Patent Publication Number: US-11643082-B2

Title: Systems and methods for determining real-time lane level snow accumulation

Description:
TECHNICAL FIELD 
     The present specification relates to driver assistance systems, and more particularly, to systems and methods for determining real-time lane level snow accumulation. 
     BACKGROUND 
     Snow accumulation on roads can be a hazard for vehicles driving on those roads. As such, it may be advantageous for drivers to be aware of accumulated snow on various lanes of roads so that drivers may choose to drive along certain roads or lanes of roads accordingly. However, information about accumulated snow levels is usually only available at a macro-level. For example, traffic and/or weather reports may include snow accumulation levels. However, these reports typically report snow accumulation only for large geographic areas and do not account for variations in snow accumulation across different roads or different lanes on those roads. Accordingly, there is a need for systems and methods for determining real-time lane level snow accumulation. 
     SUMMARY 
     In one embodiment, a method includes receiving an image of a road captured by a vehicle driving on the road, receiving a map of the road, the map comprising a road geometry of the road, obtaining an edge map of the road based on the image of the road, inputting the image, the map of the road, and the edge map into a trained regressor neural network, determining an estimated snow depth for each of one or more lanes of the road based on an output of the regressor neural network, and transmitting the estimated snow depth to an edge computing device. 
     In another embodiment, a vehicle system includes one or more processors, one or more memory modules, one or more vehicle sensors, and machine readable instructions stored in the one or more memory modules. When executed by the one or more processors, the machine readable instructions cause the vehicle system to receive an image of a road that the vehicle is driving on, captured by the one or more vehicle sensors, receive a map of the road, the map comprising a road geometry of the road, obtain an edge map of the road based on the image of the road, input the image, the map of the road, and the edge map into a trained regressor neural network, and determine an estimated snow depth for each of one or more lanes of the road based on an output of the regressor neural network. 
     In another embodiment, a method includes receiving, from one or more edge computing devices, aggregate snow depths for each of one or more lanes of a plurality of roads, updating a database comprising estimated snow depths for each of the plurality of roads based on the aggregate snow depths for each of the one or more lanes of the plurality roads received from the one or more edge computing devices, and transmitting the snow depths of the one or more lanes of the plurality of roads to at least one of the one or more edge computing devices. 
    
    
     
       BRIEF DESCRIPTION OF THE DRAWINGS 
       The embodiments set forth in the drawings are illustrative and exemplary in nature and not intended to limit the disclosure. The following detailed description of the illustrative embodiments can be understood when read in conjunction with the following drawings, where like structure is indicated with like reference numerals and in which: 
         FIG.  1    depicts an example system for determining real-time lane level snow accumulation, according to one or more embodiments shown and described herein; 
         FIG.  2    depicts a schematic diagram of an example vehicle system, according to one or more embodiments shown and described herein; 
         FIG.  3    depicts a schematic diagram of exemplary memory modules of the vehicle system of  FIG.  2   , according to one or more embodiments shown and described herein; 
         FIG.  4    depicts a schematic diagram of an example edge computing device, according to one or more embodiments shown and described herein; 
         FIG.  5    depicts a schematic diagram of an example cloud computing device, according to one or more embodiments shown and described herein; 
         FIG.  6    depicts a flow chart of an example method of determining real-time lane level snow accumulation, according to one or more embodiments shown and described herein; 
         FIG.  7    depicts a flow chart of another example method of determining real-time lane level snow accumulation, according to one or more embodiments shown and described herein; 
         FIG.  8    depicts a flow chart of another example method of determining real-time lane level snow accumulation, according to one or more embodiments shown and described herein 
         FIG.  9    depicts a flow chart of another example method of determining real-time lane level snow accumulation, according to one or more embodiments shown and described herein; 
         FIG.  10    depicts a flow chart of another example method of determining real-time lane level snow accumulation, according to one or more embodiments shown and described herein; 
         FIG.  11    depicts a flow chart of another example method of determining real-time lane level snow accumulation, according to one or more embodiments shown and described herein; 
         FIG.  12    depicts an exemplary image that may be captured by the vehicle system of  FIG.  2   , according to one or more embodiments shown and described herein; 
         FIG.  13    depicts an exemplary neural network architecture, according to one or more embodiments shown and described herein; and 
         FIG.  14    depicts an example heads-up display for indicating an optimal lane, according to one or more embodiments shown and described herein. 
     
    
    
     DETAILED DESCRIPTION 
     The embodiments disclosed herein include systems and methods for determining real-time lane level snow accumulation. A vehicle may drive along a road and may capture images of the road using a camera or other vehicles sensors. The vehicle may also have access to a high-definition (HD) map of the road, either locally stored or received from an external server. A vehicle system may input a captured image of a road into a scene segmentation neural network to determine an edge map of the road. The vehicle system may then input the edge map, the captured image of the road, and an HD map of the road into a trained regressor neural network. The regressor neural network may then output an estimated snow depth for each lane of the road. 
     After the regressor neural network determines predicted snow depth for each lane of the road, the vehicle system may transmit this snow depth data to an edge server. The edge server may receive snow depth data for a plurality of vehicles and may average the snow depths for a particular road determined by a plurality of vehicles to determine an aggregate snow depth for each lane of the road. By averaging snow depths determined by multiple vehicles, the aggregate snow depth may be more accurate than the snow depth determined by any individual vehicle. The edge server may transmit the determined aggregate snow depth information to each of the vehicles within the coverage area of the edge server. Each vehicle that receives this snow depth information from the edge server may display the received snow depth information to a driver of the vehicle. The driver of the vehicle may then make driving decisions (e.g., deciding to drive on a particular road on in a particular lane) based on the snow depth information. 
     The edge server may also transmit the determined aggregate snow depth data to a cloud server. The cloud server may receive aggregate snow depth data from a plurality of edge servers, with each edge server providing coverage for a different geographic area. Thus, the cloud server may maintain a comprehensive map of lane-level snow accumulation across a wide geographic region. In addition, the cloud server may receive weather forecasts that may include predictions of future snow accumulation. Thus, the cloud server may predict future snow depth levels based on weather forecasts and data received from edge servers. The cloud server may periodically transmit this information to the edge servers, which may relay the information to individual vehicles. 
       FIG.  1    depicts an example system  100  for determining real-time lane level snow accumulation. The system  100  includes a vehicle  102  that drives along a road  104  having lanes  106  and  108 . While the example of  FIG.  1    shows the road  104  having two lanes, it should be understood that in other examples, the vehicle  102  may drive along a road having any number of lanes. In some examples, the vehicle  102  may be an autonomous vehicle driven by computer control. In other examples, the vehicle  102  may be a non-autonomous vehicle driven by a human driver. 
     The system  100  of  FIG.  1    further includes edge servers or edge computing devices  110  and  112  and a cloud server or cloud computing device  114 . The edge server  110  is communicatively coupled to the vehicle  102 . In some examples, the edge servers  110  and  112  may be fixed edge servers, e.g., road-side units (RSU), and may comprise any type of computing device capable of performing the functionalities described herein. In these examples, a variety of edge servers may be positioned at various locations along the road  104  or along other roads and each edge server may have a different coverage or service area. As such, as the vehicle  102  drives along the road and moves in and out of coverage areas of different edge servers, the vehicle  102  may communicate with different edge servers. For example, as illustrated in  FIG.  1   , the vehicle  102  is communicatively coupled to the edge server  110 . However, as the vehicle  102  moves away from the edge server  110  and approaches the edge server  112 , the vehicle  102  may disconnect from the edge server  110  and communicatively couple to the edge server  112 . 
     In some examples, one or more of the edge servers  110  and  112  may be a moving edge server, e.g., another vehicle on the road  104 . In some examples, the edge servers  110  and  112  may be positioned near the road  104  such that they may be communicatively coupled to the vehicle  102  or other vehicles on the road  104 . In some examples, one or more of the functions performed by the edge servers  110  and  112  may be performed by the vehicle  102  and/or the cloud server  114 . 
     As described in further detail below, the edge server  110  may receive snow accumulation data from the vehicle  102  and other vehicles and may aggregate the data received from each vehicle. The edge servers  110  and  112  may also be communicatively coupled to the cloud server  114 , as explained in further detail below. In the illustrated example, the system  100  is shown comprising two edge servers. However, it should be understood that in other examples, the system  100  may comprise any number of edge servers. 
     Referring still to  FIG.  1   , in the illustrated example, the cloud server  114  is a cloud-based computing system. However, in other examples, the cloud server  114  may comprise any other type of computing system. In the illustrated example, the cloud server  114  transmits and receives data from the edge servers  110  and  112 , as described in further detail below. 
       FIG.  2    depicts an example vehicle system  200  included in the vehicle  102  of  FIG.  1   . The vehicle system  200  includes one or more processors  202 , a communication path  204 , one or more memory modules  206 , a satellite antenna  208 , one or more vehicle sensors  210 , network interface hardware  212 , and a data storage component  214 , the details of which will be set forth in the following paragraphs. In examples where the vehicle  102  is an autonomous vehicle, the vehicle system  200  may also include one or more modules for performing autonomous driving of the vehicle  102 . It should be understood that the vehicle system  200  of  FIG.  2    is provided for illustrative purposes only, and that other vehicle systems  200  comprising more, fewer, or different components may be utilized. 
     Each of the one or more processors  202  may be any device capable of executing machine readable and executable instructions. Accordingly, each of the one or more processors  202  may be a controller, an integrated circuit, a microchip, a computer, or any other computing device. The one or more processors  202  are coupled to a communication path  204  that provides signal interconnectivity between various modules of the vehicle system  200 . Accordingly, the communication path  204  may communicatively couple any number of processors  202  with one another, and allow the modules coupled to the communication path  204  to operate in a distributed computing environment. Specifically, each of the modules may operate as a node that may send and/or receive data. As used herein, the term “communicatively coupled” means that coupled components are capable of exchanging data signals with one another such as, for example, electrical signals via conductive medium, electromagnetic signals via air, optical signals via optical waveguides, and the like. 
     Accordingly, the communication path  204  may be formed from any medium that is capable of transmitting a signal such as, for example, conductive wires, conductive traces, optical waveguides, or the like. In some embodiments, the communication path  204  may facilitate the transmission of wireless signals, such as WiFi, Bluetooth®, Near Field Communication (NFC) and the like. Moreover, the communication path  204  may be formed from a combination of mediums capable of transmitting signals. In one embodiment, the communication path  204  comprises a combination of conductive traces, conductive wires, connectors, and buses that cooperate to permit the transmission of electrical data signals to components such as processors, memories, sensors, input devices, output devices, and communication devices. Accordingly, the communication path  204  may comprise a vehicle bus, such as for example a LIN bus, a CAN bus, a VAN bus, and the like. Additionally, it is noted that the term “signal” means a waveform (e.g., electrical, optical, magnetic, mechanical or electromagnetic), such as DC, AC, sinusoidal-wave, triangular-wave, square-wave, vibration, and the like, capable of traveling through a medium. 
     The vehicle system  200  includes one or more memory modules  206  coupled to the communication path  204 . The one or more memory modules  206  may comprise RAM, ROM, flash memories, hard drives, or any device capable of storing machine readable and executable instructions such that the machine readable and executable instructions can be accessed by the one or more processors  202 . The machine readable and executable instructions may comprise logic or algorithm(s) written in any programming language of any generation (e.g., 1GL, 2GL, 3GL, 4GL, or 5GL) such as, for example, machine language that may be directly executed by the processor, or assembly language, object-oriented programming (OOP), scripting languages, microcode, etc., that may be compiled or assembled into machine readable and executable instructions and stored on the one or more memory modules  206 . Alternatively, the machine readable and executable instructions may be written in a hardware description language (HDL), such as logic implemented via either a field-programmable gate array (FPGA) configuration or an application-specific integrated circuit (ASIC), or their equivalents. Accordingly, the methods described herein may be implemented in any conventional computer programming language, as pre-programmed hardware elements, or as a combination of hardware and software components. The memory modules  206  of the vehicle system  200  are described in further detail below with respect to  FIG.  3   . 
     Referring still to  FIG.  2   , the example vehicle system  200  comprises a satellite antenna  208  coupled to the communication path  204  such that the communication path  204  communicatively couples the satellite antenna  208  to other modules of the vehicle system  200 . The satellite antenna  208  is configured to receive signals from global positioning system satellites. Specifically, in one embodiment, the satellite antenna  208  includes one or more conductive elements that interact with electromagnetic signals transmitted by global positioning system satellites. The received signal is transformed into a data signal indicative of the location (e.g., latitude and longitude) of the satellite antenna  208  or an object positioned near the satellite antenna  208 , by the one or more processors  202 . Thus, the satellite antenna  208  allows the vehicle  102  to monitor its location. 
     The vehicle system  200  comprises one or more vehicle sensors  210 . Each of the one or more vehicle sensors  210  is coupled to the communication path  204  and communicatively coupled to the one or more processors  202 . The one or more vehicle sensors  210  may include, but are not limited to, LiDAR sensors, RADAR sensors, optical sensors (e.g., cameras, laser sensors, proximity sensors, location sensors), and the like. In some examples, the vehicle sensors  210  may be used to autonomously navigate the vehicle  102 . In addition, the vehicle sensors  210  may capture images of the road  104  that may be used to determine snow accumulation levels, as described herein. 
     Still referring to  FIG.  2   , the vehicle system  200  comprises network interface hardware  212  for communicatively coupling the vehicle system  200  to the edge server  110 . The network interface hardware  212  can be communicatively coupled to the communication path  204  and can be any device capable of transmitting and/or receiving data via a network. Accordingly, the network interface hardware  212  can include a communication transceiver for sending and/or receiving any wired or wireless communication. For example, the network interface hardware  212  may include an antenna, a modem, LAN port, Wi-Fi card, WiMax card, mobile communications hardware, near-field communication hardware, satellite communication hardware and/or any wired or wireless hardware for communicating with other networks and/or devices. In one embodiment, the network interface hardware  212  includes hardware configured to operate in accordance with the Bluetooth® wireless communication protocol. The network interface hardware  212  of the vehicle system  200  may transmit data detected by the vehicle sensors  210  and other data to the edge server  110 , as disclosed herein. 
     In some embodiments, the vehicle system  200  may be communicatively coupled to the edge server  110  by a network. In one embodiment, the network may include one or more computer networks (e.g., a personal area network, a local area network, or a wide area network), cellular networks, satellite networks and/or a global positioning system and combinations thereof. Accordingly, the vehicle system  200  can be communicatively coupled to the network via a wide area network, via a local area network, via a personal area network, via a cellular network, via a satellite network, etc. Suitable local area networks may include wired Ethernet and/or wireless technologies such as, for example, wireless fidelity (Wi-Fi). Suitable personal area networks may include wireless technologies such as, for example, IrDA, Bluetooth®, Wireless USB, Z-Wave, ZigBee, and/or other near field communication protocols. Suitable cellular networks include, but are not limited to, technologies such as LTE, WiMAX, UMTS, CDMA, and GSM. 
     Still referring to  FIG.  2   , the vehicle system  200  comprises a data storage component  214 . The data storage component  214  may store data that may be utilized by the memory modules  206  and/or other components of the vehicle system  200 . For example, the data storage component  214  may store HD map data of the road  104  and other roads. The data storage component  214  may also store learned parameters for a trained neural network, as described herein. Other data that may be stored in the data storage component  214  is described throughout this disclosure. 
     Now referring to  FIG.  3   , exemplary memory modules  206  of the vehicle system  200  are shown. The one or more memory modules  206  include an image data reception module  300 , a map data reception module  302 , a scene segmentation neural network processing module  304 , a regressor neural network processing module  306 , an edge server data comparison module  308 , a snow depth transmission module  310 , a snow depth severity determination module  312 , a snow depth broadcast module  314 , a snow depth reception module  316 , a snow depth display module  318 , and an optimal lane determination module  320 . Each of the image data reception module  300 , the map data reception module  302 , the scene segmentation neural network processing module  304 , the regressor neural network processing module  306 , the edge server data comparison module  308 , the snow depth transmission module  310 , the snow depth severity determination module  312 , the snow depth broadcast module  314 , the snow depth reception module  316 , the snow depth display module  318 , and the optimal lane determination module  320  may be a program module in the form of operating systems, application program modules, and other program modules stored in the one or more memory modules  206 . Such a program module may include, but is not limited to, routines, subroutines, programs, objects, components, data structures and the like for performing specific tasks or executing specific data types as will be described below. 
     The image data reception module  300  may receive images captured by the vehicle sensors  210  of the vehicle system  200 . In the example of  FIG.  1   , the images received by the image data reception module  300  may comprise images of the road  104  along which the vehicle  102  is driving. In particular, the images received by the image data reception module  300  may comprise images of the road  104  in front of the vehicle  102 . The images captured by the vehicle sensors  210  and received by the image data reception module  300  may show snow accumulation on the road  104 .  FIG.  12    shows an example image  1200  that may be received by the image data reception module  300 . The images received by the image data reception module  300  may be used to determine lane-level snow accumulation as described herein. 
     Referring back to  FIG.  3   , the map data reception module  302  may receive an HD map of a road along which the vehicle  102  drives (e.g., the road  104  in the example of  FIG.  1   ). In some examples, the map data reception module  302  may receive a locally stored HD map from the data storage component  214 . In other examples, the map data reception module  302  may receive a map from the edge server  110 , the cloud server  114 , or other remote computing devices (e.g., a map server). The HD map received by the map data reception module  302  may include road geometry of the road  104  in non-snowy conditions (e.g., when there is no snow on the road).  FIG.  12    shows an example road geometry  1202  of a road that may be included in an HD map received by the map data reception module  302 . The HD map and road geometry information therein received by the map data reception module  302  may be used along with the images received by the image data reception module  300  to determine lane-level snow accumulation as described herein. 
     Referring back to  FIG.  3   , the scene segmentation neural network processing module  304  may input an image received by the image data reception module  300  into a trained scene segmentation neural network. The scene segmentation neural network may then output an edge map based on the input image.  FIG.  13    shows an example neural network architecture  1300 . In the example of  FIG.  13   , an input image  1302  is input into a scene segmentation neural network  1306 , which outputs an edge map  1308  based on the input image  1302 . 
     The output of the scene segmentation neural network  1306  may be an edge map that indicates the edges of objects and boundaries between different objects in the input image  1302 . For example, an edge map may identify lane markings as well as other boundaries and roadside landmarks (e.g., traffic signs, light poles, and the like). In some examples, the scene segmentation neural network  1306  may be a pre-trained neural network. In other examples, the parameters of the scene segmentation neural network  1306  may be trained based on sample data collected by the vehicle sensors  210 . The scene segmentation neural network  1306  may comprise any type of neural network. The parameters of the scene segmentation neural network  1306  may be stored in the data storage component  214 . 
     Referring back to  FIG.  3    in conjunction with  FIG.  13   , the regressor neural network processing module  306  may input the image  1302  received by the image data reception module  300 , the HD map  1304  received by the map data reception module  302 , and the output of the scene segmentation neural network  1306  determined by the scene segmentation neural network processing module  304  into a regressor neural network  1310 , as shown in  FIG.  13   . The regressor neural network  1310  may then output an estimated snow depth  1312  for each lane of the road in the image  1302 . 
     In the illustrated example, the regressor neural network  1310  comprises a fully connected deep neural network. However, in other examples, the regressor neural network  1310  may comprise any neural network architecture having any number of hidden layers and any number of nodes in each layer. The regressor neural network  1310  takes as an input an input image of a road, an HD map indicating the road geometry of the road, and an edge map of the input image (e.g., as determined by the scene segmentation neural network  1306 ). The regressor neural network  1310  outputs an estimated snow depth for each lane in the input image.  FIG.  12    shows an example snow depth  1204  determined by the regressor neural network  1310  across the three lanes of the road on which the vehicle  102  is driving. 
     Referring to  FIG.  13   , the regressor neural network  1310  may be trained using supervised learning techniques on training data comprising example input images and corresponding HD maps. The training data may comprise associated ground truth labels comprising snow depth values for each lane in each example input image measured during snowy conditions. After the regressor neural network  1310  is trained, the learned parameters may be stored in the data storage component  214  of the vehicle system  200 . By inputting captured images as well as HD maps comprising road geometry, the regressor neural network  1310  may learn to distinguish between road geometry without snow and road geometry with snow in order to speed up convergence during training of the regressor neural network  1310 . 
     Referring back to  FIG.  3   , the edge server data comparison module  308  compares the estimated snow depths determined by the regressor neural network processing module  306  to snow depths recently received from the edge server  110  (as explained below). In embodiments, the edge server data comparison module  308  determines whether the difference between the snow depths determined by the regressor neural network processing module  306  and the snow depths received from the edge server  110  are above a threshold value. In some embodiments, if the difference determined by the edge server data comparison module  308  is above the threshold value, indicating a significant difference between snow depths estimated by the edge server  110  and the snow depths estimated by the vehicle system  200 , then the snow depth transmission module  310  may transmit the snow depths determined by the regressor neural network processing module  306  to the edge server  110 . The snow depth transmission module  310  may also transmit a timestamp and location associated with the vehicle  102  when the snow depth estimate was determined. If the difference determined by the edge server data comparison module  308  is at or below the threshold value, the snow depth transmission module  310  may withhold transmitting the snow depths determined by the regressor neural network processing module  306  to the edge server  110  to conserve bandwidth. 
     The snow depth severity determination module  312  may determine the severity of the snow depth determined by the regressor neural network processing module  306 . For example, the snow depth severity determination module  312  may determine whether the snow depth determined by the regressor neural network processing module  306  is above a severity threshold (e.g., one inch), indicating that the determined snow conditions are severe and the road  104  is particularly dangerous. If the snow depth severity determination module  312  determines that the snow depth determined by the regressor neural network processing module  306  is above the severity threshold, the snow depth broadcast module  314  may broadcast a signal to nearby vehicles indicating the severe and dangerous road conditions. This may allow these other vehicles to take appropriate driving precautions without the need for the edge server  110  to be involved. In the illustrated example, the snow depth broadcast module  314  transmits a signal to nearby vehicles using vehicle-to-vehicle (V2V) communications. In other examples, the snow depth broadcast module  314  may transmit a signal to nearby vehicles using other forms of communications. 
     The snow depth reception module  316  receives snow depths for each lane of the road  104  from the edge server  110 . As explained in further detail below, the edge server  110  aggregates snow depth determinations made by a plurality of vehicles and averages them together. As such, the edge server  110  may determine a more accurate snow depth than that determined by the vehicle system  200 . In particular, if any one vehicle inaccurately determines a snow depth (e.g., due to mechanical error, software error, or other types of bias), the edge server  110  will likely determine a more accurate snow depth measurement by averaging the inaccurate snow depth determination from that one vehicle with accurate snow depth determinations from other vehicles or eliminating an outlier and averaging the remaining depth measurements. As such, it may be advantageous for the vehicle system  200  to rely on snow depth data from the edge server  110  rather than snow depth data determined by the vehicle system  200 . The snow depth data received by the snow depth reception module  316  from the edge server  110  may also include predicted future snow accumulation, as explained in further detail below. 
     The snow depth display module  318  may display the snow depth data received by the snow depth reception module  316  from the edge server  110 . This snow depth data may be displayed in a heads up display or a dashboard display, or at any other location in the vehicle  102  such that it may be viewed by the driver of the vehicle  102 . In some examples, the snow depth display module  318  may display the snow depth data received by the snow depth reception module  316  such that a simulated level of the aggregate snow depth for one or more lanes is visible to a driver of the vehicle. 
     In some examples, the optimal lane determination module  320  may determine an optimal lane for the vehicle  102  to drive in based on the data received by the snow depth reception module  316 . For example, the optimal lane determination module  320  may determine that the lane having the smallest snow depth ahead of the vehicle  102  is the optimal lane for the vehicle  102  to drive in. In these examples, the snow depth display module  318  may display an indication of which lane the driver of the vehicle  102  should use, as shown in  FIG.  14   .  FIG.  14    shows an example heads up display on the vehicle  102  in which three lanes are annotated. In the example of  FIG.  14   , lanes  1  and  3  have a first annotation indicating that they are not the optimal lanes for the vehicle  102  to use and lane  2  has a second annotation indicating that it is the optimal lane for the vehicle  102  to use. The lanes may be annotated with different colors or in other ways to indicate a preferred lane. The heads up display may display simulated levels of the aggregate snow depth for lanes  1 ,  2 , and  3 . For example, a bar graph or other indication illustrating a level of snow depth may be displayed for each lane. The bar graphs or other indication may overlap with actual lanes when viewed from the perspective from the driver of the vehicle  102  such that the driver may easily recognize the level of snow depth in each lane. In some examples, the optimal lane determination module  320  may instruct the vehicle  102  to follow the optimal lane autonomously. 
     Now referring to  FIG.  4   , the edge server  110  comprises one or more processors  402 , one or more memory modules  404 , network interface hardware  406 , and a communication path  408 . The one or more processors  402  may be a controller, an integrated circuit, a microchip, a computer, or any other computing device. The one or more memory modules  404  may comprise RAM, ROM, flash memories, hard drives, or any device capable of storing machine readable and executable instructions such that the machine readable and executable instructions can be accessed by the one or more processors  402 . The communication path  408  provides signal interconnectivity between various modules of the edge server  110 . 
     The network interface hardware  406  can be communicatively coupled to the communication path  408  and can be any device capable of transmitting and/or receiving data via a network. Accordingly, the network interface hardware  406  can include a communication transceiver for sending and/or receiving any wired or wireless communication. For example, the network interface hardware  406  may include an antenna, a modem, LAN port, Wi-Fi card, WiMax card, mobile communications hardware, near-field communication hardware, satellite communication hardware and/or any wired or wireless hardware for communicating with other networks and/or devices. In one embodiment, the network interface hardware  406  includes hardware configured to operate in accordance with the Bluetooth® wireless communication protocol. The network interface hardware  406  of the edge server  110  may transmit and receive data to and from the vehicle  102  and to and from the cloud server  114 . 
     The one or more memory modules  404  include a database  410 , a snow depth reception module  412 , a snow depth aggregation module  414 , a data comparison module  416 , a snow depth transmission module  418 , a snow depth prediction reception module  420 , and a snow depth broadcast module  422 . Each of the database  410 , the snow depth reception module  412 , the snow depth aggregation module  414 , the data comparison module  416 , the snow depth transmission module  418 , the snow depth prediction reception module  420 , and the snow depth broadcast module  422  may be a program module in the form of operating systems, application program modules, and other program modules stored in one or more memory modules  404 . In some embodiments, the program module may be stored in a remote storage device that may communicate with the edge server  110 . Such a program module may include, but is not limited to, routines, subroutines, programs, objects, components, data structures and the like for performing specific tasks or executing specific data types as will be described below. 
     The database  410  may temporarily or permanently store snow accumulation data received from vehicles (e.g., the vehicle  102 ) and/or the cloud server  114 . In addition, the database  410  may store other data to be used by the memory modules  404  to ensure their proper functionality, as described herein. 
     The snow depth reception module  412  may receive snow depth data from vehicles, such as the vehicle  102 . The snow depth data received by the snow depth reception module  412  from a vehicle may include snow depth data determined by a vehicle system of the vehicle, as described above. The snow depth data received by the snow depth reception module  412  may also include a location and timestamp associated with the vehicle sending the snow depth data. This may allow the edge server  110  to keep track of and properly aggregate the snow accumulation data received from a plurality of vehicles. The snow depth data received by the snow depth reception module  412  may be stored in the database  410 . 
     The snow depth aggregation module  414  may aggregate snow depth data associated with a particular road received from a plurality of vehicles. As described above, the vehicle  102  may estimate snow depth for the road  104  using the techniques described herein. Other vehicles that drive along the road  104  may also estimate snow depths for the road  104 . However, there may be variations in the snow depths reported by different vehicles due to mechanical or software errors, variability in conditions under which the estimations were obtained, random noise, and other factors. As such, the snow depth aggregation module  414  may aggregate data received from multiple vehicles in order to reduce bias and obtain a more accurate estimate of snow depth for the lanes of the road  104 . In the illustrated example, the snow depth aggregation module  414  may aggregate the snow depths received from multiple vehicles by averaging the snow depths estimates received from each vehicle. In other examples, the snow depth aggregation module  414  may use other methods to aggregate the received snow depths. For example, the snow depth aggregation module  114  may determine a weighted average of snow depths by applying different weights to snow depths received from different vehicles (e.g., based on the time that the snow depths were received or other factors). 
     After the snow depth aggregation module  414  determines an aggregate snow depth for a road, the data comparison module  416  may compare the determined aggregate snow depth to the aggregate snow depth most recently transmitted to the cloud server  114  (as explained below). In particular, the data comparison module  416  may determine whether the difference between the snow depth determined by the snow depth aggregation module  414  and the snow depth most recently transmitted to the cloud server  114  is above a threshold value. If the difference determined by the data comparison module  416  is above the threshold value, this indicates that the snow depth for the road in question has markedly changed since the last transmission to the cloud server  114 . As such, the snow depth transmission module  418  may transmit the newly determined aggregate snow depth to the cloud server  114 . 
     Alternatively, if the difference determined by the data comparison module  416  is at or below the threshold value, this indicates that the snow depth for the road in question has not markedly changed since the last transmission to the cloud server  114 . Thus, the snow depth transmission module  418  may not transmit the recently determined aggregate snow depth to the cloud server  114 . Accordingly, the cloud server  114  receives an update whenever the aggregate snow depth determined by the edge server  110  markedly changes. However, the cloud server  114  does not receive an update when the aggregate snow depth determined by the edge server  110  does not markedly change, thereby reducing bandwidth usage. 
     The snow depth prediction reception module  420  may receive future predictions regarding snow accumulation from the cloud server  114 , as discussed in further detail below. The predictions regarding snow accumulations received by the snow depth prediction reception module  420  may be stored in the database  410 . 
     The snow depth broadcast module  422  may transmit estimated lane-level snow depth values to each connected vehicle within the coverage area of the edge server  110 . In particular, the snow depth broadcast module  422  may transmit, to each vehicle within the coverage area of the edge server  110 , estimated snow depth values for each lane of the road on which each vehicle is driving. In some examples, the estimated snow depths transmitted by the snow depth broadcast module  422  may include the predicted future snow accumulation received by the snow depth prediction reception module  420 . 
     Now referring to  FIG.  5   , the cloud server  114  comprises one or more processors  502 , one or more memory modules  504 , network interface hardware  506 , and a communication path  508 . The one or more processors  502  may be a controller, an integrated circuit, a microchip, a computer, or any other computing device. The one or more memory modules  504  may comprise RAM, ROM, flash memories, hard drives, or any device capable of storing machine readable and executable instructions such that the machine readable and executable instructions can be accessed by the one or more processors  502 . The communication path  508  provides signal interconnectivity between various modules of the cloud server  114 . 
     The network interface hardware  506  can be communicatively coupled to the communication path  508  and can be any device capable of transmitting and/or receiving data via a network. Accordingly, the network interface hardware  506  can include a communication transceiver for sending and/or receiving any wired or wireless communication. For example, the network interface hardware  506  may include an antenna, a modem, LAN port, Wi-Fi card, WiMax card, mobile communications hardware, near-field communication hardware, satellite communication hardware and/or any wired or wireless hardware for communicating with other networks and/or devices. In one embodiment, the network interface hardware  506  includes hardware configured to operate in accordance with the Bluetooth® wireless communication protocol. The network interface hardware  506  of the cloud server  114  may transmit and receive data to and from the edge server  110 . 
     The one or more memory modules  504  include a database  510 , a snow depth reception module  512 , a data comparison module  514 , a snow depth update module  516 , a weather data reception module  518 , and a snow depth transmission module  520 . Each of the database  510 , the snow depth reception module  512 , the data comparison module  514 , the snow depth update module  516 , the weather data reception module  518 , and the snow depth transmission module  520  may be a program module in the form of operating systems, application program modules, and other program modules stored in one or more memory modules  504 . In some embodiments, the program module may be stored in a remote storage device that may communicate with the cloud server  114 . Such a program module may include, but is not limited to, routines, subroutines, programs, objects, components, data structures and the like for performing specific tasks or executing specific data types as will be described below. 
     The database  510  may temporarily or permanently store snow accumulation data received from edge servers (e.g., the edge servers  110  and  112 ). In particular, the database  510  may store a comprehensive aggregated map of snow accumulation across a particular geographic region based on snow depth data received from the edge servers that are part of the system  100 . In addition, the database  510  may store other data to be used by the memory modules  504  to ensure their proper functionality, as described herein. 
     The snow depth reception module  512  may receive snow depth data from one or more edge servers (e.g., the edge servers  110  and  112 ). Specifically, the snow depth reception module  512  may receive snow depths determined by edge servers using the techniques described above. The snow depths received by the snow depth reception module  512  may be associated with one or more roads and one or more lanes on each of those roads. The snow depths received by the snow depth reception module  512  may be used by the cloud server  114  to maintain a comprehensive aggregated map of snow accumulation levels. The snow accumulation levels on the map maintained by the cloud server  114  may include current snow depths (e.g., snow depths received by the snow depth reception module  512  from edge servers) and future predicted snow accumulations, as described below. 
     When the snow depth reception module  512  receives snow depth data for a particular road, the data comparison module  514  compares the received snow depths to the stored snow depths for that road (e.g., as stored in the database  510 ). In particular, the data comparison module  514  may determine whether the difference between the snow depth received by the snow depth reception module  512  and the snow depth recorded by the cloud server  114  for that road is above a threshold value. If the data comparison module  514  determines that the difference is above the threshold value, the snow depth update module  516  may update the snow depth data stored in the database  510  indicating the snow depth for the road. Alternatively, if the data comparison module  514  determines that the difference is at or below the threshold value, the snow depth update module  516  may not update the snow depth data stored in the database  510  in order to conserve computing resources when the snow depth values have not significantly changed. 
     The weather data reception module  518  may receive weather forecasts for one or more geographic areas covered by the system  100 . These weather forecasts may include predicted future snowfall. As such, the weather data reception module  518  may receive data associated with predicted future snowfall. The snow depth update module  516  may then update the snow depth map stored in the database  510  to include future predicted snow depths based on the weather data received by the weather data reception module  518 . 
     The snow depth transmission module  520  may transmit snow depth data to the edge servers that are part of the system  100  (e.g., the edge servers  110  and  112 ). In particular, the snow depth transmission module  520  may transmit future predicted snow depths to edge servers based on data received by the weather data reception module  518 . In some examples, the snow depth transmission module  520  may only transmit snow depth data to a particular edge server upon a trigger event. For example, the snow depth transmission module  520  may only transmit data to an edge server after receiving snow depth data from that edge server. This may help reduce communication bandwidth. 
       FIG.  6    depicts a flowchart of an example method for operating the example vehicle system  200 , according to one or more embodiments shown and described herein. At step  600 , the image data reception module  300  receives an image of the road  104  captured by the vehicle  102  driving on the road  104 . The image may be captured by the vehicle sensors  210  (e.g., a camera). 
     At step  602 , the map data reception module  302  receives a map of the road  104 . The map of the road  104  may comprise a road geometry. The map of the road  104  may be stored in the data storage component  214 . 
     At step  604 , the scene segmentation neural network processing module  304  obtains an edge map of the road  104  based on the image of the road. The scene segmentation neural network may obtain the edge map of the road  104  by inputting the image of the road  104  into a trained scene segmentation neural network. 
     At step  606 , the regressor neural network processing module  306  inputs the image, the map of the road, and the edge map into a trained regressor neural network. The regressor neural network may be trained using supervised learning techniques with training data having ground truth labels comprising measurements of snow depths. 
     At step  608 , the regressor neural network processing module  306  determines an estimated snow depth for each of one or more lanes of the road  104  based on an output of the regressor neural network. 
       FIG.  7    depicts a flowchart of another example method for operating the example vehicle system  200 , according to one or more embodiments shown and described herein. At step  702 , the map data reception module  302  receives a map of the road  104 . At step  704 , the scene segmentation neural network processing module  304  obtains an edge map of the road  104  based on the image of the road. At step  706 , the regressor neural network processing module  306  inputs the image, the map of the road, and the edge map into a trained regressor neural network. At step  708 , the regressor neural network processing module  306  determines an estimated snow depth for each of one or more lanes of the road  104  based on an output of the regressor neural network. 
     At step  710 , the edge server data comparison module  308  determines a difference between an estimated snow depth for the one or more lanes of the road  104  and an aggregate snow depth for the one or more lanes of the road  104  previously received from the edge computing device  110  and determines whether the difference is greater than a threshold value. If the edge server data comparison module  308  determines that the difference is not greater than the threshold value (no at step  710 ), then control returns to step  700 . If the edge server data comparison module  308  determines that the difference is greater than the threshold value (yes at step  710 ), then, at step  712 , the snow depth transmission module  310  transmits the estimated snow depth to the edge computing device  110 . 
     At step  714 , the snow depth severity determination module  312  determines whether a condition of the road  104  is severe based on the estimated snow depth for the one or more lanes of the road  104 . The snow depth severity determination module  312  may determine that the condition of the road  104  is severe when the estimated snow depth for at least one lane of the one or more lanes of the road  104  is above a threshold value. If the snow depth severity determination module  312  determines that the condition of the road  104  is severe (yes at step  714 ), then at step  716 , the snow depth broadcast module  314  the estimated snow depth to one or more nearby vehicles. If the snow depth severity determination module  312  determines that the condition of the road  104  is not severe (no at step  714 ), then control passes to step  718 . 
     At step  718 , the snow depth reception module  316  receives an aggregate snow depth for the one or more lanes of the road  104  from the edge computing device  110 . At step  720 , the snow depth display module  318  displays the aggregate snow depth for the one or more lanes of the road  104  such that it is visible to a driver of the vehicle  102 . In some examples, the optimal lane determination module  320  determines an optimal lane of the road  104  for the vehicle  102  to drive in based on the aggregate snow depth for the one or more lanes of the road  104 . In these examples, the snow depth display module  318  may display an indication of the optimal lane such that it is visible to a driver of the vehicle  102 . 
       FIG.  8    depicts a flowchart of an example method for operating the example edge server  110 , according to one or more embodiments shown and described herein. At step  800 , the snow depth reception module  412  receives, from one or more first vehicles, an estimated snow depth for each of one or more lanes of a road on which the one or more first vehicles are driving. 
     At step  802 , the snow depth aggregation module  414  aggregates the estimated snow depth from each of the one or more first vehicles to determine an aggregate snow depth for the one or more lanes of the road. 
     At step  804 , the snow depth transmission module  418  transmits the aggregate snow depth to one or more second vehicles. 
       FIG.  9    depicts a flowchart of another example method for operating the example edge server  110 , according to one or more embodiments shown and described herein. At step  900 , the snow depth reception module  412  receives, from one or more first vehicles, an estimated snow depth for each of one or more lanes of a road on which the one or more first vehicles are driving. 
     At step  902 , the snow depth aggregation module  414  aggregates the estimated snow depth from each of the one or more first vehicles to determine an aggregate snow depth for the one or more lanes of the road. 
     At step  904 , the data comparison module  416  determines a difference between the aggregate snow depth and an aggregate snow depth previously transmitted to the cloud computing device  114  and determines whether the difference is above threshold. If the data comparison module  416  determines that the difference is not greater than the threshold (no at step  904 ), then control returns to step  900 . If the data comparison module  416  determines that the difference is greater than the threshold (yes at step  904 ), then, at step  906 , the snow depth transmission module  418  transmits the aggregate snow depth to the cloud computing device  114 . 
     At step  908 , the snow depth prediction reception module  420  receives a predicted future snow depth for each of the one or more lanes of the road from the cloud computing device  114 . At step  910 , the snow depth broadcast module  422  transmits the predicted future snow depth for each of the one or more lanes to the one more second vehicles. 
       FIG.  10    depicts a flowchart of an example method for operating the example cloud server  114 , according to one or more embodiments shown and described herein. At step  1000 , the snow depth reception module  512  receives, from one or more edge computing devices, aggregate snow depths for each of one or more lanes of a plurality of roads. 
     At step  1002 , the snow depth update module  516  updates the database  510  comprising estimated snow depths for each of the plurality of roads based on the aggregate snow depths for each of the one or more lanes of the plurality of roads received from the one or more edge computing devices. At step  1004 , the snow depth transmission module  520  transmits the snow depths of the one or more lanes of the plurality of roads to at least one of the one or more edge computing devices. For each of the second plurality of roads, the snow depth transmission module  520  may transmit the snow depth of a road to each edge computing device having a coverage area that encompasses the road. 
       FIG.  11    depicts a flowchart of another example method for operating the example cloud server  114 , according to one or more embodiments shown and described herein. At step  1100 , the snow depth reception module  512  receives, from one or more edge computing devices, aggregate snow depths for each of one or more lanes of a plurality of roads. 
     At step  1102 , the data comparison module  514  determines a difference between an aggregate snow depth of a first lane of a first road received from a first edge computing device and a snow depth of the first road stored in the database  510  and determines whether the difference is greater than a threshold value. If the data comparison module  514  determines that the difference is not greater than the threshold value (no at step  1102 ), then control returns to step  1100 . If the data comparison module  514  determines that the difference is greater than the threshold value (yes at step  1102 ), then, at step  1104 , the snow depth update module  516  updates the snow depth of the first lane of the first road in the database  510  to be the aggregate snow depth. 
     At step  1106 , the weather data reception module  518  receives weather data associated with a geographic area containing one or more of the second plurality of roads, determines a predicted snow accumulation at a future time for at least one of the second plurality of roads based on the weather data, and stores the predicted snow accumulation at the future time for the at least one of the second plurality of roads in the database  510 . At step  1108 , the snow depth transmission module  520  transmits the predicted snow accumulation at the future time to at least one of the one or more edge computing devices. 
     It should now be understood that embodiments described herein are directed to systems and methods for determining real-time lane level snow accumulation. Vehicles driving along a road in snowy conditions capture images of the road using cameras or other vehicles sensors. A vehicle system of a vehicle inputs a captured image into a scene segmentation neural network to obtain an edge map of the road. The vehicle system then inputs the edge map, the captured image, and an HD map of the road, indicating road geometry, into a trained regressor neural network. The regressor neural network then outputs an estimated snow depth for each lane of the road. 
     Multiple vehicles may determine estimated snow depths for the road and each vehicle may transmit its determined estimated snow depth to an edge server. The edge server may aggregate the snow depths receive from the multiple vehicles to determine an aggregate snow depth for the road. The edge server may transmit the aggregate snow depth for the road to a cloud server. 
     The cloud server may receive aggregate snow depths from a plurality of edge servers relating to a plurality of roads. The cloud server may use the received aggregate snow depth data to maintain a comprehensive map of lane-level snow depths for a plurality of roads over a geographic region. The cloud server may also receive weather data that predicts future snow accumulation for one or more geographic areas. The cloud server may use the weather data to predict future snow depths for one or more roads in the geographic region. The cloud server may then transmit the future predicted snow depths to the edge servers. 
     After receiving the future predicted snow depths from the cloud server, an edge server may relay the future predicted snow depths to vehicles within a coverage area of the edge server. A vehicle within the coverage area of an edge server may receive the snow depth data from an edge server and may display the snow depth to a driver of the vehicle. The vehicle system may also determine an optimal lane for the vehicle to use based on the snow depth data received from the edge server and may display the optimal lane to the driver of the vehicle. As such, a driver may be aware of lane-level snow depths as they driver and may be able to make better choices about which lane to driver in and/or which road to use accordingly. In addition, the map of lane-level snow depths maintained by the cloud server may be used to direct resources (e.g., snow removal trucks) to more heavily affected areas. 
     It is noted that the terms “substantially” and “about” may be utilized herein to represent the inherent degree of uncertainty that may be attributed to any quantitative comparison, value, measurement, or other representation. These terms are also utilized herein to represent the degree by which a quantitative representation may vary from a stated reference without resulting in a change in the basic function of the subject matter at issue. 
     While particular embodiments have been illustrated and described herein, it should be understood that various other changes and modifications may be made without departing from the spirit and scope of the claimed subject matter. Moreover, although various aspects of the claimed subject matter have been described herein, such aspects need not be utilized in combination. It is therefore intended that the appended claims cover all such changes and modifications that are within the scope of the claimed subject matter.