window can be specified by users 200 and 202, or it can be a pre-stored value.

[0075] Next, in box 330, which may follow box 320 or box 325, computer 104 categorizes the destinations of users 200 and 202. A destination may be the next destination on the navigation route of vehicle 102, or, if users 200 and 202 have specified waypoints, a specific destination along the route. The categorization of destinations may specify the purpose of the destination or the category of activities at said destination (e.g., office, school, restaurant, etc.).

[0076] Next, in box 335, computer 104 determines, based on historical data, whether traffic delays exist on the navigation route and / or whether delays are foreseeable. Computer 104 also determines the length of the delay.

[0077] Next, in box 340, computer 104 classifies any objects 206, 208 present in the vehicle and determines any object-user associations.

[0078] Next, in box 345, computer 104 determines the state of users 200 and 202. As explained above, occupant state refers to a situation specific to users 200 and 202 that may affect the amount of time required for users 200 and 202 to leave vehicle 102. For example, determining occupant state may include determining which stage of the human sleep cycle users 200 and 202 are in, and which stages users 200 and 202 will be in as time progresses until they reach their destination.

[0079] Next, in box 350, computer 104 determines an adjustment to the wake-up time based on zero or more factors determined in boxes 325 to 345. As described above and as shown in exemplary Equation 1, computer 104 may store a baseline wake-up time (e.g., the amount of time before the wake-up time), and the baseline wake-up time may then be based on zero or more factors. The corresponding factors may have associated wake-up time adjustments stored in a lookup table. The adjustments corresponding to the factors may be summed (possibly after weighting, as described above) to obtain the adjustment to the baseline wake-up time. The adjustment may be limited by the wake-up time threshold specified above.

[0080] Next, in box 355, computer 104 outputs the adjusted wake-up time, so that vehicle components can be actuated at the time when users 200 and 202 are woken up.

[0081] Next, in box 360, computer 104 actuates vehicle components during wake-up time. These components may be an audio device 110, a display 108, etc. Computer 104 may select the component to actuate based on its proximity to users 200, 202, as described above. Computer 104 may, for example, actuate the component until users 200, 202, stop actuation after a specified time has elapsed, or stop actuation after user input.

[0082] Next, in decision box 365, computer 104 determines whether any other users 200, 202 are sleeping and therefore may require an adjusted wake-up time. If other sleeping users 200, 202 exist, the process returns to box 315 to perform the procedure for those users 200, 202. Otherwise, the process continues to box 370.

[0083] Next, in box 370, computer 104 determines whether to continue process 300. For example, once process 300 is initiated, computer 104 can return to box 310 to continue determining priority ranking. However, process 300 may end after an input or event occurs that terminates process 300, such as user 200, 202 stopping the operation of vehicle 102 (e.g., shutting off a propulsion system such as the engine), user providing input to end process 300, etc. If process 300 is to continue, the process returns to box 310. Otherwise, process 300 ends.

[0084] This disclosure has been described in an illustrative manner, and it should be understood that the terminology used is intended to be descriptive in nature and not restrictive. In light of the foregoing teachings, many modifications and variations of this disclosure are possible, and this disclosure may be practiced in ways other than those specifically described. The adjectives “first” and “second” are used throughout this document as identifiers and are not intended to indicate importance, order, or quantity. The use of “in response to,” “after determining…,” etc., indicates a causal relationship, not just a temporal one. The operations, systems, and methods described herein should always be implemented and / or performed in accordance with applicable user manuals and / or safety guidelines.

[0085] According to the present invention, a system is provided comprising a computer having a processor and a memory storing instructions executable by the processor to: determine the wake-up time of an occupant of the vehicle based on a destination and objects inside the vehicle; and actuate vehicle components to wake the occupant.

[0086] According to an embodiment, the instructions further include instructions for detecting the object by vehicle sensors.

[0087] According to an embodiment, the instructions further include instructions for determining the presence of the object inside the vehicle based on user input.

[0088] According to an embodiment, the instructions further include instructions for determining the wake-up time based on the classification of the object.

[0089] According to an embodiment, the instructions further include instructions for determining the wake-up time based on conditions specified by user input.

[0090] According to an embodiment, the instructions further include instructions for determining the wake-up time based on user input of a desired difference between the wake-up time and the estimated time to reach the destination.

[0091] According to an embodiment, the instructions further include instructions for determining the wake-up time based on conditions specified by user data obtained from the mobile device.

[0092] According to an embodiment, determining the wake-up time based on the destination includes predicting the time to arrive at the destination.

[0093] According to an embodiment, determining the wake-up time based on the destination includes predicting the time needed to prepare for arrival at the destination.

[0094] According to an embodiment, determining the wake-up time based on the destination includes determining the classification of the destination.

[0095] According to an embodiment, the occupant is one of a plurality of occupants in the vehicle, and the system further includes determining a second wake-up time for a second occupant in the vehicle.

[0096] According to an embodiment, the second wake-up time of the second occupant is based on a predicted second time of arrival at the second destination.

[0097] According to an embodiment, the instructions further include instructions for determining the wake-up time based on the number of occupants in the vehicle.

[0098] According to an embodiment, the wake-up time is also determined based on one or more of the occupant's status, traffic data, and historical data.

[0099] According to an embodiment, the wake-up time is also determined based on the occupant's sleep state.

[0100] According to an embodiment, a method includes: determining the wake-up time of an occupant of the vehicle based on a destination and objects inside the vehicle; and actuating vehicle components to wake the occupant.

[0101] In one aspect of the invention, the method includes determining the wake-up time based on the classification of the object.

[0102] In one aspect of the invention, the method includes determining the wake-up time based on user input of a desired difference between the wake-up time and an estimated time to reach the destination.

[0103] In one aspect of the invention, the method includes determining the wake-up time based on conditions specified by user data obtained from a mobile device.

[0104] In one aspect of the invention, the occupant is one of a plurality of occupants in the vehicle, and the method further includes determining a second wake-up time for a second occupant in the vehicle.

Claims

1. A method comprising: determining a wake-up time for an occupant of a vehicle based on a destination and an object in an interior of the vehicle; and actuating a vehicle component to wake up the occupant.

2. The method of claim 1, further comprising detecting the object by a vehicle sensor.

3. The method of claim 1, further comprising determining a presence of the object in the interior of the vehicle based on user input.

4. The method of claim 1, further comprising determining the wake-up time based on a classification of the object.

5. The method of claim 1, further comprising determining the wake-up time based on a condition specified by user input.

6. The method of claim 1, further comprising determining the wake-up time based on user input of a desired difference between the wake-up time and an estimated time of arrival at the destination.

7. The method of claim 1, further comprising determining the wake-up time based on a condition specified by user data obtained from a mobile device.

8. The method of claim 1, wherein determining the wake-up time based on the destination comprises predicting a time of arrival at the destination.

9. The method of claim 1, wherein determining the wake-up time based on the destination comprises predicting a time for preparing for arrival at the destination.

10. The method of claim 1, wherein determining the wake-up time based on the destination comprises determining a classification of the destination.

11. The method of claim 1, wherein the occupant is one of a plurality of occupants in the vehicle, the method further comprising determining a second wake-up time for a second occupant in the vehicle.

12. The method of claim 11, wherein the second wake-up time for the second occupant is based on a predicted second time of arrival at a second destination.

13. The method of claim 1, further comprising determining the wake-up time based on a number of occupants in the vehicle.

14. The method of claim 1, wherein the wake-up time is further determined based on a sleep state of the occupant.

15. A remote computer comprising a processor and a memory, the memory storing instructions executable by the processor to perform the method of any one of claims 1-14.

Images