Patent Publication Number: US-2022234414-A1

Title: Air Pollution Detection And Remediation Systems And Methods

Description:
BACKGROUND 
     Air pollution can vary from place to place due to various reasons. For example, a level of air pollution in a town may be lower than that in a city because of reasons such as population density and traffic density. Furthermore, while it may be relatively easy to measure air pollution in a town such as, for example, along a main street of the town, it is harder to measure air pollution in a city where there may be many roads with various levels of traffic at various times. It is therefore desirable to provide solutions that identify various issues related to air pollution at various locations and solutions that may address these issues. 
    
    
     
       BRIEF DESCRIPTION OF THE DRAWINGS 
       A detailed description is set forth below with reference to the accompanying drawings. The use of the same reference numerals may indicate similar or identical items. Various embodiments may utilize elements and/or components other than those illustrated in the drawings, and some elements and/or components may not be present in various embodiments. Elements and/or components in the figures are not necessarily drawn to scale. Throughout this disclosure, depending on the context, singular and plural terminology may be used interchangeably. 
         FIG. 1  illustrates an example vehicle that is configured to generate air pollution data in accordance with an embodiment of the disclosure. 
         FIG. 2  shows an example system to detect and remediate air pollution in accordance with an embodiment of the disclosure. 
         FIG. 3  illustrates an example scenario associated with detection and remediation of air pollution in accordance with an embodiment of the disclosure. 
         FIG. 4  shows a flowchart of a method of use of a vehicle to detect and remediate air pollution in accordance with an embodiment of the disclosure. 
     
    
    
     DETAILED DESCRIPTION 
     Overview 
     In terms of a general overview, this disclosure is directed to systems and methods to use vehicles to detect and remediate air pollution. In an example implementation, a server computer transmits a directive to a vehicle controller of a vehicle. The directive directs the vehicle controller to measure an air pollution level around the vehicle at a first location. The vehicle controller executes an air pollution measurement and transmits measurement data to the server computer. The server computer evaluates the measurement data and directs the vehicle controller to perform a remedial action to reduce the air pollution level at the first location. An example remedial action can involve, for example, moving the vehicle from the first location to a second location. Another example remedial action that may be performed by the server computer is sending a directive to another vehicle to avoid traveling to the first location. The server computer may also determine whether the first location is a transient pollution location or a persistent pollution by directing the vehicle controller to carry out measurements at two different times and/or by employing two different sampling rates. Measurements may also be carried out by multiple vehicles at the first location in order to identify other parameters such as, for example, a rate of change in air pollution at the first location, a spreading characteristic of pollutants causing the air pollution at the first location, and various factors that contribute to the change in air pollution at the first location. 
     Illustrative Embodiments 
     The disclosure will be described more fully hereinafter with reference to the accompanying drawings, in which example embodiments of the disclosure are shown. This disclosure may, however, be embodied in many different forms and should not be construed as limited to the example embodiments set forth herein. It will be apparent to persons skilled in the relevant art that various changes in form and detail can be made to various embodiments without departing from the spirit and scope of the present disclosure. Thus, the breadth and scope of the present disclosure should not be limited by any of the above-described example embodiments but should be defined only in accordance with the following claims and their equivalents. The description below has been presented for the purposes of illustration and is not intended to be exhaustive or to be limited to the precise form disclosed. It should be understood that alternate implementations may be used in any combination desired to form additional hybrid implementations of the present disclosure. For example, any of the functionality described with respect to a particular device or component may be performed by another device or component. Furthermore, while specific device characteristics have been described, embodiments of the disclosure may relate to numerous other device characteristics. Further, although embodiments have been described in language specific to structural features and/or methodological acts, it is to be understood that the disclosure is not necessarily limited to the specific features or acts described. Rather, the specific features and acts are disclosed as illustrative forms of implementing the embodiments. 
     Certain words and phrases that are used herein should be interpreted as referring to various objects and actions that are generally understood in various forms and equivalencies by persons of ordinary skill in the art. For example, the phrase “user device” as used herein is applicable to any device that a person may use to run software that performs various operations in accordance with the disclosure. The word “vehicle” as used in this disclosure can pertain to any of various types of vehicles such as cars, vans, sports utility vehicles, trucks, electric vehicles, gasoline vehicles, and hybrid vehicles. The word “vehicle” as used herein can also encompass various types of airborne vehicles such as, for example, unmanned aerial vehicles, commuter aircraft, and personal aircraft (single person aircraft) that may proliferate in certain areas and contribute to air traffic congestion and air pollution in these areas. The phrase “autonomous vehicle” as used in this disclosure generally refers to a vehicle that can perform at least a few operations without human intervention. The Society of Automotive Engineers (SAE) defines six levels of driving automation ranging from Level 0 (fully manual) to Level 5 (fully autonomous). These levels have been adopted by the U.S. Department of Transportation. Level 0 (L0) vehicles are manually controlled vehicles having no driving related automation. Level 1 (L1) vehicles incorporate some features, such as cruise control, but a human driver retains control of most driving and maneuvering operations. Level 2 (L2) vehicles are partially automated with certain driving operations such as steering, braking, and lane control being controlled by a vehicle computer/controller. The driver retains some level of control of the vehicle and may override certain operations executed by the vehicle computer. Level 3 (L3) vehicles provide conditional driving automation but are smarter in terms of having an ability to sense a driving environment and certain driving situations. Level 4 (L4) vehicles can operate in a self-driving mode and include features where the vehicle computer takes control during certain types of equipment failures. The level of human intervention is very low. Level 5 (L5) vehicles are fully autonomous vehicles that do not involve human participation. The phrase “software application” as used herein refers to code (firmware, software, machine code etc.) that can be installed or downloaded into various devices such as, for example, a vehicle controller of a vehicle or a user device of an individual (a smartphone, for example). The code may be executed by a processor to implement various actions in accordance with the disclosure. The term “server computer” as used herein may refer to any cloud-based computing device or to any computing device that is communicatively coupled to other devices via a network (such as the Internet, for example). It should be understood that the word “example” as used herein is intended to be non-exclusionary and non-limiting in nature. 
       FIG. 1  illustrates an example vehicle  115  that is configured to generate air pollution data in accordance with an embodiment of the disclosure. The vehicle  115  can be any of various types of vehicles that are either driver-operated, partially autonomous, or fully autonomous. In one example embodiment, the vehicle  115  is a non-autonomous vehicle that is operated by a driver. In another example embodiment, which is illustrated in  FIG. 1 , the vehicle  115  can be any of a Level 1 through Level 5 autonomous vehicle. 
     Various operations of the vehicle  115  can be controlled via directives received by a vehicle controller  110  of the vehicle  115  from a remote device (such as a server computer  140 ) and/or from a user device  120  operated by an individual  125 . In some scenarios, the individual  125  can be a driver who is seated inside the vehicle  115 . In some other scenarios, the individual  125  may be located outside the vehicle  115  (as illustrated in  FIG. 1 ). 
     The user device  120  of the individual  125  may be any of various devices such as, for example, a smartphone, a smart wearable, a tablet computer, a laptop computer, and a desktop computer. In the illustrated example scenario, the user device  120  is a smartphone held by the individual  125  while standing outside the vehicle  115 . In another example scenario, the user device  120  can be a laptop computer or a desktop computer that is operated by the individual  125  when the individual  125  is located inside a building, such as, for example, when seated in the lobby of a hotel, a room in a house, or in an airport terminal. 
     In general, the user device  120  can include a processor, a memory, and communication hardware. The memory, which is one example of a non-transitory computer-readable medium, may be used to store an operating system (OS) and various code modules such as, for example, a control application for controlling the vehicle  115 . The code modules are provided in the form of computer-executable instructions that can be executed by the processor for performing various operations in accordance with the disclosure. The communication hardware can include one or more wireless transceivers, such as, for example, a cellular transceiver (when the user device  120  is a cellular phone) or a WiFi transceiver (when the user device  120  is a laptop computer, for example) that allows the user device  120  to transmit and/or receive various types of wireless signals to/from the vehicle controller  110 . The communication hardware can also include hardware for communicatively coupling the user device  120  to a communications network  150  for carrying out communications and data transfers with various devices and systems such as, for example, the server computer  140 . 
     The vehicle  115  may include various components such as, for example, the vehicle controller  110 , a sensor system  155 , and a wireless communication system that may include a set of wireless communication nodes  130   a ,  130   b ,  130   c , and  130   d . The vehicle controller  110  can include a processor, a memory, and communication hardware. The memory, which is another example of a non-transitory computer-readable medium, may be used to store an OS and various code modules. The code modules are provided in the form of computer-executable instructions that can be executed by the processor for performing various operations in accordance with the disclosure. The memory can also contain a database for storing information such as, for example, various maps, air pollution data, travel routes, times to travel, and travel destinations. 
     The vehicle controller  110  may perform various functions such as, for example, controlling engine operations (fuel injection, speed control, emissions control, braking, etc.), managing climate controls (air conditioning, heating etc.), activating airbags, and issuing warnings (check engine light, bulb failure, low tire pressure, vehicle in blind spot, etc.). The vehicle controller  110  may also control various actions performed by the vehicle  115  such as, for example, traveling on a travel route towards a designated destination without human intervention, avoiding accidents, avoiding collisions, and responding to directives received from the user device  120  and/or the server computer  140  for measuring air pollution levels in accordance with the disclosure. 
     The wireless communication nodes  130   a ,  130   b ,  130   c , and  130   d  may be mounted upon the vehicle  115  in a manner that allows the vehicle controller  110  to communicate with devices such as the user device  120  carried by the individual  125 . In an alternative implementation, a single wireless communication node may be mounted upon the roof of the vehicle  115 . The wireless communication system may be used by the vehicle controller  110  to communicate with various devices, various objects, and various other vehicles, when executing actions in accordance with the disclosure. The communications may be carried out using technologies such as, for example, vehicle-to-vehicle (V2V) technology, vehicle-to-infrastructure (V2I) technology, vehicle-to-everything (V2X) technology, and vehicle-to-pedestrian (V2P) technology. The communications may be also carried out by using wireless technologies such as, for example, cellular (5G, for example), Wi-Fi, Bluetooth®, Ultra-Wideband (UWB), Zigbee®, Li-Fi (light-based communication), dedicated short range communications (DSRC), audible communication, ultrasonic communication, or near-field-communications (NFC). 
     The vehicle controller  110  can communicate with the server computer  140  via the communications network  150 , which may include any one network, or a combination of networks, such as, for example, a local area network (LAN), a wide area network (WAN), a telephone network, a cellular network, a wireless network, and/or private/public networks such as the Internet. The communications network  150  may support communication technologies such as cellular, Wi-Fi, Wi-Fi direct, Bluetooth®, Ultra-Wideband, near-field communication (NFC), Li-Fi, V2V, V2I, V2X, V2P, machine-to-machine communication, and/or man-to-machine communication. At least one portion of the communications network  150  includes a wireless communication link that allows the server computer  140  to communicate with one or more of the wireless communication nodes  130   a ,  130   b ,  130   c , and  130   d  on the vehicle  115 . The server computer  140  may communicate with the vehicle controller  110  for various purposes such as, for example, to convey a directive to perform an air quality measurement in accordance with the disclosure. 
     The user device  120  may communicate with the vehicle controller  110  via one or more of the first set of wireless communication nodes  130   a ,  130   b ,  130   c , and  130   d  so as to allow the individual  125  (for example a driver who is outside the vehicle  115 ) to direct the vehicle  115  to perform an air quality measurement in accordance with an embodiment of the disclosure. 
     The sensor system  155  can include one or more sensors that perform various types of air quality measurements. More particularly, the sensor system  155  can include sensors that measure local air pollutants that may be present in the air surrounding the vehicle  115 . Some examples of local air pollutants can include particulate matter, black carbon, nitrogen oxides, and volatile organic compounds 
     Pollution data is conveyed from the sensor system  155  to the vehicle controller  110  for executing various operations in accordance with the disclosure. In an example implementation, the pollution data may be evaluated by the vehicle controller  110  and transmitted to the server computer  140  in the form of an air quality index (AQI). In another example implementation, the vehicle controller  110  may convey raw sensor data to the server computer  140  and the server computer  140  may evaluate the raw sensor data to determine the AQI. The AQI may be calculated by evaluating the raw sensor data over a period of time and/or by applying mathematical/statistical procedures such as, for example, averaging, mean value, and distribution. 
       FIG. 2  shows an example system  200  to detect and remediate air pollution in accordance with an embodiment of the disclosure. The system  200  can include an air quality data capture system  205 , an air quality data evaluation system  215 , and an air quality remediation system  220  that are communicatively coupled to each other via the communications network  150 . 
     The air quality data capture system  205  is a wide area air pollution detection system that utilizes one or more vehicles such as, for example, the vehicle  115  to operate as mobile air pollution detectors. Computing elements provided in the vehicles, such as, the vehicle controller  110  referred to above, operate as edge computers that convey air pollution information to the air quality data evaluation system  215  via the communications network  150 . Air pollution levels may be measured over a large area for purposes such as, for example, to enact pollution control ordinances, and may be measured over a hyperlocal area for purposes such as, for example, re-routing traffic away from a high pollution concentration locality. 
     In an example implementation in accordance with the disclosure, sensor systems in one or more vehicles capture data pertaining to air pollution levels in the vicinity of each of these other vehicles. The sensor systems convey the information to the respective vehicle controllers. The vehicle controllers may evaluate the data in various ways for obtaining air pollution measurement statistics that can be conveyed to the air quality data evaluation system  215 . In some cases, the vehicle controllers may convey raw sensor data to the air quality data evaluation system  215  for evaluation. The phrase “measurement statistic” as used herein encompasses all forms of data that can be transferred from the air quality data capture system  205  to the air quality data evaluation system  215 . Some examples can include raw sensor data as well as computation results obtained by evaluating sensor data (such as, for example, statistical parameters). 
     In some implementations, some or all of the vehicle controllers may provide additional data/information to the air quality data evaluation system  215 . Some examples of such data/information can include weather conditions, traffic conditions, time-related information, and hyperlocal information (ordinances, public events, sports events, etc.). The air quality data evaluation system  215  may use this information when evaluating the air quality related data provided by the vehicle controllers. In one case, for example, the air quality data evaluation system  215  may determine that a high level of particulate pollution in one locality is due to high humidity at a particular time of day, or may determine that a high level of nitrogen oxides at another locality is due to traffic congestion at a sports event that is taking place. 
     The air quality data evaluation system  215  can include a single computer in some implementations and multiple computers that are networked together in other implementations. Each of the illustrated computers can be referred to herein as a “server computer” (such as, for example, the server computer  140 ) but can include various client computers as well. In some other implementations, air quality data evaluation system  215  can include the user device  120 . The user device  120  may be a client device to the server computer  140  in some scenarios and may be independent of the server computer  140  in some other scenarios. 
     The air quality data evaluation system  215  may query and obtain air quality data from the vehicles that are a part of the air quality data capture system  205 . In one example procedure, the air quality data evaluation system  215  may derive macro level (wide area) air quality information by evaluating air quality data (measurement statistics) received from multiple vehicles located in a geographically dispersed area. In another example procedure, the air quality data evaluation system  215  may derive hyperlocal air quality information by evaluating measurement statistics received from a first vehicle located in a first area and comparing this information to hyperlocal air quality information derived by evaluating measurement statistics received from a second vehicle located at the same area at a later instant in time (later in the day, for example). In yet another example procedure, the air quality data evaluation system  215  may derive hyperlocal air quality information by evaluating measurement statistics received from a first vehicle located in a first area and comparing this information to hyperlocal air quality information derived by evaluating measurement statistics received from a second vehicle located in a second area. 
     The air quality data evaluation system  215  may convey air quality data evaluation results to the air quality remediation system  220 . The air quality remediation system  220  can include one or more of various entities. In an example implementation, the air quality remediation system  220  can include one or more of the vehicles that are a part of the air quality data capture system  205  (such as, for example, the vehicle  115 ), one or more individuals  221 , and/or one or more agencies  222 . 
     Air quality remediation procedures may be executed by the air quality remediation system  220  in various ways. In one example air quality remediation procedure, the vehicle  115  may receive a directive from the server computer  140  to move away from a first location having a poor AQI rating to a second location that has a better AQI so as to improve the AQI at the first location. The AQI at the first location may be determined by the server computer  140  based on evaluating sensor data and/or measurement statistics provided by the vehicle  115  and/or other vehicles to the server computer  140 . 
     In another example air quality remediation procedure, one or more of individuals  221  may decide to improve the AQI in an area, such as, for example, by deciding to cancel traveling to a public event to be held in the area, by using public transport (bus, train, etc.) to attend the public event, and/or by walking to the public event. 
     In yet another example air quality remediation procedure, an agency such as, for example, a municipal body or a traffic control agency, may transmit a pollution alert to one or more types of devices (smartphones, radio, television, billboards etc.). The pollution alert may provide information about a poor AQI in a certain area and may further include a recommendation to alleviate pollution in the affected area. 
     The agency may also take additional steps such as, for example, issuing an ordinance to improve the AQI in the area. In some cases, the ordinance may be generated based on input obtained from the public (meetings, feedback, discussions, etc.). The ordinance may be directed at changing traffic patterns and/or traffic regulations in the area (rerouting, banning traffic during certain hours, one-way traffic movement, banning movement of gasoline vehicles in the area, providing incentives to use ride share services etc.). 
     In some scenarios, traffic authorities may implement a variable pricing scheme that is directed at reducing pollution levels in various areas at various times. For example, a variable pricing scheme may be applied for use of an express lane in a multi-lane highway, to purchase tickets in a public transport vehicle (train, bus, etc.), to purchase tickets for an event (entry into a sports arena, for example), and/or for parking spots in a parking lot. The variable pricing scheme may be based on various types of analysis performed by the air quality data evaluation system  215  upon air quality measurements provided to the air quality data evaluation system  215  by the air quality data capture system  205 . 
       FIG. 3  illustrates an example scenario associated with detection and remediation of air pollution in accordance with an embodiment of the disclosure. In this example scenario, a vehicle  310  is traveling south on a road  315 . A factory  305  is located near a current location of the vehicle  310 . The factory  305  emits pollutants into the air throughout the day. Consequently, the AQI in the area is poor throughout the day. 
     Another vehicle  325  is stopped at an intersection (along with other vehicles) due to a red condition of a traffic light  326 . The vehicle  325  and the other vehicles stopped at the intersection produce pollutants that contribute to poor AQI at the intersection. The poor AQI may last as long as the vehicles are stopped due to the red light and improves when the vehicles start to move away from the intersection. The AQI also improves when traffic density at the intersection is low during non-peak traffic hours of the day. 
     A vehicle  335 , a vehicle  340 , and a vehicle  355 , are a few example vehicles that are circling around a sports arena  350  looking for vacant parking spots because a parking lot  345  of the sports arena  350  is full. These vehicles not only contribute to traffic congestion near the sports arena  350  but also contribute to a poor AQI around the sports arena  350 . 
     Each of the example vehicles shown in  FIG. 3  may include a vehicle controller configured to capture pollution data in the manner described above with respect to the vehicle  115 . One or more of the computers of the air quality data evaluation system  215  (such as, for example, the server computer  140 ) may be configured to communicate with the various vehicle controllers to obtain air pollution measurement statistics and evaluate the measurement statistics in order to detect air pollution levels at various locations in the geographical area illustrated in  FIG. 3 . 
       FIG. 4  shows a flowchart  400  of an example method to detect air pollution at one or more locations in a geographical area such as the geographical area illustrated in  FIG. 3 , and to perform remediation actions in accordance with an embodiment of the disclosure. 
     The flowchart  400  illustrates a sequence of operations that can be implemented in hardware, software, or a combination thereof. In the context of software, the operations represent computer-executable instructions stored on one or more non-transitory computer-readable media such as a memory in the vehicle controller  110  and/or in the server computer  140 , that, when executed by one or more processors such as a processor in the vehicle controller  110  and/or in the server computer  140 , perform the recited operations. Generally, computer-executable instructions include routines, programs, objects, components, data structures, and the like that perform particular functions or implement particular abstract data types. The order in which the operations are described is not intended to be construed as a limitation, and any number of the described operations may be carried out in a different order, omitted, combined in any order, and/or carried out in parallel. The flowchart  400  is described below by referring to various objects illustrated in  FIGS. 1 through 3 . But it must be understood that the description is equally applicable to many other similar, or identical, objects in other implementations and embodiments. 
     At block  405 , a vehicle controller of a vehicle receives a directive to measure an air pollution level in the vicinity of the vehicle. In one example implementation, the directive is issued by the server computer  140 . In another example implementation, the directive is issued by the individual  125  via the user device  120 . 
     At block  410 , the vehicle controller executes a measurement procedure and transmits air pollution data (in the form of a measurement statistic and/or raw data) to the server computer  140 . The server computer  140  evaluates the air pollution data and identifies a high level of air pollution (poor AQI). 
     At block  415 , the vehicle controller receives another directive from the server computer  140  to perform a remediation action to reduce the air pollution level in the area where the vehicle is currently located. 
     In a first example scenario of the steps described above, the server computer  140  transmits a first directive to a vehicle controller of the vehicle  310  to measure an air pollution level in the vicinity of the vehicle  310 . The vehicle controller executes an air quality measurement and transmits air pollution data to the server computer  140  (in the form of a measurement statistic and/or raw data). In an example implementation of this scenario, the measurement statistic is a first timestamped measurement statistic that provides information to the server computer  140  about an air quality during a time at which the measurement was carried out by the vehicle controller of the vehicle  310 . 
     The server computer  140  evaluates the air pollution data and identifies a high level of air pollution (poor AQI). However, the server computer  140  may be unable to suggest a specific remediation action (block  415 ) because the server computer  140  may be unable to identify the originating source of the pollution (the factory  305 ) and/or a characteristic of the air pollution present near the vehicle  310 . 
     Consequently, in one example embodiment in accordance with disclosure, the server computer  140  may recommend a remediation action to the vehicle controller of the vehicle  310  to move the vehicle  310  from the current location to another location. The vehicle controller of the vehicle  310  may execute the remediation action by issuing an advisory to a driver of the vehicle  310  to move from the current location to another location. The advisory may be provided in the form of a message that is transmitted out of the speakers of an infotainment system in the vehicle  310 . In some cases, the vehicle  310  can be an autonomous vehicle and the vehicle controller of the autonomous vehicle may autonomously move the vehicle  310  from the current location to another location. 
     In another example embodiment in accordance with disclosure, the server computer may seek to identify the originating source of the pollution and/or a characteristic of the air pollution present near the vehicle  310 . Towards this end, the server computer  140  may seek additional information from the vehicle controller of the vehicle  310  by sending another directive to perform another measurement of air quality in the area where the vehicle  310  is currently located (near the factory  305 ). The vehicle controller of the vehicle  310  responds to the directive by performing another air quality measurement followed by transmitting of a second timestamped measurement statistic that provides information to the server computer  140  about a second time at which the air quality measurement was carried out by the vehicle controller. The server computer  140  may identify similarities between the first timestamped measurement statistic and the second timestamped measurement statistic and classify the location at which the vehicle  310  is carrying out the measurements as a persistent pollution location. 
     The server computer  140  may also identify various other parameters by evaluating the first timestamped measurement statistic and the second timestamped measurement statistic. For example, the server computer  140  may determine a change in characteristic of the air pollution and/or a rate of change of the air pollution by evaluating the first timestamped measurement statistic and the second timestamped measurement statistic. 
     The change in characteristic of the air pollution may be caused, for example, by a change in the composition of pollutants indicated by the second timestamped measurement statistic versus those indicated by the first timestamped measurement statistic. The change in composition of the pollutants may be caused by various factors such as, for example, emissions by equipment in the factory, and/or by an engine of another vehicle that has moved close to the vehicle  310  (for example, a tractor trailer that has moved close to a sedan (vehicle  310 )). 
     The rate of change of the air pollution may be caused by variations in operations of the factory  305 , for example, (shift changes, lunch breaks, etc.) and/or due to traffic congestion at that location. In an example embodiment in accordance with disclosure, the rate of change of the air pollution may be determined by using two or more different sampling rates. For example, the server computer  140  may send a directive to the vehicle controller of the vehicle  310  to perform a first measurement by employing a first sampling rate and a second measurement by employing a second sampling rate. The vehicle controller may perform a first measurement and convey to the server computer  140  a first measurement statistic. The vehicle controller may perform a second measurement and convey to the server computer  140  a second measurement statistic (sequentially or concurrently with the first measurement statistic). The server computer  140  may evaluate the measurement statistics to determine a rate of change of air pollution in the vicinity of the vehicle  310 , and/or to classify the location at which the vehicle  310  is carrying out the measurements as a persistent pollution location. 
     The procedures described above with respect to the vehicle  310  at the location near the factory  305  may be repeated at other locations in real time as the vehicle  310  moves away from the current location. The procedures described above with respect to the vehicle  310  may also be performed with respect to other vehicles such as, for example, the vehicle  325 , the vehicle  335 , the vehicle  340 , and the vehicle  355 . In the case of the vehicle  325 , the server computer  140  may evaluate the timestamped measurement statistics and/or the measurement statistics obtained at various sampling rates for various purposes such as, for example, to determine a rate of change of air pollution at the location of the vehicle  325 , a trend in changes in air pollution at the location of the vehicle  325 , a change in the composition of pollutants at the location of the vehicle  325 , and/or to classify a nature of the location of the vehicle  325 . 
     The air pollution at the intersection where the traffic light  326  is located may be primarily caused by the emission of nitrogen oxides by vehicle  325  and other vehicles at the intersection when the vehicles are stopped due to the red light. The air pollution drops when the vehicles start to move away from the intersection. The air pollution also drops when traffic density at the intersection is low during non-peak traffic hours of the day. 
     In an example embodiment in accordance with disclosure, the server computer  140  may select the first sampling rate and/or the second sampling rate based on this fluctuation of air pollution (hourly sampling, daily sampling, weekly sampling, etc.) and/or based on the timings of the traffic light  326  (on/off periods of the red light and/or the green light). A sampling rate based on the timing of the traffic light  326  may involve air pollution level measurements when the traffic light  326  is red (poor AQI) and/or when the traffic light  326  is green (improved AQI). 
     The composition of the pollutants at the location of the vehicle  325  may be generally non-varying. The server computer  140  may classify the intersection where the traffic light  326  is locates as a transient pollution location because the AQI improves at night and during the weekends, for example. 
     The server computer  140  may evaluate measurement statistics obtained from the vehicle  335 , the vehicle  340 , and/or the vehicle  355  to determine that the area around the sports arena  350  has poor air quality due to traffic congestion. The server computer  140  may also classify the area around the sports arena  350  as a transient pollution location. 
     In an example embodiment in accordance with disclosure, the server computer  140  may transmit directives to one or more vehicles to execute remediation measures to improve the air quality near the sports arena  350 . An example remediation measure involves routing of traffic away from the sports arena  350 . The server computer  140  may detect that the vehicle  335 , the vehicle  340 , and the vehicle  355  are circling the sports arena  350  looking for a vacant sport and that it may be impractical to direct these vehicles to move away from the sports arena  350 . However, the server computer  140  may also determine that there are other vehicles that can be diverted away from the area near the sport arena  350 . 
     The server computer  140  may therefore transmit a directive to the vehicle  385  to travel on an alternative route instead of traveling closer to the area surrounding the sports arena  350 . The driver of the vehicle  385  or the vehicle controller of the vehicle  385  (when the vehicle  385  is an autonomous vehicle) may respond to the directive by modifying his/her planned travel route. The planned travel route may involve the vehicle  385  traveling south on the road  315  to reach the road  375  at the traffic light  316 . The alternative travel route that is selected by the driver/vehicle controller of the vehicle  385  as a remediation measure may involve the vehicle  385  traveling south on the road  315  and turning into the road  370  to reach the road  375 . In some implementations, the alternative route may be determined by the server computer  140  and conveyed to the vehicle controller of the vehicle  385  (in some cases, along with navigation guidance to travel on the alternate route). 
     The server computer  140  may transmit another directive to the vehicle  380  to turn around or to travel on an alternative route instead of traveling closer to the area surrounding the sports arena  350 . 
     In the above disclosure, reference has been made to the accompanying drawings, which form a part hereof, which illustrate specific implementations in which the present disclosure may be practiced. It is understood that other implementations 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,” “an example embodiment,” “example implementation,” etc., indicate that the embodiment or implementation described may include a particular feature, structure, or characteristic, but every embodiment or implementation may not necessarily include the particular feature, structure, or characteristic. Moreover, such phrases are not necessarily referring to the same embodiment or implementation. Further, when a particular feature, structure, or characteristic is described in connection with an embodiment or implementation, one skilled in the art will recognize such feature, structure, or characteristic in connection with other embodiments or implementations whether or not explicitly described. For example, various features, aspects, and actions described above with respect to an autonomous parking maneuver are applicable to various other autonomous maneuvers and must be interpreted accordingly. 
     Implementations of the systems, apparatuses, devices, and methods disclosed herein may comprise or utilize one or more devices that include hardware, such as, for example, one or more processors and system memory, as discussed herein. An implementation of the devices, systems, and methods disclosed herein may communicate over a computer network. A “network” is defined as one or more data links that enable the transport of electronic data between computer systems and/or modules and/or other electronic devices. When information is transferred or provided over a network or another communications connection (either hardwired, wireless, or any combination of hardwired or wireless) to a computer, the computer properly views the connection as a transmission medium. Transmission media can include a network and/or data links, which can be used to carry desired program code means in the form of computer-executable instructions or data structures and which can be accessed by a general purpose or special purpose computer. Combinations of the above should also be included within the scope of non-transitory computer-readable media. 
     Computer-executable instructions comprise, for example, instructions and data which, when executed at a processor, cause the processor to perform a certain function or group of functions. The computer-executable instructions may be, for example, binaries, intermediate format instructions such as assembly language, or even source code. Although the subject matter has been described in language specific to structural features and/or methodological acts, it is to be understood that the subject matter defined in the appended claims is not necessarily limited to the described features or acts described above. Rather, the described features and acts are disclosed as example forms of implementing the claims. 
     A memory device such as a memory in the vehicle controller  110 , can include any one memory element or a combination of volatile memory elements (e.g., random access memory (RAM, such as DRAM, SRAM, SDRAM, etc.) and non-volatile memory elements (e.g., ROM, hard drive, tape, CDROM, etc.). Moreover, the memory device may incorporate electronic, magnetic, optical, and/or other types of storage media. In the context of this document, a “non-transitory computer-readable medium” can be, for example but not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device. More specific examples (a non-exhaustive list) of the computer-readable medium would include the following: a portable computer diskette (magnetic), a random-access memory (RAM) (electronic), a read-only memory (ROM) (electronic), an erasable programmable read-only memory (EPROM, EEPROM, or Flash memory) (electronic), and a portable compact disc read-only memory (CD ROM) (optical). Note that the computer-readable medium could even be paper or another suitable medium upon which the program is printed, since the program can be electronically captured, for instance, via optical scanning of the paper or other medium, then compiled, interpreted or otherwise processed in a suitable manner if necessary, and then stored in a computer memory. 
     Those skilled in the art will appreciate that the present disclosure may be practiced in network computing environments with many types of computer system configurations, including vehicle computers, personal computers, desktop computers, laptop computers, message processors, user devices, multi-processor systems, microprocessor-based or programmable consumer electronics, network PCs, minicomputers, mainframe computers, mobile telephones, PDAs, tablets, pagers, routers, switches, various storage devices, and the like. The disclosure may also be practiced in distributed system environments where local and remote computer systems, which are linked (either by hardwired data links, wireless data links, or by any combination of hardwired and wireless data links) through a network, both perform tasks. In a distributed system environment, program modules may be located in both the local and remote memory storage devices. 
     Further, where appropriate, the functions described herein can be performed in one or more of hardware, software, firmware, digital components, or analog components. For example, one or more application specific integrated circuits (ASICs) can be programmed to carry out one or more of the systems and procedures described herein. Certain terms are used throughout the description, and claims refer to particular system components. As one skilled in the art will appreciate, components may be referred to by different names. This document does not intend to distinguish between components that differ in name, but not function. 
     At least some embodiments of the present disclosure have been directed to computer program products comprising such logic (e.g., in the form of software) stored on any computer-usable medium. Such software, when executed in one or more data processing devices, causes a device to operate as described herein. 
     While various embodiments of the present disclosure have been described above, it should be understood that they have been presented by way of example only, and not limitation. It will be apparent to persons skilled in the relevant art that various changes in form and detail can be made therein without departing from the spirit and scope of the present disclosure. Thus, the breadth and scope of the present disclosure should not be limited by any of the above-described example embodiments but should be defined only in accordance with the following claims and their equivalents. The foregoing description has been presented for the purposes of illustration and description. It is not intended to be exhaustive or to limit the present disclosure to the precise form disclosed. Many modifications and variations are possible in light of the above teaching. Further, it should be noted that any or all of the aforementioned alternate implementations may be used in any combination desired to form additional hybrid implementations of the present disclosure. For example, any of the functionality described with respect to a particular device or component may be performed by another device or component. Further, while specific device characteristics have been described, embodiments of the disclosure may relate to numerous other device characteristics. Further, although embodiments have been described in language specific to structural features and/or methodological acts, it is to be understood that the disclosure is not necessarily limited to the specific features or acts described. Rather, the specific features and acts are disclosed as illustrative forms of implementing the embodiments. Conditional language, such as, among others, “can,” “could,” “might,” or “may,” unless specifically stated otherwise, or otherwise understood within the context as used, is generally intended to convey that certain embodiments could include, while other embodiments may not include, certain features, elements, and/or steps. Thus, such conditional language is not generally intended to imply that features, elements, and/or steps are in any way required for one or more embodiments.