Patent Publication Number: US-2022219734-A1

Title: Estimating trip duration based on vehicle reroute probabilities

Description:
CROSS-REFERENCE TO RELATED APPLICATIONS 
     This application is a continuation of U.S. application Ser. No. 16/880,522, filed on May 21, 2020, entitled, ESTIMATING TRIP DURATION BASED ON VEHICLE REROUTE PROBABILITIES, which is hereby expressly incorporated by reference in its entirety and for all purposes. 
    
    
     BACKGROUND 
     1. Technical Field 
     The subject technology provides solutions for resolving driving route duration estimates and in particular, for accurately computing estimated time of arrival (ETA) figures based on reroute likelihoods for an autonomous vehicle (AV). 
     2. Introduction 
     Autonomous vehicles (AVs) are vehicles having computers and control systems that perform driving and navigation tasks that are conventionally performed by a human driver. As AV technologies continue to advance, ride-sharing services will increasingly utilize AVs to improve service efficiency and safety. However, for effective use in ride-sharing deployments, AVs will be required to perform many of the functions that are conventionally performed by human drivers, such as performing navigation and routing tasks necessary to provide a safe and efficient ride service. Such tasks may require the collection and processing of large quantities of data using various sensor types, including but not limited to cameras and/or Light Detection and Ranging (LiDAR) sensors disposed on the AV. 
    
    
     
       BRIEF DESCRIPTION OF THE DRAWINGS 
       Certain features of the subject technology are set forth in the appended claims. However, the accompanying drawings, which are included to provide further understanding, illustrate disclosed aspects and together with the description serve to explain the principles of the subject technology. In the drawings: 
         FIG. 1  illustrates an example of an autonomous vehicle route between a first map location and a second map location, according to some aspects of the disclosed technology. 
         FIG. 2  illustrates a conceptual block diagram of a decision process for calculating an estimated time of arrival (ETA) for an AV route, according to some aspects of the disclosed technology. 
         FIG. 3  illustrates steps of an example process for calculating an AV route ETA, according to some aspects of the disclosed technology. 
         FIG. 4  illustrates an example system environment that can be used to facilitate AV dispatch and operations, according to some aspects of the disclosed technology. 
         FIG. 5  illustrates an example processor-based system with which some aspects of the subject technology can be implemented. 
     
    
    
     DETAILED DESCRIPTION 
     The detailed description set forth below is intended as a description of various configurations of the subject technology and is not intended to represent the only configurations in which the subject technology can be practiced. The appended drawings are incorporated herein and constitute a part of the detailed description. The detailed description includes specific details for the purpose of providing a more thorough understanding of the subject technology. However, it will be clear and apparent that the subject technology is not limited to the specific details set forth herein and may be practiced without these details. In some instances, structures and components are shown in block diagram form in order to avoid obscuring the concepts of the subject technology. 
     As described herein, one aspect of the present technology is the gathering and use of data available from various sources to improve quality and experience. The present disclosure contemplates that in some instances, this gathered data may include personal information. The present disclosure contemplates that the entities involved with such personal information respect and value privacy policies and practices. 
     Autonomous vehicle (AV) maneuvers are only executed when surrounding conditions are deemed to be safe. For this reason, many maneuvers that are easily performed by human drivers cannot be executed by an AV under similar conditions. Such limitations can lead to frequent re-routing, wherein the AV may select a longer or more congested path-to-destination to avoid the need to perform certain driving maneuvers. When following a pre-specified course, re-routes can cause deviations in estimated time of arrival (ETA) figures. As used herein, “maneuver” can refer to virtually any AV operation performed in the service of driving and/or navigation functions. By way of example, AV maneuvers can include, but are not limited to: turns, u-turns, lane changes, unprotected maneuvers (e.g., unprotected left-turns in an intersection), accelerating, decelerating, stopping, creeping, and the like. 
     Aspects of the disclosed technology address the foregoing limitations by providing solutions for accurately modeling trip duration estimates based on a likelihood of modifications to an initial route plan (i.e., based on a reroute likelihood due to maneuver failure event). Different factors can contribute to the reroute likelihood, including but not limited to: traffic conditions, environmental conditions, driving maneuver complexity required for the initial route plan, AV routing and navigation capabilities, etc. As discussed in further detail below, these various conditions can be subject to a variety of other variables, including but not limited to: time-of-day, season, weather conditions, existence of map priors, statistical confidence in map priors, and/or AV software version, etc. 
     In some aspects, ETA can be based on an initial estimate associated with an initial route between a first map location and a second map location. The initial estimate can then be modified (increased or decreased) based on reroute likelihoods for one or more junctures along the initial route. For example, reroute likelihoods can be computed for every intersection along the initial route and used to inform the resulting ETA. In some aspects, reroute likelihoods may be based on AV lane position, for example, that can correspond with a lane identifier (lane ID) that identifies lane position or ordering for various route segments in a map database. 
     Although different computation methods may be used, depending on the desired implementation, in some aspects the resulting ETA is a weighted sum of ETAs for various routes to the destination map location i.e., the second map location. Details of ETA computations of the disclosed technology are discussed in further detail below. 
       FIG. 1  illustrates an example of AV routes  100  between a first map location  104  and a second map location  106 , according to some aspects of the disclosed technology. In the example illustrated in  FIG. 1 , AV  102  starting at first map location  104  plots an initial route  108 , which includes route path segments  108 A and  108 B, to second map destination  106 . On initial route  108 , a left-turn maneuver is required at juncture  109 , following route segment  108 B. In this example, failure to complete the left-turn maneuver at juncture  109  may result in a need for AV  102  to reroute to second map location  106  via re-route path  110 . It is understood that routes  108 ,  110  illustrate a simplified example of how different route paths may be used to reach an intended map destination (e.g., second map location  106 ), however, additional route-paths, as well as additional junctures/maneuvers are contemplated, without departing from the scope of the disclosed technology. 
     In practice, a reroute likelihood can be calculated at each juncture. In the illustrated example, the left-turn maneuver at juncture  109  has a determined success rate of 75%, indicating that there is a 25% probability that AV  102  will divert to re-route path  110  to reach second map location  106 . In some approaches, the re-route likelihood is paired with a specific maneuver type (e.g., a left-turn, unprotected left-turn, turnabout, etc.). That is, the re-route likelihood can be based on a probability of maneuver success (or failure). The re-route likelihood can also be associated with a specific map location. For example, the re-route likelihood can be based on aggregated AV maneuver data indicating a history of successful maneuver completion at a specific location (map juncture). The reroute likelihood can also be based on map prior information that includes environmental features for a specific intersection. That is, maneuver failure probabilities can be estimated based on map prior information for similar map junctures (e.g., intersections) even if historic maneuver performance data is not available for a specific juncture. In this manner, reroute probabilities can be used to determine accurate ETAs even for routes that traverse map areas with sparse data. Additionally, as mentioned above, re-route likelihoods may also be based on an identified lane position (e.g., corresponding with a lane ID) of the AV. For example, the success/failure probability of the AV can be based on lane position before execution of a specific maneuver is required. 
     By way of example, in the map configuration depicted by  FIG. 1 , let it be assumed that the trip duration estimate (ETA) for route segment  108 A, prior to the maneuver at juncture  109 , is 10 minutes, and the ETA for route segment  108 B following the maneuver (if successful), is 5 minutes. Additionally, let it be assumed that the ETA for route segment  110  (following a failed maneuver at juncture  109 ) is 10 minutes. In this example the total estimated duration (total ETA) is based on the reroute (maneuver failure) likelihood (25%), as well as the ETAs for each of the three trip segments. That is, the total ETA can be computed as a weighted sum: 
       ETA Total =10 min+ P   success (5 min)+ P   Fail (10 min) 
     where P success =75% and P Fail =25%. Therefore, the total ETA (ETA Total ) may be estimated to be approximately 17.5 min. It is understood that, in some implementations, the total ETA computation may include additional terms, for example, corresponding to additional possible maneuver outcomes, and/or to additional maneuvers along each segment following an initial maneuver. For a maneuver with N outcomes, the computation can be given by: 
       ETA Total =ETA Prior   +P   1 (ETA 1 )+ P   2 (ETA 2 )+ . . . + P   N (ETA N ) 
     and, in the case of additional maneuvers along any given segment, the term ETA i  is replaced by the weighted sum for the following maneuver along that segment (which in turn comprises all following maneuver sums), where ETA Prior  is the duration from the prior maneuver until the current maneuver. 
     It is understood that route failure probabilities for one or more routes can be computed in real-time or near real time. Depending on the desired implementation, such computations may be wholly or partially performed by one or more computing devices in the AV and/or by various remote computing systems, such as one or more cloud compute nodes. 
       FIG. 2  illustrates a conceptual block diagram of a process  200  for calculating an estimated time of arrival (ETA) for an AV route, according to some aspects of the disclosed technology. Process  200  begins with block  202  in which an AV trip request is originated, for example, by a user/rider of an AV ride-service. As discussed in further detail with respect to  FIG. 4 , the trip request may be initiated using a smartphone or other processor-based communication device. 
     In response to AV trip request  202 , a route is generated, for example, from a current location of the AV (e.g., a first map location), to a destination specified by trip request  202  (e.g., a second map location). In decision block  206 , a maneuver failure probability for the route generated at block  204  is analyzed. In some aspects, it can be determined if the likelihood of maneuver failure is higher than a predetermined threshold. For example, if the initial route generated in block  204  is determined to contain one or more maneuvers that have an unacceptably high failure probability, then a new route may be determined that eliminates one or more of the problematic maneuvers. In such aspects, process  200  advances to block  208 , in which maneuver failure is assumed, and then to block  210  in which a new route is resolved. Process  200  then reverts to block  204 . 
     Alternatively, if at decision block  206  it is determined that maneuver failure probabilities for the generated route ( 204 ) are acceptable, then process  200  proceeds to step  212  in which a set of probable routes are generated. In some aspects, the probable routes may include one or more re-route alternatives to the destination (e.g., second map location), that have been previously traversed by one or more AVs, for example, in an AV fleet. In some aspects, re-route paths may be identified using maneuver probabilities that are based on feature similarity. By way of example, if a mix of features such as road configuration, traffic lights, and/or traffic patterns, etc. for an unknown intersection are similar to those of a known intersection, then maneuver success likelihoods, and therefore re-route probabilities, may be assessed accordingly. 
     At block  214 , a decision tree is generated based on maneuver success statistics. That is, one or more re-route alternatives are identified based on maneuver success probabilities at different map junctures. At block  216 , trip duration (ETA) estimates are calculated for each possible re-route. Finally, at block  218 , the trip ETA is calculated based on maneuver success statistics ( 214 ) associated with each route alternative (reroutes  216 ). As discussed above, the calculated trip ETA can be a sum of route durations weighted by the probability of each. 
       FIG. 3  illustrates steps of an example process  300  for calculating an AV route ETA, according to some aspects of the disclosed technology. Process  300  begins with step  302  in which a route between a first map location and a second map location are identified. In some approaches, the first map location can represent a current or planned departure location of an AV, whereas the second map location can represent a destination of the AV, such as the indicated pick-up/drop-off location of a user/rider. 
     In step  304 , the likelihood of a reroute is determined with respect to at least one AV maneuver to be performed along the initial route. Further to the example of  FIG. 1 , the re-route probability can equal the failure probability of AV execution of one or more driving maneuvers, such as an unprotected left turn, along the initial route. Maneuver failure probabilities may be determined from map priors (i.e., from historic maneuver completions statistics previously completed by one or more AVs). In some approaches, maneuver failure probabilities can be inferred from AV software version information. For example, software updates may improve AV maneuver performance, thereby lowering maneuver failure rates. 
     In step  306 , an estimated time of arrival (ETA) figure is calculated based on reroute likelihoods associated with the AV maneuver. As discussed above with respect to  FIGS. 1 and 2 , the final ETA figure can be a weighted sum of ETAs and corresponding reroute (maneuver fail) probabilities associated with each alternative route path. 
     In optional step  308 , a map database can be updated based on the success (or failure) of one or more AV maneuvers on the route. By way of example, if an AV successfully executes a maneuver, the maneuver failure probability for that particular maneuver (at that juncture/map location) may be decreased. Alternatively, if the AV fails to execute a maneuver at a particular map location or juncture, then the failure probability may be increased. 
       FIG. 4  illustrates an example system environment that can be used to facilitate AV dispatch and operations, according to some aspects of the disclosed technology. Autonomous vehicle  402  can navigate about roadways without a human driver based upon sensor signals output by sensor systems  404 - 406  of autonomous vehicle  402 . Autonomous vehicle  402  includes a plurality of sensor systems  404 - 406  (a first sensor system  104  through an Nth sensor system  106 ). Sensor systems  404 - 406  are of different types and are arranged about the autonomous vehicle  402 . For example, first sensor system  404  may be a camera sensor system and the Nth sensor system  406  may be a Light Detection and Ranging (LIDAR) sensor system. Other exemplary sensor systems include radio detection and ranging (RADAR) sensor systems, Electromagnetic Detection and Ranging (EmDAR) sensor systems, Sound Navigation and Ranging (SONAR) sensor systems, Sound Detection and Ranging (SODAR) sensor systems, Global Navigation Satellite System (GNSS) receiver systems such as Global Positioning System (GPS) receiver systems, accelerometers, gyroscopes, inertial measurement units (IMU), infrared sensor systems, laser rangefinder systems, ultrasonic sensor systems, infrasonic sensor systems, microphones, or a combination thereof. While four sensors  480  are illustrated coupled to the autonomous vehicle  402 , it is understood that more or fewer sensors may be coupled to the autonomous vehicle  402 . 
     Autonomous vehicle  402  further includes several mechanical systems that are used to effectuate appropriate motion of the autonomous vehicle  402 . For instance, the mechanical systems can include but are not limited to, vehicle propulsion system  430 , braking system  432 , and steering system  434 . Vehicle propulsion system  430  may include an electric motor, an internal combustion engine, or both. The braking system  432  can include an engine brake, brake pads, actuators, and/or any other suitable componentry that is configured to assist in decelerating autonomous vehicle  402 . In some cases, braking system  432  may charge a battery of the vehicle through regenerative braking. Steering system  434  includes suitable componentry that is configured to control the direction of movement of the autonomous vehicle  402  during navigation. Autonomous vehicle  402  further includes a safety system  436  that can include various lights and signal indicators, parking brake, airbags, etc. Autonomous vehicle  402  further includes a cabin system  438  that can include cabin temperature control systems, in-cabin entertainment systems, etc. 
     Autonomous vehicle  402  additionally comprises an internal computing system  410  that is in communication with sensor systems  480  and systems  430 ,  432 ,  434 ,  436 , and  438 . Internal computing system  410  includes at least one processor and at least one memory having computer-executable instructions that are executed by the processor. The computer-executable instructions can make up one or more services responsible for controlling autonomous vehicle  402 , communicating with remote computing system  450 , receiving inputs from passengers or human co-pilots, logging metrics regarding data collected by sensor systems  480  and human co-pilots, etc. 
     Internal computing system  410  can include a control service  412  that is configured to control operation of vehicle propulsion system  430 , braking system  208 , steering system  434 , safety system  436 , and cabin system  438 . Control service  412  receives sensor signals from sensor systems  480  as well communicates with other services of internal computing system  410  to effectuate operation of autonomous vehicle  402 . In some embodiments, control service  412  may carry out operations in concert one or more other systems of autonomous vehicle  402 . Internal computing system  410  can also include constraint service  414  to facilitate safe propulsion of autonomous vehicle  402 . Constraint service  416  includes instructions for activating a constraint based on a rule-based restriction upon operation of autonomous vehicle  402 . For example, the constraint may be a restriction upon navigation that is activated in accordance with protocols configured to avoid occupying the same space as other objects, abide by traffic laws, circumvent avoidance areas, etc. In some embodiments, the constraint service can be part of control service  412 . 
     The internal computing system  410  can also include communication service  416 . The communication service  416  can include both software and hardware elements for transmitting and receiving signals from/to the remote computing system  450 . Communication service  416  is configured to transmit information wirelessly over a network, for example, through an antenna array that provides personal cellular (long-term evolution (LTE), 3G, 4G, 5G, etc.) communication. 
     Internal computing system  410  can also include latency service  418 . Latency service  418  can utilize timestamps on communications to and from remote computing system  450  to determine if a communication has been received from the remote computing system  450  in time to be useful. For example, when a service of the internal computing system  410  requests feedback from remote computing system  450  on a time-sensitive process, the latency service  418  can determine if a response was timely received from remote computing system  450  as information can quickly become too stale to be actionable. When the latency service  418  determines that a response has not been received within a threshold, latency service  418  can enable other systems of autonomous vehicle  402  or a passenger to make necessary decisions or to provide the needed feedback. 
     Internal computing system  410  can also include a user interface service  420  that can communicate with cabin system  438  in order to provide information or receive information to a human co-pilot or human passenger. In some embodiments, a human co-pilot or human passenger may be required to evaluate and override a constraint from constraint service  414 , or the human co-pilot or human passenger may wish to provide an instruction to the autonomous vehicle  402  regarding destinations, requested routes, or other requested operations. 
     As described above, the remote computing system  450  is configured to send/receive a signal from the autonomous vehicle  440  regarding reporting data for training and evaluating machine learning algorithms, requesting assistance from remote computing system  450  or a human operator via the remote computing system  450 , software service updates, rideshare pickup and drop off instructions, etc. 
     Remote computing system  450  includes an analysis service  452  that is configured to receive data from autonomous vehicle  402  and analyze the data to train or evaluate machine learning algorithms for operating the autonomous vehicle  402 . The analysis service  452  can also perform analysis pertaining to data associated with one or more errors or constraints reported by autonomous vehicle  402 . Remote computing system  450  can also include a user interface service  454  configured to present metrics, video, pictures, sounds reported from the autonomous vehicle  402  to an operator of remote computing system  450 . User interface service  454  can further receive input instructions from an operator that can be sent to the autonomous vehicle  402 . 
     Remote computing system  450  can also include an instruction service  456  for sending instructions regarding the operation of the autonomous vehicle  402 . For example, in response to an output of the analysis service  452  or user interface service  454 , instructions service  456  can prepare instructions to one or more services of the autonomous vehicle  402  or a co-pilot or passenger of the autonomous vehicle  402 . Remote computing system  450  can also include rideshare service  458  configured to interact with ridesharing applications  470  operating on (potential) passenger computing devices. The rideshare service  458  can receive requests to be picked up or dropped off from passenger ridesharing app  470  and can dispatch autonomous vehicle  402  for the trip. The rideshare service  458  can also act as an intermediary between the ridesharing app  470  and the autonomous vehicle wherein a passenger might provide instructions to the autonomous vehicle to  402  go around an obstacle, change routes, honk the horn, etc. Remote computing system  450  can, in some cases, include at least one computing system  450  as illustrated in or discussed with respect to  FIG. 5 , or may include at least a subset of the components illustrated in  FIG. 5  or discussed with respect to computing system  450 . 
       FIG. 5  illustrates an example processor-based system with which some aspects of the subject technology can be implemented. For example, processor-based system  500  that can be any computing device making up internal computing system  410 , remote computing system  450 , a passenger device executing the rideshare app  470 , internal computing device  430 , or any component thereof in which the components of the system are in communication with each other using connection  505 . Connection  505  can be a physical connection via a bus, or a direct connection into processor  510 , such as in a chipset architecture. Connection  505  can also be a virtual connection, networked connection, or logical connection. 
     In some embodiments, computing system  500  is a distributed system in which the functions described in this disclosure can be distributed within a datacenter, multiple data centers, a peer network, etc. In some embodiments, one or more of the described system components represents many such components each performing some or all of the function for which the component is described. In some embodiments, the components can be physical or virtual devices. 
     Example system  500  includes at least one processing unit (CPU or processor)  510  and connection  505  that couples various system components including system memory  515 , such as read-only memory (ROM)  520  and random-access memory (RAM)  525  to processor  510 . Computing system  500  can include a cache of high-speed memory  512  connected directly with, in close proximity to, and/or integrated as part of processor  510 . 
     Processor  510  can include any general-purpose processor and a hardware service or software service, such as services  532 ,  534 , and  536  stored in storage device  530 , configured to control processor  510  as well as a special-purpose processor where software instructions are incorporated into the actual processor design. Processor  510  may essentially be a completely self-contained computing system, containing multiple cores or processors, a bus, memory controller, cache, etc. A multi-core processor may be symmetric or asymmetric. 
     To enable user interaction, computing system  500  includes an input device  545 , which can represent any number of input mechanisms, such as a microphone for speech, a touch-sensitive screen for gesture or graphical input, keyboard, mouse, motion input, speech, etc. Computing system  500  can also include output device  535 , which can be one or more of a number of output mechanisms known to those of skill in the art. In some instances, multimodal systems can enable a user to provide multiple types of input/output to communicate with computing system  500 . Computing system  500  can include communications interface  540 , which can generally govern and manage the user input and system output. The communication interface may perform or facilitate receipt and/or transmission wired or wireless communications via wired and/or wireless transceivers, including those making use of an audio jack/plug, a microphone jack/plug, a universal serial bus (USB) port/plug, an Apple® Lightning® port/plug, an Ethernet port/plug, a fiber optic port/plug, a proprietary wired port/plug, a BLUETOOTH® wireless signal transfer, a BLUETOOTH® low energy (BLE) wireless signal transfer, an IBEACON® wireless signal transfer, a radio-frequency identification (RFID) wireless signal transfer, near-field communications (NFC) wireless signal transfer, dedicated short range communication (DSRC) wireless signal transfer, 802.11 Wi-Fi wireless signal transfer, wireless local area network (WLAN) signal transfer, Visible Light Communication (VLC), Worldwide Interoperability for Microwave Access (WiMAX), Infrared (IR) communication wireless signal transfer, Public Switched Telephone Network (PSTN) signal transfer, Integrated Services Digital Network (ISDN) signal transfer, 3G/4G/5G/LTE cellular data network wireless signal transfer, ad-hoc network signal transfer, radio wave signal transfer, microwave signal transfer, infrared signal transfer, visible light signal transfer, ultraviolet light signal transfer, wireless signal transfer along the electromagnetic spectrum, or some combination thereof. 
     Communications interface  540  may also include one or more Global Navigation Satellite System (GNSS) receivers or transceivers that are used to determine a location of the computing system  500  based on receipt of one or more signals from one or more satellites associated with one or more GNSS systems. GNSS systems include, but are not limited to, the US-based Global Positioning System (GPS), the Russia-based Global Navigation Satellite System (GLONASS), the China-based BeiDou Navigation Satellite System (BDS), and the Europe-based Galileo GNSS. There is no restriction on operating on any particular hardware arrangement, and therefore the basic features here may easily be substituted for improved hardware or firmware arrangements as they are developed. 
     Storage device  530  can be a non-volatile and/or non-transitory computer-readable memory device and can be a hard disk or other types of computer readable media which can store data that are accessible by a computer, such as magnetic cassettes, flash memory cards, solid state memory devices, digital versatile disks, cartridges, a floppy disk, a flexible disk, a hard disk, magnetic tape, a magnetic strip/stripe, any other magnetic storage medium, flash memory, memristor memory, any other solid-state memory, a compact disc read only memory (CD-ROM) optical disc, a rewritable compact disc (CD) optical disc, digital video disk (DVD) optical disc, a blu-ray disc (BDD) optical disc, a holographic optical disk, another optical medium, a secure digital (SD) card, a micro secure digital (microSD) card, a Memory Stick® card, a smartcard chip, a EMV chip, a subscriber identity module (SIM) card, a mini/micro/nano/pico SIM card, another integrated circuit (IC) chip/card, random access memory (RAM), static RAM (SRAM), dynamic RAM (DRAM), read-only memory (ROM), programmable read-only memory (PROM), erasable programmable read-only memory (EPROM), electrically erasable programmable read-only memory (EEPROM), flash EPROM (FLASHEPROM), cache memory (L1/L2/L3/L4/L5/L#), resistive random-access memory (RRAM/ReRAM), phase change memory (PCM), spin transfer torque RAM (STT-RAM), another memory chip or cartridge, and/or a combination thereof. 
     Storage device  530  can include software services, servers, services, etc., that when the code that defines such software is executed by the processor  510 , it causes the system to perform a function. In some embodiments, a hardware service that performs a particular function can include the software component stored in a computer-readable medium in connection with the necessary hardware components, such as processor  510 , connection  505 , output device  535 , etc., to carry out the function. 
     As understood by those of skill in the art, machine-learning based classification techniques can vary depending on the desired implementation. For example, machine-learning classification schemes can utilize one or more of the following, alone or in combination: hidden Markov models; recurrent neural networks; convolutional neural networks (CNNs); deep learning; Bayesian symbolic methods; general adversarial networks (GANs); support vector machines; image registration methods; applicable rule-based system. Where regression algorithms are used, they may include including but are not limited to: a Stochastic Gradient Descent Regressor, and/or a Passive Aggressive Regressor, etc. 
     Machine learning classification models can also be based on clustering algorithms (e.g., a Mini-batch K-means clustering algorithm), a recommendation algorithm (e.g., a Miniwise Hashing algorithm, or Euclidean Locality-Sensitive Hashing (LSH) algorithm), and/or an anomaly detection algorithm, such as a Local outlier factor. Additionally, machine-learning models can employ a dimensionality reduction approach, such as, one or more of: a Mini-batch Dictionary Learning algorithm, an Incremental Principal Component Analysis (PCA) algorithm, a Latent Dirichlet Allocation algorithm, and/or a Mini-batch K-means algorithm, etc. 
       FIG. 5  illustrates an example processor-based system with which some aspects of the subject technology can be implemented. Specifically,  FIG. 5  illustrates system architecture  500  wherein the components of the system are in electrical communication with each other using a bus  505 . System architecture  500  can include a processing unit (CPU or processor)  510 , as well as a cache  512 , that are variously coupled to system bus  505 . Bus  505  couples various system components including system memory  515 , (e.g., read only memory (ROM)  520  and random access memory (RAM)  525 , to processor  510 . 
     System architecture  500  can include a cache of high-speed memory connected directly with, in close proximity to, or integrated as part of the processor  510 . System architecture  500  can copy data from the memory  515  and/or the storage device  530  to the cache  512  for quick access by the processor  510 . In this way, the cache can provide a performance boost that avoids processor  510  delays while waiting for data. These and other modules can control or be configured to control the processor  510  to perform various actions. Other system memory  515  may be available for use as well. Memory  515  can include multiple different types of memory with different performance characteristics. Processor  510  can include any general purpose processor and a hardware module or software module, such as module  1  ( 532 ), module  2  ( 534 ), and module  3  ( 536 ) stored in storage device  530 , configured to control processor  510  as well as a special-purpose processor where software instructions are incorporated into the actual processor design. Processor  510  may essentially be a completely self-contained computing system, containing multiple cores or processors, a bus, memory controller, cache, etc. A multi-core processor may be symmetric or asymmetric. 
     To enable user interaction with the computing system architecture  500 , an input device  545  can represent any number of input mechanisms, such as a microphone for speech, a touch-sensitive screen for gesture or graphical input, keyboard, mouse, motion input, speech and so forth. An output device  535  can also be one or more of a number of output mechanisms. In some instances, multimodal systems can enable a user to provide multiple types of input to communicate with the computing system architecture  500 . Communications interface  540  can generally govern and manage the user input and system output. There is no restriction on operating on any particular hardware arrangement and therefore the basic features here may easily be substituted for improved hardware or firmware arrangements as they are developed. 
     Storage device  530  is a non-volatile memory and can be a hard disk or other types of computer readable media which can store data that are accessible by a computer, such as magnetic cassettes, flash memory cards, solid state memory devices, digital versatile disks, cartridges, random access memories (RAMs)  525 , read only memory (ROM)  520 , and hybrids thereof. 
     Storage device  530  can include software modules  532 ,  534 ,  536  for controlling processor  510 . Other hardware or software modules are contemplated. Storage device  530  can be connected to the system bus  505 . In one aspect, a hardware module that performs a particular function can include the software component stored in a computer-readable medium in connection with the necessary hardware components, such as the processor  510 , bus  505 , output device  535 , and so forth, to carry out various functions of the disclosed technology. 
     By way of example, instruction stored on computer-readable media can be configured to cause one or more processors to perform operations including: receiving, at an AV computing system, a first dispatch request, wherein the first dispatch request is associated with a first user identifier (ID), receiving, at the AV computing system, a first recognition model, wherein the first recognition model corresponds with the first user ID, receiving, at the AV computing system, an image stream comprising one or more images of pedestrian faces, and providing the one or more images to the first recognition model. In some aspects, the instructions can further cause processors  510  to perform operations for: determining, using the first recognition model, if a first user represented in the one or more images corresponds with the first user ID, unlocking a door of the AV in response to a match between at least one of the one or more images and the first user ID, and/or updating the first recognition model in response to a match between at least one of the one or more images and the first user ID. 
     In some aspects, memory stored operations/instructions can be configured to further cause processors  510  to perform operations for: receiving a second recognition model corresponding with a second user ID, providing the one or more images to the second recognition model, and determining, using the second recognition model, if a second user represented by the one or more images corresponds with the second user ID. In some approaches, the operations may further cause the processors to perform operations for unlocking a door of the AV in response to a match between at least one of the one or more images and the second user ID. 
     Depending on the desired implementation, the first recognition model can be a machine-learning model that has been trained using a plurality of facial images of the first user, and wherein the second recognition model is a machine-learning model that has been trained using a plurality of facial images of the second user. 
     Embodiments within the scope of the present disclosure may also include tangible and/or non-transitory computer-readable storage media or devices for carrying or having computer-executable instructions or data structures stored thereon. Such tangible computer-readable storage devices can be any available device that can be accessed by a general purpose or special purpose computer, including the functional design of any special purpose processor as described above. By way of example, and not limitation, such tangible computer-readable devices can include RAM, ROM, EEPROM, CD-ROM or other optical disk storage, magnetic disk storage or other magnetic storage devices, or any other device which can be used to carry or store desired program code in the form of computer-executable instructions, data structures, or processor chip design. When information or instructions are provided via a network or another communications connection (either hardwired, wireless, or combination thereof) to a computer, the computer properly views the connection as a computer-readable medium. Thus, any such connection is properly termed a computer-readable medium. Combinations of the above should also be included within the scope of the computer-readable storage devices. 
     Computer-executable instructions include, for example, instructions and data which cause a general purpose computer, special purpose computer, or special purpose processing device to perform a certain function or group of functions. Computer-executable instructions also include program modules that are executed by computers in stand-alone or network environments. Generally, program modules include routines, programs, components, data structures, objects, and the functions inherent in the design of special-purpose processors, etc. that perform tasks or implement abstract data types. Computer-executable instructions, associated data structures, and program modules represent examples of the program code means for executing steps of the methods disclosed herein. The particular sequence of such executable instructions or associated data structures represents examples of corresponding acts for implementing the functions described in such steps. 
     Other embodiments of the disclosure may be practiced in network computing environments with many types of computer system configurations, including personal computers, hand-held devices, multi-processor systems, microprocessor-based or programmable consumer electronics, network PCs, minicomputers, mainframe computers, and the like. Embodiments may also be practiced in distributed computing environments where tasks are performed by local and remote processing devices that are linked (either by hardwired links, wireless links, or by a combination thereof) through a communications network. In a distributed computing environment, program modules can be located in both local and remote memory storage devices. 
     The various embodiments described above are provided by way of illustration only and should not be construed to limit the scope of the disclosure. For example, the principles herein apply equally to optimization as well as general improvements. Various modifications and changes may be made to the principles described herein without following the example embodiments and applications illustrated and described herein, and without departing from the spirit and scope of the disclosure. Claim language reciting “at least one of” a set indicates that one member of the set or multiple members of the set satisfy the claim.