SPLASH DETECTION USING RADAR

Abstract

A detection system for a target vehicle includes a camera that captures images of a region exterior to the target vehicle, a radar module that scans the region to detect a depth of a splash event in the region, an actuator configured to operate in response to a response signal, and a control circuit configured to determine a distance between the target vehicle and a front of the splash event based on the images, compare the front of the splash event to the depth to determine an intensity of the splash event, and communicate the response signal based on the intensity of the splash event.

Description

FIELD OF DISCLOSUREThe present disclosure relates generally to image processing in a vehicle environment, and more particularly to splash detection using radar.BACKGROUND OF THE DISCLOSUREConventional image processing techniques for detecting moisture conditions at or around a vehicle may be limited.SUMMARY OF THE DISCLOSUREAccording to a first aspect of the present disclosure, a detection system for a target vehicle includes a camera capturing images of a region outside the target vehicle, a RADAR module sensing the region to detect a depth of a spray event in the region, an actuator configured to operate in response to a response signal, and a control circuit configured to determine a distance between the target vehicle and a front of the spray event based on the images, compare the front of the spray event with the depth to determine an intensity of the spray event, and communicate the response signal based on the intensity of the spray event.Embodiments of the first aspect of the present disclosure may include any one or a combination of the following features:the control circuit is configured to classify a spray source of the spray event as being vehicle-related or not vehicle-related based on the image;the control circuit is configured to estimate a speed of the spray source and determine a pass availability condition based on the speed of the spray source and the depth of the spray event;the response signal includes an indication of the pass availability condition;the control circuit is configured to compare the distance to a threshold sequence distance and to communicate the response signal when the distance exceeds the threshold sequence distance;the control circuit is configured to determine a target lane from a plurality of lanes for the target vehicle based on the intensity;a display in communication with the control circuit configured to indicate the target lane in response to the response signal;the control circuit is configured to determine a source lane of the plurality of lanes where the splash event is present;a window, a wiper selectively movable along the window, and a window clearing system in communication with the control circuit and including the actuator configured to operate the wiper in response to the intensity;the control circuit includes a machine learning model trained to determine the response signal based on manual feedback;the manual feedback includes at least one of manual operation of the wiper and maneuvering the vehicle onto one of the plurality of lanes;a motion control system including the actuator adjusting the distance in response to the response signal;the motion control system includes an automatic speed control system configured to control the speed of the vehicle based on the response signal; andthe RADAR module further detects a density of the splashing event, and wherein the control circuit is configured to further classify the intensity based on the density.According to a second aspect of the present disclosure, a detection system for a target vehicle includes a camera capturing images of a region outside the target vehicle, a RADAR module scanning the region to detect a depth of a spraying event in the region, a notifier configured to indicate a pass availability state in response to the response signal, and a control circuit configured to determine a distance between the target vehicle and a front of the spraying event based on the images, compare the front of the spraying event with the depth to determine an intensity of the spraying event, estimate a speed of a passing vehicle causing the spraying event, determine a pass availability state based on the speed of the passing vehicle and the intensity, and communicating the response signal based on the pass availability state.Embodiments of the second aspect of the present disclosure may include any one or a combination of the following features:a motion control system that adjusts the distance in response to the response signal;the motion control system includes an automatic speed control system configured to control the speed of the target vehicle based on the response signal;the control circuit is configured to determine a source lane from a plurality of lanes where the splash event is present;a window, a wiper selectively movable along the window, and a window clearing system in communication with the control circuit and including the actuator configured to operate the wiper responsive to the distance;According to a third aspect of the present disclosure, a detection system for a vehicle includes a camera capturing images of a region external to the vehicle, a RADAR module scanning the region to detect a depth and a density of a spray event in the region, an actuator configured to operate in response to a response signal, and a control circuit configured to determine a distance between the vehicle and a front of the spray event based on the images, compare the front of the spray event with the depth to determine a magnitude of the spray event, determine an intensity of the spray event based on the magnitude and the density of the spray event, and communicate the response signal based on the intensity of the spray event.These and other features, advantages and objects of the present disclosure will be better understood and understood by those skilled in the art by reference to the following description, claims and accompanying drawings.BRIEF DESCRIPTION OF THE DRAWINGSIn the drawings, the following applies: FIG. 1 is a perspective view of a vehicle including a moisture detection system according to an aspect of the present disclosure; FIG. 2 is a functional block diagram of a moisture detection system for a vehicle according to an aspect of the present disclosure; FIG. 3A is an image of dust on a window to be cleaned by a window clearing system of the present disclosure; FIG. 3B is an image of a biological residue of an insect on a window to be cleaned by a window clearing system of the present disclosure; FIG. 4 is a flow diagram of an automatic mode of a window clearing system for cleaning and / or clearing a window of a vehicle, in accordance with an aspect of the present disclosure; FIG. 5 is an exemplary cross-sectional view of a vehicle including an imaging device adjacent a windshield of the vehicle within an interior of the vehicle for detecting moisture conditions in the region outside the vehicle; FIG. 6A is an example image taken by an imaging device in an interior of the vehicle positioned away from the windshield that results in detectable water drops on the windshield; FIG. 6B is an example image taken by an imaging device positioned within an interior of the vehicle and proximate the windshield, illustrating detection of water stripes on the windshield; FIG. 7 is a diagram of a fully convolutional data description (FCDD) network;fully-folding data description network) that processes an image captured by an imaging device positioned near the windshield; andgenerating image data exhibiting an obstruction in the image; FIGS. 8A-8C are captured images besides image data showing obstacles on a windshield of a vehicle after processing by an FCDD network; FIGS. 9A-9B are example images taken by an imaging device for the vehicle and showing spray events along a roadway for the vehicle, where FIG. 7B illustrates spray zones overlaying spray events; FIG. 10A is an example map of a RADAR scan of an environment outside the vehicle that captures splash conditions; FIG. 10B is an example map of a RADAR scan of an environment outside the vehicle that captures splash conditions; FIG. 11 is a block diagram illustrating an example passing state detection based on spray monitoring in a vehicle environment; and FIG. 12 is an exemplary process performed by a moisture detection system according to an aspect of the present disclosure.DETAILED DESCRIPTION OF PREFERRED EMBODIMENTSReference will now be made in detail to the present preferred embodiments of the disclosure, examples of which are illustrated in the accompanying drawings. In the drawings, the same reference numerals are used to designate the same or similar parts where possible. In the drawings, the features shown are not to scale and certain components are exaggerated relative to the other components for clarity and understanding.As required, detailed embodiments of the present disclosure are disclosed herein; however, it is to be understood that the disclosed embodiments are merely exemplary of the disclosure that may be practiced in various and alternative forms. The figures do not necessarily correspond to a detailed configuration; some schematic representations can be shown in enlarged or reduced form in order to show a functional overview. Therefore, specific structural and functional details disclosed herein are not to be interpreted as limiting, but merely as a representative basis for teaching one skilled in the art to variously employ the present disclosure.For purposes of description herein, the terms "upper", "lower", "right", "left", "rear", "front", "vertical", "horizontal", and derivatives thereof refer to the concepts in their orientation in FIG. 1, however, it should be understood that the concepts may assume various alternative orientations unless expressly stated to the contrary. It is also to be understood that the specific devices and processes illustrated in the accompanying drawings and described in the following specification are merely exemplary embodiments of the inventive concepts defined in the appended claims. Thus, specific dimensions and other physical characteristics relating to the embodiments disclosed herein are not to be considered as limiting, unless the claims expressly state otherwise.The embodiments illustrated herein consist mainly of combinations of method steps and device components related to splash detection using RADAR. Accordingly, the apparatus components and method steps in the drawings have been represented by conventional symbols where appropriate, only those specific details relevant to understanding the embodiments of the present disclosure being shown in order not to obscure the disclosure with details readily apparent to those of ordinary skill in the art in light of the present description. Further, like reference numerals represent like elements throughout the specification and the drawings.Referring generally to FIGS. 1-12, a moisture detection system 10 uses image processing to detect conditions of a region outside 12 of a vehicle 14, such as water on an exterior surface 16 of a windshield 18 of the vehicle 14 or water in front of or around the vehicle 14. In general, the present systems and methods may provide improved intensity classification for sprays in the region outside 12 of the vehicle by integrating depth detection or density detection using radio detection and ranging (RADAR). Further, the present system and methods may provide improved space utilization within the vehicle 14 by allowing an imaging device 20 of the moisture detection system 10 to be positioned near or far from the windshield 18 while still detecting and classifying obstructions on the windshield 18. Further, the moisture detection system 10 may provide improved vision by optimizing the clearance of the windshield 18 and / or maneuvering the vehicle 14 to a destination, or by proposing maneuvering thereof to improve vision and / or control. The moisture detection system 10 may further provide more accurate detection of weather conditions, such as rain, humidity, fog, or other moisture conditions, in the region outside 12 of the vehicle 14, thereby enabling improved responsiveness for other vehicle systems. Further, the present systems and methods may provide increased part life (e.g., window clearing parts), efficient use of window cleaning fluid, and overall improved automatic window clearing control.Referring now to FIG. 1, the moisture detection system 10 for the vehicle 14 may include an imaging device 20 positioned within a cabin 22 of the vehicle 14. For example, the imaging device 20 may be positioned adjacent the windshield 18 of the vehicle 14 within a passenger compartment 24 of the vehicle 14 and oriented in a vehicle forward orientation. At least one windshield wiper 26 is positioned on the exterior surface 16 of the windshield 18 to clear the windshield 18 of water, dirt, or other substances thereon. As further described with reference to FIG. 2, a window clearing system for cleaning the windshield 18 or any of a plurality of windows of the vehicle 14 where moisture or other substances from the region outside 12 of the vehicle 14 may accumulate at the window may be employed. The window clearing system may include a nozzle 28 positioned adjacent the windshield wipers 26 between a hood 30 of the vehicle 14 and the windshield 18 of the vehicle 14 and configured to spray cleaning fluid onto the windshield 18. The cleaning fluid may include a cleaning agent, such as methanol, glycol, or other fluids, to clear substances from the exterior surface 16 of the windshield 18 when used with the windshield wipers 26. It is contemplated that the cleaning fluid may include water, which in some examples may be heated to assist in de-icing the windshield 18.Still referring to FIG. 1, a plurality of distance sensors 34, 36, 38 are integrated into the vehicle 14 to detect other objects, such as other vehicles 14, in the region 12 outside of the vehicle 14. The detection devices may include ultrasonic and / or infrared detectors as well as cameras 38, such as the imaging device, configured to detect distances from the vehicle 14 to surrounding objects in the region outside 12 of the vehicle 14. Accordingly, the images from the imaging device 20 may be used to detect a distance from the vehicle 14 to objects within a field of view of the imaging device.As further described with reference to FIG. 2, the distance sensors 34, 36, 38 may include any(s) of the radio detection and ranging sensors (RADARe 34), the light detection and ranging sensors (LIDARs 36), and the cameras 38. In some examples, ultra-wideband (UWB) sensors are employed to detect objects in the region outside 12. Generally, the distance sensors 34, 36, 38 may be configured to detect cross traffic events and / or detect objects in blind spots of the vehicle 14 to assist a user of the vehicle 14 in maneuvering the vehicle 14. In the present example, the data collected by the detection sensors is used by the moisture detection system 10 to enable the moisture detection system 10 to detect a distance (e.g., a following distance 120) from the vehicle 14 to spray events 118 and classify the meaning, relevance, or priority of the spray events 118 based on the distance.As further described herein, the distance sensors 34, 46, 38 may coordinate detection techniques using one or more of the cameras 38 and / or the imaging device 20 to detect a front of a spray event 118 and one or more of the RADARs 34 to detect a depth D and / or density of the spray event 118. For example, image-based detection may be restricted if there is a significant visual obstruction in a captured image. However, the one or more RADARs 34 may be used to transmit / receive radio waves or microwaves (e.g., via RADAR transmitters and RADAR receivers) reflected from water drops in the spray event 118 at a more precise level than visible light waves received by the cameras 38. For example, a control circuit in communication with the RADARs 34 may determine the depth D, density, intensity, span, or other property of the spray event 118 based on the information using information from the RADARs 34. In general, the RADARs 34 use Doppler weather detection techniques to generate a map or distribution of water in the spray event 118. For example, the RADARs 34 may emit microwave or radio wave energy and measure a reflected wave from the splashing event 118. Such measurements may include signals having frequencies different from the signals emitted by the RADARs 34, resulting in a frequency shift. The frequency shift may be directly related to the movement (velocity) of raindrops or other droplets in the splashing event 118 or precipitation. Accordingly, the more water drops are present, the more intense the rain or splashing is, and a stronger return signal is provided by the RADARe 34.Still referring to FIG. 1, the vehicle 14 includes a plurality of wheels 40, each having a tire 42 that interacts with a travel surface 44 for the vehicle 14. Friction between each tire 42 and the travel surface 44 may be affected by moisture conditions between the tires 42 and the travel surface 44. Accordingly, the moisture detection system 10 may be employed to control driving of the wheels 40 to improve maneuverability of the vehicle 14 along the travel surface 44.The vehicle 14 includes at least one lighting assembly 46, such as a headlight assembly, having headlights 48 configured to illuminate the region outside 12 of the vehicle 14. For example, the lighting assemblies may be configured to illuminate the region outside 12 of the vehicle 14 with a plurality of lighting levels (e.g., high beam, low beam, etc.). Control of the lighting assemblies and the power stages and / or the lighting stages thereof may be improved by the moisture detection system 10. For example, moisture conditions in the region outside 12 detected by the moisture detection system 10 may cause the moisture detection system 10 to control the power levels of the lighting assemblies due to the moisture conditions from a reduced view.Referring now to FIG. 2, the moisture detection system 10 includes an imaging system 50, a distance detection system 52, and a response control system 54 in communication with the imaging system 50 and the distance detection system 52. For example, the response control system 54 includes one or more controllers 56, 72 having at least one processor and a memory in communication with the processor. The memory may store instructions that, when executed by the processor(s) of the reaction control system 54, cause the reaction control system 54 to perform various tasks related to improving the visibility of the region outside 12 of the space of the vehicle 14 and / or the motion control system for the vehicle 14. Generally, one or more of the controllers 56, 72 and / or other electrical components that provide a decision making via software may be referred to as a control circuit.For example, a response controller 56 of the response control system 54 may include a motion control unit 58 and a vision control unit 60. The vision control unit 60 may be configured to control the window clearing system, a light control system 62, and / or any other system that affects vision through one or more of the windows of the vehicle 14. The motion control unit 58 may control communication with one or more vehicle systems, such as a powertrain 64 of the vehicle 14, a brake system 66 of the vehicle 14, or any other motion control system for the vehicle 14. for example, the motion control unit 58 may include a speed sensor in the powertrain 64 that detects rotations of gears in the powertrain 64, but other speed sensors are used to detect or derive the speed of the vehicle 14 (RF waves, inductive sensing, capacitive sensing, etc.). Further, the reaction control system 54 may include and / or be in communication with a display 68, such as a human-machine interface (MMS 70) in the space of the vehicle 14. The display 68 is configured to present messages to a user and / or to allow the user to control the window clearing system, the light control system 62, or any other aspect regarding the vision and / or movement of the vehicle 14. In general, the response control system 54 may be configured to actively control vision and / or movement characteristics for the vehicle 14 or passively present messages on the display 68 to indicate vision and / or movement target operations that the user is to initiate.With continued reference to FIG. 2, the window clearing system may include a window clearing controller 72 that controls operation of a pump 74 that pressurizes the cleaning fluid to spray the cleaning fluid onto the windshield 18 via the nozzle 28, as previously described. A valve 76 may be fluidly interposed between the pump 74 and the nozzle 28 to selectively allow the cleaning fluid to enter the nozzle 28.The window clearing controller may further be in communication with a motor 78 that drives the windshield wipers 26. For example, the motor 78 may be configured to rotate the windshield wipers 26 over the windshield 18 in response to signals from the window clearing controller 72. In some examples, the speed of the vehicle 14, as detected by the speed sensor, may be compared to detected spray events 118, and in response to this detection, the engine 78 may be energized to operate at a certain number of revolutions per minute (rpm). At least one switch 80 is in communication with the window clearing controller 72 and / or directly in communication with the motor 78, the pump 74, and / or the valve 76 to control dispensing of the cleaning fluid and / or driving of the windshield wipers 26 via manual interaction. For example, the at least one switch 80 may include a first mechanism 82 that causes the cleaning fluid to be dispensed onto the windshield 18 and a second mechanism 84 that controls the operation of the windshield wipers 26. For example, the first mechanism 82 may be a knob that, when pulled or pressed, causes the cleaning fluid to be dispensed, and the second mechanism 84 may be a knob or knob that causes the windshield wipers 26 to move across the windshield 18. It is contemplated that the window clearing control 72 may be omitted in some examples and that the response control system 54 may directly control the window clearing operations. In such an example, at least one of the switches 80 is between the valve 76 and the nozzle 28 (e.g., the first mechanism 82), and at least one of the switches 80 is between the motor 78 and the windshield wipers 26 (e.g., the second mechanism 84). In either example, the user may manually control the dispensing of the cleaning fluid and / or the operation of the windshield wipers 26, and such operations may also or alternatively be automatically controlled by the reaction control system 54. In another example, instructions for initiating windshield 18 wiping and / or windshield 18 cleaning are shown at the MMS 70 and automatic control of the window clearing system is omitted.The vision controller 60 may also or alternatively be configured to control the lighting assemblies of the vehicle 14 based on moisture conditions detected by the imaging system 50, as described above. For example, in the event that it is raining in the region outside 12 of the vehicle 14, the headlights 48 may be automatically turned on or, in response to detecting moisture conditions in the region outside 12 of the vehicle 14, the response control system 54 may communicate an instruction to present a message on the display 68 for the user to turn on the headlights 48. Further, brightness levels (e.g., binary high beam / low beam or fine brightness controls) may be actively or passively controlled by the response control system 54 (e.g., presenting messages on the display 68).Generally, the motion control unit 58 may control the various systems of the vehicle 14 with respect to motion control, such as driving the wheels 40 (e.g., torque values), braking the braking system 66 (e.g., traction control, anti-lock braking (ABS)), steering the vehicle 14 (e.g., maneuvering along the travel surface 44), or any other motion control unit 58 for the vehicle 14. Similar to operation of the vision control unit 60, features of the vehicle 14 with respect to motion control may be presented on the display 68, in addition to or alternatively to automatically controlling maneuverability of the vehicle 14. For example, in an at least partially semi-autonomous control mode for the vehicle 14, the vision controller 60 may communicate an indication to the user on the display 68, reduce speed, maneuver the vehicle 14 to the left or right, or the like, and in an alternative, may control speed, maneuverability to the left or right, etc., in response to detection of the moisture conditions.With continued reference to FIG. 2, the various responses communicated by the response control system 54 may be based on outputs from one or both of the imaging system 50 and the distance detection system 52. With particular reference to the imaging system 50, the imaging device 20 captures one or more images (captured images 86) of the region outside 12 of the vehicle 14, which are then processed by an image processor 88 to detect moisture conditions on or around the vehicle 14 and generate one or more output images 90. The image processor 88 is in communication with a memory 91 that stores instructions that, when executed, cause the image processor 88 to detect water stripes, water drops 108, spray events 118, spray sources 116, or any other optical distortion associated with moisture condition detection and / or obstruction detection. Included in or in communication with the memory 91 is a fully convolutional data description (FCDDN 92) network, which may be a neural network that segments the image data and detects optical distortions in the captured images 86. The FCDDN 92 may be trained by a training module 94 of the imaging system 50 that provides sample images and / or historical image data that exhibits optical distortion (e.g., optical distortion caused by moisture in an image). The FCDDN 92 is used to detect portions of the image that have distortion due to moisture conditions and will be described in more detail with reference to FIG. 7.In addition to detecting moisture conditions, the image processor 88 is configured to detect any other obstruction within the field of view of the imaging device 20. For example, the obstruction may be environmental dirt, such as animal excrement, leaves, sticks, non-water material, or any other substance that may stick or land on the exterior surface 16 of the windshield 18. The imaging system 50 can therefore distinguish between dirt and water. For example, the FCDDN 92 may be trained to evaluate the obstruction with a degree of opacity, light transmittance, light distortion, or the like. For example, dirt may be associated with opacity, whereas water and / or other moisture states may be associated with diffracting light through the obstruction.In general, distance information from the distance detection system 52 and moisture classification and detection from the image processing system by the response control system 54 may be used to initiate the response of the vehicle 14. For example, if humidity above a certain threshold (e.g., a low visibility threshold) is detected on the windshield 18, the imaging system 50 may communicate an output to the response control system 54 that indicates ambient conditions of the outside region. For example, the imaging system 50 may determine that it is raining, snowing, or otherwise precipitation in the region outside 12 and communicate an indication to the reaction control system 54 to start the windshield wipers 26. The image processing system may further distinguish between various forms of precipitation and / or obstructions on the windshield 18 to enable an individualized response initiated by the control system based on the environmental conditions. For example, and as described with reference to the previous figures, the imaging system 50 may classify types of obstructions as moisture-related or light-related, and the signal communicated by the imaging system 50 of the response control system 54 may be dependent on the type of obstruction.For example, the imaging system 50 may detect animal excrement on the windshield 18 and communicate an output for the reaction control system 54 to control the window clearing system pump 74 to spray the cleaning liquid onto the windshield 18. In another example, the imaging system 50 detects spatters on the windshield 18 by splashing from another vehicle 14 in front of the vehicle 14 and, in response, communicates a signal to the response control system 54 to control the vehicle 14 to decelerate or adjust the positioning of the vehicle 14, or communicates a message to the MMS 70 to allow the user to perform this control. The lighting assemblies may be further controlled in response to detecting the moisture conditions to illuminate the region outside 12 of the vehicle 14 at a particular power / illumination level and / or to turn the headlights 48 on or off. These reactions are exemplary and not limiting, such that any combination of reactions may be performed by the moisture detection system 10 in response to classification by the imaging system 50.Still referring to FIG. 2, feedback from the window clearing system, the humidity control system, the MMS 70, or other vehicle systems may be monitored by the response control system 54 to optimize the response by the humidity detection system 10. For example, upon classifying the imaging system 50 of the environmental conditions in the out-of-range region, the imaging system 50 may communicate an instruction or signal to the response control system 54 that it is easily raining in the out-of-range region 12 of the vehicle 14. In response to the detection of the light rain, the reaction control system 54 may start the wiper control at a low speed. However, manual control of the windshield wipers 26 to stop wiping the windshield 18 via the second switch 80 may end automatic control of the windshield wipers 26. The response controller may detect the feedback (e.g., the user manually turning the windshield wipers 26 off) and update or optimize future responses when light rain is detected. In this way, the detection and classification of the environmental conditions, including the moisture conditions, and the response thereto may be improved to promote optimized responses by the user. It is contemplated that other examples may be similarly optimized with respect to other environmental conditions using feedback (e.g., manual adjustment of the lighting assemblies, manual steering adjustments, brake adjustments, etc.). In some examples, the user discarding the messages presented at the MMS 70 may indicate that the user's response indicated in the message is not preferred. Other examples of feedback and control are described with reference to the following figures.Referring now to FIGS. 3A-4, and further to FIG. 2, in some aspects, the vehicle 14 includes the imaging device 50 configured to capture images through a window (e.g., the windshield 18) of the vehicle 14. The window clearing system is operable between a clearing operation and a cleaning operation. The window clearing system includes one or more of the windshield wipers 26 configured to move along the window during the clearing operation and the cleaning operation. The window clearing system also includes an interface (e.g., the at least one switch 80) for manually turning on the clearing operation. The control circuit is configured to detect an obstruction on the window based on the images, classify the obstruction as water or dirt, detect manual operation of the wiper 26, determine a wear condition during manual operation of the wiper 26, and classify the obstruction as debris, communicate a signal to indicate the wear condition.Generally, the window clearing system may operate in an automatic mode and a manual mode. In the automatic mode, the clearance system operates in tandem with the control circuitry (e.g., the image processor 88, the controller 56, the controller 72, the imaging system 50, and / or the distance detection system 52) to operate the windshield wipers 26, the nozzles 28, the valve 76, the pump 74, the electric motor(s) 78, and the like, in response to detecting obstructions on the windshield 18 as identified by the imaging device 20. For example, the imaging system 50 may detect water drops 108, streaks or other moisture conditions, dust (FIG. 3A ) or other dirt, biological debris such as insect sprays (FIG. 3B ), or other debris or occlusions on the windshield 18. Thus, the image processor 88 may process the images 86 and apply the FCDDN 92 or other neural network to classify the obstruction. Based on the classification of the obstruction, the particular operation performed by the window clearing system is determined. For example, a wipe action may be determined in response to moisture conditions and a clean action may be determined in response to non-moisture obstructions. Such operations may be automatically performed in the automatic mode, similar to the operation of automatic headlight activation in response to dark conditions. In this way, manual intervention can be limited.In the cleaning operation, a spray device of cleaning fluid is controlled to apply the cleaning fluid to the windshield 18. For example, the controller 72 controls the pump 74 and / or the valves 76 to deliver the cleaning fluid to the windshield 18 while communicating signals to (or shortly before) one or more of the electric motors 78 to drive rotation of the windshield wipers 26 across the windshield 18. The cleaning fluid may be operable to reduce adherence of the obstruction to the windshield 18 to allow the windshield wipers 26 to clear the obstruction. The clearing action may not involve turning on the spray and only involve operation of the windshield wipers 26.It is contemplated that the window clearing system may control the speed of the windshield wipers 26 (e.g., RPM of the electric motors 78), the timing of the windshield wipers 26 (e.g., initially fast, then slower, etc.), a speed or distribution of the cleaning fluid, or other more specific aspects of the window clearing system), in addition to selecting between the clearing and cleaning operations. For example, the imaging system 50 may more accurately classify conditions of the windshield 18 such as "very soiled", "soiled", "lightly soiled", etc., based on the degree of obstruction or the position of the obstruction through the windshield 18. In one example, the imaging system 50 detects obstructions at only a portion of the windshield 18 and activates only the corresponding portion of the window clearing system to clear the obstruction (e.g., only one windshield wiper 26, one of the nozzles 28, etc.).The window clearing system may also or alternatively be manually controlled by a user via an interface, such as switches 82, 84. For example, the switches 82, 84 may be creases on a control lever that, when rotated, initiate the cleaning operation or clearing operation in response to the switch 82, 24 being turned on. Manual turning on or off of the switches 82, 84 may be monitored by the window clearing system. For example, upon detecting an operational change, the controller 72 may update an algorithm to optimize the timing and conditions for turning on or off the wipers 26 and / or the sprayer. For example, if in the automatic mode the windshield wipers 26 are automatically operated in response to moisture on the windshield 18, but the user manually shuts down the windshield wipers 26, based on this manual feedback, the response control system 54 may update to delay turning on the windshield wipers 26 in the automatic mode in similar future conditions. In another example, an elapsed time of use for the cleaning or clearing operation may be tracked (e.g., until the user manually shuts down the cleaning or clearing operation) to optimize intervals for operating the window clearing system. Thus, the present system 10 may utilize learning methods, such as those described with respect to the training module 94, for optimized responses.The controller 72 or other portion of the control circuit may include a database or other memory (e.g., the memory 91) that stores wear information including a time of use of the wiper(s) 26. For example, if the user manually turns on the cleaning operation when dry or non-wet particles are present on the windshield 18 (as detected by the imaging system 50), the system 10 may track a duration of use of the windshield wipers 26 used in this wear condition. For example, a blade of windshield wiper 26 may employ natural or synthetic polyisoprene, butadiene, ethylene propylene diene rubber, neoprene, or mixtures thereof, which may wear over time, and particularly faster when applied to dry or rough surfaces where a solvent may be useful to limit wear of the blades. Thus, the wiper 26 may wear out in the form of the blade of the wiper 26, wherein the electric motor 78 overruns due to the resistance caused by the wipers 26 engaging dirt, for example. The wear condition may be communicated to the user via a notification device such as the MMS 70, audible speakers, indicators, or any other visible or audible notification.As further described herein with reference to FIGS. 6A-9B, the imaging system 50 may classify moisture conditions on the windshield 18 based on the level of optical distortion or occlusion. For example, the imaging system 50 may utilize the image processor 88 to distinguish between moisture states and non-moisture states on the windshield 18 based on a level of transparency of haze, color, or the like. Droplets 108, streaks or the like of moisture states can therefore be distinguished from deposits.Referring now to FIG. 4, an example method 400 for an automatic window clearing mode of the window clearing system includes enabling the auto mode at step S 402. In the auto mode, the system 10 recursively classifies the obstruction(s) on the windshield 18, including the level of obstruction and the type of obstruction, at S 404. If an obstruction is detected (e.g., swipe= Ja), the window clearing system turns on the wiper / wipers 26 at S 406. Simultaneously with or in time proximity to the detection of the obstruction, the obstruction is classified as moisture-based or non-moisture-based (e.g., primarily dirt) and the system 10 determines whether the cleaning fluid should be applied at step S 408. For example, primarily dry deposits may justifies the cleaning operation, and at step S 410, the spray device sprays the cleaning fluid onto the windshield 18 to loosen the deposits and allow the windshield wipers 26 to clear the deposits. It is contemplated that the method 400 set forth herein is merely exemplary and that another modification (e.g., manual interrupt) may restrict automatic mode and, as described above, timing and / or grading standards may be adjusted based on manual feedback to optimize the response of the window clearing operation.The wear condition may be determined based on a function of usage cycles and / or environmental conditions such as outdoor climate, air quality, etc. For example, the system 10 may include one or more temperature sensors, humidity sensors, wind speed sensors, wind direction sensors, or other weather detection sensors that may detect the environmental conditions. The system 10 may also use a geospatial location (e.g., GPS, as discussed below) to determine environmental conditions for the vehicle 14. For example, the vehicle 14 may be used primarily in a geographic region that includes one or more common environmental conditions. A vehicle 14 used in a dry climate at high temperatures may be exposed to sandy conditions that typically result in sand being on the windshield 18 (e.g., the windshield), similar to the dust shown in FIG. 3A. Use of the wipers 26 during such conditions may result in greater wear of the wipers 26 than use of the wipers 26 during moisture conditions. In another example, the use of the windshield wipers 26 under cold or icy conditions may result in higher wear than typical moisture conditions. The control circuit may determine operation of the vehicle 14 in snow-covered climates and / or in northerby geographic regions where salt is typically used for application to the roads. Classifying the environment as road salinity may affect the wear state estimation. For example, road salt may increase wear on blades because the salt mixes with moisture to provide salt water, which may increase wear on windshield wipers 26. Other conditions, such as sludge conditions (e.g., moisture conditions combined with dirty conditions), may affect wear conditions. Accordingly, the control circuit may update the wear condition in response to the environmental conditions.The system 10 may also or alternatively provide multiple wear classifications to the reaction control system 54. Some environmental conditions may cause likely wear or a high degree of wear on the windshield wipers 26 than other environmental conditions. Accordingly, the interaction between the windshield wipers 26 and the windshield 18 may be classified by the control circuit. For example, the imaging system 50 may detect visual clarity through the windshield 18 before and after a window clearing operation occurs. In a first classification, wear classification is minimized while allowing suboptimal windshield clearance. For example, if there is an insect residue on the windshield 18, the imaging system 50 may determine sufficient moisture conditions for wiping without spray application. However, stripes may be monitored during operation. Accordingly, the control circuit may communicate an instruction to alert the user (visually or audibly) to suboptimal cleaning and recommend (or automatically initiate) spraying in response to streaking. In this classification, wear is classified with minimal wear and the wear conditions are minimized.In another example, the system 10 classifies the interaction with a second wear classification where a result of the cleaning operation performed has resulted in increased wear of the windshield wipers 26. For example, if sand is detected on the windshield 18 (similar to the dust in FIG. 3B ), interaction of the windshield wipers 26 with the dry sand without spraying may cause significant friction between the blades and the windshield. Scraping sounds or the like may be detected by the system 10 via microphones. Based on the classification of the obstruction and / or audible sounds from the interaction with the windshield wipers 26, the control circuit may classify the wear and update the wear condition accordingly. Further, the control circuit may automatically turn on the spray device in response to the scraping, or otherwise communicate an instruction to the user to turn on application of cleaning fluid. In yet another example, a third wear classification may be determined by the control circuit. The third wear classification may correspond to interactions that result in at least a portion of the blades of the wiper 26 being separated from or lost from the body of the wiper 26. Such classifications may be detected via image / video analysis via the imaging system 50 of the windshield 18 and thus the windshield wipers 26. For example, a trailing sheet may be classified by the imaging system 50.Other classifications may be determined. Thus, each interaction may be weighted more or less to affect the calculation of the state of the wipers 26. Outdoor temperature, humidity, wind speed, or other environmental conditions may be factored in by the control circuit. For example, frost conditions (temperatures around freezing point or around 0 degrees Celsius) may be determined to be solid ice conditions while being visually set forth as typical moisture conditions. If the wipers 26 are still on, the obstruction may still be present due to the obstruction being solid ice and not removed by the clearing operation. In such an example, the control circuit may classify the interaction as significantly increasing the wear state of the windshield wipers 26. The control circuit may be configured to communicate a signal to indicate to the user that the windshield wipers 26 should not be operated in such conditions. In another example, the control circuit interrupts operation of the wipers 26. Accordingly, the classification step in step 404 of the method 400 may include the various warning classifications set forth and result in different responses by the response control system 54.It is contemplated that each wear classification may correspond to a modifier, variable, or multiplier applied to each estimation of the wear condition. For example, the control circuit may modify the calculation or algorithm for determining the wear condition (e.g., multiplying the ordinary wear condition by twice, three times, ten times, etc.). In this way, the normal wear of the blades of the wiper 26 can be increased. The durability of the wipers 26 can therefore be re-estimated by the control circuit. For example, when a large modifier is present (e.g., very cold temperatures with ice, very hot temperatures on the windshield 18, etc.), use of the windshield wipers 26 may result in a significantly reduced useful life of the windshield wipers 26.Referring now to FIG. 5, an example arrangement of the imaging device 20 relative to the windshield 18 is illustrated to show the proximity of the imaging device 20 relative to the windshield 18 and the improved optical and spatial characteristics of the arrangement. Although illustrated as being disposed adjacent an upper portion 96 of the windshield 18, it is contemplated that the imaging device 20 may be disposed along any portion of any window of the vehicle 14 that allows the imaging device 20 to capture a view of the region outside 12 of the vehicle 14. In general, the software employed by the moisture detection system 10 of the present disclosure enables the positioning of the imaging device 20 to be at least partially distance independent between a lens 98 of the imaging device 20 and the window. For example, and as described with reference to FIGS. 6A and 6B, water on the windshield 18 (e.g., condensed water, liquid water, frost) may appear different in the images depending on a distance of the imaging device 20 relative to the windshield 18. For example, the water drops 108 may appear as discrete circles, dots, or other geometric shapes more clearly defined along the edges 110 of the shapes when the imaging device 20 is spaced further from the windshield 18 than when it abuts the windshield 18 or is disposed very close (e.g., between 0 and 35 mm) to the windshield 18. The processes and methods performed by the moisture detection system 10 of the present disclosure may enable detection of the moisture states at one or both of a first position 102 of the imaging system 50 (e.g., at a first distance) and a second position 104 of the imaging device 20 (e.g., at a second distance that is less than the first distance). For example, due to imaging device 20 being proximate to the water on windshield 18, moisture on windshield 18 may appear as blurred stripes or less defined optical distortions when imaging device 20 is at the first distance from windshield 18 than when it is positioned at the second distance. By providing a distance independent arrangement, the moisture detection system 10 enables improved intra-space spacings, a larger and more detailed field of view for the imaging device, and a universal application to enable imaging devices at different distances from the windshield 18.With continued reference to FIG. 5, the windshield 18 may extend at an oblique angle 106 relative to a vertical and / or horizontal orientation. For example, the windshield 18 may be inclined upwardly relative to the travel surface 44. The oblique angle 106 of the windshield 18 may further distort or otherwise affect how the moisture states appear in the images captured by the imaging device. The distortion may be more pronounced relative to the first position 102 in examples where the imaging device 20 is in the second position 104. Accordingly, challenges of capturing moisture conditions of the environment outside the vehicle 14 from images captured by an in-room imaging device 20 may be greater than or different from image processing for moisture detection of images captured by out-of-room imaging devices. For example, water drops 108 on the windshield 18 may expand as they land on the windshield 18 to be more elongated than drops 108 on a perpendicular or more inclined surface, such as the surface of a passenger window, a rear window, a cover for an exterior camera, etc.Referring now to FIGS. 6A and 6B, the differences in the captured images 86 from the first position 102 relative to the second position 104 of the imaging device 20 are shown in FIG. 5. As illustrated in FIG. 6A (the first position 102), rainwater may appear on the outer surface 16 of the windshield 18 as droplets 108 having discrete shapes or edges 110 more clearly defined than blur 112 due to water conditions as indicated in FIG. 6B (the second position 104). Using the current algorithm and processes by the moisture detection system 10, such optical distortion, even if light, can be detected. For example, a drop detection algorithm may be suitable for detecting moisture conditions when the imaging device 20 is positioned far from the windshield 18, and a blur detection algorithm 112 may be employed by the imaging system 50 to detect moisture conditions on the windshield 18 when the imaging device 20 is positioned closer to the windshield 18 (FIG. 6B ). Accordingly, the moisture detection system 10 may provide an improved distance within the vehicle 14 by providing the imaging device 20 proximate the windshield 18.In some examples, the second position 104 is within 50 mm of the windshield 18. In some examples, the second position 104 is between 0 mm and 35 mm of the windshield 18. In each of these examples, the second position 104 is proximate the windshield 18 to provide an improved field of view and distance within the space. In these examples, the first position 102 is farther from the windshield 18 than the second position 104.Referring now to FIG. 7, it will be shown how the FCDDN 92 processes the captured image of FIG. 6B to generate the corresponding output image. The FCDDN 92 is a deep learning model designed for the purpose of unsupervised abnormality detection in data, and operates by learning the underlying patterns and structures within the image data to distinguish normal patterns from abnormal patterns. The FCDD may include a plurality of layers 114, each layer being a convolutional layer. Since each layer may be a convolution, the FCDDN 92 may capture spatial dependencies and maintain the spatial structure of the input data.In the FCDDN, the input data is passed through a series of convolutional layers 114 that extract relevant features at different levels of abstraction. These convolutional layers 114 may be followed by pooling layers 114 to downcate feature maps and reduce spatial dimensions of the feature maps. The output of convolutional layers 114 may then be flattened and fed into fully connected layers 114 that perform further feature extraction and associate the learned features with irregularity scores. Abnormality detection may be achieved by comparing the calculated abnormality scores to a predefined threshold, where scores above the threshold indicate abnormal cases. In the present examples, the predefined thresholds may correspond to an edge continuity of shapes in the image to detect distortions (e.g., blur) caused by moisture. These thresholds may be actively set based on the previously described user feedback (e.g., the user manually turns on the windshield wipers 26 and / or the clearing process, the user manually turns on the headlights 48, etc.).Referring now to FIGS. 8A-8C, example captured images 86 are imaged by the imaging device 20 directed toward the windshield 18 along with filtered image data (output images 90) that indicate moisture conditions on the windshield 18. The imaging system 50 may process the captured images 86 in the image processor 88, including processing the captured images 86 in one or more machine learning models and / or neural networks. The imaging system 50 may employ edge detection techniques, histogram equalization, linear filters, image segmentation, convolution, or any combination of any image processing techniques to detect at least a portion of the captured image that has distortion or obstruction. For example, and with reference to FIGS. 8A and 8B, the imaging system 50 may detect the blur 112 on the windshield 18 corresponding to humidification conditions (e.g., humidity on the windshield 18). In another example, the imaging system 50 detects non-moisture conditions, such as one of the windshield wipers 26 moving across the windshield 18 (FIG. 8C ). In these examples, detection may be applied to the captured images 86 to map groups of pixels of the captured images 86 to surrounding pixels to classify the image data as moisture-related or non-moisture-related. For example, lane detection by the imaging system 50 may be employed to identify one or more lane lines defining the plurality of lanes 126. Generally, object classification may be performed by the imaging system 50, such as classifying other vehicles 14, street lights, trees, or any other object captured in the captured images 86.In general, the FCDDN 92 described above with respect to FIG. 7 may be employed to provide wet estimation. Based on the wet estimate, which may be a continuously changing value or a binary wet or non-wet value, the response control system 54 may determine the response of the moisture detection system 10. By employing the various layers 114 of the FCDDN 92 in combination with manual feedback from the user as described above with respect to FIG. 2, the techniques employed by the moisture detection system 10 may provide an improved response. For example, the windshield wipers 26 may be activated based on the training of the FCDDN 92 if the user would normally activate the windshield wipers 26 or before the user would normally activate the windshield wipers 26. Further, the speed of the windshield wipers 26 and / or the activation of the pump 74 to clean the windshield 18 may be optimized based on the moisture level and / or previous activations of the windshield wipers 26 or the pump 74. The headlights 48 of the vehicle 14 may also or alternatively be turned on with limited mis-lighting. For example, the FCDDN 92 detects and classifies the image data as oncoming headlights 48 and distinguishes oncoming headlights 48 from moisture conditions or non-moisture conditions. Further, objects with distinct Moire effects (e.g., fences) may be distinguished by the FCDDN 92. In some examples, the manual feedback includes manual operation of the wiper 26 (e.g., via the at least one switch 80) and / or maneuvering the vehicle 14 away from a spray event 118. Further, and as described below, setting the vehicle 14 to another lane or maintaining positions on a current lane may be manual feedback (e.g., ignoring a recommendation for lane change or manually cancelling moving to another lane).Referring now to FIGS. 9A and 9B, identification of at least one spray source 116 of spray events 118 may be performed by the imaging system 50 of the moisture detection system 10. For example, the spray sources 116 may include vehicles 14 and non-vehicles 14. The spray source 116 may be tires 42, a vehicle body, bridges, overpassers, construction machinery, hydrants, or any other source. Accordingly, the imaging system 50 may be configured to classify the sources using any of the imaging techniques described above.With continued reference to FIGS. 9A and 9B, the imaging system 50 may further or alternatively determine a location of the spray source 116 relative to the vehicle 14. Based on the location of the spray source 116 (e.g., a source lane 126 a), the reaction control system 54 may control any of the window clearing system, the lighting system, the MMS 70, and / or the motion control system. For example, the imaging system 50 may locate the spray source 116 on a different one of the plurality of lanes 126 than the lane of the vehicle 14 and recommend to maintain the heading on the current lane of the vehicle 14 based on the location of the spray source 116 on a different lane. Thus, the moisture detection system 10 may generally control the vehicle 14 or provide a message to control it to adjust lanes based on the location of the spray source 116. In the example, as illustrated in FIG. 9B, multiple spray sources 116 are detected by the imaging system 50, and the response control system 54 may recommend one of the plurality of lanes 126 based on the proximity of the vehicle 14 to one or more of the plurality of spray events 118 (e.g., based on a following distance 120 of the vehicle 14 to the other vehicles 14, based on a relative position of other vehicles 14 to other sources of spraying, such as a keyhole). In other words, the moisture detection system 10 may detect a first spray source 116 and a second spray source 116, classify each spray source 116 with a relevance score, and recommend a lane or other location for the vehicle 14 based on the relevance scores of the first and second spray sources 116. As described below, the moisture detection system 10 may further recommend a target following distance 120, a target speed, or the like, in response to the relevance scores.With particular reference to FIG. 9B, at least one spray zone 122 may be determined or calculated by the imaging system 50 based on image data from the initial images. For example, any of the techniques described above, including the application of the FCDDN 92, may allow the imaging system 50 to estimate dimensions of the spray zone 122, such as a depth D, a width W, and a height H of the spray. The dimensions of the spray zones 122 may be determined based on the following distance 120 from the source of spraying to the vehicle 14 and the captured images 86. For example, the following distance 120 may be determined using the distance sensors 34, 36, 38 described above with reference to FIGS. 1 and 2, and the foremost location of the spraying may be detected based on image processing of the captured images 86. The difference between the following distance 120 and the front of the spray zone 122 may be calculated by the control circuit to determine the depth D of the spray zone 122. In this manner, the distance detection and location detection of the system 10 may provide for determining the point of origin of the splashing, the size of the splashing, the likelihood of water touching the splashing based on distance and / or velocity, and the like.In the present example, three spray zones 122 are detected by the imaging system 50, each of the spray zones 122 having a different size. The size of the spray zones 122 may be caused by the sizes of the tires 42, the speeds of the vehicles 14, the lanes on which the vehicles 14 are located (e.g., road roughness, potholes, etc.), or any other factor that may affect the size of the tire spraying. For example, rough roads that may cause puddles, curvatures on the road (e.g., a slope on the road, curves), or the like may further affect the amount of splashing. Based on the amount of spraying, the imaging system 50 may determine the following distance 120 from each or any of the spraying sources 116 (e.g., the other vehicles 14). In general, quantities of splashing, a density of splashing, a duration of splashing, or any other aspect related to the magnitude of splashing may be detected by the imaging system 50 and used to classify a priority or ranking (e.g., the relevance values) of the splashing events 118 to determine a target lane of the plurality of lanes 126, a target following distance 120 (e.g., a minimum following distance 120) for the vehicle 14 relative to other vehicles 14, a power-on of the window clearing system, a presentation of messages to the MMS 70, a control of the vehicle 14, or any of the reactions described above. The direction of splashing may be further detected, which may be affected by the wind speed and direction, to improve the estimation of the splashing zones 122 and further improve the response determined by the response control system 54. Such wind speed and direction may be detected using weather sensors or a global positioning system (GPS) in communication with the moisture detection system 10.The moisture detection system 10 may further or alternatively estimate the density of moisture conditions, such as the density of the spray zone 122 or the density of rain. For example, based on the amount, distribution, or pattern of blurs 112 or other moisture spots detected on the windshield 18, the moisture detection system 10 may estimate the density of rain and, responsive to the amount, distribution, or pattern exceeding a threshold or matching a target distribution or pattern, turn on the windshield wipers 26 or communicate an indication to operate the windshield wipers 26 to the user. For example, if more than 25%, 50%, or 90% of the windshield 18 has blurs 112 in the field of view of the imaging device 20, the imaging system 50 may communicate a signal to the response control system 54 to start the windshield wipers 26. In other examples, the size of the water drops 108 and / or blurs 112 may be categorized by the FCDDN 92 and compared to stored moisture conditions to determine the response of the moisture detection system 10.In some examples, the moisture detection system 10 may access the position of the vehicle 14 relative to the roadway type. For example, the GPS may provide the position of the vehicle 14, thereby providing the type of roadway (e.g., highway, city driving, etc.). In this way, the number of lanes, the direction of travel, the construction and / or operation of the roadway (e.g., one-way roads, mid-lane highways, mid-lane expressways) may be determined. Accordingly, the moisture detection system 10 may prioritize some lanes over other lanes or adjust following distances 120 further based on types of lanes. For example, because the typical speed of a vehicle 14 on one highway roadway is greater than on another roadway, the moisture detection system 10 may suggest a "slow lane" of two lanes based on moisture conditions detected on the highway roadway.Although the source lane 126 aof the plurality of lanes 126 is illustrated as having the same traffic direction as the other of the plurality of lanes 126 in the features described above, in some examples, the plurality of lanes 126 includes lanes having a first traffic direction and lanes having a second traffic direction. In this example, the imaging system 50 is configured to classify the spray source 116 as being on a lane having the first direction of traffic and / or the spray source 116 as being on a lane having the second direction of traffic. For example, the imaging system 50 may detect oncoming headlights 48 on an adjacent lane and, in response to this detection, classify the adjacent lane as having an opposite direction of traffic. Thus, spurting of oncoming traffic may be compared to spurting of preceding traffic, and the humidity detection system 10 may provide improved response determination for a target lane for the vehicle 14 based on one or both spurting events 118 in the first and second traffic directions. It is also contemplated that the distance detection system 52 described above may be used to further determine the target lane for the vehicle 14 by tracking the following distance 120 from the vehicle 14 to the preceding vehicle 14.Referring now to FIGS. 10A and 10B, visual representations of RADAR maps as applied to the images set forth in FIGS. 9A and 9B, respectively, are shown. In these examples, the density of the hatching corresponds to the density of the splashing events 118. The control circuitry may generate the density map via the distance detection system 52 and the imaging system 50 and compare such density map to predefined thresholds to classify the spray events 118 with an intensity level.According to an example of the present disclosure, a detection system (e.g., the moisture detection system 10) for a target vehicle 14 (e.g., a trailing vehicle) includes a camera (e.g., an imaging device 20) that captures images of a region outside 12 of the target vehicle 14. The detection system further includes a RADAR module (e.g., one or more of the RADARs 34) that scans the region 12 to detect a depth D of a splash event 118 in the region 12. An actuator, such as one or more actuators of the response control system 54, is configured to operate in response to a response signal. The control circuit is configured to determine a distance between the target vehicle 14 and a front of the spray event 118 based on the images, compare the front of the spray event 118 to the depth D to determine an intensity of the spray event 118, and communicate the response signal based on the intensity of the spray event 118.The image processing and depth analysis techniques employed by the control circuitry to determine the intensity levels of the spray events 118 may include any of the methods or components (e.g., neural networks) described above for moisture detection. For example, Doppler effect analysis may be employed by the control circuitry in tandem with the RADARs 34 to detect the density of the spray events 118 and / or the depth of the spray events 118. The density may refer to the ratio of the volume of the liquid to the volume of the space (e.g., air and liquid) within the spray event 118. The front of the spray events 118 may be detected using pixel analysis (e.g., edge detection, pattern recognition, or other image processing method using neural networks such as the FCDDN 92) to determine positions of water drops / moisture conditions relative to the vehicle 14. The distance between the front of the spray event 118 and the vehicle 14 is determined based on the images (e.g., the captured images 86 or the output images 90), and the depth D of the spray event 118, as determined based on the scans by the RADARs 34, may be synthesized by the control circuitry. For example, a processor of the distance detection system 52, the imaging system 50, or the controller 56 of the reaction control system 54 may process the distance information and the depth information to calculate a position of the spray source 116. Further, the height H and width W of the spray events 118 may be used to estimate the spray zones 122 with improved accuracy compared to image-based methods only. Thus, accurate intensity levels can be determined by employing the RADARs 34.With continued reference to FIGS. 10A and 10B, the density of each spray zone 122 may be determined by the control circuit using the information from the RADARs 34, and is illustrated by hatching (i.e., a dense hatching corresponding to a high moisture density). Using the density information, the position and identity of the spray source 116 may be estimated / determined by the control circuit. Accordingly, in addition to a volume of the spray event 118 calculated using the enhanced spray zones 122, an origin point of the spray event 118 may be determined based on the density.The size and density of the spray events 118 may correspond to the intensity of the spray events 118. For example, large sprays may have higher intensity levels relative to small sprays. The intensity levels may be weighted based on a speed of the vehicle 14 and / or a distance from the target vehicle 14 to the spray source 116 and / or the origin of the spraying (e.g., a puddle, a keyhole, a hydrant, or any of the previously described sprayer sources). For example, the spray intensity classification may be used by the reaction control system 54 to recommend or actively control a spacing between the trailing vehicle 14 (e.g., the target vehicle 14) and a leading vehicle 14. For example, the information may be used by the detection system to recommend increasing a following distance 120 from a preceding vehicle 14 based on the intensity of the sprayer. In one example, a pass-by condition is determined by the control circuit and communicated to the driver or other user via the display 68 or other notification device (e.g., an audio instruction). For example, if a driver contemplates whether the driver should control the vehicle 14 to pass a preceding vehicle 14 when spray events 118 are present, the detection system may recommend a "pass" or "not pass.". In this example, the controller may compare the spray events 118 from the preceding vehicle 14 with spray events 118 on a pass lane due to another preceding vehicle 14 on an adjacent lane.Referring now to FIG. 11, an example of three cases performed using the RADARs 34 and the camera(s) 38 to recommend a pass-by state is shown. In the first case (t=1), a target vehicle V1including the present moisture detection system 10 measures / tracks splashing events 118 from a preceding vehicle V2on a current lane L1and a passing vehicle V3on a passing lane L2using information from the RADARs 34 and the camera 38 as the passing vehicle V3passes the preceding vehicle V2. During this time, the following vehicle V 1 optimizes a following distance 120 from the preceding vehicle V 2 in response to the intensity level of the preceding vehicle V 2 via control via one or more of the actuators of the vehicle systems (e.g., braking, motion control, etc.). Alternatively, the detection system recommends the following distance 120 and the user manually controls the following vehicle V 1.Based on the previously measured spray event 118 generated by the passing vehicle and an estimated speed of the passing vehicle V 3 (as determined using speed detection via the RADARs 34 or other methods), the detection system estimates a leading end F of the spray event 118 at a second time (t=2). It is contemplated that the speed estimate for the other vehicles may be based on image analysis, comparison to the speed of the following vehicle V 1, information from the RADARs 34, or any other speed detection method. Based on the position of the front F of the spray event 118 relative to the trailing vehicle V 1, the detection system controls the trailing vehicle V 1 onto the pass lane L 2 at a third time (t=3) or provides an indication for control. For example, if a distance between the front F and the following vehicle exceeds a target following distance (adjusted for the front end F of the spray event 118) or another threshold distance, the detection system may indicate that appropriate passing conditions are present. Of course, other actions such as traffic from behind or elsewhere, as well as other aspects, may be incorporated into permission to pass. As described herein, the recommendation or pass-by approval is relevant to moisture condition detection and not other factors that may affect pass-by approval.In general, the use of the RADARs 34 relative to imaging may limit the effect of visual obstructions (sunlight, other illumination, other moisture conditions that interfere with the view of the camera(s) 34) on depth detection to determine the optimal following distance 120 and / or its execution. By combining the image-based detection with the RADAR-based detection, more accurate determinations of spray intensity may be tracked to enable an improved response (e.g., activation of the windshield wipers 26, control of the vehicle, etc.). For example, the previously described aspects may be further optimized with respect to optimal window cleaning or clearing by the detection system more accurately estimating when moisture conditions on the windshield 18 should be removed (e.g., when the windshield wipers 26 should be energized).Referring now to FIG. 12, an example process 800 performed by the moisture detection system 10 for use with splash detection and moisture condition detection on the windshield 18 is shown. At step 802, the imaging device 20 captures images of the region outside 12 of the vehicle 14. At step 804, the image processor 88 detects an event in the region that is next inboard with respect to the vehicle 14. For example, the event may be moisture conditions, such as splashing events 118, water on the exterior surface 16 of the windshield 18, objects on the windshield 18, or any other event associated with vision impairment or distortion. At step 806, the event is classified as being associated with the windshield 18 (e.g., on the windshield 18) or spaced apart from the windshield 18. For example, if the event is a spray event 118 relative to other vehicles 14 in front of the vehicle 14, as opposed to spray water on the windshield 18 itself. It is contemplated that the spray event 118 may be classified as both an event on the windshield 18 and an event off the windshield 18 depending on the following distance 120 between the spray source 116 and the vehicle 14. If the event is an obstruction to the exterior surface 16 of the windshield 18, the image processor 88 may classify the obstruction at step 808. For example, the imaging system 50 may classify the obstruction as dirt or water. Based on the classification of the obstruction, the moisture detection system 10 determines a response at step 810. For example, the response may be to initiate application of clearance fluid to the windshield 18 in the event the obstruction is dirt, such as wet dirt, or to turn on or adjust the speed of one or more of the windshield wipers 26 on the windshield 18 in the event of moisture conditions. At step 812, output is communicated to initiate the response determined at step 810. It is contemplated that the response may alternatively be a more passive response, such as presenting a message on the display 68 to instruct a user to perform one or more of the functions, which may alternatively be automatically performed.At step 814, feedback in the form of manual adjustment or non-operation (e.g., the user does not follow a recommendation) is communicated to the response control system 54 and / or the imaging system 50 to further refine the response determined in future events. For example, if the user is instructed to turn on the windshield wipers 26 and the user is not turning on the windshield wipers 26, the target moisture levels for determining the turning on of the windshield wipers by the imaging system 50 may be increased to a threshold to limit false responses for future computations. Such a threshold may be the threshold for the previously described FCDDN 92 or another threshold.If the event is not related to the conditions of the windshield 18, as determined in step 806, the process may proceed to determine a location of a spray event 118 in step 816. For example, using the captured images 86, the imaging system 50 may detect the source and / or location (e.g., the spray lane 126 a) of the spray events 118 caused around the vehicle 14 (e.g., tire spraying). At step 818, the response is determined by the response control system 54. For example, the response may be to adjust the following distance 120 between the spray source 116 and the vehicle 14 by reducing the speed of the vehicle 14. In other examples, the response includes maneuvering the vehicle 14 to another lane of a plurality of lanes 126. In other examples, the response includes presenting messaging instructions to the MMS 70 to indicate to the user to maneuver the vehicle 14, adjust the speed of the vehicle 14, or the like. Other examples of the response include settings of the window clearing system, such as turning on the wiper 26, adjusting the speed of the wiper 26, turning on the pump 74 to apply cleaning fluid, or the like. At step 820, the output may be communicated to initiate the response. Similar to step 812, at step 822, the feedback in the form of an action or non-action by the user to undo the response communicated by the response control system 54 is returned to the moisture detection system 10 to enhance response determination in future conditions where splash events 118 are detected.In general, the present moisture detection system 10 improves the responses for the vehicle 14 to limit obstruction and / or distortion in the vision of the region outside 12 of the vehicle 14. The image processing techniques employed by the moisture detection system 10 may improve the space in the interior of the vehicle 14 by allowing the imaging device 20 to be positioned proximate the windshield 18. Further, the image processing techniques employed herein may provide improved detection of spray sources 116 and / or moisture conditions on the exterior surface 16 of the windshield 18. Based on the detection of these moisture events, fast response times may be provided for clearing the windshield 18 and / or optimizing maneuvering of the vehicle 14 by the moisture detection system 10.As used herein, the term "and / or," when used in a listing of two or more items, means that each of the listed items may be employed individually or any combination of two or more of the listed items may be employed. For example, when a composition as described herein contains components A, B, and / or C, the composition may contain A alone; B alone; C alone; A and B in combination; A and C in combination; B and C in combination; or A, B, and C in combination.Herein, relational expressions such as first and second, upper and lower, and the like are used only to distinguish one entity or action from another entity or action without necessarily requiring or implying an actual such relationship or order between such entities or actions. It is intended that the terms "comprises," "comprising," or any other variation thereof cover a non-exclusive inclusion, such that a process, method, article, or device that / includes a enumeration of elements not only includes those elements, but may include other elements not expressly listed or inherent to such process, method, article, or device. An element preceded by "comprises... a / a / an" does not exclude, without further limitations, the presence of additional identical elements in the process, method, article, or apparatus comprising the element / s.As used herein, the term "about" means that amounts, sizes, formulations, parameters, and other quantities and properties are not accurate and need not be accurate, but may be approximately and / or greater or less, and may reflect tolerances, conversion factors, rounding off, measurement errors, and the like, and other factors known to those of ordinary skill in the art, as desired. When the term "about" is used to describe a value or an endpoint of a range, the disclosure should be understood to include the specific value or endpoint referred to. Regardless of whether a numerical value or an end point of a range includes "about" in the description, the numerical value or the end point of a range is intended to include two embodiments: one modified by "about" and one not modified by "about". It will be further understood that the endpoints of each of the ranges are significant with respect to both the other endpoint and independent of the other endpoint.The terms "substantial", "substantially" and variations thereof are intended to indicate in the present context that a described feature is equal to or approximately equal to a value or description. For example, a "substantially planar" surface is intended to mean that a surface is planar or approximately planar. Moreover, "substantially" is intended to mean that two values are equal or approximately equal. In some embodiments, "substantially" may refer to values within about 10% of each other, such as within about 5% of each other, or within about 2% of each other.As used herein, the terms "the," "the," or "a" or "an" mean "at least one(s)," and are not intended to be limited to "only one(s)," unless expressly stated to the contrary. Thus, for example, reference to "a component" includes embodiments having two or more such components, unless the context clearly indicates otherwise.It is to be understood that variations and modifications may be made to the above structure without departing from the concepts of the present disclosure, and further it is to be understood that such concepts are intended to be covered by the following claims unless these claims expressly state otherwise by their language.According to the present disclosure, there is provided a detection system for a target vehicle, comprising: a camera capturing images of a region outside the target vehicle; a RADAR module sensing the region to detect a depth of a spray event in the region; an actuator configured to operate in response to a response signal; and a control circuit configured to: determine a distance between the target vehicle and a front of the spray event based on the images; compare the front of the spray event with the depth to determine an intensity of the spray event; and communicate the response signal based on the intensity of the spray event.According to one embodiment, the control circuit is configured to: classify a spray source of the spray event based on the image as being vehicle-related or not vehicle-related.According to one embodiment, the control circuit is configured to: estimate a speed of the spray source; and determine a pass availability condition based on the speed of the spray source and the depth of the spray event.According to one embodiment, the response signal includes an indication of the pass availability condition.According to an embodiment, the control circuit is configured to compare the distance to a threshold following distance; and communicate the response signal when the distance exceeds the threshold following distance.According to an embodiment, the control circuit is configured to: determine a target lane from a plurality of lanes for the target vehicle based on the intensity.According to an embodiment, the invention is further characterized by: a display in communication with the control circuit and configured to indicate the target lane in response to the response signal.According to an embodiment, the control circuit is configured to determine a source lane of the plurality of lanes where the splash event is present.According to an embodiment, the invention is further characterized by: a window; a windshield wiper selectively movable along the window; and a window clearing system in communication with the control circuit and including the actuator configured to operate the windshield wiper in response to the intensity.According to one embodiment, the control circuit includes a machine learning model trained to determine the response signal based on manual feedback.According to an embodiment, the manual feedback includes at least one of manual operation of the wiper and maneuvering of the vehicle onto one of the plurality of lanes.According to an embodiment, the invention is further characterized by: a motion control system including the actuator that adjusts the distance in response to the response signal.According to an embodiment, the motion control system includes an automatic speed control system configured to control the speed of the vehicle based on the response signal.According to an embodiment, the RADAR module further detects a density of the splashing event, and wherein the control circuit is configured to classify the intensity further based on the density.According to the present invention, there is provided a detection system for a target vehicle, comprising: a camera capturing images of a region outside the target vehicle; a RADAR module scanning the region to detect a depth of a spray event in the region; a notifier configured to indicate a pass availability state in response to the response signal; and a control circuit configured to: determine a distance between the target vehicle and a front of the spray event based on the images; compare the front of the spray event to the depth to determine an intensity of the spray event; estimate a speed of a passing vehicle causing the spray event; determining a pass availability state based on the speed of the passing vehicle and the intensity; and communicating the response signal based on the pass availability state.According to an embodiment, the invention is further characterized by: a motion control system that adjusts the distance in response to the response signal.According to an embodiment, the motion control system includes an automatic speed control system configured to control the speed of the target vehicle based on the response signal.According to an embodiment, the control circuit is configured to determine a source lane from a plurality of lanes where the splash event is present.According to an embodiment, the invention is further characterized by: a window; a windshield wiper selectively movable along the window; and a window clearing system in communication with the control circuit and including the actuator configured to operate the windshield wiper in response to the distance.According to the present disclosure, there is provided a detection system for a vehicle, comprising: a camera capturing images of a region outside the vehicle; a RADAR module scanning the region to detect a depth and a density of a spray event in the region; an actuator configured to operate in response to a response signal; and a control circuit configured to: determine a distance between the vehicle and a front of the spray event based on the images; compare the front of the spray event with the depth to determine a size of the spray event; determine an intensity of the spray event based on the size and the density of the spray event; and communicate the response signal based on the intensity of the spray event.

Claims

A detection system for a target vehicle, comprising: a camera capturing images of a region outside the target vehicle; a RADAR module scanning the region to detect a depth of a spray event in the region; an actuator configured to operate in response to a response signal; and a control circuit configured to: determine a distance between the target vehicle and a front of the spray event based on the images; compare the front of the spray event to the depth to determine an intensity of the spray event; and communicate the response signal based on the intensity of the spray event.The detection system of claim 1, wherein the control circuit is configured to: classify a spray source of the spray event as vehicle-related or non-vehicle-related based on the image.The detection system of claim 2, wherein the control circuit is configured to: estimate a speed of the spray source; and determine a pass availability condition based on the speed of the spray source and the depth of the spray event.The detection system of claim 3, wherein the response signal includes an indication of the pass availability condition.The detection system of claim 3 or claim 4, wherein the control circuit is configured to compare the distance to a threshold following distance; and communicate the response signal when the distance exceeds the threshold following distance.The detection system of claim 1, wherein the control circuit is configured to: determine a target lane from a plurality of lanes for the target vehicle based on the intensity.The detection system of claim 6, further comprising: a display in communication with the control circuit and configured to indicate the target lane in response to the response signal.The detection system of claim 6 or claim 7, wherein the control circuit is configured to determine a source lane of the plurality of lanes where the splash event is present.The detection system of claim 8, further comprising: a window; a windshield wiper selectively movable along the window; and a window clearing system in communication with the control circuit and including the actuator configured to operate the windshield wiper in response to the intensity.The detection system of claim 9, wherein the control circuitry includes a machine learning model trained to determine the response signal based on manual feedback.The detection system of claim 10, wherein the manual feedback includes at least one of manual operation of the windshield wiper and maneuvering of the vehicle onto one of the plurality of lanes.The detection system of claim 1, further comprising: a motion control system including the actuator that adjusts the distance in response to the response signal.The detection system of claim 12, wherein the motion control system includes an automatic speed control system configured to control the speed of the vehicle based on the response signal.The detection system of any of claims 1-13, wherein the RADAR module further detects a density of the splashing event, and wherein the control circuit is configured to classify the intensity further based on the density.

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