Predictive Wi-Fi data offloading system and method

Abstract

Systems and methods for transmitting data from a vehicle using a Wi-Fi network are provided. One method includes: collecting, by a processor, performance data associated with a plurality of access points in the Wi-Fi network; segmenting, by the processor, a route into a plurality of route segments; mapping, by the processor, a plurality of access points to the plurality of route segments; selecting, by the processor, a set of access points from the plurality of access points based on the collected performance data, wherein the set of access points includes the selected access point for each of the plurality of route segments; predicting, by the processor, a scan channel based on the set of access points and a current location of the vehicle; and selectively transmitting, by the processor, packet data based on the set of access points, the scan channel, and the associated performance data.

Description

Technical Field

[0001] The present disclosure relates generally to Wi-Fi data for vehicles, and more particularly to systems and methods for predictive Wi-Fi data offloading. Background Art

[0002] Some vehicles use Wi-Fi communication systems to communicate vehicle information with remote transportation systems or back offices, or with personal devices within the vehicle. Vehicle Wi-Fi communication systems communicate with one or more access points along the vehicle's route. For example, the vehicle typically scans for available access points on different channels until one is found.

[0003] This unplanned Wi-Fi access strategy results in inefficient uploading of vehicle data to the back office. Furthermore, this scanning process results in inefficient data uploads and inefficient performance of in-vehicle Wi-Fi applications.

[0004] Therefore, it is desirable to provide methods and systems for providing predictive Wi-Fi data offloading between access points.Furthermore, other desirable features and characteristics of the present disclosure will become apparent from the subsequent detailed description and the appended claims, taken in conjunction with the accompanying drawings and the foregoing technical field and background. Summary of the Invention

[0005] Systems and methods for transmitting vehicle data are provided. In one embodiment, a system includes: collecting, by a processor, performance data associated with a plurality of access points in a Wi-Fi network; segmenting, by the processor, a route into a plurality of route segments; mapping, by the processor, the plurality of access points to the plurality of route segments; selecting, by the processor, a set of access points from the plurality of access points based on the collected performance data, wherein the set of access points includes the selected access point for each of the plurality of route segments; predicting, by the processor, a scan channel based on the set of access points and a current location of a vehicle; and selectively transmitting, by the processor, packet data based on the set of access points, the scan channel, and the associated performance data.

[0006] In various embodiments, performance data is collected from vehicles connected to multiple access points.

[0007] In various embodiments, performance data is collected from multiple access points.

[0008] In various embodiments, the performance data includes one or more of throughput, signal strength, Wi-Fi channel, latency, capacity, and price for each of the plurality of access points.

[0009] In various embodiments, the mapping of access points is based on geo-hashing.

[0010] In various embodiments, the mapping of access points is based on matching with a map of the vehicle or remote transportation system.

[0011] In various embodiments, selecting the set of access points is based on a calculated score calculated from the performance data.

[0012] In various embodiments, the calculated score is based on the cost of switching to the access point.

[0013] In various embodiments, selecting the set of access points is based on a weighted graph.

[0014] In various embodiments, selecting the set of access points is based on reinforcement learning.

[0015] In various embodiments, the predicted scanning channel is based on the distance of the vehicle's current location from the access point.

[0016] In various embodiments, the method further includes determining a packet number based on the performance data, the encounter duration, and the bandwidth, wherein selectively transmitting the packet data is based on the packet number, the priority, and the deadline.

[0017] In another embodiment, a system includes: a first non-transitory computer module configured to collect performance data related to multiple access points in a Wi-Fi network through a processor; a second non-transitory computer module configured to divide a route into multiple route segments by the processor and map the multiple access points to the multiple route segments; a third non-transitory computer module configured to select a group of access points from the multiple access points based on the collected performance data, wherein the group of access points includes the selected access point for each of the multiple route segments; a fourth non-transitory computer module configured to predict a scanning channel based on the group of access points and a current location of a vehicle by the processor; and a fifth non-transitory computer module configured to selectively transmit packet data by the processor based on the group of access points, the scanning channel, and the associated performance data.

[0018] In various embodiments, performance data is collected from vehicles connected to multiple access points.

[0019] In various embodiments, performance data is collected from multiple access points.

[0020] In various embodiments, the performance data includes one or more of throughput, signal strength, Wi-Fi channel, latency, capacity, and price per access.

[0021] In various embodiments, the second non-transitory computer module is configured to map the access points based on at least one of geo-hashing and map matching with a vehicle or a remote transportation system.

[0022] In various embodiments, the third non-transitory computer module is configured to select the set of access points based on a calculated score calculated from the performance data, wherein the calculated score is based on a cost of switching to the access point.

[0023] In various embodiments, the third non-transitory computer module is configured to select the set of access points based on at least one of a weighted graph and reinforcement learning.

[0024] In various embodiments, the fifth non-transitory computer module is configured to determine a packet number based on the performance data, the encounter duration, and the bandwidth, and selectively transmit the packet data based on the packet number, the priority, and the deadline. BRIEF DESCRIPTION OF THE DRAWINGS

[0025] Exemplary embodiments will be described below with reference to the following drawings, wherein like reference numerals represent like elements, and in the accompanying drawings:

[0026] Figure 1 is a functional block diagram illustrating a vehicle having a communication system according to various embodiments;

[0027] Figure 2 is a data flow diagram illustrating a communication module of a communication system according to various embodiments;

[0028] Figure 3 and 4 is an illustration of a method for selecting an access point and performed by a communication module according to various embodiments; and

[0029] Figure 5 is a flow chart illustrating a communication method for performing predictive Wi-Fi data offloading within a vehicle, according to various embodiments. DETAILED DESCRIPTION

[0030] The following detailed description is merely exemplary in nature and is not intended to limit application and use. Furthermore, there is no intention to be bound by any theory, expressed or implied, presented in the preceding technical field, background technology, brief overview, or detailed description below. As used herein, the term module refers to any hardware, software, firmware, electronic control component, processing logic, and / or processor device, individually or in any combination, including but not limited to: application specific integrated circuits (ASICs), electronic circuits, processors (shared, dedicated, or grouped) and memories that execute one or more software or firmware programs, combinational logic circuits, and / or other suitable components that provide the functionality.

[0031] Embodiments of the present disclosure may be described in terms of functional and / or logical block components and various processing steps. It should be understood that such block components may be implemented by any number of hardware, software, and / or firmware components configured to perform the specified functions. For example, embodiments of the present disclosure may employ various integrated circuit components, such as memory elements, digital signal processing elements, logic elements, lookup tables, etc., which may perform various functions under the control of one or more microprocessors or other control devices. In addition, those skilled in the art will appreciate that embodiments of the present disclosure may be practiced in conjunction with any number of systems, and the systems described herein are merely exemplary embodiments of the present disclosure.

[0032] For the sake of brevity, conventional techniques related to signal processing, data transmission, signal transmission, control, and other functional aspects of the system (as well as the individual operating components of the system) are not described in detail herein. In addition, the connecting lines shown in the various figures included herein are intended to represent example functional relationships and / or physical connections between the various elements. It should be noted that many alternative or additional functional relationships or physical connections may exist in the embodiments of the present disclosure.

[0033] refer to Figure 1 According to various embodiments, a communication system 100 is associated with a vehicle 10. As will be discussed in greater detail below, the communication system 100 plans a vehicle data offload and channel switching strategy for a given route based on crowdsourced channel, availability, and performance information of Wi-Fi access points and real-time traffic information. The vehicle data offload and channel switching strategy is intended to optimize data upload costs and performance.

[0034] like Figure 1 As shown, the vehicle 10 generally includes a chassis 12, a body 14, front wheels 16, and rear wheels 18. The body 14 is disposed on the chassis 12 and substantially surrounds the components of the vehicle 10. The body 14 and the chassis 12 may together form a frame. The wheels 16-18 are each rotatably coupled to the chassis 12 near a corresponding corner of the body 14.

[0035] In various embodiments, the vehicle 10 is an autonomous or semi-autonomous vehicle, and the sensor system 100 is included in the autonomous or semi-autonomous vehicle 10. In the exemplary embodiment, the vehicle 10 is autonomous in that it provides partial or full automatic assistance to a driver operating the vehicle 10. In the illustrated embodiment, the vehicle 10 is described as a passenger car, but it should be understood that any other vehicle may be used, including electric bicycles, motorcycles, trucks, sport utility vehicles (SUVs), recreational vehicles (RVs), marine vessels, aircraft, etc.

[0036] As shown, vehicle 10 generally includes a propulsion system 20, a transmission system 22, a steering system 24, a braking system 26, a sensor system 28, an actuator system 30, at least one data storage device 32, at least one controller 34, and a communication module 36. In various embodiments, propulsion system 20 may include an internal combustion engine, an electric motor such as a traction motor, and / or a fuel cell propulsion system. Transmission system 22 is configured to transfer power from propulsion system 20 to wheels 16-18 according to selectable speed ratios. In various embodiments, transmission system 22 may include a stepped automatic transmission, a continuously variable transmission, or other suitable transmission. Braking system 26 is configured to provide braking torque to wheels 16-18. In various embodiments, braking system 26 may include friction brakes, brake-by-wire brakes, a regenerative braking system such as an electric motor, and / or other suitable braking systems. Steering system 24 influences the position of wheels 16-18. Although depicted as including a steering wheel for illustrative purposes, in some embodiments contemplated within the scope of this disclosure, steering system 24 may not include a steering wheel.

[0037] The sensor system 28 includes one or more sensing devices 40a-40n for sensing observable conditions of the external environment and / or the internal environment of the vehicle 10. The sensing devices 40a-40n may include, but are not limited to, radar, lidar, global positioning systems, optical cameras, thermal cameras, ultrasonic sensors, inertial measurement units, and / or other sensors.

[0038] The actuator system 30 includes one or more actuator devices 42a-42n that control one or more vehicle features, such as, but not limited to, the propulsion system 20, the transmission system 22, the steering system 24, and the braking system 26. In various embodiments, the vehicle features may further include interior and / or exterior vehicle features, such as, but not limited to, doors, trunks, and cabin features, such as air, music, lighting, etc. (not numbered).

[0039] The data storage device 32 stores data for automatically controlling the vehicle 10. In various embodiments, the data storage device 32 stores a defined map of the navigable environment. In various embodiments, the defined map may be predefined and obtained from the remote system (see Figure 2 For example, the defined map may be assembled by the remote transport system 48 and transmitted to the vehicle 10 (wirelessly and / or wired) and stored in the data storage device 32. It will be appreciated that the data storage device 32 may be part of the controller 34, separate from the controller 34, or part of the controller 34 and part of a separate system.

[0040] The controller 34 includes at least one processor 44 and a computer-readable storage device or medium 46. The processor 44 can be any custom or commercially available processor, a central processing unit (CPU), a graphics processing unit (GPU), a secondary processor among several processors associated with the controller 34, a semiconductor-based microprocessor (in the form of a microchip or chipset), a macroprocessor, any combination thereof, or generally any device for executing instructions. The computer-readable storage device or medium 46 can include volatile and non-volatile storage such as read-only memory (ROM), random access memory (RAM), and keep-alive memory (KAM). KAM is permanent or non-volatile memory that can be used to store various operating variables when the processor 44 is powered off. The computer-readable storage device or medium 46 can be implemented using any of known storage devices such as PROM (programmable read-only memory), EPROM (electrical PROM), EEPROM (electrically erasable PROM), flash memory, or any other electrical, magnetic, optical, or combination storage device capable of storing data, some of which represents executable instructions, used by the controller 34 in controlling the vehicle 10.

[0041] The instructions may include one or more separate programs, each of which includes an ordered list of executable instructions for implementing logical functions. When executed by the processor 44, the instructions receive and process signals from the sensor system 28, execute logic, calculations, methods and / or algorithms for automatically controlling components of the vehicle 10, and generate control signals to the actuator system 30 to automatically control components of the vehicle 10 based on the logic, calculations, methods and / or algorithms. Although Figure 1 Only one controller 34 is shown, but embodiments of the vehicle 10 may include any number of controllers 34 that communicate via communication messages over any suitable communication medium or combination of communication media and cooperate to process sensor signals, execute logic, calculations, methods and / or algorithms, and generate control signals to automatically control features of the vehicle 10.

[0042] The communication module 36 is configured to wirelessly transmit information using Wi-Fi or other protocols (e.g., C-V2X, WiGig, etc.) to other entities, such as, but not limited to, other vehicles 47, remote transportation systems 48, and / or one or more access points 50 configured to communicate with remote transportation systems and / or other vehicles. In various embodiments, the communication system 100 may be embodied in one or more instructions of the controller 34. The one or more instructions generate instructions for the communication module 36 to selectively communicate with other vehicles 47, remote transportation systems 48, and / or access points 50. When executed by the processor 44, these instructions use crowdsourced information and real-time traffic information from the access points 50 to plan vehicle data offload and channel switching.

[0043] Reference Figure 2 , and continue to refer to Figure 1 , the data flow diagram illustrates various embodiments of the communication module 101, which is part of the communication system 100 as part of the controller 34. Various embodiments of the communication module 101 according to the present disclosure may include any number of submodules. It is understood that Figure 2 The illustrated submodules can be combined and / or further partitioned to similarly orchestrate vehicle data offloading and channel switching. Inputs to the communication module 101 can be received from the remote transportation system 48, access points, sensing devices 40a-40n, from other control modules (not shown) in the vehicle 10, and / or determined by other submodules (not shown) of the control module 34. In various embodiments, the communication module 101 includes a Wi-Fi data collection module 202, an access point data repository 203, an access point selection module 204, a channel switching module 206, and a packet transmission module 208. These modules work together to efficiently upload file data 210 from the file data repository 212 to, for example, the remote transportation system 48 for further processing via various access points 50 along the route. In various embodiments, each file in the file data 210 includes upload requirement parameters, such as a priority parameter and a deadline parameter.

[0044] In various embodiments, Wi-Fi data collection module 202 receives as input route data 214 indicating an upcoming route, cost data 216 received from various access points 50, performance data 218 measured directly by vehicle 10 (actively or passively), and performance data 220 received from other vehicles 47 and / or remote transportation systems 48. In various embodiments, cost data 216 indicates the cost of use (e.g., two-tier, single price, etc.), if access point 50 supports dynamic pricing. In various embodiments, performance data 218 measured by vehicle 10 indicates the performance of access point 50 and may include measured throughput, signal strength, jitter, uptime, Wi-Fi channel information, packet loss, latency, and handoff time (e.g., from one access point to another). Performance metrics may be based on data from a specific historical time window (e.g., the last minute, the last 15 minutes, the last week). Access point selection may also consider the number of active connections in conjunction with the performance metrics and adjust the selection accordingly. In various embodiments, performance data 220 received from other vehicles 47 and / or remote transportation systems 48 includes data indicative of traffic capacity, ie, an indication of how many vehicles can be around a particular access point without compromising bandwidth.

[0045] The Wi-Fi data collection module 202 collects the received data and associates the received data with each access point 50. The Wi-Fi data collection module 202 then segments the route and associates the collected data with the route segments based on the location of the access points relative to the route segments. The Wi-Fi data collection module then stores the associated data as access point data 222 in the access point data repository 212.

[0046] In various embodiments, the Wi-Fi data collection module 202 segments the route using, for example, map matching or geohashing.The Wi-Fi data collection module 202 then assigns each point to a route segment based on the location of the access point relative to the route segment.

[0047] In various embodiments, the access point data 222 may be combined from multiple sources and / or combined with historical data and stored, as shown in Table 1, for example:

[0048]

[0049] Table 1

[0050] In various embodiments, the Wi-Fi data collection module 202 combines the historical data with the new data using filters, including but not limited to low-pass filters:

[0051] y n =k·y n-1 +(1-k)x n

[0052] where y n-1 is historical data, x n It’s new data.

[0053] In various embodiments, the access point selection module 204 receives access point data 222, route data 224, and traffic flow data 225. Based on the received data 222-225, the access point selection module 204 selects a set of access points to connect to along the route indicated by the route data 224 and generates a set of access point data 226 based thereon. For example, the access point selection module 204 calculates scores from the access point data 222 and the traffic flow data 225. These scores are associated with switching from one access point on a particular route segment to another access point on another route segment. The access point selection module 204 then selects an access point to connect to while traveling the route segment based on the calculated scores.

[0054] For example, suppose that in the jth segment, the length is L, the estimated travel speed is v, and the i-th access point of the segment is i jThe following random variables are defined from the access point data 222 and the traffic flow data 225: A_1, A_2, ..., A_n represent n access points, B_1, B_2, ..., B_n represent throughput, R_1, R_2, ..., R_n represent signal strength, L_1, L_2, ..., L_n represent waiting time, C_1, C_2, ..., C_n represent access point capacity, N_1, N_2, ..., N_n represent the number of connected vehicles, O_1, O_2, ..., O_n represent Wi-Fi prices, W_1, W_2, ..., W_n represent the switching time to switch to the access point, and α_i represents the weight of the parameters. The access point selection module 204 calculates the segment score s by the following method: j :

[0055]

[0056] The access point selection module 204 calculates the switching cost w by: j :

[0057]

[0058] The access point selection module 204 then finds the value that maximizes the score s j and minimize the switching cost w j ,ij=0 or 1, where:

[0059]

[0060] For example, Figure 3 As shown, the weighted graph 300 may be composed of access points 302-312 of route segments j 314-318. The switching cost from the mth access point at the (j-1)th route segment to the nth access point at the jth route segment is represented as It can be achieved through the corresponding s j and w j To calculate:

[0061]

[0062] The shortest path 320 of the weighted graph is found by dynamic programming (ie, Dijkstra's algorithm, etc.) and an access point is selected from the shortest path.

[0063] In another example, Figure 4 As shown, the reinforcement learning method 330 is used to select an access point. Where [B, R, L, C, N, O, W] as defined above is the state S, is the probability of selecting the i-th access point at the j-th segment, and the reward r is calculated based on the total cost k and the number of transmitted bytes d as follows:

[0064]

[0065] Where k is the cost of each choice, and d is the number of bytes transmitted at the selected AP. 231, 234, 237, and 238 are the environmental components, represented by the states S1, S2, and St. 232 and 235 are the actor components, represented by A1 and A2, and 233, 236, and 239 are the reward components, represented by R1, R2, and R3. Reinforcement learning method 330 maximizes the total reward Rt by selecting a set of actions in a Markov decision process.

[0066] As can be appreciated, the access point selection module 204 can perform other methods to determine the set of access points, as embodiments are not limited to this example.

[0067] In various embodiments, the access point selection module 204 calculates an encounter duration for each selected access point and provides the encounter duration as part of the set of access point data 226. For example, the encounter duration can be estimated by accessing the current and average speed metrics of the route segment or geo-hash location. In various embodiments, the estimate can be enhanced using traffic signal timing plan information received, for example, via a cellular interface to a traffic signal timing plan provider, via a Wi-Fi access point to the Internet of the traffic signal timing plan provider, or via local broadcasts received from smart infrastructure (e.g., C-V2X or DSRC).

[0068] Reference again Figure 2 In various embodiments, the channel switching module 206 receives as input a set of access point data 226 and vehicle location data 227 indicating a current location of the vehicle. The channel switching module 206 predictively switches the scanning channel of the communication system 100 to the channel of the next access point relative to the vehicle location indicated by the set of access point data 226. For example, the channel switching module 206 generates switching command data 228 to switch the channel from the current channel to the channel associated with the next access point based on the current location of the vehicle 10 and the distance to the next access point (e.g., switch to Ch9 at one mile from the next access point).

[0069] In various embodiments, the channel switching module 206 generates notification data 230 to notify other modules of the communication system 100 and / or in-vehicle devices (e.g., a mobile phone, tablet, smartwatch, etc.) of the channel change and the next channel to be switched to. Notification to the in-vehicle devices allows the in-vehicle devices to smoothly transition and maintain their connection to the vehicle as the vehicle transitions to a different channel to connect to an access point. It also reduces channel search time to maximize Wi-Fi transmission opportunities.

[0070] In various embodiments, the packet transmission module 208 receives as input the channel advertisement data 230 and the set of access point data 226. The packet transmission module 208 selectively transmits packets of the file data 210 from the file data repository 212 as packet data 232. The packet transmission module 208 selectively transmits the packet data 232 by determining a number of file packets to upload to each connected access point 50 along the route.

[0071] In various embodiments, the packet transmission module 208 determines the packets to transmit for each access point 50 based on access point performance, such as bandwidth and connection duration indicated by the access point set data 226, and file transfer requirements, such as the priority and deadline of each file indicated by the upload file data 210. In various embodiments, the packet transmission module 208 optimizes the total size of the packet x during transmission at the jth access point based on the probability distribution:

[0072] P(B j ·h j -x>e)≥η

[0073] Where e is the margin and η is the minimum probability. The access point data indicates M selected access points along the route. Each access point j∈[1,M] has a predicted arrival time a j and departure time b j (based on traffic flow conditions), and the connection duration h j =b j -a j , each access point has a predicted bandwidth B j .

[0074] The total size of group x is as follows:

[0075]

[0076] The file data 212 indicates that there are N files to be transferred, i∈[1,N]. Each file has a size s i , upload priority p i , upload deadline t i .in denotes whether the i-th packet is to be sent to the j-th access point and is solved based on a heuristic solution of sorting and padding:

[0077] For the jth access point, assume that: β1>0, β2>0, β3>0, t i >a j , as follows:

[0078]

[0079] in Indicates files that are closer to the maximum packet size x. β2·p i Select the file with higher priority. β3·(t i -b j ) Select the file that is closer to the deadline. j ·h j The distribution prediction of is:

[0080]

[0081] Among them, the total throughput of the access point φ, the bandwidth of the access point B, the vehicle speed v, and the transmission duration d are combined into the feature vector of vehicle i: i =[φ i , B i , v i , d i ], the capacity is determined by the total number of bytes transferred y i express.

[0082] It will be appreciated that the packet transmission module 208 may perform other methods to determine packets to transmit, as the embodiments are not limited to this example.

[0083] Reference Figure 5 , and continue to refer to Figure 1-2 , the flow chart illustrates that according to the present disclosure Figure 1-2 The control method 400 is executed by the system 100. According to the present disclosure, it can be understood that the order of operations in the method is not limited to the following. Figure 5 In various embodiments, method 400 may be scheduled to execute based on one or more predetermined events and / or may execute continuously during operation of autonomous vehicle 10.

[0084] In one example, method 400 may begin at 405. At 410, performance data and cost data are collected from various sources for various access points. At 420, a route is segmented into route segments. At 430, Wi-Fi performance data for each access point is evaluated and mapped to the route segments to generate access point data. At 440, an optimization function is solved as described above to select a set of access points to connect to along the upcoming route.

[0085] Thereafter, as the vehicle travels along the route, at 450, the scanning channel is predictively switched to the scanning channel of the next access point based on the vehicle's current location and the selected set of access points. At 460, a packet transmission number is determined for the next access point to maximize Wi-Fi usage and ensure that data requirements such as priority and deadlines are met. At 470, file data is transmitted based on the file to be transmitted and the packet transmission number.

[0086] Thereafter, the method continues to collect more performance data at 410 and optimize access point selection along the route until the route is completed at 480. Once the route is completed at 480, the method may end at 490.

[0087] Although at least one exemplary embodiment has been described in the foregoing detailed description, it should be understood that there are numerous variations. It should also be understood that one or more exemplary embodiments are merely examples and are not intended to limit the scope, applicability, or configuration of the present disclosure in any way. On the contrary, the foregoing detailed description will provide those skilled in the art with a convenient roadmap for implementing one or more exemplary embodiments. It should be understood that various changes may be made to the function and arrangement of elements without departing from the scope of the present disclosure as set forth in the appended claims and their legal equivalents.

Claims

1. A method for transmitting data from a vehicle using a Wi-Fi network, comprising: collecting, by the processor, performance data associated with a plurality of access points in a Wi-Fi network; The processor divides the route into a plurality of route segments; mapping, by a processor, the plurality of access points to the plurality of route segments; selecting, by a processor, a set of access points from a plurality of access points based on the collected performance data, wherein the set of access points includes the selected access point for each of the plurality of route segments; predicting, by the processor, a scan channel based on the set of access points and a current location of the vehicle; determining a group number based on performance data, encounter duration, and bandwidth; and Packet data is selectively sent, by a processor, based on the set of access points, the scanned channels, the performance data, the packet number, the priority, and the deadline. 2 . The method of claim 1 , wherein the performance data is collected from at least one of a vehicle connected to a plurality of access points and a plurality of access points.

3. The method of claim 1 , wherein the performance data comprises one or more of throughput, signal strength, Wi-Fi channel, latency, capacity, and price for each of the plurality of access points. The method of claim 1 , wherein the mapping of access points is based on at least one of geo-hashing and matching with a map of the vehicle or remote transportation system. The method of claim 1 , wherein selecting the set of access points is based on a calculated score calculated from the performance data. The method of claim 5 , wherein calculating the score is based on a cost of switching to the access point. The method of claim 1 , wherein selecting the set of access points is based on at least one of a weighted graph and reinforcement learning. The method of claim 1 , wherein the predicted scanning channel is based on a distance from a current location of the vehicle to the access point.

9. A system for transmitting data from a vehicle using a Wi-Fi network, comprising: a first non-transitory computer module configured to collect, by a processor, performance data associated with a plurality of access points in a Wi-Fi network; a second non-transitory computer module configured to, by the processor, segment the route into a plurality of route segments and map the plurality of access points to the plurality of route segments; a third non-transitory computer module configured to select, by the processor, a set of access points from the plurality of access points based on the collected performance data, wherein the set of access points includes the selected access point for each of the plurality of route segments; a fourth non-transitory computer module configured to predict, by the processor, a scanning channel based on the set of access points and a current location of the vehicle; and A fifth non-transitory computer module is configured to, by the processor, determine a packet number based on the performance data, the encounter duration, and the bandwidth, and selectively transmit the packet data based on the set of access points, the scanned channels, and the performance data, the packet number, the priority, and the deadline.

Images