Description:FIELD OF THE INVENTION

The present disclosure generally relates to the field of information processing systems and methods in vehicles. More particularly, the present disclosure relates to a system and a method to predict a future ride of a vehicle.

BACKGROUND

In recent years, the demand for map services related to setting a map and selecting a route for travel has increased significantly in an automobile sector. This is due to the increasing popularity of vehicles and need for users to plan their commutes in advance. The map services can help the users to plan their commutes more efficiently and effectively. For example, the map services can help the users to find a best route to a selected destination, and select the best route based on their preferences, such as based on estimated time of arrival, a preferred route, weather conditions, a fuel capacity of fuel tank of the vehicle, remaining fuel level or battery level, etc.

Further, in view of the same, user uses the map services to plan a ride well in advance. A maps service is incorporated in smart phones, tabs, a dashboard of the vehicles etc. The maps service, based on the destination, provides a plurality of routes with traffic condition to reach the destination. The maps service also provides the estimated time of arrival to the destination, weather forecast, corresponding to each of the plurality of routes etc. The maps service also provides flexibility to the user to feed data, for example, the destination, at starting of the ride. Further, the maps service also provides a real-time location, for example, petrol pumps, charging station, eateries, etc, during the ride.

However, to access the maps service, the user has to always feed the data manually, whenever the user wants to commute, which increases the hassle for the user and thus may result in a poor user experience. Further, there might be the possibility that the user may forget/miss to pre-plan the ride to reach the destination which may further result in increasing the hassle for the user.

Therefore, in view of the above-mentioned problems, it is advantageous to provide a system and a method that can overcome one or more above-mentioned problems and limitations of the existing maps services in the vehicles.

SUMMARY

This summary is provided to introduce a selection of concepts, in a simplified format, that is further described in the detailed description of the invention. This summary is neither intended to identify key or essential inventive concepts of the invention and nor is it intended for determining the scope of the invention.

The present disclosure aims to provide a system and a method for generating ride prediction for a user associated with a vehicle.

In an embodiment of the present disclosure, a system for generating ride predictions for a user associated with a vehicle, is disclosed. The system includes a collecting unit, a control unit, and a predicting unit. The collecting unit adapted to obtain a plurality of operational attributes associated with the vehicle. The control unit is adapted to determine at least one predetermined cluster based on the plurality of operational attributes. The predicting unit is adapted to determine a plurality of parameters with respect to the at least one predetermined cluster. The predicting unit is adapted to generate a plurality of outputs based on the plurality of parameters. Further, the predicting unit is adapted to merge the plurality of outputs with at least one predetermined field to generate at least one ride prediction of the vehicle.

In another embodiment, a method for generating ride predictions for a user associated with a vehicle by a system, is disclosed. The method includes obtaining, by a collecting unit, a plurality of operational attributes associated with the vehicle. The method includes processing, by the control unit, the plurality of operational attributes to determine at least one predetermined cluster based on the plurality of operational attributes. The method includes determining, by the prediction unit, a plurality of parameters with respect to the at least one predetermined cluster. The method includes generating, by the prediction unit, a plurality of outputs depending on the plurality of parameters. The method includes merging, by the predicting unit, each of the plurality of outputs with at least one predetermined field to generate at least one ride prediction of the vehicle.

The present disclosure provides a configuration of a system along with a method to operate the system to generate the at least one ride prediction for the user associated with the vehicle.

To further clarify the advantages and features of the present invention, a more particular description of the invention will be rendered by reference to specific embodiments thereof, which are illustrated in the appended drawings. It is appreciated that these drawings depict only typical embodiments of the invention and are therefore not to be considered limiting of its scope. The invention will be described and explained with additional specificity and detail with the accompanying drawings.

BRIEF DESCRIPTION OF THE DRAWINGS

These and other features, aspects, and advantages of the present invention will become better understood when the following detailed description is read with reference to the accompanying drawings in which like characters represent like parts throughout the drawings, wherein:

Figure 1 illustrates a block diagram of an embodiment of an Electronic Control Unit (ECU) of a vehicle, in accordance with an embodiment of the present disclosure;

Figure 2 illustrates a block diagram of a system, according to an embodiment of the present disclosure;

Figure 3A illustrates a detailed block diagram of the system along with the vehicle, according to an embodiment of the present disclosure;

Figure 3B illustrates a block diagram depicting an operation of a control unit of the system, according to an embodiment of the present disclosure; and

Figures 3C illustrate a first use case example implementation of the system, according to an embodiment of the present disclosure;

Figure 3D illustrates a second use case example implementation of the system, according to an embodiment of the present disclosure;

Figure 4A illustrates a predicting unit of the system, according to another embodiment of the present disclosure;

Figure 4B illustrates a non-limiting example of the predicting unit of the system, according to an embodiment of the present disclosure; and

Figure 5 illustrates a method performed by the system, according to an embodiment of the present disclosure.

Further, skilled artisans will appreciate that elements in the drawings are illustrated for simplicity and may not have necessarily been drawn to scale. For example, the flow charts illustrate the method in terms of the most prominent steps involved to help to improve understanding of aspects of the present invention. Furthermore, in terms of the construction of the device, one or more components of the device may have been represented in the drawings by conventional symbols, and the drawings may show only those specific details that are pertinent to understanding the embodiments of the present invention so as not to obscure the drawings with details that will be readily apparent to those of ordinary skill in the art having the benefit of the description herein.

DETAILED DESCRIPTION OF FIGURES

For the purpose of promoting an understanding of the principles of the present disclosure, reference will now be made to the various embodiments and specific language will be used to describe the same. It will nevertheless be understood that no limitation of the scope of the present disclosure is thereby intended, such alterations and further modifications in the illustrated system, and such further applications of the principles of the present disclosure as illustrated therein being contemplated as would normally occur to one skilled in the art to which the present disclosure relates.

It will be understood by those skilled in the art that the foregoing general description and the following detailed description are explanatory of the present disclosure and are not intended to be restrictive thereof.

Whether or not a certain feature or element was limited to being used only once, it may still be referred to as “one or more features” or “one or more elements” or “at least one feature” or “at least one element.” Furthermore, the use of the terms “one or more” or “at least one” feature or element do not preclude there being none of that feature or element, unless otherwise specified by limiting language including, but not limited to, “there needs to be one or more…” or “one or more elements is required.”

Reference is made herein to some “embodiments.” It should be understood that an embodiment is an example of a possible implementation of any features and/or elements of the present disclosure. Some embodiments have been described for the purpose of explaining one or more of the potential ways in which the specific features and/or elements of the proposed disclosure fulfil the requirements of uniqueness, utility, and non-obviousness.

Use of the phrases and/or terms including, but not limited to, “a first embodiment,” “a further embodiment,” “an alternate embodiment,” “one embodiment,” “an embodiment,” “multiple embodiments,” “some embodiments,” “other embodiments,” “further embodiment”, “furthermore embodiment”, “additional embodiment” or other variants thereof do not necessarily refer to the same embodiments. Unless otherwise specified, one or more particular features and/or elements described in connection with one or more embodiments may be found in one embodiment, or may be found in more than one embodiment, or may be found in all embodiments, or may be found in no embodiments. Although one or more features and/or elements may be described herein in the context of only a single embodiment, or in the context of more than one embodiment, or in the context of all embodiments, the features and/or elements may instead be provided separately or in any appropriate combination or not at all. Conversely, any features and/or elements described in the context of separate embodiments may alternatively be realized as existing together in the context of a single embodiment.

Any particular and all details set forth herein are used in the context of some embodiments and therefore should not necessarily be taken as limiting factors to the proposed disclosure.

The terms “comprises”, “comprising”, or any other variations thereof, are intended to cover a non-exclusive inclusion, such that a process or method that comprises a list of steps does not include only those steps but may include other steps not expressly listed or inherent to such process or method. Similarly, one or more devices or sub-systems or elements or structures or components proceeded by “comprises... a” does not, without more constraints, preclude the existence of other devices or other sub-systems or other elements or other structures or other components or additional devices or additional sub-systems or additional elements or additional structures or additional components.

Embodiments of the present disclosure will be described below in detail with reference to the accompanying drawings.

For the sake of clarity, the first digit of a reference numeral of each component of the present disclosure is indicative of the Figure number, in which the corresponding component is shown. For example, reference numerals starting with digit “1” are shown at least in Figure 1. Similarly, reference numerals starting with digit “2” are shown at least in Figure 2.

Figure 1 illustrates a block diagram of an embodiment of an Electronic Control Unit (ECU) 100-A of a vehicle 100, in accordance with an embodiment of the present disclosure. In an embodiment, the vehicle 100 may be an Electric vehicle or a battery powered vehicle, without departing from the scope of the present disclosure. The Electric Vehicle (EV) or the battery powered vehicle including, and not limited to two-wheelers such as scooters, mopeds, motorbikes/motorcycles; three-wheelers such as auto-rickshaws, four-wheelers such as cars and other Light Commercial Vehicles (LCVs) and Heavy Commercial Vehicles (HCVs) primarily work on the principle of driving an electric motor using the power from the batteries provided in the EV. In an embodiment, the vehicle 100 may be interchangeably referred as the EV 100, without departing from the scope of the present disclosure. Furthermore, the EV 100 may have at least one wheel which is electrically powered to traverse such a vehicle. The term ‘wheel’ may be referred to any ground-engaging member which allows traversal of the EV 100 over a path. The types of EVs include Battery Electric Vehicle (BEV), Hybrid Electric Vehicle (HEV) and Range Extended Electric Vehicle. However, the subsequent paragraphs pertain to the different elements of a Battery Electric Vehicle (BEV).

In construction, the EV 100 typically comprises hardware components such as a battery or battery module enclosed within a battery casing and includes a Battery Management System (BMS), an on-board charger, a Motor Controller Unit (MCU), an electric motor and an electric transmission system. In addition to the hardware components/elements, the EV 100 may be supported with software modules comprising intelligent features including and not limited to navigation assistance, hill assistance, cloud connectivity, Over-The-Air (OTA) updates, adaptive display techniques and so on. The firmware of the EV 100 may also comprise Artificial Intelligence (AI) & Machine Learning (ML) driven modules which enable the prediction of a plurality of parameters such as and not limited to driver/rider behaviour, road condition, charging infrastructures/charging grids in the vicinity and so on. The data pertaining to the intelligent features may be displayed through a display device 214 (as shown in Figure 2) present in the dashboard of the vehicle 100. In one embodiment, the display device 214 may contain an Liquid Crystal Display (LCD) screen of a predefined dimension. In another embodiment, the display device 214 may contain a Light-Emitting Diode (LED) screen of a predefined dimension. The display device 214 may be a water-resistant display supporting one or more User-Interface (UI) designs. The EV 100 may support multiple frequency bands such as 2G, 3G, 4G, 5G and so on. Additionally, the EV 100 may also be equipped with wireless infrastructure such as, and not limited to Bluetooth, Wi-Fi and so on to facilitate wireless communication with other EVs or the cloud.

In one example, the ECU 100-A of the EV 100 is responsible for managing all the operations of the EV 100, wherein the key elements of the ECU (100-A) typically includes (i) a microcontroller core (or processor unit) (120); (ii) a memory unit (140); (iii) a plurality of input (160) and output modules (180) and (iv) communication protocols including, but not limited to a CAN protocol, Serial Communication Interface (SCI) protocol and so on. The sequence of programmed instructions and data associated therewith can be stored in a non-transitory computer-readable medium such as the memory unit 140 or storage device which may be any suitable memory apparatus such as, but not limited to read-only memory (ROM), programmable read-only memory (PROM), electrically erasable programmable read-only memory (EEPROM), random-access memory (RAM), flash memory, disk drive and the like. In one or more embodiments of the disclosed subject matter, non-transitory computer-readable storage media can be embodied with a sequence of programmed instructions for monitoring and controlling the operation of different components of the EV.

The processor may include any computing system which includes, but is not limited to, Central Processing Unit (CPU), an Application Processor (AP), a Graphics Processing Unit (GPU), a Visual Processing Unit (VPU), and/or an AI-dedicated processor such as a Neural Processing Unit (NPU). In an embodiment, the processor can be a single processing unit or several units, all of which could include multiple computing units. The processor may be implemented as one or more microprocessors, microcomputers, microcontrollers, digital signal processors, central processing units, state machines, logic circuitries, and/or any devices that manipulate signals based on operational instructions. Among other capabilities, the processor is configured to fetch and execute computer-readable instructions and data stored in the memory. The instructions can be compiled from source code instructions provided in accordance with a programming language such as Java, C++, C#.net or the like. The instructions can also comprise code and data objects provided in accordance with, for example, the Visual Basic™ language, LabVIEW, or another structured or object-oriented programming language. The one or a plurality of processors control the processing of the input data in accordance with a predefined operating rule or artificial intelligence (AI) model stored in the non-volatile memory and the volatile memory. The predefined operating rule or artificial intelligence model is provided through training or learning algorithms which include, but are not limited to, supervised learning, unsupervised learning, semi-supervised learning, or reinforcement learning.

Furthermore, the modules, processes, systems, and devices can be implemented as a single processor or as a distributed processor. Also, the processes, modules, and sub-modules described in the various figures of and for embodiments herein may be distributed across multiple computers or systems or may be co-located in a single processor or system. Further, the modules can be implemented in hardware, instructions executed by a processing unit, or by a combination thereof. The processing unit 120 can comprise a computer, a processor, such as the processor, a state machine, a logic array, or any other suitable devices capable of processing instructions.

The processing unit 120 can be a general-purpose processor which executes instructions to cause the general-purpose processor to perform the required tasks or, the processing unit 120 can be dedicated to performing the required functions. In another embodiment of the present disclosure, the modules may be machine-readable instructions (software) which, when executed by a processor/processing unit 120, perform any of the described functionalities. In an embodiment, the modules may include a receiving module, a generating module, a comparing module, a pairing module, and a transmitting module. The receiving module, the generating module, the comparing module, the pairing module, and the transmitting module may be in communication with each other. The data serves, amongst other things, as a repository for storing data processed, received, and generated by one or more of the modules. Exemplary structural embodiment alternatives suitable for implementing the modules, sections, systems, means, or processes described herein are provided below.

Figure 2 illustrates a block diagram of a system 200, according to an embodiment of the present disclosure. In an embodiment, the system 200 may be associated with the vehicle 100, without departing from the scope of the present disclosure. The system 200 as disclosed may be adapted to generate at least one ride prediction for a user associated with the vehicle 100. The system 200 provides an automated and predicted outcome, that is, the at least one ride prediction for the user ensuring hassle-free ride for the user.

In an embodiment, the system 200 may include, but is not limited to, a collecting unit 204, a control unit 206, and a predicting unit 208, details of which will be explained in subsequent paragraphs.

In an embodiment, the system 200 may be a cloud-server unit, without departing from the scope of the present disclosure. In another embodiment, the system 200 may be a standalone unit which may be installed in the vehicle 100, without departing from the scope of the present disclosure. For instance, the collecting unit 204 may be adapted to be communicatively coupled with the vehicle 100. In an embodiment, the control unit 206 may be adapted to be communicatively coupled with the collecting unit 204. Further, the predicting unit 208 may be communicatively coupled with the control unit 206. The collecting unit 204 may be adapted to obtain a plurality of operational attributes associated with the vehicle 100. The control unit 206 may be adapted to receive and process the plurality of operational attributes, which is further transferred to the predicting unit 208. The predicting unit 208 may further be adapted to automatically generate the at least one ride prediction associated with the vehicle 100, based on the processed plurality of attributes received from the control unit 206.

In an embodiment, the system 200 may include a communication unit 210. The communication unit 210 may be adapted to receive the at least one ride prediction associated with the vehicle 100 and communicate the at least one ride prediction to the user.

The operational details of the system 200 may be explained in conjunction with Figures 3A to 3D.

Figure 3A illustrates a detailed block diagram of the system 200 along with the vehicle 100, according to an embodiment of the present disclosure. Figure 3B illustrates a block diagram depicting operations of the control unit 206, predicting unit 208, and the communication unit 210 of the system 200, according to an embodiment of the present disclosure. Figure 3C illustrates a first use case example implementation 350 of the system 200, according to an embodiment of the present disclosure. Figure 3D illustrates a second use case example implementation 360 of the system 200, according to an embodiment of the present disclosure. In an embodiment, the vehicle 100 may be adapted to generate the plurality of operational attributes. The plurality of operational attributes may be stored in the ECU 100-A of the vehicle 100. Further, a cloud connect unit 306 may be adapted to receive the plurality of operational attributes associated with the vehicle 100 from the ECU 100-A by one of a wired network and a wireless network through a vehicle core unit 304. In an embodiment, the wired network may be a CAN bus 302, without departing from the scope of the present disclosure. Further, the cloud connect unit 306, simultaneously, receives a GPS data from the display device 214 of the vehicle 100. Further, the cloud connect unit 306 transfers the stored plurality of operational attributes and the GPS data to a cloud storage 310 of the system 200.

In an embodiment, the collecting unit 204 may obtain the plurality of operational attributes associated with the vehicle 100 from the cloud storage 310, which may be stored in the cloud storage 310 for predetermined days, without departing from the scope of the present disclosure. In an embodiment, the predetermined days may be approximately 90 days, without departing from the scope of the present disclosure. In another embodiment, the collecting unit 204 may be adapted to obtain the plurality of operational attributes from the cloud storage 310 in one of a real-time or after a single ride from the vehicle 100, without departing from the scope of the present disclosure.

In an embodiment, the cloud storage 310 may be a ClickHouse®, where the plurality of operational attributes returned as a Data Frame, without departing from the scope of the present disclosure. In an embodiment, the plurality of operational attributes may be one or more of an initial state of charge (SoC) of a battery in the vehicle 100, a final SoC of the battery in the vehicle 100, a ride history of the vehicle 100, a user identification number, a ride identification number, a start time of ride, an end time of the ride, a start location of the ride, an end location of the ride, riding modes of the vehicle 100, and a distance covered by the vehicle 100.

Further, the plurality of operational attributes may be obtained by the collection unit 206 associated with the vehicle 100, if the plurality of operational attributes fulfills a predetermined criterion. The predetermined criterion may include, but is not limited to, at least one of minimum number of riding sessions, a minimum duration of riding session, a minimum distance covered, and minimum number of active days in a predetermined time period. For example, the vehicle 100 may had taken at least one ride before the predetermined time, a duration of a riding session should be greater than 5 seconds, a distance covered in the riding session should be greater than 50 m and less than 150 km, a threshold for a minimum number of riding sessions in the predetermined time may be 1, and the threshold for a minimum number of active days in the predetermined time may be 1, without departing from the scope of the present disclosure. In an embodiment, values corresponding to each of the duration of the riding session, the distance covered in the riding session, the threshold for the minimum number of riding sessions, and the threshold for the minimum number of active days may vary depending on the requirement of the user, without departing from the scope of the present disclosure.

Referring to Figures 3A, and 3B, in an embodiment, the control unit 206 may be adapted to receive the plurality of operational attributes. Further, the control unit 206 may be adapted to determine at least one predetermined cluster based on the plurality of operational attributes, wherein the at least one predetermined cluster represents a ride pattern based on the plurality of operational attributes. The control unit 206, to determine the predetermined cluster, may be adapted to obtain the plurality of operational parameters from the collecting unit 204. The control unit 206 may be adapted to convert the plurality of the operational attributes into a plurality of predetermined data types. In an embodiment, the control unit 206 may be adapted to filter the plurality of operational attributes before converting into the plurality of predetermined data types.

The control unit 206 may segregate the plurality of predetermined data types based on at least one predetermined field. In an embodiment, the control unit 206 may be adapted to segregate the plurality of predetermined data types based on the at least one predetermined field by a clustering technique, without departing from the scope of the present disclosure. The control unit 206 may be adapted to merge the plurality of predetermined data type with the at least one predetermined field to generate the at least one predetermined cluster.

The control unit 206 may include different components that operate synergistically to determine the at least one predetermined cluster. For instance, the control unit 206 may include a processor/controller 314, a memory 316, modules(s) 346, and data 328. The memory 316, in one example, may store the instructions to carry out the operations of the modules 346. The modules 346 and the memory 316 may be coupled to the processor 314.

The processor 314 may be a single processing unit or several units, all of which could include multiple computing units. The processor 314 may be implemented as one or more microprocessors, microcomputers, microcontrollers, digital signal processor, central processing units, state machines, logic circuitries, and/or any devices that manipulate signals based on operational instructions. Among other capabilities, the processor 314 is configured to fetch and execute computer-readable instructions and data stored in the memory 316. The processor 314 may include one or a plurality of processors. At this time, one or a plurality of processors may be a general-purpose processor, such as a central processing unit (CPU), an application processor (AP), or the like, a graphics-only processing unit such as a graphics processing unit (GPU), a visual processing unit (VPU), and/or an AI-dedicated processor such as a neural processing unit (NPU). The one or a plurality of processors control the processing of the input data in accordance with a predefined operating rule or artificial intelligence (AI) model stored in the non-volatile memory and the volatile memory. The predefined operating rule or machine learning model is provided through training or learning.

The memory 316 may include any non-transitory computer-readable medium known in the art including, for example, volatile memory, such as static random-access memory (SRAM) and dynamic random-access memory (DRAM), and/or non-volatile memory, such as read-only memory (ROM), erasable programmable ROM, flash memories, hard disks, optical disks, and magnetic tapes.

The modules 346, amongst other things, include routines, programs, objects, components, data structures, etc., which perform particular tasks or implement data types. The modules 346 may also be implemented as, signal processor(s), state machine(s), logic circuitries, and/or any other device or component that manipulates signals based on operational instructions.

Further, the modules 346 can be implemented in hardware, instructions executed by a processing unit, or by a combination thereof. The processing unit can comprise a computer, a processor, such as the processor 314, a state machine, a logic array, or any other suitable devices capable of processing instructions. The processing unit can be a general-purpose processor which executes instructions to cause the general-purpose processor to perform the required tasks or, the processing unit can be dedicated to performing the required functions. In another embodiment of the present disclosure, the modules 346 may be machine-readable instructions (software) which, when executed by a processor 314/processing unit, perform any of the described functionalities. Further, the data serves, amongst other things, as a repository for storing data processed, received, and generated by one or more of the modules 346. The data 328 may include information and/or instruction to perform activities by the processor 314.

The modules 346 may perform different functionalities. Accordingly, the modules 346 may include a fetching module 318, a filtering module 320, a converting module 322, a segregating module 324, and a merging module 326, without departing from the scope of the present disclosure.

In an embodiment, the fetching module 318 may be adapted to fetch the plurality of operational attributes from the collecting unit 204. Further, the filtering unit 320 may be adapted to filter the plurality of operational attributes to remove duplicate, irrelevant, abnormal, unwanted anomalous rides, handle missing null data etc. The converting module 322 may be adapted to convert the plurality of operational attributes, filtered, into the predetermined data type. Further, multiple new features fields, for example, date, day of a week, weekend, and minutes of the day, may be created/extracted based on the predetermined data type. The segregating module 324 may be adapted to segregate the plurality of predetermined data type based on the at least one predetermined field.

In an embodiment, the at least one predetermined field may include, but is not limited to, at least one of a predetermined time field and a predetermined location field, without departing from the scope of the present disclosure. In another embodiment, the at least one predetermined field may be a predetermined distance field or any other type of the predetermined field which can be incorporated as per user’s requirement and need, without departing from the scope of the present disclosure. In an embodiment, the predetermined time field may be a predetermined time cluster, without departing from the scope of the present disclosure. In an embodiment, the predetermined location field may be a predetermined location cluster, without departing from the scope of the present disclosure.

In an embodiment, the predetermined time field may include a stitching session which merges two consecutive rides completed within a predefined time, that is, approximately 4 minutes, without departing from the scope of the present disclosure. Further, a cluster tagging of the predetermined time field and the predetermined location field occurs, respectively, without departing from the scope of the present disclosure. In one example, for the predetermined location field- a latitude and a longitude, for each vehicle 100, a hierarchical agglomerative clustering may be performed with a complete linkage and a cluster of approximately 0.025 (having distance in kilometers) that correspond to 25 m cluster may be adapted to form the predetermined location field.

Further, the predetermined location field may be a valid cluster according to the above parameters when the vehicle 100 may have at least 5 rides in the predetermined location field and contributes to at least 5% of the total rides in the predetermined location field. Thus, a cluster tag for the predetermined location field may be generated for each riding session. In one example, if the user may go to a gym, a home, or an office, thus, the cluster tag for the gym, the office and the home may be generated separately for each riding session. In an embodiment, values of the cluster, number of the rides, and contribution in the total rides may vary depending on the type of the vehicle 100, a type of battery, a range of the vehicle 100 etc., without departing from the scope of the present disclosure.

In an embodiment, a separate table/data frame with the predetermined location field may be created. The table contains the representative centroids for each location cluster. This may be performed by finding an arithmetic mean of the latitude and the longitude of the rides within a respective cluster and then finding a closest (least haversine distance) ride destination’s latitude-longitude to that of the mean. Further, a reverse geocoding and a string matching and historic ride data may be performed to merge the predetermined location field to remove redundant predetermined location field. For example, when the vehicle 100 reaches a designated parking daily, and a user parks the vehicle at a different parking spot in the parking, in that case, the reverse geocoding and the string matching and historic ride data may be performed to merge the predetermined location field to remove redundant predetermined location field.

In an embodiment, the predetermined time field may be determined by performing a hierarchical agglomerative clustering with a complete linkage and a cluster size of approximately 120 (Euclidean distance) that corresponds to 120 minutes clusters. In an embodiment, a time data may be referred for the minutes of the day without departing from the scope of the present disclosure. In an embodiment, the predetermined time field may be a valid cluster, when the vehicle 100 has performed at least at least 5 rides in the predetermined time field and contributes to at least 5% of the total rides in the predetermined time field. Thus, a cluster tag for the predetermined time field may be generated for each riding session. In one example, if the user may go to a gym, a home and an office, thus, time taken to reach the gym may be X hours, the office may be Y hours and the home may be Z hours, the cluster tag for the X hours, the Y hours, and the Z hours may be generated separately for each riding session. In an embodiment, values of the cluster, number of the rides, and contribution in the total rides may vary depending on the type of the vehicle 100, a type of battery, a range of the vehicle 100 etc., without departing from the scope of the present disclosure.

Further, a separate table/data frame may be formed with a definition of the predetermined time field. This table contains the start and the end time, in minutes of the day, for each predetermined time field. This is done by finding the minimum and maximum timestamps among all the rides with the same predetermined time field label.

Further, as the predetermined time field and the predetermined location may be created, thus the plurality of predetermined data types may be segregated based on the predetermined time field and the predetermined location field.

Further, the merging module 326 may be adapted to merge the plurality of predetermined data type with the at least one predetermined field to generate the at least one predetermined cluster. In an embodiment, the at least one predetermined cluster may be a predetermined time-location cluster, a predetermined time cluster, and a predetermined location cluster, without departing from the scope of the present disclosure. Hence, in an embodiment, each riding session may be tagged with the predetermined time-location cluster. In another embodiment, each riding session may be tagged with one of the predetermined time location and the predetermined location cluster, without departing from the scope of the present disclosure. Further, the merging may be performed on the predetermined location field to reduce duplicate/overlapping clusters from the system 200.

In an embodiment, the predicting unit 208 may be adapted to receive the at least one predetermined cluster from the control unit 206. In an embodiment, the predicting unit 208 may be a contextual frequency-based unit, without departing from the scope of the present disclosure. The predicting unit 208 may be adapted to determine a plurality of parameters with respect to the at least one predetermined cluster. The predicting unit 208 may be adapted to generate a plurality of outputs based on the plurality of parameters. The predicting unit 208 may be adapted to merge the plurality of outputs with the at least one predetermined field to generate the at least one ride prediction of the vehicle. The elaborate working of the predicting unit 208 may be explained in subsequent paragraphs.

In an embodiment, the predicting unit 208 may include a receiving unit 330, a determining unit 340, a generating unit 342, and a merging unit 344. In an embodiment, the receiving unit 330 may be adapted to receive the at least one predetermined cluster from the control unit 206. The determining unit 340 may be adapted to determine the plurality of parameters with respect to the at least one predetermined cluster. In an embodiment, the plurality of parameters may include, but not limited to, one or more of a frequency of rides to the at least one predetermined cluster, number of rides to the at least one predetermined cluster, number of days to the at least one predetermined cluster, and total number of rides in the at least one predetermined field, without departing from the scope of the present disclosure. In an embodiment, the generating unit 342 may be adapted to generate the plurality of outputs based on the plurality of parameters.

In an embodiment, the plurality of outputs may include possibility of starting the ride, possible destination etc, without departing from the scope of the present disclosure. In an embodiment, the plurality of outputs may be generated for each day. Further, the predicting unit 208 may be adapted to merge the plurality of outputs with the at least one predetermined field to generate the at least one ride prediction of the vehicle. In an embodiment, the plurality of outputs may be merged with at least one of the predetermined time field and the predetermined location field to generate the at least one ride prediction. The predicting unit 208 may also provide a plurality of locations in order of the likelihood of the user going to a plurality of destinations at a predetermined time.

In an embodiment, the at least one ride prediction may be a predicted pattern of the ride of the vehicle 100 to at least one destination through at least one of a plurality of routes at the predetermined time, without departing from the scope of the present disclosure. In one embodiment, the at least one of plurality of routes may be at least one of plurality of locations, without departing from the scope of the present disclosure.

In an embodiment, the communication unit 210 may be adapted to communicate the at least one ride prediction to the user at least before, during and after riding the vehicle 100. In an embodiment, the communication unit 210 may be adapted to transmit the at least one ride prediction to at least one of the display device 214 of the vehicle 100 (Figure 3D) or a display screen 212 of the electronic device (Figure 3C), at least before riding the vehicle 100.

Further, the communication unit 210 may be adapted to transmit the at least one ride prediction to the display device 214 of the vehicle 100 and the display screen 212 of the electronic device, in a real-time, without departing from the scope of the present disclosure. In another embodiment, the communication unit 210 may be adapted to transmit the at least one ride prediction to the display device 214 of the vehicle 100 and the display screen 212 of the electronic device, from a data storage unit 312 at a predetermined time interval, without departing from the scope of the present disclosure. In one embodiment, the at least one ride prediction may be displayed as a notification, in an app of the electronic device, a prompt notification on the vehicle 100, preloading the map. etc., without departing from the scope of the present disclosure.

The present subject matter may be explained with a plurality of examples in conjunction with the Figures 3C and 3D for better understanding.

In one example, the user goes to office at 8AM daily by taking a single route. In that case, the predicting unit 208 has data about estimated time taken which may be around 30 to 35 mins to reach the office and total consumption of the fuel/charge capacity that may be 30 to 35 % etc. In view of the same, the system 200 may generate the at least one ride prediction of the vehicle 100. According to the generated ride prediction, the system 200 may transmit the at least one ride prediction to the display screen 212 of the electronic device of the user in form of a notification. The notification provides a reminder to the user that at 8AM, the user has to leave for the office.

Additionally, the at least one ride prediction also provides the fuel/charge capacity required to reach the office, estimated time of arrival to the office, weather condition etc., without departing from the scope of the present disclosure. Simultaneously, the system 200 may transmit the at least one ride prediction to the display device 214 of the vehicle 100, such that, when the user turns ON the vehicle 100, the at least one ride prediction may be provided in a preloaded map visible in the display device 214 of the vehicle 100, thus ensuring hassle less rider for the user.

In another example, the system 200 may also recommend and suggest, via the display screen 212 of the electronic device, a plurality of destinations to the user in a form a list including one or more locations visited by the user in the past, along with an estimated time of departure depending on the at least one ride prediction of the vehicle 100, without departing from the scope of the present disclosure. Further, in an embodiment, the list may also include a preferred location, for example a home of the user, as requested earlier by the user, without departing from the scope of the present disclosure.

Further, the system 200 may also provide a notification to the user to add any other destination in the list which he/she has travelled, depending on the at least ride prediction of the vehicle 100, without departing from the scope of the present disclosure. For example, daily at 8AM, the user goes to office. However, on any one day, at 8AM, the user goes to a mall. In that case, the system 200 may provide the notification to the user to add the mall as the destination in the list corresponding to 8AM, without departing from the scope of the present disclosure. In an embodiment, the notification may be provided on the display screen 212 of the electronic device of the user, without departing from the scope of the present disclosure.

In yet another example, the user goes to the office at 8AM, followed by a grocery shopping and then reaches home. On another day, the user goes to the grocery shopping at 8AM, followed by the office and then reaches home. In that case, the system 200 may generate the list of destinations corresponding to the predetermined time depending on the at least one ride prediction of the vehicle. In this case, the system 200 may transmit the list of destinations to the display screen 212 of the electronic device and the display device 214 of the vehicle 100. This allows the user to easily access the destination, hence making the ride easy and comfortable.

In yet another example, the user travels intercity daily in the morning. In that case, the system 200 generates the at least one ride prediction of the vehicle. The system 200, depending on the at least one ride prediction, sends a notification to the electronic device of the user providing an estimated time of arrival, fuel/charging capacity, traffic conditions, weather conditions etc.

In yet another example, the user travels to the office at 8AM on Monday and Tuesday. Further, the user travels to the office at 12PM on Tuesday to Friday. In that case, the system 200 generates the at least one ride prediction of the vehicle 100 to cater to the aforementioned exemplary travel behavior. For instance, the system 200 depending on the at least one ride prediction sends the notification to the electronic device of the user, at 7:30AM on Monday and Tuesday, providing estimated time of arrival, fuel/charging capacity, traffic conditions, weather conditions etc. Similarly, the system 200 depending on the at least one ride prediction sends the notification to the electronic device of the user, at 11:30AM on Wednesday to Friday, providing the estimated time of arrival, fuel/charging capacity, traffic conditions, weather conditions etc.

Figure 4A illustrates the predicting unit 208 of the system 200, according to another embodiment of the present disclosure. In another embodiment, the predicting unit 208 may be a classification unit 402, without departing from the scope of the present disclosure. In another embodiment, the predicting unit 208 receives features (Feature 1, Feature 2…Feature x) to generate the plurality of outputs. In an embodiment, the features may be the at least one predetermined cluster, without departing from the scope of the present disclosure. The plurality of outputs generated by the predicting unit 208 may be a plurality of destinations, where each destination is classified into different classes using a hierarchical agglomerative clustering. Further, each predetermined location field may be labelled as a separate class and create a classifier using supervised learning algorithms. Further, the classification unit 402 may be adapted to generate the at least one ride prediction, that is, probability of each end location of the user going to that destination cluster. The probability of each end location of the user going to that destination cluster may be a Probability of location Cluster 1, a Probability of location Cluster 2, …., a Probability of location Cluster n, without departing from the scope of the present disclosure.

Figure 4B illustrates a non-limiting example of the predicting unit 208 of the system 200, according to an embodiment of the present disclosure. In the illustrated example, the predicting unit 208 receives the features that may be a start time cluster, a start location cluster, a day of a week, a current battery percentage, a public holiday, without departing from the scope of the present disclosure. Further the predicting unit 208, depending on the received features, generates the at least one ride prediction, that is, the probability of each end location of the user going to that destination cluster. The probability of each end location of the user going to that destination cluster may be the Probability of location Cluster 1, the Probability of location Cluster 2, …., the Probability of location Cluster n, without departing from the scope of the present disclosure.

The present disclosure also relates to a method for generating the ride prediction for the user associated with the vehicle 100 as shown in Figure 5. The order in which the method steps are described below is not intended to be construed as a limitation, and any number of the described method steps can be combined in any appropriate order to execute the method or an alternative method. Additionally, individual steps may be deleted from the method without departing from the spirit and scope of the subject matter described herein.

The method 500 may be performed by programmed computing devices, for example, based on instructions retrieved from non-transitory computer readable media. The computer readable media can include machine-executable or computer-executable instructions to perform all or portions of the described method. The computer readable media may be, for example, digital memories, magnetic storage media, such as a magnetic disks and magnetic tapes, hard drives, or optically readable data storage media.

The method for generating the at least one ride prediction for the user associated with the vehicle 100 by the system 200 begins at block 502 where the method includes obtaining, by the collecting unit 204, a plurality of operational attributes associated with the vehicle 100. The plurality of operational attributes comprises one or more of the initial state of charge (SoC) of the battery in the vehicle 100, the final SoC of the battery in the vehicle 100, the ride history of the vehicle 100, the user identification number, the ride identification number, the start time of ride, the end time of the ride, the start location of the ride, the end location of the ride, riding modes of the vehicle 100, and a distance covered by the vehicle 100.

At block 504, the method includes processing, by the control unit 206, the plurality of operational attributes to determine the at least one predetermined cluster based on the plurality of operational attributes. To determine the at least one predetermined cluster, the control unit 206 may obtain from the collecting unit 204, the plurality of operational attributes. The control unit 206 may filter the plurality of operational attributes prior to converting the plurality of operational attributes into the plurality of predetermined data type. The control unit 206 may convert the plurality of operational attributes into the plurality of predetermined data type.

The control unit 206 may segregate the plurality of predetermined data type based on the at least one predetermined field. The control unit 206 may segregate the plurality of predetermined data type based on the at least one predetermined field through the clustering technique. The at least one predetermined field may be at least one of the predetermined time field and the predetermined location field. The control unit 206 may merge the plurality of predetermined data type with the at least one predetermined field, to generate the at least one predetermined cluster. The at least one predetermined cluster may be the at least one of the predetermined time-location cluster, the predetermined time cluster, and the predetermined location cluster.

At block 506, the method includes determining, by the predicting unit 208, the plurality of parameters with respect to the at least one predetermined cluster. The plurality of parameters comprises one or more of the frequency of rides to the at least one predetermined cluster, number of rides to the at least one predetermined cluster, number of days to the at least one predetermined cluster, and total number of rides in the at least one predetermined field. The predicting unit may be one of a contextual frequency-based unit and a classification-based unit.

At block 508, the method includes generating, by the predicting unit 208, the plurality of outputs depending on the plurality of parameters.

At block 510, the method includes merging, by the predicting unit 208, each of the plurality of outputs with the at least one predetermined field to generate the at least one ride prediction of the vehicle 100.

Further, the at least one ride prediction may be communicated, by the communication unit 210, to the user at least before, during, and after riding the vehicle 100. The communication unit 210 transmits the at least one ride prediction to the at least one of the display device 214 of the vehicle 100 or the display screen 212 of the electronic device of the user at least before riding the vehicle 100.

As would be gathered, the system 200 of the present disclosure ensures in generation of the at least one ride prediction of the vehicle 100. Also, information related to the generated ride prediction is transmitted to the at least one of the display screen 212 of the electronic device of the user or the display device 214 of the vehicle 100, which helps the user to plan a ride in advance. This helps in reducing a cognitive load of the user and results in a better user experience while using the system 200. Thus, the system and method as disclosed herein ensure automated and predicted outcome, that is, the at least one ride prediction for the user ensuring hassle-free ride for the user. This eliminates the requirement to enter the data in the maps manually, which decreases hassle for the user, while riding the vehicle 100. Further, the system 200 and the method 500 predict a future ride of the vehicle 100.

The system 200 and the method 500 also provide guidance to the user on the destination they may be likely headed to, based on their previous riding behavior. Further, the system 200 as disclosed provides ease of riding of the vehicle 100 to the user by providing a notification prompting that the user’s next trip is to a list of predicted destination arranged in order of the predicted probability of the user going to that destination, with the default being the destination with the highest probability at the predetermined time.

Further, the system 200 disclosed herein also provides other information regarding an upcoming trip such as ETA, charge/fuel requirement and current status of the capacity of the fuel/charge in the vehicle 100, and the like, which may be communicated via the electronic device of the user. Additionally, the system 200 also aids the rider at the start of the ride, for example, the user may accept the suggestion or select the destination from the list of locations provided by the system 200. Further, the user also receives the required data corresponding to the respective destination.

It will be appreciated that the modules, processes, systems, and devices described above can be implemented in hardware, hardware programmed by software, software instruction stored on a non-transitory computer readable medium or a combination of the above. Embodiments of the methods, processes, modules, devices, and systems (or their sub-components or modules), may be implemented on a general-purpose computer, a special-purpose computer, a programmed microprocessor or microcontroller and peripheral integrated circuit element, an ASIC or other integrated circuit, a digital signal processor, a hardwired electronic or logic circuit such as a discrete element circuit, a programmed logic circuit such as a programmable logic device (PLD), programmable logic array (PLA), field-programmable gate array (FPGA), programmable array logic (PAL) device, or the like. In general, any process capable of implementing the functions or steps described herein can be used to implement embodiments of the methods, systems, or computer program products (software program stored on a non-transitory computer readable medium).

Furthermore, embodiments of the disclosed methods, processes, modules, devices, systems, and computer program products may be readily implemented, fully or partially, in software using, for example, object or object-oriented software development environments that provide portable source code that can be used on a variety of computer platforms. Alternatively, embodiments of the disclosed methods, processes, modules, devices, systems, and computer program product can be implemented partially or fully in hardware using, for example, standard logic circuits or a very-large-scale integration (VLSI) design. Other hardware or software can be used to implement embodiments depending on the speed and/or efficiency requirements of the systems, the particular function, and/or particular software or hardware system, microprocessor, or microcomputer being utilized.

In this application, unless specifically stated otherwise, the use of the singular includes the plural and the use of “or” means “and/or.” Furthermore, use of the terms “including” or “having” is not limiting. Any range described herein will be understood to include the endpoints and all values between the endpoints. Features of the disclosed embodiments may be combined, rearranged, omitted, etc., within the scope of the invention to produce additional embodiments. Furthermore, certain features may sometimes be used to advantage without a corresponding use of other features , Claims:1. A system (200) for generating ride predictions for a user associated with a vehicle (100), the system (200) comprising:
a collecting unit (204) adapted to obtain a plurality of operational attributes associated with the vehicle (100);
a control unit (206) adapted to determine at least one predetermined cluster based on the plurality of operational attributes;
a predicting unit (208) adapted to:
determine a plurality of parameters with respect to the at least one predetermined cluster;
generate a plurality of outputs based on the plurality of parameters; and
merge the plurality of outputs with at least one predetermined field to generate at least one ride prediction of the vehicle (100).

2. The system (200) as claimed in claim 1, comprising a communication unit (210) adapted to communicate the at least one ride prediction to the user at least before, during and after riding the vehicle (100).

3. The system (200) as claimed in claim 2, wherein the communication unit (210) is adapted to transmit the at least one ride prediction to at least one of a display device (214) of the vehicle (100) or a display screen (212) of an electronic device of the user, at least before riding the vehicle (100).

4. The system (200) as claimed in claim 1, wherein the at least one ride prediction comprises a predicted pattern of the ride of the vehicle (100) to at least one destination through at least one of a plurality of routes corresponding to a predetermined time.

5. The system (200) as claimed in claim 1, wherein the plurality of operational attributes comprises one or more of an initial state of charge (SoC) of a battery in the vehicle (100), a final SoC of the battery in the vehicle(100), a ride history of the vehicle (100), a user identification number, a ride identification number, a start time of ride, an end time of the ride, a start location of the ride, an end location of the ride, riding modes of the vehicle (100), and a distance covered by the vehicle (100).

6. The system (200) as claimed in claim 1, wherein to determine the at least one predetermined cluster, the control unit (206) is adapted to:
obtain, from the collecting unit (204), the plurality of operational attributes;
convert the plurality of operational attributes into a plurality of predetermined data type;
segregate the plurality of predetermined data type based on the at least one predetermined field; and
merge the plurality of predetermined data type with the at least one predetermined field, to generate the at least one predetermined cluster.

7. The system (200) as claimed in claim 6, wherein the control unit (206) is adapted to filter the plurality of operational attributes before converting into the plurality of predetermined data type.

8. The system (200) as claimed in claim 6, wherein the at least one predetermined field comprises at least one of a predetermined time field and a predetermined location field, and wherein the at least one predetermined cluster is at least one of a predetermined time-location cluster, a predetermined time cluster, and a predetermined location cluster.

9. The system (200) as claimed in claim 6, wherein the control unit (206) is adapted to segregate, by a clustering technique, the plurality of predetermined data type based on the at least one predetermined field.

10. The system (200) as claimed in claim 1, wherein the plurality of parameters comprises one or more of a frequency of rides to the at least one predetermined cluster, number of rides to the at least one predetermined cluster, number of days to the at least one predetermined cluster, and total number of rides in the at least one predetermined field.

11. The system (200) as claimed in claim 1, wherein the predicting unit (208) is one of a contextual frequency-based unit and a classification-based unit.

12. A method (500) for generating ride predictions for a user associated with a vehicle (100) by a system (200), comprising:
obtaining (502), by a collecting unit (204), a plurality of operational attributes associated with the vehicle (100);
processing (504), by the control unit (206), the plurality of operational attributes to determine at least one predetermined cluster based on the plurality of operational attributes;
determining (506), by the predicting unit (208), a plurality of parameters with respect to the at least one predetermined cluster;
generating (508), by the predicting unit (208), a plurality of outputs depending on the plurality of parameters; and
merging, by the predicting unit (208), each of the plurality of outputs with at least one predetermined field to generate at least one ride prediction of the vehicle (208).

13. The method (500) as claimed in claim 12, comprising:
communicating, by a communication unit (210), the at least one ride prediction to the user at least before, during, and after riding the vehicle (100).
transmitting, by the communication unit (210), the at least one ride prediction to at least one of a display device (214) of the vehicle (100) and an electronic device of the user at least before riding the vehicle (100).

14. The method (500) as claimed in claim 12, wherein the plurality of operational attributes comprises one or more of an initial state of charge (SoC) of a battery in the vehicle (100), a final SoC of the battery in the vehicle (100), a ride history of the vehicle (100), a user identification number, a ride identification number, a start time of ride, an end time of the ride, a start location of the ride, an end location of the ride, riding modes of the vehicle (100), and a distance covered by the vehicle (100).

15. The method (500) as claimed in claim 12, wherein the processing, by the control unit (206), the plurality of operational attributes to determine at least one predetermined cluster based on the plurality of operational attributes, comprising:
obtaining, from the collecting unit (204), by the control unit (206), the plurality of operational attributes;
converting, by the control unit (206), the plurality of operational attributes into a plurality of predetermined data type;
segregating, by the control unit (206), the plurality of predetermined data type based on the at least one predetermined field; and
merging, by the control unit (206), the plurality of predetermined data type with the at least one predetermined field, to generate the at least one predetermined cluster.

16. The method (500) as claimed in claim 15, wherein prior to the converting the plurality of operational attributes into the plurality of predetermined data type, the method comprises:
filtering, by the control unit (206), the plurality of operational attributes.

17. The method (500) as claimed in claim 16, wherein the at least one predetermined field comprises at least one of a predetermined time field and a predetermined location field, and wherein the at least one predetermined cluster is at least one of a predetermined time-location cluster, a predetermined time cluster, and a predetermined location cluster.

18. The method (500) as claimed in claim 15, wherein the segregating, by the control unit (206), the plurality of predetermined data type based on the at least one predetermined field through a clustering technique.

19. The method (500) as claimed in claim 12, wherein the plurality of parameters comprises one or more of a frequency of rides to the at least one predetermined cluster, number of rides to the at least one predetermined cluster, number of days to the at least one predetermined cluster, and total number of rides in the at least one predetermined field.

20. The method (500) as claimed in claim 12, wherein the predicting unit (208) is one of a contextual frequency-based unit and a classification-based unit.