DESC:EARLIEST PRIORITY DATE:
This Application claims priority from a Provisional patent application filed in India having Patent Application No. 202141059853, filed on December 2021, and titled “SYSTEM AND METHOD FOR ALERTING A DRIVER OF AN ELECTRIC TWO-WHEELER”.
FIELD OF INVENTION
[0001] Embodiments of the present disclosure relates to electric vehicles, and more particularly to a system and a method for alerting a driver of an electric two-wheeler.
BACKGROUND
[0002] Sometimes, accidents happen when a driver generally does not switch off an ignition at crossroads while standing at traffic signals and in unconscious state of mind slips hands upon throttle. If an electric two-wheeler is ON and the driver is standing at the crossroad following traffic signals and if a child standing in front of the electric two-wheeler or rider slips hands upon throttle, it may lead to violation of traffic regulation which may induce the accident because of wrong handling.
[0003] During such accidents, not only the rider and the electric two-wheeler, however any adjacent vehicle also next to the electric two-wheeler might be damaged. Sometimes, when the driver meets with the accident, the electric two-wheeler of the driver may roll over. In rush, the families are not informed by neighbouring people in the scene of the accident.
[0004] On icy, watery, muddy, or snowy roads, because of more acceleration while starting the electric two-wheeler, the driver generates slip in the electric two-wheeler through which handling characteristics of the electric two-wheeler changes and the driver may meet with the accident.
[0005] Hence, there is a need for an improved system for alerting a driver of an electric two-wheeler and a method to operate the same and therefore address the aforementioned issues.
BRIEF DESCRIPTION
[0006] In accordance with one embodiment of the disclosure, a computing system for alerting a driver of an electric two-wheeler is disclosed. The computing system includes a hardware processor. The computing system also includes a memory coupled to the hardware processor. The memory comprises a set of program instructions in the form of a plurality of subsystems and configured to be executed by the hardware processor.
[0007] Embodiments of the present disclosure comprises the computing system for alerting the driver of the electric two-wheeler. The plurality of subsystems comprises a vehicle controller subsystem. The vehicle controller subsystem is configured to collect vehicle data in real time via one or more sensors. The vehicle data comprises data from a wheel speed sensor, signal data from an ignition, data from an input roll over sensor, sensor data of the electric two-wheeler, a signal data from a brake switch, Inertial Measurement Unit (IMU) sensor data and a signal data from a mode of the electric two-wheeler. The plurality of subsystems further comprises a vehicle decision processing subsystem. The vehicle decision processing subsystem is configured to extract one or more vehicle parameters from the collected vehicle data. The vehicle decision processing subsystem is further configured to validate the extracted one or more vehicle parameters by applying the collected vehicle data to an artificial intelligence-based decision model. The vehicle decision processing subsystem is further configured to determine an abnormal condition in the extracted one or more vehicle parameters based on results of validation. The vehicle decision processing subsystem is further configured to generate one or more types of alerts for the determined abnormal condition. The one or more type of alerts comprises audio and text. The vehicle decision processing subsystem is further configured to output the generated one or more types of the alerts on a light emitting diode (LED) and a buzzer of the electric two-wheeler.
[0008] In accordance with another embodiment, a method for alerting a driver of the electric two-wheeler is disclosed. The method comprises collecting vehicle data in real time via one or more sensors. The vehicle data comprises data from the wheel speed sensor, the signal data from an ignition, the data from the input roll over sensor of the electric two-wheeler, the signal data from the brake switch, the inertial measurement unit sensor data and the signal data from mode of the electric two-wheeler. The method further comprises extracting one or more vehicle parameters from the collected vehicle data. The method further comprises validating the extracted one or more vehicle parameters by applying the collected vehicle data to the artificial intelligence-based decision model. The method further comprises determining an abnormal condition in the extracted one or more vehicle parameters based on results of the validation. The method further comprises generating one or more types of alerts for the determined abnormal condition. The one or more type of alerts comprises audio and text. The method further outputting the generated one or more types of the alerts on the light emitting diode (LED) and the buzzer of the electric two-wheeler.
[0009] To further clarify the advantages and features of the present disclosure, a more particular description of the disclosure will follow by reference to specific embodiments thereof, which are illustrated in the appended figures. It is to be appreciated that these figures depict only typical embodiments of the disclosure and are therefore not to be considered limiting in scope. The disclosure will be described and explained with additional specificity and detail with the appended figures.
BRIEF DESCRIPTION OF THE DRAWINGS
[0010] The disclosure will be described and explained with additional specificity and detail with the accompanying figures in which:
[0011] FIG. 1 is a block diagram illustrating an exemplary computing system for alerting a driver of an electric two-wheeler, in accordance with an embodiment of the present disclosure;
[0012] FIG. 2 is a schematic representation of an electronic architecture for an electric two-wheeler, in accordance with an embodiment of the present disclosure;
[0013] FIG. 3 is a tabular representation of a truth table depicting various scenarios of an ignition, mode of a ride, speed of an electric two-wheeler, in accordance with an embodiment of the present disclosure;
[0014] FIG. 4 is an exemplary process flow chart depicting a solution at a time when an ignition of an electric two-wheeler is turned on and a driver meets with an accident, in accordance with an embodiment of the present disclosure;
[0015] FIG. 5 is an exemplary process flow chart depicting a solution at a time when an electric two-wheeler rolls over during an accident, in accordance with an embodiment of the present disclosure;
[0016] FIG. 6 is an exemplary process flow chart depicting a solution at a time when a driver meets with an accident on icy, watery, muddy, or snowy roads, in accordance with an embodiment of the present disclosure; and
[0017] FIG. 7 is exemplary process flowchart illustrating an exemplary method for alerting a driver of an electric two-wheeler, in accordance with an embodiment of the present disclosure.
[0018] Further, those skilled in the art will appreciate that elements in the figures are illustrated for simplicity and may not have necessarily been drawn to scale. Furthermore, in terms of the construction of the device, one or more components of the device may have been represented in the figures by conventional symbols, and the figures may show only those specific details that are pertinent to understanding the embodiments of the present disclosure so as not to obscure the figures with details that will be readily apparent to those skilled in the art having the benefit of the description herein.
DETAILED DESCRIPTION
[0019] For the purpose of promoting an understanding of the principles of the disclosure, reference will now be made to the embodiment illustrated in the figures and specific language will be used to describe them. It will nevertheless be understood that no limitation of the scope of the disclosure is thereby intended. Such alterations and further modifications in the illustrated online platform, and such further applications of the principles of the disclosure as would normally occur to those skilled in the art are to be construed as being within the scope of the present disclosure.
[0020] 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 a process or method. Similarly, one or more devices or subsystems or elements or structures or components preceded by “comprises... a” does not, without more constraints, preclude the existence of other devices, subsystems, elements, structures, components, additional devices, additional subsystems, additional elements, additional structures or additional components. Appearances of the phrase “in an embodiment”, “in another embodiment” and similar language throughout this specification may, but not necessarily do, all refer to the same embodiment.
[0021] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by those skilled in the art to which this disclosure belongs. The system, methods, and examples provided herein are only illustrative and not intended to be limiting.
[0022] In the following specification and the claims, reference will be made to a number of terms, which shall be defined to have the following meanings. The singular forms “a”, “an”, and “the” include plural references unless the context clearly dictates otherwise.
[0023] A computer system (standalone, client or server computer system) configured by an application may constitute a “subsystem” that is configured and operated to perform certain operations. In one embodiment, the “subsystem” may be implemented mechanically or electronically, so a subsystem may comprise dedicated circuitry or logic that is permanently configured (within a special-purpose processor) to perform certain operations. In another embodiment, a “subsystem” may also comprise programmable logic or circuitry (as encompassed within a general-purpose processor or other programmable processor) that is temporarily configured by software to perform certain operations.
[0024] Accordingly, the term “subsystem” should be understood to encompass a tangible entity, be that an entity that is physically constructed permanently configured (hardwired) or temporarily configured (programmed) to operate in a certain manner and/or to perform certain operations described herein.
[0025] FIG. 1 is a block diagram illustrating an exemplary computing system 100 for alerting a driver of an electric two-wheeler in accordance with an embodiment of the present disclosure. To avoid sudden accidents, the computing system 100 provides mandatory alerts to the driver of the electric two-wheeler in real time. The computing system 100 provides such alerts by considering various electric two-wheeler parameters.
[0026] The computing system 100 includes a hardware processor 108. The computing system 100 also includes a memory 102 coupled to the hardware processor 108. The memory 102 comprises a set of program instructions in the form of a plurality of subsystems and configured to be executed by the hardware processor 108.
[0027] The computing system 100 further includes a database 106. The database 106 is, for example, a structured query language (SQL) data store. The database 106 is configured as cloud-based database implemented in the computing system 100, where software application are delivered as a service over a cloud platform. The database 106, according to another embodiment of the present disclosure, is a location of a file system directly accessible by the plurality of subsystems. The database 106 stores operation data of the vehicle with synchronization to the cloud platform in the absence of internet connectivity i.e local storage on vehicle.
[0028] The hardware processor(s) 108, as used herein, means any type of computational circuit, such as, but not limited to, a microprocessor, a microcontroller, a complex instruction set computing microprocessor, a reduced instruction set computing microprocessor, a very long instruction word microprocessor, an explicitly parallel instruction computing microprocessor, a digital signal processor, or any other type of processing circuit, or a combination thereof. Input/output (I/O) devices 110 (including but not limited to keyboards, displays, pointing devices, etc.) may be coupled to the system either directly or through intervening I/O controllers
[0029] The memory 102 includes a plurality of subsystems stored in the form of executable program which instructs the hardware processor 108 via bus 104 to perform the method steps. The memory 102 has following plurality of subsystems: a vehicle controller subsystem 112, a vehicle decision processing subsystem 114 and an acceleration computing subsystem 116.
[0030] Computer memory elements may include any suitable memory device(s) for storing data and executable program, such as read only memory, random access memory, erasable programmable read only memory, electrically erasable programmable read only memory, hard drive, removable media drive for handling memory cards and the like. Embodiments of the present subject matter may be implemented in conjunction with program modules, including functions, procedures, data structures, and application programs, for performing tasks, or defining abstract data types or low-level hardware contexts. Executable program stored on any of the above-mentioned storage media may be executable by the hardware processor(s) 108.
[0031] The plurality of subsystems comprises the vehicle controller subsystem 112. The vehicle controller subsystem 112 is configured to collect vehicle data in real time via one or more sensors. In one embodiment, the vehicle data comprises data from a wheel speed sensor 208, signal data from an ignition, the data from an input roll over sensor, sensor data of the electric two-wheeler, the signal data from a brake switch, Inertial Measurement Unit (IMU) sensor data and the signal data from mode of the electric two-wheeler (As shown in FIG. 2).
[0032] In such embodiment, the one or more sensors are configured with the two-wheeler and includes the wheel speed sensor 208, an ignition key sensor, a roll over sensor, brake sensor, Inertial Measurement Unit (IMU) sensor, neutral position sensor and the like.
[0033] The plurality of subsystems also includes the vehicle decision processing subsystem 114. The vehicle decision processing subsystem 114 is configured to extract one or more vehicle parameters from the collected vehicle data. The vehicle decision processing subsystem 114 is further configured to validate the extracted one or more vehicle parameters by applying the collected vehicle data to an artificial intelligence-based decision model. The vehicle decision processing subsystem 114 is further configured to determine an abnormal condition in the extracted one or more vehicle parameters based on results of validation. The vehicle decision processing subsystem 114 is further configured to generate one or more types of alerts for the determined abnormal condition. The one or more type of alerts comprises audio and text. The vehicle decision processing subsystem 114 is further configured to output the generated one or more types of the alerts on a light emitting diode (LED) and a buzzer of the electric two-wheeler . The one or more vehicle parameters comprises current ignition stage, current mode of operation, current speed value, and current brake status. The vehicle decision processing subsystem 114 is further configured to dynamically correlate each of the extracted one or more vehicle parameters with corresponding pre-stored one or more vehicle parameters. The vehicle decision processing subsystem 114 is further configured to perform one or more rule-based validation checks on each of the correlated one or more vehicle parameters and output results of the validation of the one or more vehicle parameters. The vehicle decision processing subsystem 114 is further configured to output results of the validation of the one or more vehicle parameters. The result of the validation comprises success and failure. The vehicle decision processing subsystem 114 is further configured to identify a deviation in current values of the one or more vehicle parameters based on results of the one or more rule-based validation checks and determine whether the identified deviation amounts to an abnormal condition based on one or more predefined criteria of the one or more rule-based validation checks. The vehicle decision processing subsystem 114 is further configured to determine whether data from the input roll over sensor of the electric two-wheeler is greater than pre-determined roll over sensor data and trigger one or more Save Our Soul (SOS) service notification if the data from the input roll over sensor of the electric two-wheeler is greater than the pre-determined roll over sensor data. The vehicle decision processing subsystem 114 is further configured to determine whether acceleration data of the electric two-wheeler is lesser than relative acceleration data of the wheel corresponding to the electric two-wheeler and trigger ON the light emitting diode (LED) and the buzzer if the acceleration data of the electric two-wheeler is lesser than the relative acceleration data of the wheel.
[0034] The plurality of subsystems further comprises the acceleration computing subsystem 116 configured to calculate the acceleration data of the electric two-wheeler and the relative acceleration data of the wheel corresponding to the electric two-wheeler from the Inertial Measurement Unit (IMU) sensor data.
[0035] FIG. 2 is a schematic representation of an electronic architecture 200 for an electric two-wheeler, in accordance with an embodiment of the present disclosure. The electronic architecture 200 comprises an electronic controller unit (ECU) 202 and a human machine interface (HMI) 214. Various inputs are collected and sent to the electronic controller unit (ECU) 202. The various inputs (also referred as vehicle data) include data collected from a wheel speed sensor 208, signal data collected from an ignition 210, the signal data collected from a brake switch 212 and the signals data collected from mode 206 (also referred as NEUTRAL, MEDIUM, or HIGH mode) of the ride. The data from the wheel speed sensor 208 are collected with the help of a motor controller. The data of the wheel speed sensor 208 are collected based on motion of the wheels. . The signal data of the brake switch 212 may be collected based on if the brake switch 212 is applied to the electric two-wheeler or if the brake switch 212 is not applied to the electric two-wheeler. The signals data of the ignition 210 may be collected based on if the ignition 210 of the electric two-wheeler is ON or the ignition 210 of the electric two-wheeler is OFF and the like. The signal data of the mode 206 of the ride may be based on if the electric two-wheeler is in NEUTRAL ride mode or LOW ride mode or MEDIUM ride mode or HIGH ride mode. (The NEUTRAL ride mode or LOW ride mode or MEDIUM ride mode or HIGH ride mode may also be referred as NEUTRAL mode, HIGH mode, and LOW mode). The various data collected are sent to the electronic controller unit (ECU) 202. The electronic controller unit (ECU) 202 processes all the various data and sends the various data to the human machine interface (HMI) 214. The human machine interface (HMI) 214 based on the received data sends two different outputs. First output is from a light emitting diode (LED) 216 which provides a visual warning. The human machine interface (HMI) 214 comprises a visual zone through which the visual warning may be visualised by a driver riding the electric two-wheeler. Second output is from a buzzer 218 which provides an audio warning. The visual warning may be blinking of the light emitting diode (LED) 216.
[0036] FIG. 3 is a tabular representation of a truth table 300 depicting various scenarios of an ignition 210, a mode 206 of a ride, speed of an electric two-wheeler, in accordance with an embodiment of the present disclosure. For example, consider a data of x as 5 kmph. Here, in one of the cases it is inferred that if the ignition 210 of the electric two-wheeler is OFF, and the electric two-wheeler is in neutral mode or ride mode and if the speed of the electric two-wheeler is less than the data of x and if a brake swich 212 is either applied or not applied to the electric two-wheeler, then a light emitting diode (LED) 216 and a buzzer 218 remains OFF. Further in one of the cases if the ignition 210 of the electric two-wheeler is ON and the electric two-wheeler is in neutral mode and if the speed of the electric two-wheeler is less than the data of x and if the brake switch 212 is either applied or not applied to the electric two-wheeler, then the light emitter diode (LED) 216 and the buzzer 218 remains off. Further in one of the cases, if the ignition 210 of the electric two-wheeler is ON and the mode 206 of the electric two-wheeler is in LOW ride mode, MEDIUM ride mode, or HIGH ride mode and if the speed of the electric two-wheeler is less than the data of x and if the brake switch 212 is not applied, then the light emitting diode (LED) 216 and the buzzer 218 is turned on. Further in one of the cases, if the ignition 210 of the electric two-wheeler is ON and the mode 206 is in LOW ride mode, MEDIUM ride mode, or HIGH ride mode and if the speed of the electric two-wheeler is greater than the data of x and if the brake switch 212 is applied on the electric two-wheeler, then the light emitting diode (LED) 216 and the buzzer 218 remains off. Further in one of the cases, if the ignition 210 of the electric two-wheeler is ON and if the electric two-wheeler is in LOW ride mode, MEDIUM ride mode, or HIGH ride mode and the speed of the electric two-wheeler is greater than the data of x and if the brake switch 212 is not applied then the light emitting diode (LED) 216 and the buzzer 218 remains off.
[0037] FIG. 4 is an exemplary process flow chart 400 depicting a solution at a time when an ignition 210 of an electric two-wheeler is turned on, and a driver meets with an accident, in accordance with an embodiment of the present disclosure. At step 402, vehicle data are collected. The vehicle data include signal data from the ignition 210, data from a wheel speed sensor 208 of the electric two-wheeler and the signal data collected from a brake switch 212 of the electric two-wheeler. At step 404, a check for the ignition 210 is performed. At step 412, since the ignition 210 of the electric two-wheeler is OFF, a light emitting diode (LED) 216 and a buzzer 218 remains OFF. At step 406, since the ignition 210 of the electric two-wheeler is ON, a check for neutral mode is performed. If the electric two-wheeler is in the neutral mode, then the light emitting diode (LED) 216 and the buzzer 218 remains OFF. In such embodiment, a computing system 100 matches one of pre-determined modes, whereby the pre-determined mode comprises HIGH mode, LOW mode, and MEDIUM mode. If the electric two-wheeler is not in the neutral mode, then at step 408, speed check is performed. If the speed is greater than predetermined speed, then, the light emitting diode (LED) 216 and the buzzer 218 remains OFF. For example, data of the predetermined speed may be five kmph. Further, if the speed is not greater than x kmph, then at step 410, a check is performed if the brake switch 212 is applied or not applied on the electric two-wheeler. If the brake switch 212 is applied on the electric two-wheeler, then the light emitting diode (LED) 216 and the buzzer 218 remains OFF. If the brake switch 212 is not applied on the electric two-wheeler, then at step 414, the light emitting diode (LED) 216 and the buzzer 218 is turned ON. The light emitting diode (LED) 216 sends visual warnings to a driver and the buzzer 218 sends an audio warning to the driver in a visibility zone of the driver.
[0038] FIG. 5 is exemplary process flow chart 500 depicting a solution at a time when an electric two-wheeler rolls over during an accident, in accordance with an embodiment of the present disclosure. At step 502, various inputs are collected. The input collected is data from an input roll over sensor 204. At step 504, a check is performed if the data from the input roll over sensor 204 is greater than a pre-determined roll over sensor data or not greater. If the data from the input roll over sensor 204 is greater than the pre-determined roll over sensor data, then at step 506, one or more SOS service notification is sent via Short Message Service (SMS). Here, it is inferred here that a driver who is riding the electric two-wheeler has met with the accident. The one or more SOS service notification is sent to all family members of the driver based on details registered by the driver with current Global Positioning System (GPS) location. To eliminate a need of additional data from the input roll over sensor 204, it is possible to take raw data of gyroscope from Inertial Measurement Unit (IMU) sensor and calculate roll moment which is received from the data from the input roll over sensor 204. However, in one of the cases, if the battery of the electric two-wheeler is low and the driver is pushing the electric two-wheeler and while pushing the vehicle (also referred as electric two-wheeler), if vehicle rolls over, then also the one or more SOS service notification is sent. Further here, for example, the one or more SOS service notification is also sent if the speed of the electric two-wheeler is greater than five kmph and the vehicle rolls over.
[0039] FIG. 6 is exemplary process flow chart 600 depicting a solution at the time when a driver meets with an accident on icy, watery, muddy, or snowy roads, in accordance with an embodiment of the present disclosure. At step 602 a check for an ignition 210 is performed. At step 606, if the ignition 210 of an electric two-wheeler is OFF then a light emitting diode (LED) 216 and a buzzer 218 remains OFF. If the ignition 210 of the electric two-wheeler is ON, then at step 604 two inputs are collected. The first input is collected from Inertial Measurement Unit (IMU) sensor. The inertial measurement unit (IMU) sensor collects relative acceleration data of the electric two-wheeler. The second input which is collected is wheel speed relative acceleration value. An electronic controller unit (ECU) 202 calculates relative acceleration of vehicle from Inertial Measurement Unit (IMU) sensor data and calculates relative acceleration of a wheel from speed of the wheel. At step 608, a comparison on the first input and the second input is performed. If the data of second input is greater from the data of first input, then at step 612 the light emitting diode (LED) 216 and the buzzer 218 is remains ON to pass one or more types of alerts to the driver regarding the accident. If the data of second input is not greater from data of the first input, then at step 610 the light emitting diode (LED) 216 and the buzzer 218 is turned OFF. The light emitting diode (LED) 216 sends visual warnings and the buzzer 218 sends an audio warning. The visual warnings may be blinking of the light emitting diode (LED) 216 which is displayed on a visual zone, a message which states to decrease the acceleration of the electric two-wheeler which is displayed on the visual zone. The audio warnings may be a buzz sound produced by the buzzer 218.
[0040] FIG. 7 is exemplary process flowchart illustrating an exemplary method 700 for alerting a driver of an electric two-wheeler in accordance with an embodiment of the present disclosure.
[0041] At step 702, vehicle data is collected in real time via one or more sensors. The vehicle data comprises data from wheel speed sensor 208, signal data from an ignition 210, data from the input roll over sensor 204, sensor data of the electric two-wheeler, signal data from a brake switch 212, Inertial Measurement Unit (IMU) sensor data and signal data from a mode 206 of the electric two-wheeler.
[0042] At step 704, one or more vehicle parameters are extracted from the collected vehicle data.
[0043] At step 706, the extracted one or more vehicle parameters are validated by applying the collected vehicle data to an artificial intelligence-based decision model.
[0044] At step 708, an abnormal condition is determined in the extracted one or more vehicle parameters based on results of validation.
[0045] At step 710, one or more types of alerts are generated for the determined abnormal condition. The one or more type of alerts comprises audio and text.
[0046] At step 712, the generated one or more types of the alerts are outputted on a light emitting diode (LED) 216 and a buzzer 218 of the electric two-wheeler .
[0047] The method 700 further comprises determining the signal data of the ignition 210. The method 700 further comprises triggering OFF the light emitting diode (LED) 216 and the buzzer 218 if the signal data of the ignition 210 is OFF. The method 700 further comprises determining the signal data from the mode 206 of the electric two-wheeler if the signal data of the ignition 210 is ON. The method 700 further comprises triggering OFF the light emitting diode (LED) 216 and the buzzer 218 if the signal data from the mode 206 of the electric two-wheeler is neutral mode. The method 700 further comprises determining whether the data from the wheel speed sensor 208 is greater than predetermined speed if the signal data from the mode 206 of the electric two-wheeler matches one of pre-determined modes. The pre-determined mode (also referred as mode 206) comprises HIGH mode, LOW mode, and MEDIUM mode. The method 700 further comprises triggering OFF the light emitting diode (LED) 216 and the buzzer 218 if the data from the wheel speed sensor 208 is greater than predetermined speed. The method 700 further comprises determining the signal data from the brake switch 212 if the data from the wheel speed sensor 208 is below than predetermined speed. The method 700 further comprises triggering OFF the light emitting diode (LED) 216 and the buzzer 218 if the signal data from the brake switch 212 is ON and triggering ON the light emitting diode (LED) 216 and the buzzer 218 if the signal data from the brake switch 212 is OFF.
[0048] The method 700 further comprises determining whether data from an input roll over sensor 204 of the electric two-wheeler is greater than the pre-determined roll over sensor data and triggering one or more SOS service notification if data from the input roll over sensor 204 of the electric two-wheeler is greater than pre-determined roll over sensor data.
[0049] The method 700 further comprises calculating acceleration data of the electric two-wheeler and relative acceleration data of the wheel corresponding to the electric two-wheeler from the collected Inertial Measurement Unit (IMU) sensor data.
[0050] The method 700 further comprises determining the signal data of the ignition 210. The method 700 further comprises triggering OFF the light emitting diode (LED) 216 and the buzzer 218 if the signal data of the ignition 210 is OFF. The method 700 further comprises determining whether the relative acceleration data of the electric two-wheeler is lesser than relative acceleration data of the wheel corresponding to the electric two-wheeler. The method 700 further comprises triggering ON the light emitting diode (LED) 216 and the buzzer 218 if the acceleration data of the electric two-wheeler is lesser than relative acceleration data of the wheel.
[0051] The present disclosure alerts the driver while he or she is in unconscious state of mind and may have chances of meeting with the accident. Further, the present disclosure provides safety from a child when the child slips his or her hands over a throttle. The function of sending automated message alerts to the family members of the driver about the accident is one of the important functions. The present disclosure helps prevent accidents on low friction roads such as watery, icy roads by guiding the driver to reduce acceleration which further indicates, the present disclosure helps with all present-day challenges of the electric two-wheeler.
[0052] The figures and the foregoing description give examples of embodiments. Those skilled in the art will appreciate that one or more of the described elements may well be combined into a single functional element. Alternatively, certain elements may be split into multiple functional elements. Elements from one embodiment may be added to another embodiment. For example, order of processes described herein may be changed and are not limited to the manner described herein. Moreover, the actions of any flow diagram need not be implemented in the order shown; nor do all of the acts need to be necessarily performed. Also, those acts that are not dependant on other acts may be performed in parallel with the other acts. The scope of embodiments is by no means limited by these specific examples.
,CLAIMS:WE CLAIM:
1. A computing system (100) for alerting a driver of an electric two-wheeler, the computing system (100) comprising:
a hardware processor (108); and
a memory (102) coupled to the hardware processor (108), wherein the memory (102) comprises a set of program instructions in the form of a plurality of subsystems, configured to be executed by the hardware processor (108), wherein the plurality of subsystems comprises:
a vehicle controller subsystem (112) configured to collect vehicle data in real time via one or more sensors, wherein the vehicle data comprises data from a wheel speed sensor (208), signal data from an ignition (210), the data from an input roll over sensor (204), sensor data of the electric two-wheeler, the signal data from a brake switch (212), Inertial Measurement Unit (IMU) sensor data and the signal data from the mode (206) of the electric two-wheeler;
a vehicle decision processing subsystem (114) configured to:
extracting one or more vehicle parameters from the collected vehicle data;
validating the extracted one or more vehicle parameters by applying the collected vehicle data to an artificial intelligence-based decision model;
determining an abnormal condition in the extracted one or more vehicle parameters based on results of validation;
generating one or more types of alerts for the determined abnormal condition, wherein the one or more types of alerts comprises audio and text; and
outputting the generated one or more types of the alerts on a light emitting diode (LED) (216) and a buzzer (218) of the electric two-wheeler.
2. The computing system (100) as claimed in claim 1, wherein the one or more vehicle parameters comprise current ignition stage, current mode of operation, current speed value, and current brake status.
3. The computing system (100) as claimed in claim 1, wherein in validating the extracted one or more vehicle parameters by apply the collected vehicle data to the artificial intelligence-based decision model, the vehicle decision processing subsystem (114) is configured to:
dynamically correlating each of the extracted one or more vehicle parameters with corresponding pre-stored one or more vehicle parameters;
performing one or more rule-based validation checks on each of the correlated one or more vehicle parameters; and
outputting results of the validation of the one or more vehicle parameters, wherein the result of the validation comprises success and failure.
4. The computing system (100) as claimed in claim 1, wherein in determining the abnormal condition in the extracted one or more vehicle parameters based on results of the validation, the vehicle decision processing subsystem (114) is configured to:
identify a deviation in current values of the one or more vehicle parameters based on results of the one or more rule-based validation checks; and
determining whether the identified deviation amounts to an abnormal condition based on one or more predefined criteria of the one or more rule-based validation checks.

5. The computing system (100) as claimed in claim 1, wherein in determining the abnormal condition in the extracted one or more vehicle parameters based on results of the validation, the vehicle decision processing subsystem (114) is configured to:
determine whether data from the input roll over sensor (204) of the electric two-wheeler is greater than pre-determined roll over sensor data; and
trigger one or more SOS service notification if the data from the input roll over sensor (204) of the electric two-wheeler is greater than the pre-determined roll over sensor data.
6. The computing system (100) as claimed in claim 1, further comprising acceleration computing subsystem (116) configured to calculate acceleration data of the electric two-wheeler and relative acceleration data of the wheel corresponding to the electric two-wheeler from the Inertial Measurement Unit (IMU) sensor data.
7. The computing system (100) as claimed in claim 1, wherein in in determining the abnormal condition in the extracted one or more vehicle parameters based on results of the validation, the vehicle decision processing subsystem (114) is configured to:
determine whether acceleration data of the electric two-wheeler is lesser than relative acceleration data of the wheel corresponding to the electric two-wheeler; and
trigger ON the light emitting diode (LED) (216) and the buzzer (218) if the acceleration data of the electric two-wheeler is lesser than relative acceleration data of the wheel.
8. A method (700) for alerting the driver of the electric two-wheeler, the method (700) includes:
collecting, by the hardware processor (108), the vehicle data in real time via one or more sensors, wherein the vehicle data comprises the data from the wheel speed sensor (208), the signal data from the ignition (210), the data from the input roll over sensor (204) of the electric two-wheeler, the signal data from the brake switch (212), the Inertial Measurement Unit (IMU) sensor data and the signal data from the mode (206) of the electric two-wheeler;
extracting, by the hardware processor (108), one or more vehicle parameters from the collected vehicle data;
validating, by the hardware processor (108), the extracted one or more vehicle parameters by applying the collected vehicle data to the artificial intelligence-based decision model;
determining, by the hardware processor (108), the abnormal condition in the extracted one or more vehicle parameters based on the results of the validation;
generating, by the hardware processor (108), one or more types of alerts for the determined abnormal condition, wherein the one or more type of alerts comprises audio and text; and
outputting, by the hardware processor (108), the generated one or more types of the alerts on the light emitting diode (LED) (216) and the buzzer (218) of the electric two-wheeler .
9. The method (700) as claimed in claim 8, wherein triggering, by the processor, the light emitting diode (LED) (216) and the buzzer (218) based on the artificial intelligence-based decision model result, the method (700) comprises:
determining the signal data of the ignition (210);
triggering OFF the light emitting diode (LED) (216) and the buzzer (218) if the signal data of the ignition (210) is OFF;
determining the signal data from the mode (206) of the electric two-wheeler if the signal data of the ignition (210) is ON;
triggering OFF the light emitting diode (LED) (216) and the buzzer (218) if the signal data from the mode (206) of the electric two-wheeler is in NEUTRAL mode;
determining whether the data from the wheel speed sensor (208) is greater than predetermined speed if the signal data from the mode (206) of the electric two-wheeler matches one of the pre-determined mode, wherein the pre-determined mode comprises HIGH mode, LOW mode and MEDIUM mode;
triggering OFF the light emitting diode (LED) (216) and the buzzer (218) if the data from the wheel speed sensor (208) is greater than predetermined speed;
determining the signal data from the brake switch (212) if the data from the wheel speed sensor (208) is below than predetermined speed;
triggering OFF the light emitting diode (LED) (216) and the buzzer (218) if the signal data from the brake switch (212) is ON; and
triggering ON the light emitting diode (LED) (216) and the buzzer (218) if the signal data from the brake switch (212) is OFF.
10. The method (700) as claimed in claim 8, wherein triggering, by the processor, the light emitting diode (LED) (216) and the buzzer (218) based on the artificial intelligence-based decision model result, the method (700) comprises:
determining whether data from the input roll over sensor (204) of the electric two-wheeler is greater than the pre-determined roll over sensor data; and
triggering one or more SOS service notification if the data from the input roll over sensor (204) of the electric two-wheeler is greater than the pre-determined roll over sensor data.
11. The method (700) as claimed in claim 8, further comprising calculating acceleration data of the electric two-wheeler and relative acceleration data of the wheel corresponding to the electric two-wheeler from the collected Inertial Measurement Unit (IMU)sensor data.
12. The method (700) as claimed in claim 8, triggering, by the processor, the light emitting diode (LED) (216) and the buzzer (218) based on the artificial intelligence-based decision model result, the method (700) comprises:
determining the signal data of the ignition (210);
triggering OFF the light emitting diode (LED) (216) and the buzzer (218) if the signal data of the ignition (210) is OFF;
determining whether the relative acceleration data of the electric two-wheeler is lesser than relative acceleration data of the wheel corresponding to the electric two-wheeler; and
triggering ON the light emitting diode (LED) (216) and the buzzer (218) if the acceleration data of the electric two-wheeler is lesser than relative acceleration data of the wheel.

Dated this 09th day of June 2022

Vidya Bhaskar Singh Nandiyal
Patent Agent (IN/PA-2912)
Agent for applicant