Control and tracking system for a spreader

Abstract

A control and tracking system (16) for a vehicle (10) includes a hopper spreader assembly (12) having a hopper (12a) and a spreader (12b). The control and tracking system (16) includes a control module (14) that can control the spreader (12b) of the hopper spreader assembly (12), a sensor (28) that can produce sensor data (28a-d), and a controller (18) that is in communication with the control module (14) and with the sensor (28). The controller (18) can obtain the sensor data (28), and optionally obtain third-party data (39) from a third-party (40). The controller (18) can provide an instruction to the control module (14) based on an input including the sensor data (28), and optionally the third-party data (40), to control the hopper spreader assembly (12).

Description

CONTROL AND TRACKING SYSTEM FOR A SPREADERTECHNICAL FIELD AND BACKGROUND

[0001] The present disclosure generally relates to a control and tracking system for controlling the spreading of granular particles and / or spraying of liquid onto the ground or road surfaces.

[0002] The spreading of salt, sand, seed, fertilizer, or other generally dry, free-flowing material is common in many areas for road or driveway maintenance, as well as landscaping and agriculture. However, depending on the treatment method (e.g., salt granulates, pre-wetted salt, or salt liquid, or mixture thereof), the frequency of traffic, the speed of traffic, the weather, and the road conditions, the treatment may be insufficient or overapplied, where waste salt is left on the road and / or parking area from a previous treatment.SUMMARY

[0003] The present disclosure describes a control and tracking system having a controller that controls a hopper spreader assembly of a vehicle for dispensing one or more liquids and / or dry, free-flowing solid material onto a road surface. The controller may control the hopper spreader assembly by providing data to a hopper spreader control module of the control and tracking system that may be mounted to the hopper spreader assembly. The data communicated to the hopper spreader control module by the controller may be based on data from one or more sensors of the vehicle and / or a server. The data from the one or more sensors and / or the server may be processed by an artificial intelligence (Al) model that may be implemented by the controller and / or the server to improve the data communicated to the hopper spreader control module. For example, the Al model may improve determining the suggested spreading or application rate for dispersing liquids and / or solid material so that the controller can communicate an improved suggested application rate to the hopper spreader control module. This prevents the spreader control module from causing the spreader to provide insufficient and / or overapply material to road surfaces.

[0004] In one example of the present disclosure, a control and tracking system for a vehicle includes a hopper spreader assembly having a hopper and a spreader. The control and tracking system includes a control module that can control the spreader of the hopper spreader assembly, a sensor that can produce sensor data, and a controller that is in communication with the control module and with the sensor. The controller can obtain the sensor data, and optionally obtain third-party data from a third-party. The controllercan provide an instruction to the control module based on an input including the sensor data, and optionally the third-party data, to control the hopper spreader assembly.

[0005] In another example, the control and tracking system is in combination with a remote server in communication with the controller. The remote server can obtain the sensor data from the controller, wherein the instruction is a first instruction, wherein the server can determine and provide a second instruction to the controller based on the sensor data.

[0006] In yet another example, in any of the above control and tracking systems, at least one of the remote server and the controller includes or has access to an artificial intelligence model having an algorithm to produce an operating parameter for the hopper spreader assembly. The controller can provide the instruction based on the operating parameter

[0007] In yet another example, in any of the above control and tracking systems, the algorithm is trained on historical data using machine learning.

[0008] In yet another example, in any of the above control and tracking systems, the operating parameter is an application rate.

[0009] In yet another example, in any of the above control and tracking systems, the third-party data is based on a location of the vehicle.

[0010] In yet another example, in any of the above control and tracking systems, the third-party data includes data selected from the group consisting of live weather data, live road data, and weather forecast data.

[0011] In yet another example, in any of the above control and tracking systems, the sensor data can indicate a road surface condition, the road surface condition including at least one of: air temperature; road temperature; dew point temperature; an amount of water on a road surface; an amount of snow on the road surface; an amount of slush on the road surface; friction of the road surface; and an amount of salt left on the road surface.

[0012] In another example of the present disclosure, a method is implemented by a computing device to set a parameter for a hopper spreader assembly of a vehicle. The method includes obtaining first data from a first sensor of the vehicle, generating a suggested parameter by implementing an artificial intelligence (Al) model using first input data including the first sensor data, setting the parameter for the hopper spreader assembly based on the suggested parameter, obtaining second data from the first sensor or a second sensor or of the vehicle, and updating the Al model based on second input data including the suggested parameter and the second data.

[0013] In another example, the first sensor data includes at least one of: (i) road data indicating a condition of a road surface; (ii) vehicle speed data indicating the speed of the vehicle; (iii) vehicle weather data indicating outside weather conditions surrounding the vehicle; (iv) location data indicating the location of the vehicle; and (v) salinity data indicating an amount of salt on the road surface.

[0014] In yet another example, in any of the above methods, the second sensor data includes at least one of: road data indicating a condition of a road surface; (ii) vehicle speed data indicating the speed of the vehicle; (iii) vehicle weather data indicating outside weather conditions surrounding the vehicle; (iv) location data indicating the location of the vehicle; and (v) salinity data indicating an amount of salt on the road surface.

[0015] In yet another example, in any of the above methods, setting the parameter for the hopper spreader assembly includes providing the hopper spreader assembly one or more instructions based on the suggested parameter.

[0016] In yet another example, in any of the above methods, further including obtaining third data from a third-party supplier, wherein the first input data further includes the third data.

[0017] In yet another example, in any of the above methods, the third data includes at least one of: (i) weather data; and (ii) road data.

[0018] In yet another example, in any of the above methods, the weather data includes live weather data and weather forecast data, and the road data comprises live road data.

[0019] In yet another example, in any of the above methods, obtaining third data from the third-party supplier is based on location data of the vehicle.

[0020] In yet another example, in any of the above methods, the second input data further includes prior data.

[0021] In yet another example, in any of the above methods, the prior data includes the first data.

[0022] In yet another example, in any of the above methods, the Al model has an algorithm, wherein updating the Al model based on second input data is by using machine learning.

[0023] These and other objects, advantages, purposes and features of the present disclosure will become apparent upon review of the following specification in conjunction with the drawings.BRIEF DESCRIPTION OF THE DRAWINGS

[0024] FIG. 1 A is a perspective view of a vehicle having a hopper spreader assembly and a hopper spreader control module mounted to the hopper spreader assembly;

[0025] FIG. 1 B is a perspective view of a vehicle similar to the vehicle of FIG. 1 A having a hopper spreader assembly and a hopper spreader control module mounted to the hopper spreader assembly;

[0026] FIG. 2 is an enlarged perspective view of the hopper spreader assembly of FIGS. 1 A-B with the vehicle omitted for clarity;

[0027] FIG. 3 is a front elevation view of a controller that is in communication with the hopper spreader control module of FIGS. 1A-B and 2;

[0028] FIG. 4 is a schematic drawing of the control and tracking system of the hopper spreader assembly of FIGS. 1 A-B and 2;

[0029] FIG. 5 is a block diagram of the control and tracking system of the hopper spreader assembly of FIGS. 1A-B and 2;

[0030] FIG. 6 is another block diagram of the control and tracking system of the hopper spreader assembly of FIGS. 1A-B and 2; and

[0031] FIG. 7 is a flowchart representative of a process to determine a suggested application rate for the hopper spreader assembly of FIGS. 1 A-B and 2.DETAILED DESCRIPTION OF PREFERRED EMBODIMENTS

[0032] Referring to FIGS. 1 A-B, the numeral 10 generally designates a vehicle that includes a hopper spreader assembly 12 having a hopper 12a and a spreader 12b that is configured to dispense one or more liquids and / or dry, free-flowing material. The hopper spreader assembly 12 can be controlled by a hopper spreader control module 14 of an electronic control and tracking system 16. (FIGS. 4-6). As will be more fully described below, the electronic control and tracking system 16 is configured, based on various data, to automate and enhance the operation of the hopper spreader assembly 12. As used herein, the term "data" is to be interpreted broadly and is meant to encompass any information in any suitable format.

[0033] Referring to FIG. 2, the spreader 12b may be in the form of a liquid dispenser with a pre-wet nozzle, a hose reel with a dispensing hose, a spray bar 12d, and / or a spinner 12e for dispensing solid material that is fed from an auger (not shown but mounted inside the hopper 12a) to the spinner 12e via a chute 12c. The spreader 12b may be selectively supplied with liquid through a selector valve controlled by the hopper spreader control module 14 (FIG. 1 A and 1 B) that may be powered by a battery of the vehicle 10. The hopper spreader control module 14 may further control other devices included in the hopper spreader assembly 12 such as, for example, lights, vibration, and / or cameras. For further details of the hopper spreader assembly 12, reference is made to U.S. Pat. App. Ser. No. 16 / 822,623, entitled MODULAR DUAL WALL SPREADER WITH LIQUID STORAGE TANKS, and filed on March 18, 2020 (U.S.Pub. No. 2020 / 0298261, published on Sep. 24 2020), which is incorporated by reference herein in its entirety.

[0034] Referring to FIGS. 4-6, the hopper spreader control module 14 controls the hopper spreader assembly 12 based on data obtained from a controller 18 of the electronic control and tracking system 16. The controller 18 may include a central processing unit (CPU) 18a and a memory 18b. The controller 18 may provide data to the hopper spreader control module 14 based on data received via a user interface 23 of the controller 18. The user interface 23 may include a plurality of input controls such as, for example, buttons and / or graphical user interface elements (e.g., icons, buttons) formed on a display 22 (e.g., a touch screen display) of the user interface 23. The controller 18 may be powered by an onboard power supply 24 of the controller 18 and / or a battery of the vehicle 10, similar to the hopper spreader control module 14, via electrical wiring. The controller 18 may be mounted in the cabin of the vehicle 10 or may be a hand-held mobile device.

[0035] Referring to FIG. 5, the controller 18 can obtain location data from a global positioning system (GPS) antenna 20 of the electronic control and tracking system 16. The GPS antenna 20 may be physically mounted to the hopper 12a, as shown in FIGS. 1 B and 2, and the GPS antenna 20 may communicate with the controller 18 via the hopper spreader control module 14, as shown in FIG. 1 B.

[0036] The controller 18 also can obtain data from a plurality of sensors 28 of the electronic control and tracking system 16 that may be mounted to the vehicle 10 for measuring and / or collecting data. The sensors 28 may include one or more hopper sensors that measure parameters of the hopper spreader assembly 12. For example, the hopper sensors may detect and / or measure rotational speed of an auger and / or spinner 12e and / or amps drawn by a motor of the spreader 12b. Other data that may be collected by the hopper sensor(s) and / or determined by the controller 18 include the amount of material, the spreading radius or width of material, and / or the status of the hopper spreader assembly 12. For example, the CPU 18a of the controller 18 may determine the amount of material based on the spreading width, the vehicle speed, and / or the spreading rate.

[0037] Referring to FIGS. 5-6, the sensors 28 may include one or more road sensors that detect and / or measure road data 28a. It should be noted that the term "road data" is to be interpreted broadly and is meant to include any ground data. The road data may indicate conditions of a road surface being travelled on by the vehicle 10 such as, for example, the road temperature, the amount of water, snow, ice, and / or slush on the road (e.g., depth of water, snow, ice, and / or slush), the type of road (e.g., pavement, gravel, dirt), and / or friction of the road surface. For example, one of the road sensors may be a camera thatcaptures road data 28a in the form of a processed or an unprocessed video stream showing the road that is being travelled on by the vehicle 10. The sensors 28 may also include one or more road sensors that detect and / or measure salinity data 28b that indicates the amount of salt left on the road from prior treatments.

[0038] The sensors 28 may include one or more vehicle sensors that detect and / or measure parameters of the vehicle 10. For example, the vehicle sensors may detect and / or measure vehicle speed data 28c that indicates the speed of the vehicle 10. The sensors 28 may include one or more vehicle weather sensors that may detect and / or measure vehicle weather data 28d that indicates outside weather conditions surrounding the vehicle 10 such as, for example, air temperature, outside dew point temperature (i.e., humidity), and / or outside wind conditions (e.g., direction, speed).

[0039] The controller 18 may provide one or more instructions to the hopper spreader control module 14 based on the data from the GPS antenna 20 and / or sensors 28 so that the control module 14 can regulate the operational parameters of the hopper spreader assembly 12. For example, the controller 18 may cause the control module 14 to regulate the speed of the auger and / or spreader 12b by providing instructions to the hopper spreader assembly 12 based on rotational speed data received from the control module 14.

[0040] The controller 18 can transmit the data obtained from the GPS antenna 20 and / or the plurality of sensors 28 to a remote server 30 via one or more wireless links 32 (e.g., 4G standard, Wi-Fi), as shown in FIG. 1 B. Alternatively, the controller 18 can transmit data to the server 30 via one or more wired and / or wireless links that may include one or more local area networks (LANs), wide area networks (WANs), cellular or mobile networks (e.g., 4G, Wi-Fi), and / or the Internet.

[0041] The server 30 can optionally be a part of the control and tracking system 16. The server 30 can process the data obtained from controller 18 to determine spreading or application rates, determine travel speed, and create time stamps. The server 30 can then send data to the controller 18 based on the data received from the controller 18. The controller 18 may provide one or more instructions to the hopper spreader control module 14 based on the data received from the server 30.

[0042] Thus, the controller 18 can provide one or more instructions to the hopper spreader control module 14 based on data received from the server 30, the sensors 28, and / or the GPS antenna 20. This allows the controller 18 and / or the server 30 to manage, and optionally optimize, the dispensing of the liquid and / or solid material to road surfaces. To accomplish this, the controller 18 and / or server 30 may implement one or more algorithms as discussed below.

[0043] Referring to FIG. 6, the controller 18 and / or the server 30 may implement an artificial intelligence (Al) model 34 of the electronic control and tracking system 16 that includes one or more algorithms tomanage, and optionally optimize, the dispensing of liquids and / or solid materials. The Al model 34 may include one or more algorithms may have machine learning, deep learning, and / or other artificial machine- driven logic. For example, the Al model 34 may be a software program that is executed by one or more computing devices of the controller 18 (e.g., the CPU 18a) and / or the server 30.

[0044] The Al model 34 may have an algorithm that is “trained” on historical data using machine learning to train and update the algorithm. For example, the Al model 34 may be trained with the historical data to recognize patterns and / or associations and follow such patterns and / or associations when processing input data 38 such that other inputs result in outputs consistent with the recognized patterns and / or associations. The algorithm can be trained in a training module of the Al model 34 that has access to one or more memories and / or data storage devices that store historical data (e.g., operational data). For example, one or more memories and / or data storage devices may be included in the controller 18 (e.g., the memory 18b), the server 30, and / or another device that is accessible by the controller 18 and / or server 30. The training module itself may, for example, include one or more computers and / or servers that operate in a network, comprise hardware and / or software, and / or include one or more programs, such as cooperatively interoperating programs. The Al model 34, using the “trained” algorithm, is operable to select an optimum solution for a given set of current data based upon the algorithm’s training on historical data. Such a system may be referred to as an Al-based system that utilizes machine learning to train the Al to provide an optimum solution for a given set of data based upon previous training using historical data.

[0045] The Al model 34 may be implemented by the controller 18 and / or the server 30 to output a suggested application or spreading rate 36 for the spreader 12b of the vehicle 10 for a current treatment. The Al model 34 generates the suggested application rate 36 for the spreader 12b by processing input data 38 including: (i) road data 28a that indicates the amount of water, snow, ice, and / or slush on the road, the road temperature, and / or the type of road; (ii) vehicle speed data 28c that indicates the speed of the vehicle 10; (iii) vehicle weather data 28d that indicates the air temperature, the humidity, and / or the wind; (iv) human-made observation data (e.g., provided by a user via the user interface 23 of the controller 18); (v) location data (e.g., GPS data from the GPS antenna 20); and / or (vi) third-party data 39 from a third-party supplier 40, such as weather data (e.g., live or current weather, past or current weather forecast) from the National Weather Service or a local weather service provider and / or road data (e.g., live or past frequency of traffic, live or past speed of traffic, live or past road surface condition). The third-party data 39 may be obtained based on data from the GPS antenna 20 that indicates the location of the vehicle 10 during the current treatment.

[0046] The input data 38 may be processed by the Al model 34 based on patterns and / or associations previously learned by the Al model 34 after being "trained" on the historical data, as discussed previously. The historical data may include one or more prior application rates and prior data (e.g., road data, salinity data, vehicle speed data, vehicle weather data, human-made observation data, location data, third-party data), and salinity data 28b that indicates the amount of salt left on the road. For example, the Al model 34 may be trained by a batch of data including an application rate (e.g., 5 pounds / 1000 square feet) corresponding to a prior treatment, prior data (e.g., road data, salinity data, vehicle speed data, vehicle weather data, human-made observation data, location data, third-party data) corresponding to the prior treatment, and a salinity data 28b (e.g., 2 pounds / 1000 square feet) corresponding to a current or present treatment. The Al model 34 can then be updated based on the application rate 36 and the input data 38 corresponding to the current treatment, and a salinity amount data from one of the sensors 28 corresponding to later treatment.

[0047] Thus, the Al model 34 can be continuously trained by new batches of data while the vehicle 10 is in motion and / or data is being collected, such as from the sensors 28, the third-party supplier 40, and / or the controller 18. This allows the Al model 34 to improve its outputs such as, for example, improve the accuracy of its suggested application rates. Improved application rates for the hopper spreader control module 14 can prevent the spreader 12b from providing insufficient material and / or overapplying material to road surfaces.

[0048] In one example, the server 30 may also collect data from a plurality of other controllers 18, as shown in FIG. 5, that correspond to a plurality of other vehicles 10, and then process the various data collected from the controllers 18 to manage the respective vehicles 10. Each of the controllers 18 may be connected to the server 30 such as, for example, via a two-way online communication channel that may include one or more wired and / or wireless links. The Al model 34 can then process the data from the vehicles 10 to enhance the control over the vehicles 10 and their respective hopper spreader assemblies 12. Thus, the Al model 34 can determine one or more optimal instructions (e.g., suggested application rates) for the controllers 18 to control the respective hopper spreader assemblies 12.

[0049] Further, the electronic control and tracking system 16 may collect other data, such as weather- related data and / or road data from the third-party supplier 40, such as from the National Weather Service or a local weather service provider. The data from the third-party supplier 40 may include live or current weather data, past or current weather forecast data, live or past frequency of traffic data, live or past speed of traffic data, and / or live or past road surface condition data. All data, including the third-party data (e.g.,current weather, weather forecast data, road conditions) and the data collected from the vehicles 10 (via the control and tracking system 16, namely from controller 18), which is out on route, can be sent to the server 30. This data can then be used as input to the Al model 34 to obtain optimal instructions for all vehicles 10 and, hence, optimal road safety with the current weather conditions. As a result, the correct and optimal application rates and type of spread may be determined by the Al model 34, and then used by controllers 18 to enhance the operation of the hopper spreader assemblies 12.

[0050] So, for example, when a vehicle 10 goes out to treat a surface, the one or more sensors 28 can measure the percentage of salt left on the surface that was spread from the previous treatment. With this data, the electronic control and tracking system 16 can adjust live the spreading rate for the treatment according to current road conditions, temperature, weather conditions, and / or an updated forecast.

[0051] The Al model 34 can be continuously updated with data via the server 30 as the controllers 18 collect data from the vehicles 10 on how much materials were spread with the specific weather on routes and / or forecasts during prior treatments, and data on post treatments that indicates whether the roads were “over salted” or “under salted” during prior treatment so that over salting or under salting can be avoided.

[0052] The server 30 may also be configured to give instructions to vehicles 10 and their drivers (for example, via an integrated navigation system optionally provided in a respective controller 18) based on the Al model 34, such as what site to treat first based on current and forecast weather data, as well as fleet optimization based on the available spreaders’ capacity and performance, as the server 30 may have all data about the weather conditions for each application site and about the salt they have left in the hoppers.

[0053] In one example, the Al model 34 is configured to suggest the type of treatment, such as the spreading method, (e.g. salt liquid or dry salt or a mixture, namely pre-wetted salt), for example, based on cost, weather, previous treatment history with same / similar weather, and / or availability.

[0054] In another example, the Al model 34 is configured to choose a treatment method and an amount of treatment given for the specific road based on the historic data and adjustments due to feedback provided by the sensors and / or weather-related conditions. The Al model 34 may also be configured to change an alarm callout time ahead of a storm based on historical pre-treatment and / or post-treatment times and / or weather conditions.

[0055] Each of the controllers 18 may include a universal serial bus (USB) socket to connect to a mobile device or to a Wi-Fi dongle so that the Internet could be accessed on the go. As noted above, the controllers 18 may have displays 22, which may be configured to display maps and / or communicate operator instructions to the drivers of the vehicles 10 such as, for example, from the server 30.

[0056] In one example, the server 30 is configured to generate reports for user accounts, such as total distance travelled on a route, total material usage on the route, efficiency on the route (e.g., spreading distance compared to total distance travelled), average speed of an accessory and / or the maintenance vehicle, material application rate, timestamps, etc. The controller 18 may also include a software program that is configured to display collected data, or selected settings, on a graphical user interface (GUI) provided on the display 22.

[0057] The server 30 may include one or more hardware servers, cloud-based servers, web servers, application servers, proxy servers, network servers, laptop computers, and / or desktop computers. The server 30 may be in the form of a server farm, a rack server, etc.

[0058] While exemplary implementations of the electronic control and tracking system 16 and the server 30 are shown in FIGS. 1-6, one or more of the elements illustrated in FIGS. 1-6 may be combined, divided, re-arranged, omitted, and / or implemented in any other way. For example, the server 30 can be included in the electronic control and tracking system 16. Further, the electronic control and tracking system 16 and the server 30 shown in FIGS. 1-6 may include one or more elements in addition to, or instead of, those illustrated in FIGS. 1-6, and / or may include more than one of any or all of the illustrated elements.

[0059] The electronic control and tracking system 16 and the server 30 shown in FIGS. 1-6 may be implemented by hardware, software, firmware, and / or any combination of hardware, software, and / or firmware. Thus, for example, the hopper spreader control module 14, the controller 18, the GPS antenna 20, the sensors 28, the server 30, and / or the Al model 34 could be implemented by one or more analog or digital circuit(s), logic circuit(s), programmable processor(s), graphics processing unit(s) (GPU(s)), digital signal processor(s) (DSP(s)), application specific integrated circuit(s) (ASIC(s)), programmable logic device(s) (PLD(s)), field programmable logic device(s) (FPLD(s)), multi-core processor(s), electronic control unit(s), and / or crypto processor(s).

[0060] When reading any of the apparatus or system claims of this patent to cover a purely software and / or firmware implementation, at least one of the hopper spreader control module 14, the controller 18, the GPS antenna 20, the sensors 28, the server 30, and / or the Al model 34 is hereby expressly defined to include a non-transitory computer readable medium including the software and / or firmware.

[0061] FIG. 7 is a flowchart representative of an example process 100 that implements the electronic control and tracking system 16 and / or the server 30 to determine a suggested application rate for the spreader 12b. The process 100 may be implemented as hardware logic, machine-readable instructions, hardware implemented state machines, and / or any combination thereof. At block 102, the process 100begins when one or more sensors 28 of the vehicle 10 measure and / or collect sensor data. For example, the sensor data may include road data 28a, vehicle speed data 28c, and / or vehicle weather data 28d. At block 104, the server 30 obtains location data about the vehicle 10. For example, the location data may be from the GPS antenna 20. At block 106, the server 30 obtains third-party data 39 based on the location data. For example, the third-party data 39 may include weather data and / or road data.

[0062] At block 108 of FIG. 7, the Al model 34 obtains the input data 38 including the sensor data from the sensors 28 and the third-party data 39 from the server 30. As discussed previously, the Al model 34 may be implemented by the controller 18 and / or the server 30. At block 110, the Al model 34 generates a suggested application rate 36 for the spreader 12b based on the input data 38.

[0063] At block 112 of FIG. 7, the display 22 shows the suggested application rate 36. For example, the display 22 may show the suggested application rate 36 based on data from the Al model 34. At block 114, the hopper spreader control module 14 sets the application rate of the spreader 12b based on the suggested application rate 36 from the controller 18. For example, the hopper spreader control module 14 may set the suggested application rate 36 in response to a user confirming the suggested application rate 36 via the user interface 23 or the suggested application rate 36 is set automatically by the hopper spreader control module 14.

[0064] At block 116 of FIG. 7, one or more of the sensors 28 measure and / or collect salinity data 28b. At block 118, the Al model 34 is updated based on the salinity data 28b, the suggested application rate 36, and prior data.

[0065] Thus, the present disclosure provides a control and tracking system with a hopper spreader control module that can provide enhanced control over the dispensing of the liquid and / or dry, free-flowing solid material or a mixture thereof to be spread on a road surface. This is accomplished by a controller providing one or more instructions to the hopper spreader control module based on the data from a GPS antenna, sensors, and / or a server. The one or more instructions may be improved by inputting data from the GPS antenna, sensors, and / or server into the Al model that is updated, either continuously or periodically.

[0066] The process 100 shown in FIG. 7 may include one or more processes in addition to, or instead of, those illustrated in FIG. 7, and / or may include more than one of any or all of the processes. For example, the Al model 34 may generate an amount of material to be spread by the spreader 12b. Further, many other methods of implementing the electronic control and tracking system 16 and / or the server 30 may alternatively be used. For example, the order of execution of the blocks may be changed, and / or some of the blocks described may be changed, eliminated, and / or combined.

[0067] As mentioned above, the processes 100 of FIG. 7 may be implemented using machine-readable instructions for execution by the electronic control and tracking system 16 and / or the server 30. One or more of the instructions may be embodied in software stored on one or more non-transitory machine- readable mediums associated with the electronic control and tracking system 16, such as a hard disk drive (HDD), a solid-state drive (SSD), a flash memory, a read-only memory, a compact disk, a digital versatile disk, a cache, a random-access memory and / or any other storage device or storage disk in which data is stored for any duration (e.g., for extended time periods, permanently, for brief instances, for temporarily buffering, for caching of the data). Additionally or alternatively, one or more of the instructions may be embodied in firmware and / or dedicated hardware structured to perform the corresponding operation without executing software or firmware (e.g., discrete and / or integrated analog and / or digital circuitry, a Field Programmable Gate Array (FPGA), an ASIC, a comparator, an operational-amplifier (op-amp), a logic circuit).

[0068] The machine-readable instructions described herein may be downloaded to the electronic control and tracking system 16 and / or the server 30 from a software distribution platform. The machine-readable instructions may be stored in one or more formats such as, for example, a compressed format, an encrypted format, a fragmented format, a compiled format, an executable format, and / or a packaged format. The machine-readable instructions may be stored as data or a data structure (e.g., portions of instructions, code, representations of code) that may be utilized to create, manufacture, and / or produce machine executable instructions. For example, the machine-readable instructions may be fragmented and stored on one or more storage devices and / or computing devices (e.g., servers) located at the same or different locations of a network or collection of networks (e.g., in the cloud, in edge devices).

[0069] One or more of the machine-readable instructions may be one or more executable programs and / or one or more portions of one or more executable programs for execution by one or more computing devices (e.g., the controller 18, the server 30) of the electronic control and tracking system 16 and / or the server 30. The machine-readable instructions may require one or more of installation, modification, adaptation, updating, combining, supplementing, configuring, decryption, decompression, unpacking, distribution, reassignment, compilation, etc. in order to make them directly readable, interpretable, and / or executable by a computing device. For example, the machine-readable instructions may be stored in multiple parts, which are individually compressed, encrypted, and stored on separate computing devices. The multiple parts when decrypted, decompressed, and combined form a set of executable instructions that implement one or more functions that may together form a program. The machine-readable instructionsmay be stored in a state in which they may be read by the one or more computing devices but require addition of a library (e.g., a dynamic link library (DLL)), a software development kit (SDK), an application programming interface (API)) in order to execute the instructions on a particular computing device. In another example, the machine-readable instructions may need to be configured (e.g., settings stored, data input, network addresses recorded) before the machine-readable instructions and / or the corresponding program(s) can be executed in whole or in part. Thus, machine-readable media, as used herein, may include one or more machine-readable instructions and / or program(s) regardless of the particular format or state of the machine-readable instructions and / or program(s) when stored or otherwise at rest or in transit.

[0070] The machine-readable instructions described herein can be represented by any past, present, or future instruction language, scripting language, programming language, etc. For example, the machine- readable instructions may be represented using any of the following languages: C, C++, Java, C#, Perl, Python, JavaScript, Hyper Text Markup Language (HTML), Structured Query Language (SQL), Swift, etc.

[0071] While the foregoing description describes several examples of the present disclosure, it will be understood by those skilled in the art that variations and modifications to these examples may be made without departing from the spirit and scope of the disclosure, as defined in the claims below, as interpreted according to the principles of patent law including the doctrine of equivalents. The present disclosure encompasses all combinations of various examples or aspects of the disclosure described herein. It is understood that any and all examples of the present disclosure may be taken in conjunction with any other example to describe additional examples of the present disclosure. Furthermore, any elements of an example may be combined with any and all other elements of any of the examples to describe additional embodiments.

Claims

We claim:1 . A control and tracking system for a vehicle having a hopper spreader assembly, the hopper spreader assembly having a hopper and a spreader, the control and tracking system comprising: a control module configured to control the spreader of the hopper spreader assembly; a sensor of the vehicle configured to produce sensor data; and a controller in communication with said control module and with said sensor, the controller configured to obtain said sensor data, and optionally obtain third-party data from a third-party, and wherein said controller is configured to provide an instruction to said control module based on an input including said sensor data, and optionally the third-party data, to control the hopper spreader assembly.

2. The control and tracking system according to claim 1, in combination with a remote server in communication with said controller, the remote server configured to obtain said sensor data from said controller, wherein said instruction is a first instruction, wherein the server is further configured to determine and provide a second instruction to said controller based on said sensor data.

3. The control and tracking system according to claim 2, wherein at least one of the remote server and said controller includes or has access to an artificial intelligence model having an algorithm to produce an operating parameter for the hopper spreader assembly, wherein said controller is configured to provide said instruction based on said operating parameter.

4. The control and tracking system according to claim 3, wherein the algorithm is trained on historical data using machine learning.

5. The control and tracking system of claim 3, wherein said operating parameter is an application rate.

6. The control and tracking system of claim 1, wherein said third-party data is based on a location of the vehicle.

7. The control and tracking system according to any above claim, wherein the third-party data includes data selected from the group consisting of live weather data, live road data, and weather forecast data.

8. The control and tracking system of claim 1 , wherein said sensor data is configured to indicate a road surface condition, said road surface condition including at least one of: air temperature; road temperature; dew point temperature; an amount of water on a road surface; an amount of snow on the road surface; an amount of slush on the road surface; friction of the road surface; and an amount of salt left on the road surface.

9. A method is implemented by a computing device to set a parameter for a hopper spreader assembly of a vehicle, said method comprising: obtaining first data from a first sensor of the vehicle; generating a suggested parameter by implementing an artificial intelligence (Al) model using first input data including the first sensor data; setting the parameter for the hopper spreader assembly based on the suggested parameter; obtaining second data from the first sensor or a second sensor or of the vehicle; and updating the Al model based on second input data including the suggested parameter and the second data.

10. The method of claim 9, wherein the first sensor data includes at least one of: (i) road data indicating a condition of a road surface; (ii) vehicle speed data indicating the speed of the vehicle; (iii) vehicle weather data indicating outside weather conditions surrounding the vehicle; (iv) location data indicating the location of the vehicle; and (v) salinity data indicating an amount of salt on the road surface.11 . The method of claim 9, wherein the second sensor data includes at least one of: road data indicating a condition of a road surface; (ii) vehicle speed data indicating the speed of the vehicle; (iii) vehicle weather data indicating outside weather conditions surrounding the vehicle; (iv) location data indicating the location of the vehicle; and (v) salinity data indicating an amount of salt on the road surface.

12. The method of claim 9, wherein said setting the parameter for the hopper spreader assembly comprises providing the hopper spreader assembly one or more instructions based on the suggested parameter.

13. The method of claim 9, further comprising obtaining third data from a third-party supplier, wherein the first input data further includes the third data.

14. The method of claim 13, wherein the third data includes at least one of: (i) weather data; and (ii) road data.

15. The method of claim 14, wherein the weather data comprises live weather data and weather forecast data, and the road data comprises live road data.

16. The method of claim 14, wherein said obtaining the third data from the third-party supplier is based on location data of the vehicle.

17. The method of claim 9, wherein the suggested parameter is a suggested application rate.

18. The method of claim 9, wherein the second input data further includes prior data.

19. The method of claim 18, wherein the prior data includes the first data.

20. The method of claim 9, wherein the Al model has an algorithm, wherein said updating the Al model based on second input data is by using machine learning.

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