Device and method for providing information showing contamination level to occur due to tire wear of vehicle

Abstract

To provide an efficient and exact approach for providing a vehicle with information showing a contamination level which occurs due to tire wear of the vehicle.SOLUTION: The present invention relates to a method comprising steps of: acquiring information showing tire wear of a vehicle at each geographic position to be derived from vehicle sensor data of one or more vehicles for each of at least some geographic positions among a plurality of geographic positions; calculating contamination data including information showing a contamination level which occurs due to the tire wear of the vehicle at each geographic position which is derived from the acquired information showing the tire wear of the vehicle for each of the plurality of geographic positions; and providing the information showing the contamination level which occurs due to the tire wear of the vehicle calculated for the geographic position at which the vehicle is located, in response to a request by the vehicle requesting the information showing the contamination level which occurs from the vehicle due to the tire wear for the geographic position at which the vehicle is located.SELECTED DRAWING: Figure 13

Description

[Technical field]

[0001] Various aspects of the present disclosure relate to an apparatus and method for providing a vehicle with information indicative of the pollution levels generated by tire wear on the vehicle. [Background technology]

[0002] For vehicles, it may be desirable to have information about air pollution, e.g. to control the cabin air system. Air quality measuring stations exist that provide such information, but they do not cover all locations, or at least not for all types of pollution. For example, a (local) measuring station may only provide information about the current (local) pollution situation for a defined parameter (e.g. PM10) and may specifically exclude fine particles, e.g. resulting from tire wear on the vehicle.

[0003] Therefore, an efficient and accurate approach for providing a vehicle with information indicative of the pollution levels generated by the wear of the vehicle's tires is desirable. Summary of the Invention

[0004] Various embodiments provide a method for providing information to a vehicle indicative of a pollution level generated by tire wear of the vehicle, the method comprising: obtaining, for each of at least a portion of the plurality of geographic locations, information indicative of tire wear of the vehicle at each geographic location derived from vehicle sensor data of one or more vehicles; determining (e.g., storing) pollution data for each of a plurality of geographic locations, the pollution data including information indicative of (e.g., relating to or based on) a pollution level caused by tire wear of the vehicle at each geographic location, the information being derived from the obtained information indicative of tire wear of the vehicle; and providing, in response to a request from a vehicle (i.e. a requester or requesting vehicle, e.g. a requester or requesting unit of a vehicle) requesting information indicative of the pollution level generated by the vehicle due to tire wear, for a geographical location where the vehicle is located, the determined (e.g. calculated and / or stored) information indicative of the pollution level generated by the vehicle due to tire wear, for the geographical location where the vehicle is located.

[0005] According to one embodiment, the method further includes receiving (e.g., in a database system performing the method) a transmission from each vehicle of the one or more vehicles of information indicative of tire wear on the vehicle at a geographic location in which each vehicle of the one or more vehicles is located.

[0006] According to one embodiment, the transmission includes an indication of a geographic location in which each of the one or more vehicles is located.

[0007] According to one embodiment, the pollution data is stored in one or more vehicle external (and / or vehicle external) databases.

[0008] According to one embodiment, the method further comprises receiving, via wireless communication, a request to provide information indicative of a pollution level caused by tire wear of the vehicle.

[0009] According to one embodiment, the method further includes obtaining information indicative of tire wear of the vehicle for a plurality of weather conditions, and determining, for each weather condition of the plurality of weather conditions, pollution data including information indicative of a pollution level caused by tire wear of the vehicle.

[0010] According to one embodiment, the request indicates at least one of a plurality of weather conditions, and the method includes providing information regarding a pollution level caused by tire wear of the vehicle in a geographic location in which the vehicle is located, determined for the indicated weather conditions.

[0011] According to one embodiment, the geographic location where the vehicle is located is a road or a road segment.

[0012] According to one embodiment, the information indicative of the pollution level caused by tyre wear of a vehicle comprises information indicative of the condition of a road or road segment.

[0013] According to one embodiment, the information indicative of the pollution level generated by tire wear of the vehicle comprises an estimate of the coefficient of friction between the vehicle tires and the road or road segment.

[0014] According to one embodiment, there is provided a database system comprising: obtaining, for each of at least a portion of the plurality of geographic locations, information indicative of tire wear for the vehicle at each geographic location, the information being derived from vehicle sensor data for the one or more vehicles; determining (e.g., storing) pollution data for each of a plurality of geographic locations, the pollution data including information indicative of (e.g., relating to or based on) a pollution level generated by tire wear of the vehicle at each geographic location, the information being derived from the obtained information indicative of tire wear of the vehicle; A database system is provided having at least one processor configured to provide, in response to a request from a vehicle (i.e. a requester or requesting vehicle, e.g. a requester or requesting unit of a vehicle) seeking information indicative of a pollution level generated from the vehicle due to tire wear, for a geographical location in which the vehicle is located, determined (e.g. calculated and / or stored) information indicative of a pollution level generated from the vehicle due to tire wear, for a geographical location in which the vehicle is located.

[0015] According to one embodiment, there is provided a method for controlling a pollution control system of a vehicle, the method comprising the steps of obtaining information indicative of a pollution level generated by the vehicle due to tyre wear, determining values ​​of operational parameters of the vehicle pollution control system in response to the obtained information, and controlling the vehicle pollution control system in response to the determined values ​​of the operational parameters.

[0016] According to one embodiment, the method includes obtaining information indicative of the level of pollution emanating from the vehicle due to tire wear from a database external to the vehicle.

[0017] According to one embodiment, the method includes obtaining, via wireless communication, information indicative of a pollution level emanating from the vehicle due to tire wear from a database.

[0018] According to one embodiment, the method includes obtaining information from the database indicative of a pollution level generated by the vehicle due to tire wear by sending a request from the vehicle to the database indicating the location of the vehicle, the weather conditions of the vehicle, or both.

[0019] According to one embodiment, the information indicative of the pollution level emanating from the vehicle due to tyre wear comprises information indicative of the condition of the road on which the vehicle is located.

[0020] According to one embodiment, the information indicative of the level of pollution emanating from the vehicle due to tyre wear comprises an estimate of the coefficient of friction between the vehicle's tyres and the road on which the vehicle is located.

[0021] According to one embodiment, the information indicative of the pollution level emanating from the vehicle due to tire wear is derived at least in part from sensor data provided by sensors on the vehicle.

[0022] According to one embodiment, the pollution control system of the vehicle is a cabin air system.

[0023] According to one embodiment, the method includes estimating a concentration of pollutant particles caused by vehicle tire wear from the acquired information, and determining operating parameters of the cabin air system to reduce the concentration of pollutant particles caused by vehicle tire wear in the vehicle passenger compartment if the estimated concentration of pollutant particles caused by vehicle tire wear is greater than or equal to a predetermined threshold.

[0024] According to one embodiment, the vehicular pollution control system is a vehicular navigation system that takes air pollution into account. For example, pollution control may involve selecting an appropriate route by optimizing time, distance, energy and / or pollution levels encountered during the journey. A combination of several of these parameters may also be optimized, with different weights for each parameter, for example as selected by a user.

[0025] According to one embodiment, there is provided a vehicle controller comprising at least one processor configured to obtain information indicative of a pollution level generated from a vehicle due to tire wear, determine values ​​of operating parameters of a pollution control system of the vehicle in response to the obtained information, and control the pollution control system of the vehicle in accordance with the determined operating parameters.

[0026] According to one embodiment, there is provided a computer program element comprising program instructions which, when executed by one or more processors, cause the one or more processors to perform the method of any of the above-mentioned embodiments.

[0027] According to one embodiment, a computer readable medium is provided that, when executed by one or more processors, causes the one or more processors to perform the method of any of the above-described embodiments.

[0028] It should be noted that the embodiments described with respect to any of the above methods are equally valid for the apparatus (i.e., database system or vehicle controller) and vice versa.

[0029] The present disclosure will be better understood with reference to the detailed description, taken in conjunction with the non-limiting examples and the accompanying drawings. [Brief description of the drawings]

[0030] [Figure 1] FIG. 1 is a diagram showing a vehicle equipped with a cabin air system. [Diagram 2]FIG. 1 illustrates the generation and augmentation of a contamination map (also called a hypermap) from raw data. [Diagram 3] FIG. 13 is a flow diagram illustrating the generation of a hypermap containing information about road characteristics using wear-friction coefficient and / or road specific friction coefficient. [Figure 4] FIG. 1 is a flow diagram illustrating the use of a hypermap to determine contamination from tire wear without directly measuring tire wear, according to one embodiment. [Diagram 5] FIG. 11 is a flow diagram illustrating the use of a hypermap to determine contamination from tire wear without directly measuring tire wear, according to another embodiment. [Figure 6] FIG. 13 is a flow diagram illustrating an example in which a hypermap includes a coefficient of friction. [Figure 7a] FIG. 1 shows an arrangement of automotive sensors used to obtain vehicle pollution data. [Figure 7b] FIG. 1 shows an arrangement of automotive sensors used to obtain vehicle pollution data. [Figure 8] FIG. 1 illustrates the use of a laser sensor to determine road surface information. [Figure 9] FIG. 1 illustrates the use of acoustic sensors to determine road surface information. [Figure 10] FIG. 1 illustrates the use of a camera sensor to determine road surface information. [Figure 11] FIG. 1 is a diagram illustrating generation of a hypermap using sensor data from multiple vehicles. [Figure 12] FIG. 2 illustrates the structure of a hypermap for geographic locations. [Figure 13] FIG. 2 is a flow diagram illustrating a method for providing a vehicle with information indicative of the pollution levels generated by tire wear on the vehicle. [Figure 14] 1 is a flow diagram illustrating a method for controlling a pollution control system of a vehicle. DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

[0031] In the following detailed description, reference is made to the accompanying drawings, which show by way of example specific details and embodiments in which the present disclosure may be practiced. These embodiments are described in sufficient detail to enable those skilled in the art to practice the present disclosure. Other embodiments may be utilized and structural and logical changes may be made without departing from the scope of the present disclosure. Various embodiments are not necessarily mutually exclusive, and some embodiments may be combined with one or more other embodiments to form new embodiments.

[0032] An embodiment described with respect to one of the devices or methods is equally valid for the other devices or methods, and similarly, an embodiment described with respect to the device is equally valid for the vehicle or method, and vice versa.

[0033] Features described with respect to one embodiment may be applied to the same or similar features in other embodiments. Features described with respect to one embodiment may be applied to the other embodiments even if not explicitly described there. Also, additional elements and / or combinations and / or alternative elements described with respect to features of one embodiment may be applied to the same or similar features in other embodiments.

[0034] In various embodiments, the articles "a," "an," and "the" used in reference to features or elements include a reference to one or more of multiple features or elements.

[0035] As used herein, the term "and / or" includes any and all combinations of one or more of the associated listed items.

[0036] As used herein, the term "vehicle" includes motor-driven vehicles, e.g., having two, three, four, or more wheels. Examples of vehicles include cars, trucks, buses, lorries, or rail vehicles, e.g., locomotives. In some embodiments, the vehicle may be an electric vehicle or a hybrid (internal combustion engine and electric) vehicle. In some embodiments, the vehicle may be an internal combustion engine (ICE) vehicle (i.e., non-hybrid). The term vehicle may further include autonomous vehicles.

[0037] As used herein, the term "processor(s)" includes one or more electrical circuits capable of processing data, i.e., processing circuits. Processors may include analog circuits or components, digital circuits or components, or hybrid circuits or components. Other implementations of the functions described in more detail below may also be understood as "circuits" according to alternative embodiments. Digital circuits may be understood as any kind of logic implementation, and may be special purpose circuits, processors executing software stored in memory, or firmware.

[0038] As used herein, the term "module" refers to, forms part of, or includes an application specific integrated circuit (ASIC), an electronic circuit, a combinatorial logic circuit, a field programmable gate array (FPGA), a processor (shared, dedicated, or group) that executes code, other suitable hardware components that provide the described functionality, or a combination of some or all of the above, such as a system on a chip, etc. The term module may include memory (shared, dedicated, or group) that stores code executed by a processor.

[0039] As used herein, the term "data" may be understood to include information in any suitable analog or digital format, for example, provided as a file, a portion of a file, a set of files, a signal or stream, a portion of a signal or stream, a set of signals or streams, etc. However, the term data is not limited to the above examples and may take several forms and represent any information as understood in the art.

[0040] As used herein, the term "obtain" refers to a processor that actively obtains input from one or more sensors or data sources, or a processor that passively receives input. The term obtain may also refer to a processor that receives or obtains input from a communication interface, such as a user interface. A processor may also receive or obtain input via memory, registers, and / or analog / digital ports.

[0041] The embodiments will be described in detail below.

[0042] FIG. 1 shows a vehicle 100 equipped with a cabin air system.

[0043] The cabin air system includes one or more filters 101, fans 102, and ducts 103 that work together to supply and circulate air throughout the vehicle cabin. In some embodiments, the one or more filters 101, fans 102, and ducts 103 are arranged in series. The cabin air system is designed to remove contaminants, dust, and other harmful particles from the air, as well as regulate temperature and humidity levels. In particular, the cabin air system operates (and can be considered) as a vehicle interior air filtration system. Similarly, the cabin air system operates and can be considered as an on-board ambient air filtration system (ambient air in the sense of air outside the vehicle).

[0044] The cabin air system includes a controller 104 that controls various control actions, such as setting the speed of a fan, the opening of vents and air ducts, etc. The controller 104 may, for example, measure the indoor air quality and perform corresponding control actions. The controller 104 may form at least a part of a vehicle controller.

[0045] According to various embodiments, a more optimized cabin air (filter) system is provided that takes into account not only the interior air quality but also the outside air quality and the condition of its filter 101, aiming to provide improved interior air quality with lower energy consumption while optimizing filter usage, for example by extending the RUL (Remaining Useful Life).

[0046] To estimate the outside air quality, the controller 104 uses a pollution map. At least a portion of the data for the pollution map is provided from on-board data of the vehicle, including tire-generated pollution data (or tire usage data indicative of tire-generated pollution). For example, the tire usage data may include wear, usage, coefficient of friction, or a combination thereof. In some embodiments, the wear or coefficient of friction may be estimated (e.g., by the controller 104 using additional data such as weather, traffic, pavement characteristics, etc.). The controller 104 may, for example, calculate tire-generated pollution data based on the tire usage data and estimate the outside air quality from these pollution data. For example, an estimate of the pollution generated by the vehicle 100 (specifically tire-generated pollution, but may also be total pollution, including other pollution depending on external conditions such as traffic congestion) may be used to predict the need to activate an operation of the cabin air system (such as a filter operation) to improve the air quality. This allows for proactive activation of the cabin air system operation without waiting for feedback from sensors (indicating, for example, poor indoor air quality).

[0047] It should be noted that the calculated tire generated pollution data can be used, for example, by another controller of the vehicle 100, i.e. the polluting vehicle, or by the same controller 104, to determine the total pollution emissions of the vehicle (e.g. absolute or net value) and possibly control it to reduce the net emissions (and also, for example, for fault detection and fault elimination).

[0048] Air quality data are usually available from public measuring stations (especially PM10, PM2.5 and NO2 measurements), so that the cabin air system controller can use this information. However, apart from the information about the air quality available from the measuring stations, there are other factors that can increase air pollution. For example, certain (local) measuring stations only provide information about the current (local) pollution situation regarding a defined parameter (e.g. PM10), so there may be pollution that these measuring stations cannot recognize. This means that when only the information of such measuring stations is used, there are blind spots, for example regarding particles smaller than PM2.5 and larger than PM10.

[0049] According to various embodiments, this blind spot, especially in the control of the cabin air system as described above, is covered by using data collected by vehicle sensors (friction coefficient, vehicle movement, traffic volume, altitude difference, etc., to estimate pollution especially from tires) to provide more accurate data on pollution and to allow prediction of high pollution levels. Apart from PM10 and PM2.5, predictions are possible especially on ultrafine particles (UFP, particles smaller than 0.1 micrometers), since it is known that high friction and high traffic volume contribute substantially to UFP concentrations. Therefore, according to various embodiments, data for estimating pollution is collected from vehicles traveling on the road and / or from public databases.

[0050] It should be noted that one or more sensors may be provided in the vehicle and the collected sensor data may provide information about the expected pollution risk. The one or more sensors may include at least one hardware sensor, at least one software sensor, or a combination of at least one hardware and software sensor. In some embodiments, the one or more sensors may further include on-board sensors, remote sensors, and / or remote sensors. Thus, apart from PM10 and PM2.5 levels, the risk of increased ultrafine particle concentration may also be assessed using the vehicle sensors. For example, a friction sensor (which may be equipped in the vehicle to warn of slippery roads) may provide a friction coefficient indicative of the road surface and tire wear. From an air quality perspective, the higher the friction, the higher the expected UFP concentration and pollution levels. Thus, for example, the controller 104 may use this friction information to estimate the pollution generated from the tires. Similarly, data from other vehicle sensors collected and used to provide services such as warnings about traffic jams, accidents, slippery roads, etc. may also be used in the derivation of air quality information and predicted pollution, for example to intelligently operate the cabin air filter system. The data from these sensors can be used to improve the accuracy of information about air quality and to fill in the above-mentioned blind spots of measuring stations, i.e. to take into account factors influencing air pollution that are ignored when using only data from UFP measuring stations (for a certain particle size).

[0051] Combining measured Air Quality (AQ) data with data provided by vehicle sensors could provide more accurate information and allow prediction of expected risks of high pollution, especially with regard to UFPs. The estimated pollution levels could be used to control the vehicle's cabin air system (e.g. by activating specially designed filters (e.g. High-Efficiency Particulate Air (HEPA) filters)) or could be displayed to the user (e.g. the driver in a digitally visualized tool such as a dashboard or mobile app) to provide the user with different AQ information or recommendations such as alternative routes or changing the cabin filter.

[0052] Data relating to the pollution data acquired by such air quality measuring stations and vehicle sensors may be collected in a database 105, for example connected to the controller 104 (and accessible by a database server). This database 105 may store pollution data for multiple locations, i.e., the pollution data for each of multiple geographic locations (e.g., areas of a certain size, road segments, city areas, etc.) may be stored in a data structure (i.e., a pollution map), hereinafter referred to as a hypermap.

[0053] FIG. 2 illustrates the creation and augmentation of a hypermap 201 from raw data 202, performed, for example, by a database (eg, a corresponding database server) 105.

[0054] The input data, or raw data 202, may be measured data obtained via vehicle sensors or measurement stations, or may be calculated or derived data. In some embodiments, the calculated data may include tire wear estimates based on driving data, weather, tire characteristics, etc.

[0055] Examples of measurement data include: Optical measurement of tire tread depth, for example using stereo vision or lasers: wear, e.g. radius loss per time, can be measured. Data measured via wireless communication, e.g. radio frequency (RF) methods: data output as above. Slip ratio (based on the difference between GPS vehicle speed and tire speed): Easy to calculate. Skidmeter data: allows for the measurement of coefficient of friction (usually used at airports).

[0056] The raw data 202 is continuously acquired and stored and used to generate and (continuously) update the hypermap 201. When the raw data 202 is acquired from the vehicle, it may be anonymized, i.e. personal data protection may be provided by the data protection module 203. Information from the hypermap 201 may be requested, for example by the controller 104, for example from the database 105, to obtain data determining the pollution in the geographical area in which the vehicle 100 is located.

[0057] The raw data 202 can be stored and / or processed at various stages according to various embodiments. For example, tire wear data can be processed into pollution data and stored in the hypermap 201, or tire wear data can be stored in the hypermap 201 and processed (e.g., at the controller 104) when the pollution data is needed. In some embodiments, the hypermap data 201 is accessed by one or more external applications via the sending of one or more requests 205 by the application 204. Such requests 205 may be authenticated. The raw data 202 may not be accessible.

[0058] FIG. 3 is a flow diagram 300 illustrating the generation of a hypermap (e.g., hypermap 201) including information on road characteristics using wear-friction coefficients and / or road-specific friction coefficients, performed, for example, by a database (e.g., a corresponding database server) 105.

[0059] In 301, the tire wear is calculated using raw tire wear measurements 302 provided by a data source 303 as described above. The tire wear calculation can use tire (reference) data and past values ​​from a memory of past results (wherein the current results are also stored for later use). The wear calculation can (optionally) be temperature compensated, since tread depth can vary with the temperature of the tire itself. Taking into account the distance traveled 306 and comparing the tire wear before and after, the coefficient of friction can be determined in 307. Depending on the data provided by the data source 303, the coefficient of friction can also be determined without quantifying the tire wear.

[0060] Using location information 308, such as from a Global Positioning System (GPS), an entry is generated for the hypermap at 309 including the determined friction coefficient for each location. As shown in FIG. 3, the hypermap may include friction coefficients for each of a number of different weather conditions (e.g., "rain", "dry weather", "snow", or temperature ranges such as below 0° C., 0° C.-15° C., 15° C.-25° C., above 25° C., etc.). Thus, weather data 310 is taken into account in generating the hypermap entries. In an alternative embodiment of 309, the friction coefficient may be a dimension of the hypermap.

[0061] FIG. 4 is a flow diagram 400 illustrating the use of a hypermap (eg, hosted by database 105) to determine contamination from tire wear without directly measuring tire wear, according to one embodiment.

[0062] The controller 104 obtains the location 401 of the vehicle 100, for example from a GPS, and determines the current weather conditions 402 in which the vehicle 100 is located, for example from rain sensor data and / or weather maps. Then, at 403, the vehicle 100 requests a hypermap entry from the database 105 for or based on the determined current weather conditions 402 and the determined vehicle location 401. The requested entry may be a calculation of pollution data based on the current weather conditions 402 and the determined vehicle location 401.

[0063] At 404, the database 105 provides the pollution data stored for the requested entry, i.e. current weather conditions 402 and vehicle position 401, and the controller 104 receives this pollution data.

[0064] The controller 104 may also receive additional pollution data 405, for example information about traffic, derived information such as the speed / velocity or acceleration of the vehicle 100.

[0065] At 406, the controller 104 controls the cabin air system of the vehicle 100 based on the received pollution data (i.e. the pollution data contained in (the respective entries of) the hypermap, and possibly additional pollution data 405), i.e. the controller 104 adjusts operating parameters, e.g. filtration parameters, of the cabin air system in response to the received pollution data.

[0066] In some embodiments, the additional pollution data 405 is based on one or more factors, such as traffic factors, traffic density, acceleration, and / or statistical measurements as mentioned above.

[0067] FIG. 5 is a flow diagram 500 illustrating the use of a hypermap (eg, hosted by database 105) to determine contamination from tire wear without directly measuring tire wear, according to another embodiment.

[0068] 4, the controller 104 obtains the location 501 of the vehicle 100, for example from a GPS, and further determines the current weather conditions 502 in which the vehicle 100 is. Then, at 503, the vehicle 100 requests a hypermap entry from the database 105 for the determined current weather conditions 502 and the determined vehicle location 501.

[0069] At 504, the database 505 provides the requested entry, i.e. the pollution data stored for the current weather conditions 502 and the vehicle position 501, and the controller 104 receives the pollution data. The requested entry may be a calculation of the pollution data based on the current weather conditions 502 and the determined vehicle position 501.

[0070] Similar to the example of FIG. 4, the controller 104 may receive additional pollution data 505, for example information relating to traffic, speed / velocity information or derived information such as acceleration of the vehicle 100.

[0071] At 506, the controller 104 may further obtain or receive, e.g., via vehicle-to-vehicle communication, friction coefficients 507 of one or more other vehicles in its vicinity, and may take this information regarding tire conditions of the other vehicles into account (in addition to the received pollution information) when determining adjustments to operating parameters of the cabin air system, e.g., filtration parameters.

[0072] At 508, the controller 104 adjusts the operation of the cabin air system according to the determined adjustments.

[0073] FIG. 6 is a flow diagram 600 illustrating an example of including a friction coefficient in a hypermap.

[0074] The controller 104 obtains the location of the vehicle 601, for example from a GPS, and further determines the current weather conditions 602 in which the vehicle 100 is located (eg, from rain sensor data and / or weather maps, etc.).

[0075] Thereafter, at 603, a hypermap entry is requested from the database 105 for the determined current weather conditions 602 and the determined vehicle position 601. In this example, the hypermap entry includes a friction factor (as pollution data).

[0076] At 604, the database 105 provides the friction coefficient stored for the requested entry, i.e., the current weather conditions 602 and the vehicle position 601 (e.g., a particular road) in the hypermap, and the controller 10 receives the friction coefficient.

[0077] The controller 104 may also receive additional pollution data 605, such as information about the tires, the driving style of the driver (eg based on speed / velocity and acceleration data).

[0078] At 606, the controller 104 determines the actual tire generated pollution of the vehicle 100 and controls the cabin air system of the vehicle 100 accordingly, i.e., the controller 104 adjusts the operating parameters, e.g., filtration parameters, of the cabin air system in response to the received pollution data.

[0079] Compared to the case where the pollution data stored in the hypermap are estimates of the pollution generated by the vehicle (at the respective location and under the respective weather conditions), the approach of FIG. 6 allows a more accurate determination of the tire-generated pollution of the vehicle (e.g. because individual parameters such as driving behavior can be taken into account). By obtaining the friction coefficient from the database 105, the controller 104 does not need to determine the friction coefficient (between the road and the tires) itself. Note that the database 105 may store and provide road characteristics to the controller 104 so that the controller 104 can estimate the friction coefficient. Furthermore, the estimate of the tire-generated pollution of the vehicle can be used (e.g. by a further controller of the vehicle) to determine the total emissions of the vehicle and possibly control it to reduce the net emissions (and for example for fault detection and elimination).

[0080] As mentioned above, pollution data can be acquired by vehicle sensors or measurement sensors and used to generate a hypermap such as that shown in FIG. 2, from which the pollution data can be downloaded by the vehicle (in particular the controller of the cabin air system).

[0081] The pollution data may include information (e.g., concentration) on pollutants such as pollen, PM10, PM2.5, PM1, NOx, CO, CO2, VOC (volatile organic compounds), etc. Availability of information on various pollutants depends on sensors on the vehicle and / or data from air quality measurement stations (or air quality data providers). Information on all of these pollutants is not required and can be used (e.g., by the controller 104) to determine the air quality level. Information on multiple pollutants can be used in combination to improve accuracy, but information on a single pollutant can also be used individually (or alone) to ensure some level of control over the cabin air system. For example, PM1 and / or PM2.5 concentrations can be used to determine whether to switch on a HEPA filter system. Depending on the use case, the controller 104 may, for example, activate a HEPA filter based on thresholds for one or both of these concentrations, and when it determines that the air quality is good enough, switch off the filter system to preserve the life of the filter system. For example, a threshold of 10ug / m for PM2.5 and PM1 may be used to determine whether to switch on a HEPA filter. 3 In this case, a threshold of 10ug / m2 for PM2.5 and / or PM1 concentrations is used. 3 If this is exceeded, the controller 104 activates the HEPA system.

[0082] In the case of pollen, depending on how accurate the details of pollen concentration and pollutant composition are, the controller 104 may also turn on the HEPA filter when it detects high pollen concentration (possibly based on calculations) to preserve passenger health. For example, the controller 104 may activate the PM10 filter if the pollen concentration is above a predefined threshold.

[0083] The CO2 concentration needs to be kept below a certain threshold to avoid passenger troubles (headaches, concentration problems, etc.). The controller 104 can use the following strategies: For example, if the CO2 exceeds 1000 ppm, the controller 104 controls the cabin air system to open a duct (e.g., a flap) for fresh air to increase the flow of fresh air. For example, the controller may include a smart management implemented in the flap to keep the CO2 concentration below 1000 and keep a balance between thermal management and air quality. Other pollutants such as NOx and / or VOCs can also be considered for strategy execution, but the specific control action may depend on the cabin air system integrated in the respective vehicle. As an example of considering multiple pollutants, the controller 104 can turn off the intake of fresh air if the concentration of any of the above pollutants is above a certain CO2 threshold and the CO2 concentration is below another certain CO2 threshold. As another example, the controller 104 can turn on the activated carbon filter if the NOx concentration and / or the VOC concentration are above a certain threshold.

[0084] As mentioned above, pollution data can be acquired using various vehicle sensors, which are described in more detail below.

[0085] Figures 7a and 7b show a vehicle sensor arrangement in the form of one or more car sensors that may be used to obtain pollution data for the vehicle. The car sensors may be configured to obtain sensor data around the vehicle and / or to obtain sensor data from a server at a remote location.

[0086] The laser sensor 701 can measure either in front of or behind the vehicle.

[0087] The acoustic sensor 702 can measure acoustic changes in the front or rear wheels.

[0088] For example, a camera 703 located on the windshield can collect information about the surface on which the car is traveling.

[0089] FIG. 8 illustrates the use of a laser sensor 801 to determine road surface information 804.

[0090] In some embodiments, the laser sensor 801 used for road surface detection may be a Light Detection and Ranging (LiDAR) sensor. A LiDAR sensor uses a laser beam 802 to measure the distance between the sensor 801 and the road surface 803. A LiDAR sensor creates a 3D map of the road surface by rapidly emitting and detecting laser pulses. For road surface detection, a laser (or LiDAR) sensor 801 shines a laser beam on the road surface and measures the time it takes for the light to bounce back to the sensor. By calculating the time it takes for the light to travel the road surface and back, the laser sensor 801 can determine the distance between the sensor 801 and the road surface 803. Data collected from the LiDAR sensor 801 can be analyzed to detect irregularities in the road surface, such as potholes, cracks, and steps.

[0091] As mentioned above, road surface information 804 (e.g., 3D maps or information derived therefrom, such as road roughness levels) may be stored (as contamination information) in database 805 for each geographic location. That is, road surface information 804 may be tied or mapped to a geographic location. In some embodiments, the geographic location may be determined from GPS 806, for example, for later request by another vehicle.

[0092] FIG. 9 illustrates the use of an acoustic sensor 901 to determine road surface information 904.

[0093] In some embodiments, the acoustic sensors 901 that determine information that is characteristic of the road surface 902 are designed to measure the noise levels generated when a vehicle drives over different road surfaces. The principle of these sensors is based on the fact that different road surfaces generate different noise levels when a vehicle passes over them.

[0094] In some embodiments, the acoustic sensor 901 includes a microphone that is built into the vehicle and can be connected to a data collection system. As the vehicle is moving, the microphone picks up noise generated by the tires and transmits the data to the data collection system. The data is then analyzed to determine the noise level generated by the vehicle on the road surface. Determining the noise level can include comparing with a reference database 903 that stores the noise level data. The noise level data collected by the acoustic sensor can be used as road surface information to assess road quality and identify areas corresponding to the road surface by comparing the noise level with a database of reference noise levels.

[0095] As mentioned above, road surface information 904 (road quality) can be stored (as pollution information) in database 905 for each geographic location. That is, road surface information 904 can be tied or mapped to a geographic location. In some embodiments, the geographic location can be determined from GPS 906, for example, for later request by another vehicle.

[0096] FIG. 10 illustrates the use of an image sensor 1001 to determine road surface information 1004 .

[0097] In some embodiments, an image sensor 1001 can be used to measure the characteristics of the road surface 902. The image sensor 1001 can include using a camera 1001 (or multiple cameras) to capture images or videos of the road surface 902 and analysing these images or videos to identify and classify different types of road surfaces, i.e., to compare them with reference data from a reference database 1003. In some embodiments, the analysis of the images and the comparison with the reference data can be performed using an artificial intelligence module. In some embodiments, the AI ​​module is trained using historical data, which is the reference data. The training can be based on supervised, unsupervised and / or semi-supervised learning.

[0098] In some embodiments, analyzing and classifying the images based on the type of road surface includes the following steps. Taking images or videos: Using the camera 1001, an image or video of the road surface 1002 is taken. Pre-processing: Pre-processing the captured images or videos to improve their quality and remove unwanted noise and distortions. Feature extraction: Extract features of the road surface 1002 such as texture, color, shape, etc. from the pre-processed images or videos. Classification: Using the extracted features, the road surface 1002 is classified into different classes such as asphalt, concrete, gravel, and dirt.

[0099] The results of the classification can be used, for example, to make decisions about how to navigate a road. For example, a vehicle, such as an autonomous vehicle, can use this information to adjust its speed or steering angle. According to various embodiments, tire-borne pollution can be used as pollution data to estimate tire-borne pollution, as described above.

[0100] There are many different types of cameras that can be used for road surface detection: monocular cameras, stereo cameras, depth cameras, etc. Each type has its advantages and disadvantages, and the choice of camera depends on the specific application and requirements.

[0101] As described above, road surface information 1004 (e.g., road classification) can be stored (as contamination information) in database 1005 for each geographic location. That is, road surface information 1004 can be tied or mapped to a geographic location. In some embodiments, the geographic location can be determined from GPS 1006, for example, for later request by another vehicle.

[0102] FIG. 11 is a diagram for explaining generation of a hypermap using sensor data from multiple vehicles 1101, 1102, and 1103.

[0103] Each vehicle performs measurements using sensor measurements using one or more of the approaches of Figures 8, 9, and 10 and communicates each acquired road surface information 804, 904, 1004 to a database system (e.g., cloud) 1104 (e.g., corresponding to database 105) that stores all road surface information in a road information storage 1105. The database system 1104 may include one or more servers.

[0104] Furthermore, database system 1104 (which may be implemented as one or more cloud servers) obtains information about air quality from one or more air quality data providers 1109 (e.g. operating measurement stations as described above) and one or more traffic condition data providers 1110 (e.g. operating traffic monitoring stations) and stores it in air quality data storage 1105. Using information from road surface information storage 1106 (which may have already been analyzed to derive road surface information), information from air quality data storage 1105, and possibly further parameters from parameter storage 1107, the server generates (or updates) a hypermap 1108.

[0105] FIG. 12 is a diagram showing the structure of a hypermap relating to geographical locations.

[0106] Starting from a classical map 1201 showing roads, the geographical regions can be structured into geographical locations according to the location Air Quality (AQ) provider (possibly using interpolation and / or extrapolation). This structure 1202 is further refined (possibly using interpolation and / or extrapolation) according to the geographical location where the sensor data is received from the vehicle. This results in a structure 1203 of geographical locations that can be used as a basis for a hypermap.

[0107] In summary, according to various embodiments, a method is provided as illustrated in Figures 13 and 14.

[0108] FIG. 13 is a flow diagram 1300 illustrating a method for providing a vehicle with information indicative of the pollution levels generated by tire wear on the vehicle.

[0109] At 1301, for each of at least some of the geographic locations, information indicative of tire wear of a vehicle at each geographic location is obtained, the vehicle wear being derived from vehicle sensor data of one or more vehicles.

[0110] At 1302, for each of a plurality of geographic locations, pollution data is determined (e.g., stored) including information indicative of (e.g., relating to or based on) the pollution level caused by tire wear of the vehicle at each geographic location, derived from the obtained information indicative of tire wear of the vehicle.

[0111] In 1303, in response to a request from a vehicle (i.e., a requester or requesting vehicle, e.g., a requester or requesting unit of a vehicle) or other source requesting information indicating the pollution level generated from the vehicle due to tire wear, for the geographic location where the vehicle is located, information is provided that is determined (e.g., calculated and / or stored) for the geographic location where the vehicle is located, indicating the pollution level generated from the vehicle due to tire wear.

[0112] At 1304, the method 1300 further includes receiving a transmission from each of the one or more vehicles of information indicative of tire wear on the vehicle at a geographic location in which the each of the one or more vehicles is located. The transmission optionally includes an indication of the geographic location in which the each of the one or more vehicles is located.

[0113] 14 is a flow diagram 1400 illustrating a method for controlling a pollution prevention system of a vehicle. According to some embodiments, the pollution control system may be or be part of a navigation system of the vehicle that takes air pollution into account.

[0114] At 1401, information is obtained that indicates the pollution levels emanating from a vehicle due to tire wear.

[0115] At 1402, values ​​of operating parameters of a pollution control system of the vehicle are determined in response to the obtained information.

[0116] At 1403, a pollution control system of the vehicle is controlled according to the determined values ​​of the operating parameters.

[0117] In other words, according to various embodiments, vehicle tire wear data is collected based on sensor data from multiple vehicles at different locations and used to build a database of pollution caused by vehicle tire wear per geographic location, which can then be requested by the vehicle, for example to control the vehicle's pollution control system.

[0118] The methods described herein may be performed, and the various processing or computing units, devices, and computational entities described herein may be implemented by one or more circuits. In one embodiment, a "circuit" may be understood as any kind of logic implementation entity, which may be hardware, software, firmware, or any combination thereof. Thus, in one embodiment, a "circuit" may be a hardwired logic circuit, or a programmable logic circuit such as a programmable processor, e.g., a microprocessor. Also, a "circuit" may be software implemented or executed by a processor, e.g., any kind of computer program, e.g., a computer program using virtual machine code. Other implementations of each function described herein may also be understood as a "circuit" according to alternative embodiments. An apparatus (e.g., a database system or a vehicle, or a vehicle component (e.g., a vehicle controller)) performing one of the methods includes, for example, a communication interface (for sending and receiving various data and information), a processing unit, and a memory.

[0119] Although the present disclosure has been particularly shown and described with respect to certain embodiments, it should be understood by those skilled in the art that various changes in form and detail can be made therein without departing from the spirit and scope of the present disclosure as defined in the appended claims. The scope of the present disclosure is therefore indicated by the appended claims, and all changes that come within the meaning and range of equivalency of the claims are intended to be embraced therein.

Claims

1. A method (1300) for providing a vehicle (100) with information indicative of a pollution level generated by tire wear of the vehicle, comprising: obtaining (1301) for each of at least a portion of the plurality of geographic locations, information indicative of tire wear of the vehicle at each geographic location, the information being derived from vehicle sensor data of one or more vehicles; determining (1302) for each of said plurality of geographic locations pollution data (505, 605) comprising information indicative of a pollution level caused by tire wear of the vehicle at each geographic location derived from the obtained information indicative of tire wear of the vehicle; In response to a request (205) for information indicating the level of pollution generated from the vehicle (100) due to tire wear for the geographic location where the vehicle is located, a step (1303) of providing information indicating the level of pollution generated by tire wear of the vehicle determined for the geographic location where the vehicle is located.

2. 2. The method (1300) of claim 1, further comprising the step of receiving (1304) a transmission from each vehicle (100) of the one or more vehicles of information indicative of vehicle tire wear at a geographic location in which each vehicle of the one or more vehicles is located.

3. The method of claim 2 , wherein the transmission includes an indication of a geographic location in which each of the one or more vehicles is located.

4. The method (1300) of claim 1, wherein the pollution data is stored in a database (105) external to the one or more vehicles.

5. 13. The method of claim 1, further comprising receiving the request via wireless communication and providing information indicative of a pollution level caused by tire wear of the vehicle.

6. 2. The method (1300) of claim 1, further comprising obtaining information indicative of tire wear of the vehicle for a plurality of weather conditions, and determining, for each weather condition of the plurality of weather conditions, the pollution data including information indicative of a pollution level caused by tire wear of the vehicle.

7. the request indicates at least one of the plurality of weather conditions; 6. The method (1300) of claim 5, comprising providing information regarding a pollution level caused by tire wear of the vehicle at a geographic location in which the vehicle is located, determined for the indicated weather conditions.

8. The method (1300) according to any one of claims 1 to 7, wherein the geographic location where the vehicle is located is a road or a road segment.

9. 9. The method of claim 8, wherein the information indicative of the pollution level caused by tire wear of the vehicle comprises information indicative of the condition of the road or road segment, and optionally the information indicative of the pollution level caused by tire wear of the vehicle comprises an estimate of the coefficient of friction between the vehicle tires and the road or road segment.

10. A database system (1104), obtaining, for each of at least a portion of the plurality of geographic locations, information indicative of tire wear for the vehicle at each geographic location, the information being derived from vehicle sensor data for the one or more vehicles; determining, for each of said plurality of geographical locations, pollution data comprising information indicative of a pollution level caused by tyre wear of a vehicle at each geographical location, derived from the obtained information indicative of tyre wear of said vehicle, for example according to a method as claimed in any one of claims 1 to 9; A database system (1104) comprising at least one processor configured to provide, in response to a request for information indicating a pollution level caused by tire wear of the vehicle for a geographic location where the vehicle is located, information indicating a pollution level caused by tire wear of the vehicle determined for the geographic location where the vehicle is located.

11. A controller (104), obtaining information indicative of a pollution level emanating from the vehicle (100) due to tire wear; determining values ​​of operating parameters of a pollution control system of the vehicle in response to the obtained information; A controller (104) comprising at least one processor configured to control a pollution control system of the vehicle in accordance with the determined operating parameters.

12. The controller (104) of claim 11, wherein the information indicating the pollution level generated from the vehicle due to tire wear is obtained from a database (105) external to the vehicle, and the information indicating the pollution level generated from the vehicle due to tire wear is obtained from the database (105), optionally via wireless communication.

13. The controller (104) of claim 11 further comprising obtaining information from the database indicative of a pollution level generated by the vehicle due to tire wear by sending a request from the vehicle to the database indicating a location of the vehicle, weather conditions of the vehicle, or both.

14. The controller (104) of claim 11, wherein the information indicating the level of pollution generated from the vehicle due to tire wear includes information indicating the condition of the road on which the vehicle (100) is located, and the information indicating the level of pollution generated from the vehicle due to tire wear includes an estimated coefficient of friction between the tires of the vehicle (100) and the road (802, 903, 1003) on which the vehicle (100) is located.

15. The controller (104) of claim 11, wherein the information indicative of a pollution level emanating from the vehicle (100) due to tire wear is derived at least in part from sensor data provided by sensors (701, 702, 703) of the vehicle (100).

16. The controller (104) of claim 11, wherein the vehicle pollution control system is a cabin air system.

17. The controller (104) of claim 16, wherein the at least one processor is further configured to estimate a concentration of polluting particles caused by tire wear of the vehicle from the acquired information, and determine operating parameters of the cabin air system to reduce the concentration of polluting particles caused by tire wear of the vehicle in the passenger compartment of the vehicle when the estimated concentration of polluting particles caused by tire wear of the vehicle is equal to or greater than a predetermined threshold.

18. The controller (104) of any one of claims 11 to 17, wherein the pollution control system of the vehicle (100) is a navigation system of the vehicle that takes air pollution into account.

19. A computer program element comprising program instructions which, when executed by one or more processors, cause said one or more processors to carry out the method according to any one of claims 1 to 9.

20. A computer readable medium which, when executed by one or more processors, causes the one or more processors to perform the method of any one of claims 1 to 9.

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