Vehicle occupancy management system and method

Abstract

Example vehicle occupancy management systems and methods are described. In one embodiment, a method receives a current vehicle image, the current vehicle image representing a current interior of a vehicle. An occupancy management system detects at least one passenger in the vehicle and determines a seating position of the passenger based on the current vehicle image. A seating map is generated, the seating map identifying the seating positions of the passengers in the vehicle.

Description

Technical Field

[0001] The present disclosure relates to vehicle systems and, more particularly, to systems and methods for managing occupants in a vehicle. Background Art

[0002] Automobiles and other vehicles provide a large portion of transportation for businesses, governments, and private entities. Vehicles such as autonomous vehicles travel on roads, parking lots, and other areas when transporting passengers or objects from one location to another. An example application of an autonomous vehicle is to operate as a taxi or shuttle service that picks up one or more passengers in response to a transportation request. When operating as a taxi or shuttle service, the autonomous vehicle will drive to a pick-up location so that one or more passengers requesting service can enter the vehicle. The vehicle then drives to one or more destinations and allows one or more passengers to exit the vehicle.

[0003] When an autonomous vehicle operates as a taxi or shuttle service, it is important to manage the occupancy of the vehicle. For example, the autonomous vehicle needs to know which seats are occupied and how many additional passengers can enter the vehicle. Additionally, the autonomous vehicle may want to make sure the correct passengers exit the vehicle at the appropriate destination. Summary of the invention

[0004] A system receives a current vehicle image representing the current interior of the vehicle. Using the current vehicle image, the system detects at least one passenger in the vehicle. The system then determines the seating position of the passenger and generates a seat map identifying the seating position of the passenger in the vehicle. BRIEF DESCRIPTION OF THE DRAWINGS

[0005] Non-limiting and non-exhaustive embodiments of the present disclosure are described with reference to the following figures, wherein like reference numerals refer to like parts throughout the various figures unless otherwise specified.

[0006] Figure 1 is a block diagram illustrating an embodiment of a vehicle control system including an occupancy management system.

[0007] Figure 2 is a block diagram illustrating an embodiment of an occupancy management system.

[0008] Figure 3 An embodiment of a vehicle is shown having multiple interior cameras that capture different aspects of the interior of the vehicle.

[0009] Figure 4 An embodiment of a method for identifying passengers in a vehicle is shown.

[0010] Figure 5 An embodiment of a method for managing occupancy of a vehicle as passengers enter or exit the vehicle is shown.

[0011] Fig. 6A and Figure 6B Example images showing the interior of a vehicle with four passenger seating positions.

[0012] Fig. 7A and Figure 7B Example images showing the interior of a vehicle with five passengers.

[0013] Figure 8 Shows the situation after some passengers changed their seating positions. Fig. 7A and Figure 7B Example images of the interior of a vehicle are shown. DETAILED DESCRIPTION

[0014] In the following disclosure, reference is made to the accompanying drawings, which form a part of the present disclosure and in which specific embodiments in which the present disclosure can be practiced are shown by way of illustration. It should be understood that other embodiments may be utilized and structural changes may be made without departing from the scope of the present disclosure. References in the specification to "one embodiment", "an embodiment", "an example embodiment", etc. indicate that the embodiment described may include specific features, structures, or characteristics, but each embodiment may not necessarily include the specific features, structures, or characteristics. In addition, such phrases do not necessarily refer to the same embodiment. In addition, when specific features, structures, or characteristics are described in conjunction with an embodiment, whether or not explicitly described, it is within the knowledge of those skilled in the art to implement such features, structures, or characteristics in conjunction with other embodiments.

[0015] Implementations of the systems, devices, and methods disclosed herein may include or utilize a dedicated or general-purpose computer including computer hardware (such as, for example, one or more processors and system memory discussed herein). Embodiments within the scope of the present disclosure may also include physical and other computer-readable media for carrying or storing computer-executable instructions and / or data structures. Such computer-readable media may be any available media that can be accessed by a general-purpose or special-purpose computer system. A computer-readable medium that stores computer-executable instructions is a computer storage medium (device). A computer-readable medium that carries computer-executable instructions is a transmission medium. Therefore, by way of example and not limitation, embodiments of the present disclosure may include at least two distinct computer-readable media: a computer storage medium (device) and a transmission medium.

[0016] Computer storage media (devices) include RAM, ROM, EEPROM, CD-ROM, solid-state drives ("SSD") (e.g., RAM-based), flash memory, phase-change memory ("PCM"), other types of memory, other optical disk storage devices, magnetic disk storage devices or other magnetic storage devices, or any other medium that can be used to store desired program code devices in the form of computer-executable instructions or data structures and that can be accessed by a general or special purpose computer.

[0017] The embodiments of the device, system and method disclosed herein can communicate through a computer network. "Network" is defined as one or more data links capable of transmitting electronic data between a computer system and / or module and / or other electronic devices. When information is transmitted or provided to a computer through a network or another communication connection (hard wiring, wireless or a combination of hard wiring or wireless), the computer appropriately regards the connection as a transmission medium. The transmission medium may include a network and / or a data link, which may be used to carry the required program code means in the form of a computer executable instruction or a data structure and may be accessed by a general or special purpose computer. The above combination should also be included in the scope of a computer-readable medium.

[0018] Computer executable instructions include instructions and data that, when executed in a processor, cause a general purpose computer, a special purpose computer, or a special purpose processing device to perform a certain function or group of functions. Computer executable instructions can be, for example, binary files, intermediate format instructions (such as assembly language), or even source code. Although the subject matter is described in language specific to structural features and / or method actions, it should be understood that the subject matter defined in the appended claims is not necessarily limited to the features or actions described above. Rather, the features and actions are disclosed as exemplary forms of implementing the claims.

[0019] Those skilled in the art will appreciate that the present disclosure can be practiced in a network computing environment using many types of computer system configurations, including built-in vehicle computers, personal computers, desktop computers, laptop computers, message processors, handheld devices, multiprocessor systems, microprocessor-based or programmable consumer electronics, network PCs, minicomputers, mainframe computers, mobile phones, PDAs, tablet computers, pagers, routers, switches, various storage devices, and the like. The present disclosure can also be practiced in a distributed system environment, where local and remote computer systems linked (by hardwired data links, wireless data links, or by a combination of hardwired and wireless data links) through a network all perform tasks. In a distributed system environment, program modules can be located in both local and remote memory storage devices.

[0020] In addition, where appropriate, the functions described herein may be performed in one or more of the following: hardware, software, firmware, digital components, or analog components. For example, one or more application specific integrated circuits (ASICs) may be programmed to perform one or more of the systems and processes described herein. Certain terms are used throughout the description and claims to refer to specific system components. Those skilled in the art will appreciate that components may be referred to by different names. This document is not intended to distinguish between components that have different names but the same function.

[0021] It should be noted that the sensor embodiments discussed herein may include computer hardware, software, firmware, or any combination thereof to perform at least a portion of their functionality. For example, the sensor may include computer code configured to be executed in one or more processors, and may include hardware logic / circuitry controlled by the computer code. These exemplary devices are provided herein for illustrative purposes and are not intended to be limiting. As known to those skilled in the relevant art, the embodiments of the present disclosure may be implemented in other types of devices.

[0022] At least some embodiments of the present disclosure relate to computer program products that include such logic stored on any computer usable medium (eg, in software form). Such software, when executed in one or more data processing devices, causes the devices to operate as described herein.

[0023] Figure 1 is a block diagram illustrating an embodiment of a vehicle control system 100 within a vehicle including an occupancy management system 104. The automated driving / assistance system 102 may be used to automate or control the operation of the vehicle or provide assistance to a human driver. For example, the automated driving / assistance system 102 may control one or more of the vehicle's braking, steering, seat belt tensioning, acceleration, lights, alarms, driver notifications, radio, vehicle locks, or any other assistance systems. In another example, the automated driving / assistance system 102 may not be able to provide any control over driving (e.g., steering, acceleration, or braking), but may provide notifications and alarms to assist a human driver in driving safely. The vehicle control system 100 includes an occupancy management system 104 that interacts with various components in the vehicle to identify passengers and their seating positions in the vehicle. Although the occupancy management system 104 is Figure 1 104 is shown as a separate component, but in alternative embodiments, the occupancy management system 104 can be incorporated into the autonomous driving / assistance system 102 or any other vehicle component. The vehicle control system 100 can be used with any type of vehicle, such as a car, truck, bus, train, airplane, boat, etc.

[0024] The vehicle control system 100 also includes one or more sensor systems / devices for detecting the presence of nearby objects (or obstacles) or determining the position of the host vehicle (e.g., a vehicle including the vehicle control system 100). For example, the vehicle control system 100 may include one or more radar (radar detection and ranging) systems 106, one or more lidar (light detection and ranging) systems 108, one or more camera systems 110, a global positioning system (GPS) 112, and / or an ultrasound system 114. The one or more camera systems 110 may include a rear camera, a front camera, and a side camera mounted to the vehicle (e.g., the rear of the vehicle). As discussed herein, the camera system 110 may also include one or more interior cameras that capture images of passengers and other objects inside the vehicle. The lidar system 108 may include one or more interior lidar sensors that capture data associated with an interior area of ​​the vehicle. The vehicle control system 100 may include a data storage area 116 for storing relevant or useful data related to navigation and safety, such as map data, driving history, or other data. The vehicle control system 100 may also include a transceiver 118 for wirelessly communicating with mobile or wireless networks, other vehicles, infrastructure, or any other communication system.

[0025] The vehicle control system 100 may include vehicle control actuators 120 to control various aspects of vehicle driving (such as electric motors, switches or other actuators to control braking, acceleration, steering, seat belt tensioning, door locks, etc.). The vehicle control system 100 may also include one or more displays 122, speakers 124, or other devices so that notifications can be provided to a human driver or passenger. The display 122 may include a heads-up display, a dashboard display or indicator, a display screen, or any other visual indicator that can be seen by a driver or passenger of the vehicle. The speaker 124 may include one or more speakers of the vehicle's sound system, or may include a speaker dedicated to driver or passenger notifications.

[0026] It should be understood that Figure 1 The embodiments are given by way of example only. Other embodiments may include fewer or additional components without departing from the scope of the present disclosure. Additionally, the components shown may be combined or included in other components without limitation.

[0027] In one embodiment, the automatic driving / assistance system 102 is configured to control the driving or navigation of the host vehicle. For example, the automatic driving / assistance system 102 can control the vehicle control actuator 120 to drive along a certain path on a road, parking lot, lane, or other location. For example, the automatic driving / assistance system 102 can determine the path based on information or perception data provided by any one of the components 106-118. The path can also be determined based on the route of manipulating the vehicle to avoid or mitigate a potential collision with another vehicle or object. The sensor systems / devices 106-110 and 114 can be used to obtain real-time sensor data so that the automatic driving / assistance system 102 can assist the driver or drive the vehicle in real time.

[0028] In some embodiments, the vehicle control system 100 also includes one or more passenger input devices, such as a microphone, a touch screen display, buttons, etc. These passenger input devices allow passengers in the vehicle to provide input to the vehicle control system 100, such as answering questions, requesting information, requesting vehicle operations, etc.

[0029] Figure 2 is a block diagram illustrating an embodiment of an occupancy management system 104. Figure 2 As shown, the occupancy management system 104 includes a communication manager 202, a processor 204, and a memory 206. The communication manager 202 allows the occupancy control system 104 to communicate with other systems, such as the autonomous driving / assistance system 102. The processor 204 executes various instructions to implement the functions provided by the occupancy management system 104, as discussed herein. The memory 206 stores these instructions and other data used by the processor 204 and other modules and components included in the occupancy management system 104.

[0030] In addition, the occupancy management system 104 includes an image processing module 208 that receives current image data (e.g., image data representing the current interior of the vehicle) from one or more camera systems 110. In addition, the image processing module can receive image data representing the interior of the vehicle without any passengers (e.g., an empty vehicle image). As discussed herein, the current vehicle image and the empty vehicle image are used to identify passengers in the vehicle and passenger seating locations. In some embodiments, the image processing module 208 includes an image management algorithm or process that manages one or more empty vehicle images from multiple camera systems 110 located inside the vehicle. These same camera systems 110 are used to capture current vehicle images of the interior for comparison with the empty vehicle images. In some embodiments, in order to properly compare the images, the current vehicle image and the empty vehicle image are captured from approximately the same position or perspective.

[0031] The occupancy management system 104 also includes an image subtraction module 210 that subtracts the empty vehicle image from the current vehicle image to identify differences between the images. These differences may represent passengers sitting in specific locations within the vehicle. The image subtraction module 210 may use any of a variety of background subtraction algorithms, such as frame difference, weighted moving average, adaptive background learning, fuzzy Gaussian, Gaussian mixture model, multi-layer BGS, etc.

[0032] The passenger identification module 212 identifies one or more passengers in the vehicle based on, for example, the result of subtracting the empty vehicle image from the current vehicle image. Based on these results (e.g., the difference between the empty vehicle image and the current vehicle image), the passenger identification module 212 can identify the passenger's face or body and associate each identified passenger with a specific seat position in the vehicle. In addition, the passenger identification module 212 can identify specific features of each identified passenger, such as clothing, facial features, hair features, etc. These passenger features can be used to identify the same passenger if they move to a different seat in the vehicle.

[0033] In some embodiments, the facial recognition module 214 attempts to determine the identity of each passenger in the vehicle based on the current vehicle image. The facial recognition module 214 can access a database or other data storage mechanism to correlate the facial features of the current passenger with known individuals (e.g., previous passengers of the current vehicle or other vehicles). The facial recognition module 214 can use any of a variety of facial recognition algorithms, such as PCA (Principal Component Analysis), LDA (Linear Discriminant Analysis), ICA (Independent Component Analysis), EP (Evolutionary Pursuit), kernel-based, SVM (Support Vector Machine), CLM (Constrained Local Model), neural network, etc. If the facial recognition module 214 can determine the identity of one or more passengers in the vehicle, the vehicle control system 100 can adjust one or more vehicle settings or operating parameters (e.g., radio station preferences, temperature preferences, etc.) based on the known preferences of the passengers.

[0034] The occupancy management system 104 also includes a seat map generator 216 that creates a current seat map for the vehicle based on the current passengers in the vehicle. The seat map identifies the passengers in each seat position. The seat map also identifies the empty seats in the vehicle. The seat map generator 216 can also associate passenger identification information, passenger characteristics, and facial recognition identities with each passenger in the vehicle. For example, the passenger identification information, passenger characteristics, and facial recognition identities can be included as metadata associated with each seat position.

[0035] The estimation module 218 can determine (or estimate) other passenger characteristics, such as passenger height, passenger weight, passenger age, passenger gender, and passenger mood. In some embodiments, these additional passenger characteristics are useful for determining correct / safe airbag deployment, identifying the need for child seats or booster seats, etc. In a specific embodiment, one or more sensors in the vehicle seat can estimate the weight of the passenger. For example, the height of the passenger can be estimated based on the received image and the position of the top of the passenger's head relative to the known dimensions of the seat surface. In some embodiments, machine learning techniques such as neural networks can be used to estimate the passenger age, passenger gender, and passenger mood. The alarm generator 220 generates various alarms and warnings based on the activities and situations detected by the occupancy management system 104. For example, if a particular passenger attempts to leave the vehicle at the wrong destination, the alarm generator 220 can generate an audible or visual alarm to inform the passenger to stay in the vehicle. In addition, if one or more passengers are not wearing seat belts, the alarm generator 220 can generate an audible or visual alarm to inform the passengers to fasten their seat belts. Another type of alarm indicates whether the airbag has been enabled or disabled for a specific seating position. In some embodiments, a specific seating position in a vehicle may be reserved for a specific passenger (e.g., a passenger who plans to get on board). If other people attempt to sit in the reserved seating position, an alarm may indicate that the seating position has been reserved for other passengers.

[0036] Figure 3 An embodiment of a vehicle 300 is shown having multiple interior cameras that capture different aspects of the interior of the vehicle. Figure 3 As shown, vehicle 300 has four interior cameras 302, 304, 306, and 308. In some embodiments, cameras 302-308 are positioned and oriented in vehicle 300 so that all seating positions are within the field of view of at least one camera 302-308. Other areas inside vehicle 300 may also be within the field of view of one or more cameras 302-308. Cameras 302-308 may be any type of camera, such as RGB (red, green, and blue) cameras, IR (infrared) cameras, stereo cameras, etc. In some embodiments, other types of sensors (e.g., lidar system 108) may be used in place of cameras 302-308 or in combination with cameras to detect passengers and other items in vehicle 300.

[0037] In the configuration of vehicle 300, cameras 302 and 304 are positioned and oriented to capture images of seating positions in the front of vehicle 300. Additionally, cameras 306 and 308 are positioned and oriented to capture images of seating positions in the rear of vehicle 300. Figure 3Four interior cameras 302-308 are shown in FIG. 3 , but in alternative embodiments, vehicle 300 may have any number of interior cameras located at various locations throughout the vehicle and aimed at different angles. In some embodiments, cameras 302-308 may capture various types of images, such as RGB images, RGB-D images, IR images, thermal images, stereo images, etc.

[0038] Figure 4 An embodiment of a method 400 for identifying passengers in a vehicle is shown. Initially, an occupancy management system receives 402 one or more current images of the interior of a vehicle. The occupancy management system also accesses 404 one or more empty vehicle images associated with the interior of the vehicle. These empty vehicle images represent the interior of the vehicle when there are no passengers in the vehicle (i.e., the vehicle is empty). These empty vehicle images may also be referred to as reference images. In some embodiments, the perspective of each empty vehicle image (or the area captured in each empty vehicle image) is substantially the same as the perspective of each current image (or the area captured) of the interior of the vehicle. The occupancy management system may access the empty vehicle images from a storage device inside the vehicle or outside the vehicle.

[0039] The method 400 continues as the occupancy management system detects 406 a passenger in the vehicle based on the current vehicle image and the empty vehicle image. In some embodiments, the passenger is detected by subtracting the empty vehicle image from the current vehicle image to identify differences between the images. In other embodiments, a neural network or other technique may be used to detect the passenger. In particular embodiments, the occupancy management system analyzes the differences between the images to determine whether the differences represent a passenger. For example, if the differences represent a person's face or body, the occupancy management system may determine that the differences are passengers. As discussed in more detail below, the method may identify a region of interest within the empty vehicle image where a passenger is expected to be located. When analyzing the differences between the empty vehicle image and the current vehicle image, the method 400 may focus on the differences within the identified region of interest.

[0040] The occupancy management system then determines 408 a seating location for each passenger in the vehicle. The occupancy management system determines which seats in the vehicle are currently occupied by the passengers based on an analysis of the current vehicle image and the empty vehicle image. Based on the identified seating locations, the occupancy management system generates 410 a seat map associated with the vehicle. The seat map defines all seating locations in the vehicle and identifies the seating locations currently occupied by the passengers.

[0041] The method 400 continues as the occupancy management system attempts to determine the identity of each passenger in the vehicle by performing 412 facial recognition on the current vehicle image. In some embodiments, the facial recognition process may access a database or other data storage mechanism to correlate the facial features of the current passenger with known individuals (e.g., previous passengers of the current vehicle or other vehicles). In other embodiments, any other type of identification system or recognition system may be used to determine the identity of a particular passenger.

[0042] If facial recognition fails to successfully determine the identity of a particular passenger, the occupancy management system identifies the characteristics of the particular passenger (414). Passenger characteristics may include, for example, clothing, facial features, hair features, etc. In this case, the passenger may be identified by a unique tag (e.g., Passenger A, Passenger B, or Passenger 4) to distinguish the passenger from other passengers. The tag may be used during the passenger's current ride and then discarded after the passenger arrives at the destination. By providing each passenger with a unique tag, the systems and methods described herein are able to track the location of each passenger within the vehicle (e.g., tracking the current seating location of each passenger). In some embodiments, these passenger characteristics (and tags) may be used to identify the same passenger if they are moved to different seats in the vehicle. In addition, the passenger characteristics may be used to distinguish one passenger from another even if the passenger's actual identity is unknown.

[0043] If the identity of the passenger is detected using facial recognition, then that identity information is associated with the seating map 416. For example, if the identity of a particular user is determined, then that identity information is associated with the particular seating location that the passenger is currently in. Similarly, any passenger characteristics are associated with the seating map 416 so that those characteristics are associated with the particular seating location that the passenger is currently in.

[0044] The method 400 continues as the occupancy management system estimates 418 the age, weight, height, gender, and / or mood of each passenger in the vehicle. The occupancy management system adjusts 420 one or more vehicle settings or operating parameters based on the identified passengers. For example, the vehicle settings or operating parameters may include radio station settings, temperature settings, autonomous driving characteristics (slow / smooth driving or faster driving and faster turns). In some embodiments, these vehicle settings or operating parameters may be determined based on known preferences of the passenger identified in a passenger profile or other passenger data settings.

[0045] In some embodiments, the occupancy management system is aware of a number of available seating locations within a particular vehicle. After the occupancy management system determines the number of current passengers in the vehicle, the occupancy management system can determine the number of available seats in the vehicle. Based on the number of available seats, the occupancy management system can determine whether the vehicle can accommodate additional passengers.

[0046] In some embodiments, one or more sensors in the vehicle seating surface can confirm the presence of a passenger in a particular seating position. For example, if the weight detected by the sensors in the seating surface is consistent with the weight of the passenger, then this data can confirm (based on the current image of the vehicle interior) a determination that a particular seating position is occupied by a passenger.

[0047] Figure 5 An embodiment of a method 500 for managing occupancy of a vehicle as passengers enter or exit the vehicle is shown. Initially, the vehicle stops 502 to allow one or more passengers to enter and / or exit the vehicle. The occupancy management system identifies 504 the current vehicle passengers (after the passengers have completed entering and / or exiting the vehicle). In some embodiments, the occupancy management system generates 506 an updated seat map based on the current vehicle passengers. For example, passengers who have exited the vehicle are deleted from the previous seat map, and passengers who have entered the vehicle are added to the seat map. In addition, the seat map is updated for passengers who have changed their seating positions in the vehicle. In some embodiments, the seat map is updated with respect to the seat map described herein. Figure 4 The discussed approach is to identify and analyze new passengers entering a vehicle.

[0048] The method 500 continues as the occupancy management system determines 508 whether the correct passenger has left the vehicle. In some embodiments, the occupancy management system maintains a list of destinations for each passenger in the vehicle. If the correct passenger has left 510 the vehicle, no action is required. However, if one or more passengers accidentally leave 510 the vehicle at the wrong destination, the occupancy management system generates 512 an alarm indicating that the passenger has mistakenly left the vehicle at the wrong location. The alarm may be an audible alarm, a visual alarm, a tactile alarm, or any other type of alarm. In some cases, the vehicle may not be allowed to start driving again until the wrong passenger re-enters the vehicle or indicates that the vehicle can continue without them. If the vehicle is not allowed 514 to continue, the method 500 returns to 504 and waits until the wrong passenger has re-entered the vehicle. When the vehicle is allowed 514 to continue, the method 500 adjusts 516 one or more vehicle settings or operating parameters based on the currently identified passengers. For example, after one or more passengers leave the vehicle and / or enter the vehicle, the preferences of the passengers may change.

[0049] Fig. 6A and Figure 6B Example images showing the interior of a vehicle with four passenger seating positions. Fig. 6A An empty vehicle interior 600 is shown with four passenger seating positions. Figure 6BThe same vehicle interior 600 is shown with four passenger seating positions identified by bounding boxes 602-608. As used herein, bounding boxes may also be referred to as regions of interest. Specifically, first bounding box 602 identifies a first passenger seating position, second bounding box 602 identifies a second passenger seating position, third bounding box 606 identifies a third passenger seating position, and fourth bounding box 608 identifies a fourth passenger seating position. In some embodiments, the bounding boxes are defined by an operator for a particular vehicle. Where a particular vehicle model has consistent seating arrangements and consistent camera positions, the same bounding boxes may be used for all production vehicles of that model or for all vehicles in an autonomous fleet. In particular embodiments, an automated process is used to define the bounding boxes for a particular vehicle by identifying seating surfaces within the vehicle. An image (such as Fig. 6A and Figure 6B The image shown in ) represents an example of an empty vehicle image as discussed in this article.

[0050] Fig. 7A and Figure 7B Example images showing the interior of a vehicle with five passengers. Fig. 7A The current vehicle interior 700 is shown in which five passengers occupy the vehicle. The faces of the five passengers are identified with bounding boxes or regions of interest. Specifically, a first bounding box 702 identifies the first passenger, a second bounding box 704 identifies the second passenger, a third bounding box 706 identifies the third passenger, a fourth bounding box 708 identifies the fourth passenger, and a fifth bounding box 710 identifies the fifth passenger.

[0051] Figure 7B The same current vehicle interior 700 is shown with five passengers identified by bounding boxes 702-710. Figure 7BAs shown, a passenger identity or passenger label is associated with each bounding box 702-710. If the identity of the passenger is determined (e.g., using facial recognition), the name of the passenger is associated with the appropriate bounding box. If the identity of the passenger is not determined, a label is created and associated with the appropriate bounding box. For example, the first passenger identified by bounding box 702 is labeled as "Occupant A" and associated with seat #2 in the vehicle. The second passenger identified by bounding box 704 is labeled as "Robert" (e.g., facial recognition identifies the passenger as Robert) and associated with seat #5 in the vehicle. The third passenger identified by bounding box 706 is labeled as "Occupant C" and associated with seat #4 in the vehicle. The fourth passenger identified by bounding box 708 is labeled as "Occupant B" and associated with seat #3 in the vehicle. The fourth passenger identified by bounding box 710 is labeled as "James" and associated with seat #1 in the vehicle. As discussed herein, information about occupied vehicle seats and the identities (or labels) associated with each passenger can be associated with a seat map generated for the vehicle.

[0052] Figure 8 Shown after some passengers changed their seating positions, Fig. 7A and Figure 7B Example images of the interior of a vehicle are shown. Figure 8 A current vehicle interior 800 is shown in which five passengers occupy the vehicle. The current vehicle interior 800 is the same vehicle as shown in the current vehicle 700, but after the vehicle has stopped to allow passengers to enter or exit the vehicle. As shown in the current vehicle interior space 800, James and Robert have changed seating positions, occupant C remains in the same seating position, occupant B has moved to a different seating position, occupant A has exited the vehicle, and occupant D has entered the vehicle. As discussed herein, the seat map generated for the vehicle is updated to include current information about occupied vehicle seats and the identities (or tags) associated with each passenger.

[0053] Although various embodiments of the present disclosure have been described herein, it should be understood that these embodiments are presented by way of example only and not by way of limitation. It should be understood by those skilled in the relevant art that various changes in form and detail may be made without departing from the spirit and scope of the present disclosure. Therefore, the breadth and scope of the present disclosure should not be limited by any of the exemplary embodiments described, but should only be limited according to the attached claims and their equivalents. This description is presented for illustration and description purposes. It is not exhaustive or does not limit the present disclosure to the precise form disclosed. In view of the disclosed teachings, many modifications and variations are possible. In addition, it should be noted that any or all of the alternative embodiments discussed herein may be used in any desired combination to form additional hybrid embodiments of the present disclosure.

Claims

1. A method comprising: receiving a current vehicle image, the current vehicle image representing a current interior of the vehicle; detecting, by an occupancy management system, at least one occupant in the vehicle based on the current vehicle image; determining, by the occupancy management system, a seating position of the passenger; as well as generating, by the occupancy management system, a seat map identifying the seating positions of the passengers in the vehicle; Also comprising accessing an empty vehicle image representing the interior of the vehicle without any occupants, wherein detecting at least one occupant in the vehicle comprises comparing the empty vehicle image to the current vehicle image; Comparing the empty vehicle image with the current vehicle image includes subtracting the empty vehicle image from the current vehicle image using any one of background subtraction algorithms including frame difference, weighted moving average, adaptive background learning, fuzzy Gaussian, Gaussian mixture model, and multi-layer BGS, and identifying passenger faces or bodies based on the comparison and associating each identified passenger with a specific seat position in the vehicle. 2 . The method of claim 1 , further comprising determining the identity of the passenger by performing facial recognition using the current vehicle image.

3. The method of claim 2, further comprising adjusting at least one vehicle setting or operating parameter based on the identity of the passenger. The method of claim 1 , further comprising identifying characteristics of the passenger based on the current vehicle image.

5. The method of claim 4, wherein the characteristics of the passenger include at least one of passenger clothing, passenger facial features, and passenger hair features.

6. The method of claim 4, wherein the characteristics of the passenger are associated with the seat map.

7. The method of claim 1, further comprising estimating at least one of the age, weight, height, gender, and mood of the passenger.

8. The method of claim 1, further comprising: detecting a plurality of passengers in the vehicle based on the current vehicle image; determining, by the occupancy management system, a seating position of each of the plurality of passengers; as well as A seating map is generated by the occupancy management system, the seating map identifying the seating position of each of the plurality of passengers in the vehicle.

9. The method of claim 1, wherein the vehicle is an autonomous vehicle.

10. A method comprising: receiving a current vehicle image, the current vehicle image representing a current interior of the vehicle; detecting, by an occupancy management system, a plurality of occupants in the vehicle based on the current vehicle image; determining, by the occupancy management system, a seating position of each of the plurality of passengers; determining, by the occupancy management system, an identity of each of the plurality of passengers by performing facial recognition using the current vehicle image; as well as generating, by the occupancy management system, a seating map including the identity and the seating position of each of the plurality of passengers in the vehicle; Also included is accessing an empty vehicle image representing the interior of the vehicle without any passengers, wherein detecting a plurality of passengers in the vehicle includes comparing the empty vehicle image to the current vehicle image; Comparing the empty vehicle image with the current vehicle image includes subtracting the empty vehicle image from the current vehicle image using any one of background subtraction algorithms including frame difference, weighted moving average, adaptive background learning, fuzzy Gaussian, Gaussian mixture model, and multi-layer BGS, and identifying passenger faces or bodies based on the comparison and associating each identified passenger with a specific seat position in the vehicle.

11. The method of claim 10, further comprising adjusting at least one vehicle setting or operating parameter based on the identity of at least one of the plurality of passengers. 12 . The method of claim 10 , further comprising identifying characteristics of at least one of the plurality of passengers based on the current vehicle image.

13. The method of claim 12, wherein the characteristics of the passenger include at least one of passenger clothing, passenger facial features, and passenger hair features.

14. A device comprising: a communications manager configured to receive a current vehicle image representing a current interior of a vehicle; an image processing module, the image processing module being configured to detect a passenger in the vehicle based on the current vehicle image, wherein the image processing module is further configured to determine a seating position of the passenger; as well as a seat map generator configured to generate a seat map identifying the seating positions of the passengers in the vehicle; wherein the image processing module is further configured to access an empty vehicle image representing the interior of the vehicle without any passengers, wherein the image processing module detects passengers in the vehicle by comparing the empty vehicle image with the current vehicle image; Comparing the empty vehicle image with the current vehicle image includes subtracting the empty vehicle image from the current vehicle image using any one of background subtraction algorithms including frame difference, weighted moving average, adaptive background learning, fuzzy Gaussian, Gaussian mixture model, and multi-layer BGS, and identifying passenger faces or bodies based on the comparison and associating each identified passenger with a specific seat position in the vehicle.

15. The apparatus of claim 14, further comprising a facial recognition module configured to determine the identity of the passenger.

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