FORM 2
THE PATENTS ACT, 1970
(39 of 1970)
&
5 THE PATENTS RULES, 2003
COMPLETE SPECIFICATION (See section 10, rule 13)
10
Title of the Invention:
“SYSTEM FOR GENERATING WARNINGS OF ROAD IMPEDIMENTS FOR VEHICLE AND METHOD THEREOF”
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APPLICANT(S) –
(a) Name : Minda Corporation Limited 20 (b) Nationality : Indian
(c) Address : Plot No: E-5/2, Chakan Industrial Area Phase III, MIDC,
Nanekarwadi, Tal:Khed, Dist-Pune, 410501, Maharashtra, India.
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The following specification particularly describes the nature of the invention and the manner in which it is to be performed.
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1

TECHNICAL FIELD [001] The present disclosure generally relates to the field of safety warning system for vehicles, and more particularly to system for generating warnings of road impediments for 5 vehicle and method thereof.
BACKGROUND [002] Various impediments such as potholes, speed breakers, and other hazardous objects may exist on roads, which may not be clearly visible to drivers while driving the vehicle at
10 night or at high speed or in bad weather conditions, that affects the safety of the rider. Existing pothole detection systems available to detect the impediments on the roads, are primarily targeted at four-wheelers or trucks. Further, the existing pothole detection system uses cameras to create a depth map for pothole detection. Particularly, the existing system may also use thermal sensors along with the camera, for surface temperature measurement, which may be
15 coupled with a driver assistance system to alert the driver. Furthermore, an advanced driver assistance systems (ADAS) based real-time transportation safety system uses road survey data and a cloud-based system to assist the drivers, along with implementing the camera.
[003] Therefore, current systems primarily focus on four-wheeled vehicles and rely on 20 cameras, yet they fail to ensure the safety of two-wheeled vehicles. Moreover, adapting these existing systems from four-wheelers to two-wheelers presents significant challenges due to the complexity of circuitry, multiple sensors, and cameras required, which are not easily accommodated in two-wheeled vehicles.
25 [004] Thus, there exists a need in the art for systems to enhance the safety of two-wheeler riders, which may provide early warning to the riders before encountering any impediment.
SUMMARY [005] The present disclosure overcomes one or more shortcomings of the prior art and 30 provides additional advantages discussed throughout the present disclosure. Additional features and advantages are realized through the techniques of the present disclosure. Other embodiments and aspects of the disclosure are described in detail herein and are considered a part of the claimed disclosure.
2

[006] In one non-limiting embodiment, the present disclosure discloses an apparatus to generate warnings for road impediments for a two-wheeled vehicle. Further, the apparatus operatively coupled to the vehicle. Particularly, the apparatus may comprise: a processing unit; and one or more sensors operatively coupled with the processing unit. Furthermore, the 5 processing unit may be configured to: monitor, using the one or more sensors, position of the two-wheeled vehicle along a road of travel; determine, using a trained machine learning (ML) model, whether one or more impediments exists along the road of travel within a predefined distance from the sensed position of the two-wheeled vehicle; and generate an early warning to alert a driver of the two-wheeled vehicle about the one or more impediments along the road 10 of travel upon determining that the one or more impediments exists thereon within the predefined distance from the position of the two-wheeled vehicle.
[007] In another non-limiting embodiment of the present disclosure, a method of generating a warning for road impediments for a two-wheeled vehicle. Particularly, the method
15 comprises: monitoring, by a processing unit using one or more sensors of the two-wheeled vehicle, position of the two-wheeled vehicle along a road of travel; determining by the processing unit using a trained machine learning (ML) model, whether one or more impediments exists along the road of travel within a predefined distance from the position of the two-wheeled vehicle; and generating, by the processing unit, an early warning to alert a
20 driver of the two-wheeled vehicle about the one or more impediments along the road of travel upon determining that the one or more impediments exist along the road of travel within the predefined distance from the position of the two-wheeled vehicle.
[008] The foregoing summary is illustrative only and is not intended to be in any way 25 limiting. In addition to the illustrative aspects, embodiments, and features described above, further aspects, embodiments, and features will become apparent by reference to the drawings and the following detailed description.
BRIEF DESCRIPTION OF DRAWINGS 30 [009] The embodiments of the disclosure itself, as well as a preferred mode of use, further objectives and advantages thereof, will best be understood by reference to the following detailed description of an illustrative embodiment when read in conjunction with the accompanying drawings. One or more embodiments are now described, by way of example only, with reference to the accompanying drawings in which:
3

[0010] FIG.1 illustrates an exemplary environment where techniques of the present disclosure may be implemented, in accordance with an embodiment of the present disclosure.
5 [0011] FIG.2 illustrates a block diagram of an alerting system, in accordance with an embodiment of the present disclosure.
[0012] FIG. 3 illustrates a block diagram of an alerting apparatus, in accordance with an embodiment of the present disclosure. 10
[0013] FIG. 4 illustrates a flowchart of a method for generating a warning for road impediments for a vehicle, in accordance with an embodiment of the present disclosure.
[0014] The figures depict embodiments of the disclosure for purposes of illustration only. 15 One skilled in the art will readily recognize from the following description that alternative embodiments of the structures and methods illustrated herein may be employed without departing from the principles of the disclosure described herein.
DETAILED DESCRIPTION
20 [0015] The foregoing has broadly outlined the features and technical advantages of the present disclosure in order that the detailed description of the disclosure that follows may be better understood. It should be appreciated by those skilled in the art that the conception and specific embodiment disclosed may be readily utilized as a basis for modifying or designing other structures for carrying out the same purposes of the present disclosure.
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[0016] Various embodiments of the present invention now will be described more fully hereinafter with reference to the accompanying drawings, in which some, but not all embodiments of the invention are shown. Indeed, the invention may be embodied in many different forms and should not be construed as limited to the embodiments set forth herein.
30 Rather, these embodiments are provided so that this disclosure will satisfy applicable legal requirements. The term “or” is used herein in both the alternative and conjunctive sense, unless otherwise indicated. The terms “illustrative,” “example,” and “exemplary” are used to be examples with no indication of quality level. Like numbers refer to like elements throughout.
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[0017] The phrases “in an embodiment,” “in one embodiment,” “according to one embodiment,” and the like generally mean that the particular feature, structure, or characteristic following the phrase may be included in at least one embodiment of the present disclosure and may be included in more than one embodiment of the present disclosure (importantly, such 5 phrases do not necessarily refer to the same embodiment).
[0018] The word “exemplary” is used herein to mean “serving as an example, instance, or illustration.” Any implementation described herein as “exemplary” is not necessarily to be construed as preferred or advantageous over other implementations.
10
[0019] If the specification states a component or feature “can,” “may,” “could,” “should,” “would,” “preferably,” “possibly,” “typically,” “optionally,” “for example,” “often,” or “might” (or other such languages) be included or have a characteristic, that particular component or feature is not required to be included or to have the characteristic. Such
15 component or feature may be optionally included in some embodiments, or it may be excluded.
[0020] The words “impediments” and “obstacles” are interchangeably used in the present
disclosure. The meaning of these words shall be construed as potholes, speed breakers, and
other hazardous objects that may exist on roads.
20
[0021] The words “driver” and “rider” are interchangeably used in the present disclosure.
The meaning of these words shall be construed as any person who is riding, driving or travelling
by the vehicle.
25 [0022] In an embodiment, the present disclosure includes an apparatus to generate warnings for road impediments for a vehicle (e.g., two-wheeled vehicle). Further, the apparatus is operatively coupled to the two-wheeled vehicle. Furthermore, the apparatus generates an early warning to alert a driver of the two-wheeled vehicle about the one or more impediments along the road of travel upon determining that the one or more impediments exists thereon within the
30 predefined distance from the position of the two-wheeled vehicle.
[0023] The present disclosure discloses a system that comprises the apparatus for generating early warnings for road impediments in order to improve vehicle driver safety, and particularly to improve two-wheeler driver safety. The system may utilize a machine-learning model to
5

determine whether there is any upcoming impediment on the road before the vehicle actually hits the impediments. Upon determination that there are one or more impediments on road within a predefined safe distance from the vehicle, the system may generate a warning to alert the driver about upcoming impediments on the road. 5
[0024] In a non-limiting embodiment, the system may generate different warnings based on the severity level of the impediments. The severity levels may be determined by the machine-learning model. The system may display the warning in form of audio, video, image, haptic, or any combination with the rider of the vehicle. In an embodiment, system may generate the
10 warning and display the warning to the driver in form of audio, video, image, haptic, or any combination. In another embodiment, the system may also automatically reduce the speed of the vehicle up to a safe driving speed limit such that the vehicle can safely cross the impediments. In another embodiment, the system may also limit the acceleration of the speed of the vehicle such that the speed of the vehicle cannot be increased near the impediments on
15 the road.
[0025] Referring now to the drawings, the detailed description set forth below in connection with the appended drawings is intended as a description of various configurations and is not intended to represent the only configurations in which the concepts described herein may be 20 practiced. The detailed description includes specific details for the purpose of providing a thorough understanding of various concepts with like numerals denoting like components throughout the several views. However, it will be apparent to those skilled in the art that these concepts may be practiced without these specific details.
25 [0026] FIG.1 illustrates an exemplary environment 100 where techniques of the present disclosure may be implemented, in accordance with an embodiment of the present disclosure. A skilled person would appreciate the fact that the illustrated environment 100 is for example and explanation purpose, and should not be considered as limiting the scope of the present disclosure. The present disclosure may be implemented in any other suitable environment
30 which may be different from the environment illustrated in FIG. 1.
[0027] As shown in FIG. 1, a vehicle 102 may be moving on road 104 which may have various kinds of impediments 106, where the vehicle 102 may be a two-wheeled vehicle as per the present disclosure, but not limiting to. In an embodiment, the impediments 106 may be
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construed as potholes, speed breakers, and other hazardous objects that may exist on roads. Further, the impediments 106 may be one or more impediments in same location. Furthermore, the impediments 106 may not be clearly visible to drivers while driving the vehicle during sunlight or at night or at high speed or in bad weather conditions, that affects the safety of the 5 rider, but not limited to. The vehicle 102 may comprise an alerting apparatus 108 for generating different warnings based on the severity level of the impediments. Further, the alerting apparatus 108 may display the warning/alert to the driver in form of audio, video, image, haptic, or any combination. A skilled person would appreciate the fact that there would be many other components in the vehicle 102 but those components are not discussed and explained herein 10 for sake of brevity.
[0028] FIG. 2 illustrates a detailed representation or block diagram of the exemplary alerting system 200, in accordance with an embodiment of the present disclosure. In an embodiment, the alerting system 200 may include the alerting apparatus 202 for generating the
15 early warning for one or more road impediments to improve safety of the driver of the two-wheeled vehicle 102. In an embodiment, the alerting system 200 may further include an instrument cluster 204 for displaying the warning/alert to the driver of the two-wheeled vehicle 102. Further, the instrument cluster 204 may display the warning signals to the driver on determining that the one or more impediments along the road of travel. Further, the instrument
20 cluster 204 may output the early warning to the driver of the two-wheeled vehicle 102, in form of audio, video, image, haptic, or any combination.
[0029] In an embodiment, the alerting system 200 may further include a user equipment 206 (e.g., smart phone, etc.) for transceiving data between the alerting apparatus 202 and the
25 instrument cluster 204. Thus, the user equipment 306, the alerting apparatus 202, and the instrument cluster 204 interacting with each other via a cloud interface 208, that may form the alerting system 200 as shown in FIG. 2, in accordance with an embodiment of the present disclosure, but not limited thereto. Further, an optional wireless interface may be provided to the alerting apparatus 202 to send the alert to user equipment 206 of the driver via
30 communication protocol (e.g., CAN interface, etc.) but not limited thereof. However, the alerting system 200 disclosed herein does not necessitate a need for a camera for generating the above warning.
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[0030] FIG. 3 illustrates a block diagram of an alerting apparatus 300, in accordance with an embodiment of the present disclosure. In an embodiment of the present disclosure, FIG. 3 provides a detailed representation of the alerting apparatus 300 that may be implemented in the vehicle 102 (e.g., two wheeled). Further, the alerting apparatus 300 may generate an early 5 warning for road impediments to improve safety of the driver of two-wheeled vehicle 102. The alerting apparatus 300 may include at least a sensor unit 302, a processing unit 308, and a memory unit 310, but not limited thereto. Further, the sensor unit 302 may comprise one or more sensors such as an impediment measuring sensor 304, a positioning sensor 306, but not limiting to. In an embodiment, the impediment measuring sensor 304 may be a six-axis MEMS
10 motion sensor, but not limited thereto. The six-axis MEMS motion sensor may measure various vehicle parameters, which are indicative of the crossing of an impediment by the vehicle 102. In another embodiment, the positioning sensor 306 may be a Global Navigation Satellite System (GNSS) sensor or Global Positioning Sensor (GPS) sensor, but not limited thereto, which may be used to determine location of the vehicle 102 in the environment 100. The sensor
15 unit 302 collect data through the one or more sensors, that may be stored in the memory unit 310, but not limited thereto.
[0031] In an embodiment of the present disclosure, the processing unit 308 may comprise one or more processors 312 along with internal memory 314. The processing unit 208 may also 20 comprise a Machine Learning (ML) model 316 that can self-learn using ML techniques like reinforced learning, and that may be used to evaluate the severity of such impediments. The functionality of the alerting apparatus 300 is explained in detail in the paragraphs below in conjunction with FIG. 1.
25 [0032] In an embodiment of the present disclosure, when the vehicle 102 approaches impediment 106 for the first time, the alerting apparatus 300 may detect the impediment 106 on the road 104 using the impediment measuring sensor 304. Whenever the vehicle crosses any impediment such as a pothole, speed breaker or any other obstacle, there may be a change in one or more vehicle parameters such as acceleration, horizontal and/or vertical displacement
30 and angular position of the vehicle, but not limited to. Particularly, the six-axis MEMS motion sensor of the impediment measuring sensor 304 may be used to measure the one or more vehicle parameters, but not limited to.
[0033] Particularly, when the vehicle approaches the impediment for the first time there will 35 be no alert generated by the alerting apparatus 300, as there may not be any impediment data
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saved in the memory unit 310 about that impediment. As soon as the vehicle crosses the impediment, there may be changes in the one or more vehicle parameters (e.g., acceleration, horizontal and/or vertical displacement, angular motion, etc.) of the vehicle 102. Further, the impediment sensor 304 may detect the changes in the one or more vehicle parameters and may 5 relate intensity of the detected change corresponding to severity of the impediment on the road 104, i.e., the more the change, the higher the severity. In an embodiment, the one or more vehicle parameters may include motion related data that are indicative of the change in the vehicle parameters. Further, the processing unit 308 may use the motion related data stored in memory unit 310 to train the ML model 316 using self-learning techniques like reinforced 10 learning. The ML model 316 may be used to evaluate the severity of the impediment based on the changes in the vehicle parameters, but not limited thereof.
[0034] In an embodiment, the motion related data indicative of the change in the one or more vehicle parameters may be stored in the memory unit 310 along with the location of the 15 impediment on the road 104 and severity of corresponding impediment on the road 104. Further, the location of the impediment is determined by the GNSS / GPS sensor of the positioning sensor 306 of the alerting apparatus, but not limited thereof.
[0035] In an embodiment of the present disclosure, the alerting apparatus 300 comprising 20 the processing unit 308 may encompass the ML model 316 that may be trained using the motion related data stored in the memory unit 310. Further, based on the motion related data along with the location of the impediment and severity of the impediment on the road 104, the alerting apparatus 300 may generate the warning /alert signal for the driver of the vehicle 102 in future.
25 [0036] In an embodiment, the present disclosure includes an alerting apparatus 300 to generate warnings for road impediments for a two-wheeled vehicle. Further, the alerting apparatus 300 is operatively coupled to the two-wheeled vehicle. Furthermore, the alerting apparatus 300 comprises a processing unit; and one or more sensors operatively coupled with the processing unit.
30
[0037] In an embodiment, the alerting apparatus 300 may monitor, using the one or more sensors, position of the two-wheeled vehicle along a road of travel. The one or more sensors may be selected from a group of: impediment measuring sensor 304 and positioning sensor 306. Further, the positioning sensor 306 may continuously monitor the position of the vehicle
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and record the position of the vehicle as vehicle position data for further processing, but not limited to. In an embodiment, the position of the vehicle may be continuously monitored or at a regular time interval or after the vehicle travels a predefined distance. In an embodiment, the data recorded by the impediment measuring sensor 304 and the positioning sensor 306 may be 5 transmitted to a road transport authority (RTA) to evaluate the insurance value based on the road conditions (i.e., the potholes, impediments, speed bumps/ breakers, or any obstacles while commuting), and driving record.
[0038] In an embodiment, the alerting apparatus 300 may recite processing the vehicle position 10 data by a machine learning (ML) model 316 of the processing unit 308. Further, the ML model 316 is trained based on at least one of: real-time data (e.g., vehicle position data) when the two-wheeled vehicle 102 is in motion, and data related to impediments on roads stored in a cloud database, wherein the data stored in the cloud database is shared by other two-wheeled vehicles. Furthermore, the ML model 316 is also trained to detect, based on the vehicle position data, 15 whether any impediments exist within a predefined distance from the vehicle 102. In another embodiment, the ML model 316 may be trained based on real-time data when the vehicle is in motion. In yet another embodiment, the ML model 316 may be trained using data related to impediments on roads stored in a cloud database 320, which is shared by other vehicles; but not limited to. Thus, whenever the vehicle may happen to travel on the same road in future, the 20 ML model 316 may process the vehicle position data to determine whether any impediment on the road within a predefined safe distance to the vehicle exists or not.
[0039] In an embodiment, the alerting apparatus 300 may comprise to determine, using the
trained ML model 316, whether one or more impediments exists along the road of travel within
25 the predefined distance from the sensed position of the two-wheeled vehicle. The sensed
position of the vehicle 102 may correspond to the vehicle position data, sensed by the one or
more sensors, but not limiting to. The alerting apparatus 300 may utilize the ML model 316 to
determine whether there is any upcoming impediment on the road before the vehicle actually
hits the impediments.
30
[0040] In an embodiment, the alerting apparatus 300 may comprise to generate an early
warning to alert the driver of the two-wheeled vehicle 102 about the one or more impediments
along the road of travel upon determining that the one or more impediments exists thereon
within the predefined distance from the position of the two-wheeled vehicle. Upon
10

determination that there are one or more impediments on road within a predefined safe distance from the vehicle, the alerting apparatus 300 may generate the warning to alert the driver about upcoming impediments on the road 104. Particularly, the alerting apparatus 300 may generate early warnings for road impediments to improve vehicle driver safety, and specifically to 5 improve two-wheeled driver safety.
[0041] In an embodiment, the processing unit 308 is configured to generate the early warning to alert the driver of the two-wheeled vehicle based on a severity level of the one or more impediments. Particularly, upon determining that the impediment 106 exists near the vehicle 10 position on the road 104, the ML model 316 may provide input to the processing unit 308 to generate the warning/alert for the driver.
[0042] In an embodiment, the alerting apparatus 300 may display the early warning message using the instrument cluster 204 that may be fitted on the vehicle 102, to alert the driver about
15 upcoming impediments. The warning message may be in any form that may be predefined and is modifiable. In another embodiment, the warning may be provided in any form such as audio, video, image, audio-visual, haptic feedback, or a combination thereof, but not limited thereto. In another embodiment, the alerting apparatus 300 may generate different warnings depending on the severity of the upcoming impediment.
20
[0043] In an embodiment, an optional wireless interface may be provided to the alerting apparatus 300 to send the alert to the user equipment 206 of the driver, but not limited thereof. Further, the alerting apparatus 300 disclosed herein does not necessitate a need for a camera for generating the warning signals that improves vehicle driver safety, but not limited to.
25
[0044] In another embodiment, the alerting apparatus 300 may output the early warning message via the instrument cluster 204 of the two-wheeled vehicle 102. Further, the early warning message may be displayed on the instrument cluster 204 of the vehicle to alert the driver about upcoming impediments. The warning may be in any form that may be predefined
30 and modifiable.
[0045] In an embodiment, the alerting apparatus 300 may also share information related to the detected impediment with the cloud database 208 via the user equipment 206. Further, the shared information may be also accessed by other vehicles to train their ML models. Such
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information sharing improves the efficiency of the system and further improves driver safety, because the impediments detected by one vehicle may be used to alert the driver of another vehicle, which may be travelling on the road 104 for the first time. In other words, such information sharing may result in the generation of the warning even on the new roads, thereby 5 achieving higher rider safety.
[0046] In an embodiment, the alerting apparatus 300 may automatically reduce speed of the two-wheeled vehicle to a safe driving speed limit to safely cross the one or more impediments, upon determining that the one or more impediments exists on the road within the predefined 10 distance from the position of the two-wheeled vehicle. Particularly, the processing unit 308 may also automatically reduce the speed of the vehicle 102 up to a safe driving speed limit such that the vehicle 102 may safely cross the impediments.
[0047] In another embodiment, the processing unit of the alerting apparatus 300 may control 15 acceleration of the two-wheeled vehicle by restricting increase of the speed of the two-wheeled vehicle beyond the safe driving speed limit. Particularly, the processing unit 308 may also limit the acceleration of the vehicle such that the speed of the vehicle cannot be increased near the impediments on the road. Further, the processing unit is further configured to control acceleration of the two-wheeled vehicle by restricting any increase in the speed of the two-20 wheeled vehicle beyond the safe driving speed limit. In an embodiment, the alerting apparatus 300 may comprise reducing the speed and/or acceleration of the vehicle up to a safe limit such that the vehicle can safely cross the impediments.
[0048] In an embodiment, whenever an impediment is detected, the ML model 316 may be 25 trained using the motion related data so that accurate warnings may be generated for the driver of the vehicle 102. In this manner, the early warning may be automatically generated while approaching the impediments on the road by the driver of the vehicle 102. Further, the ML model 316 is trained to detect the severity of the impediment.
30 [0049] In an aspect, it is possible that the impediment 106 that was previously detected by the system 200 on the road 104 might no longer be present. In such a situation, the ML model 316 may continue to learn that the impediment is no longer present on the road 104 and the warning for that impediment may not be generated in the next iteration of travel.
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[0050] In an embodiment of the present disclosure, the alerting system 200 described herein does not require a separate camera unit for generating the alert. Hence, the alerting system 200 may be available at a lower cost in comparison, and it also consumes less power and requires minimal space due to inexistence the camera. Therefore, the present disclosure discloses a low-5 cost solution using the six-axis MEMS motion sensor alongside the GNSS sensor, and a limited memory with the processing unit running ML algorithms to train the ML model 316 on the ride, and therefore does not require a prior survey. Further, the disclosed system may be built within the tell tales of the existing instrument cluster, therefore not necessitating any new equipment to be mounted in the vehicle, like a camera, IR sensor or a cloud database.
10
[0051] In embodiment of the present disclosure, the alerting apparatus 300 is implemented on
the vehicle 102, where the vehicle 102 may be a two-wheeled vehicle, three-wheeled vehicle,
four-wheeled vehicle, but not limiting to.
15 [0052] Fig. 4 illustrates a flowchart of a method for generating a warning for road impediments for a vehicle i.e., two-wheeled, in accordance with an embodiment of the present disclosure. The method 400 may be described in the general context of computer-executable instructions. Generally, computer-executable instructions may include routines, programs, objects, components, data structures, procedures, modules, and functions, which perform specific
20 functions or implement specific abstract data types.
[0053] The order in which method 400 is described is not intended to be construed as a limitation, and any number of described method blocks may be continued in any order to implement the method. Additionally, individual blocks may be deleted from the methods 25 without departing from the spirit and scope of the subject matter.
[0054] At step 402, the method recites monitoring, by a processing unit using one or more sensors of the two-wheeled vehicle, position of the two-wheeled vehicle along a road of travel. In an embodiment, the method 400 may recite monitoring the position of the vehicle that may 30 be obtained by the positioning sensor 306 of the alerting apparatus 300. In an embodiment, the position of the vehicle may be continuously monitored or at a regular time interval or after the vehicle travels a predefined distance.
[0055] In an embodiment, the method 400 recites processing the position information by a 35 machine learning (ML) model 316, where the ML model 316 is trained to detect the severity
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of the impediment. Further, the ML model 316 is trained based on at least one of: real-time
data when the two-wheeled vehicle is in motion, and data related to impediments on roads
stored in a cloud database 208, wherein the data stored in the cloud database 208 is shared by
other two-wheeled vehicles. Furthermore, the ML model 316 is also trained to detect, based on
5 the vehicle's position, whether any impediments exist within a predefined distance from the
vehicle. In another embodiment, the ML model 316 may be trained based on real-time data
when the vehicle is in motion. In yet another embodiment, the ML model 316 may be trained
using data related to impediments on roads stored in a cloud database 208, which is shared by
other vehicles; but not limited to.
10
[0056] At step 404, the method 400 comprises determining, by the processing unit using a
trained machine learning (ML) model, whether one or more impediments exists along the road
of travel within a predefined distance from the position of the two-wheeled vehicle. In an
embodiment, the method 400 recites determining whether the impediment is present on the
15 road of travel within the predefined distance from the vehicle position, based on the vehicle
position.
[0057] At step 406, the method 400 comprises generating, by the processing unit, an early warning to alert a driver of the two-wheeled vehicle about the one or more impediments along 20 the road of travel upon determining that the one or more impediments exist along the road of travel within the predefined distance from the position of the two-wheeled vehicle. In an embodiment, the method recites, upon determining that the impediments exist on the road, generating the warning to alert the driver.
25 [0058] In another embodiment, the method 400 comprises generating the early warning to alert the driver of the two-wheeled vehicle based on a severity level of the one or more impediments. In another embodiment, the method 400 comprises outputting the early warning via an instrument cluster in the two-wheeled vehicle, where the early warning message may be displayed on instrument cluster 108 of the vehicle to alert the driver about upcoming
30 impediments. The warning may be in any form that may be predefined and modifiable.
[0059] In another embodiment, the warning may be provided in any form such as audio, video,
image, audio-visual, haptic feedback, or a combination thereof, but not limited thereto. In
another embodiment, different warnings may be generated depending on the severity of the
35 upcoming impediment. In an embodiment, an optional wireless interface may be provided to
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the alerting apparatus 300 to send the alert to user equipment (e.g., smartphone, etc.) of the driver of the two-wheeled vehicle.
[0060] In an embodiment, the method 400 comprises, upon determining that the one or more 5 impediments exists on the road within the predefined distance from the position of the two-wheeled vehicle, automatically reducing speed of the two-wheeled vehicle to a safe driving speed limit to safely cross the one or more impediments. In another embodiment, the method 400 comprises, controlling acceleration of the two-wheeled vehicle by restricting any increase in the speed of the two-wheeled vehicle beyond the safe driving speed limit. In an embodiment, 10 the method 400 comprises, reducing the speed and/or acceleration of the vehicle up to a safe limit such that the vehicle can safely cross the impediments.
[0061] In an embodiment, the one or more sensors that are selected from a group of: impediment measuring sensor 304 and positioning sensor 306. In an embodiment, the
15 impediment measuring sensor 304 may be a six-axis MEMS motion sensor, but not limited thereto. Further, the six-axis MEMS motion sensor may measure various vehicle parameters, which are indicative of the crossing of an impediment by the vehicle. In another embodiment, the positioning sensor 306 may be a Global Navigation Satellite System (GNSS) sensor or Global Positioning Sensor (GPS) sensor, but not limited thereto, which may be used to
20 determine location of the vehicle 102 in the environment 100. The sensor unit 302 collect data through the one or more sensors, that may be stored in the memory 310 but not limiting to.
[0062] In an embodiment of the present disclosure, when the vehicle 102 approaches impediment 106 for the first time, the method 400 comprises detecting, by the alerting
25 apparatus 300, the impediment 106 on the road 104 using the impediment measuring sensor 304. Whenever the vehicle crosses any impediment such as a pothole, speed breaker or any other obstacle, there may be a change in various vehicle parameters such as acceleration, horizontal and/or vertical displacement and angular position of the vehicle, but not limited to. Particularly, the six-axis MEMS motion sensor of the impediment measuring sensor 304 may
30 be used to measure the various vehicle parameters.
[0063] In an embodiment, when the vehicle approaches the impediment for the first time there
will be no alert displayed on the instrument cluster 108, as there may not be any impediment
data saved in the memory unit 310 about that impediment. As soon as the vehicle crosses the
35 impediment there may be changes in the vehicle parameters and the impediment sensor may
15

detect the changes in the vehicle parameters and may relate intensity of detected change corresponding to severity of the impediment on the road 104, i.e., the more the change, the higher the severity. In an embodiment, the vehicle parameters may include motion related data that are indicative of the change in the vehicle parameters, to be stored in the memory unit 310 5 along with the location of the impediment and severity of the impediment, where the location of the impediment is determined by the GNSS / GPS sensor of the positioning sensor 306, but not limited thereof.
[0064] In this manner, the early warning may be automatically generated while approaching 10 the impediments on the road, thereby the safety of the driver/rider is enhanced.
[0065] Although the present disclosure explains the system, apparatus and method for early warning for road impediments considering the two-wheeler, however, a skilled person would appreciate the fact the teachings of the present disclosure may be easily extended to any kind 15 of vehicle.
[0066] The hardware used to implement the various illustrative logics, logical blocks, modules, and circuits described in connection with the aspects disclosed herein may include a general-purpose processor, a digital signal processor (DSP), a special-purpose processor such as an
20 application-specific integrated circuit (ASIC) or a field programmable gate array (FPGA), a programmable logic device, discrete gate or transistor logic, discrete hardware components, or any combination thereof designed to perform the functions described herein. A general-purpose processor may be a microprocessor, but, in the alternative, the processor may be any conventional processor, controller, microcontroller, or state machine. A processor may also be
25 implemented as a combination of computing devices, e.g., a combination of a DSP and a microprocessor, a plurality of microprocessors, one or more microprocessors in conjunction with a DSP core, or any other such configuration. Alternatively or additionally, some steps or methods may be performed by circuitry that is specific to a given function.
30 [0067] In one or more example embodiments, the functions described herein may be implemented by special-purpose hardware or a combination of hardware programmed by firmware or other software. In implementations relying on firmware or other software, the functions may be performed as a result of execution of one or more instructions stored on one or more non-transitory computer-readable media and/or one or more non-transitory processor-35 readable media. These instructions may be embodied by one or more processor-executable
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software modules that reside on the one or more non-transitory computer-readable or processor-readable storage media. Non-transitory computer-readable or processor-readable storage media may in this regard comprise any storage media that may be accessed by a computer or a processor. By way of example but not limitation, such non-transitory computer-5 readable or processor-readable media may include random access memory (RAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), FLASH memory, disk storage, magnetic storage devices, or the like. Disk storage, as used herein, includes compact disc (CD), laser disc, optical disc, digital versatile disc (DVD), floppy disk, and Blu-ray disc™, or other storage devices that store data magnetically or optically with 10 lasers. Combinations of the above types of media are also included within the scope of the terms non-transitory computer-readable and processor-readable media. Additionally, any combination of instructions stored on the one or more non-transitory processor-readable or computer-readable media may be referred to herein as a computer program product.
15 [0068] Many modifications and other embodiments of the inventions set forth herein will come to mind to one skilled in the art to which these inventions pertain having the benefit of teachings presented in the foregoing descriptions and the associated drawings. Although the figures only illustrate certain components of the apparatus and systems described herein, it is understood that various other components may be used in conjunction with the supply management system.
20 Therefore, it is to be understood that the inventions are not to be limited to the specific embodiments disclosed and that modifications and other embodiments are intended to be included within the scope of the appended claims. Moreover, the steps in the method described above may not necessarily occur in the order depicted in the accompanying diagrams, and in some cases one or more of the steps depicted may occur substantially simultaneously, or
25 additional steps may be involved. Although specific terms are employed herein, they are used in a generic and descriptive sense only and not for purposes of limitation.
[0069] The various illustrative logical blocks, modules, circuits, and algorithm steps described in connection with the embodiments disclosed herein may be implemented as electronic 30 hardware, computer software, or combinations of both. To clearly illustrate this interchangeability of hardware and software, various illustrative components, blocks, modules, circuits, and steps have been described above generally in terms of their functionality. Whether such functionality is implemented as hardware or software depends upon the particular application and design constraints imposed on the overall system. Skilled artisans may
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implement the described functionality in varying ways for each particular application, but such implementation decisions should not be interpreted as causing a departure from the scope of the present invention.
5 10 15 20 25 30 35 40 45
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WE CLAIM:
1. An apparatus to generate warnings for road impediments for a two-wheeled vehicle,
said apparatus operatively coupled to the two-wheeled vehicle, said apparatus
5 comprising:
a processing unit; and
one or more sensors operatively coupled with the processing unit, wherein the processing unit is configured to:
monitor, using the one or more sensors, position of the two-wheeled
10 vehicle along a road of travel;
determine, using a trained machine learning (ML) model, whether one or more impediments exists along the road of travel within a predefined distance from the sensed position of the two-wheeled vehicle; and
generate an early warning to alert a driver of the two-wheeled vehicle
15 about the one or more impediments along the road of travel upon determining
that the one or more impediments exists thereon within the predefined distance from the position of the two-wheeled vehicle.
2. The apparatus of claim 1, wherein upon determining that the one or more impediments
20 exists on the road within the predefined distance from the position of the two-wheeled
vehicle, the processing unit is further configured to:
automatically reduce speed of the two-wheeled vehicle to a safe driving speed limit to safely cross the one or more impediments; and
control acceleration of the two-wheeled vehicle by restricting increase of the
25 speed of the two-wheeled vehicle beyond the safe driving speed limit.
3. The apparatus of claim 1, wherein the one or more sensors is selected from a group of:
impediment measuring sensor and positioning sensor.
30 4. The apparatus of claim 1, wherein the ML model is trained based on at least one of:
real-time data when the two-wheeled vehicle is in motion, and data related to impediments on roads stored in a cloud database, wherein the data stored in the cloud database is shared by other two-wheeled vehicles.

5. The apparatus of claim 1, wherein the processing unit is configured to:
generate the early warning to alert the driver of the two-wheeled vehicle based on a severity level of the one or more impediments; and
output the early warning via an instrument cluster in the two-wheeled vehicle. 5
6. A method of generating a warning for road impediments for a two-wheeled vehicle, the
method comprises:
monitoring, by one or more sensors of the two-wheeled vehicle, position of the
two-wheeled vehicle along a road of travel;
10 determining by a processing unit using a trained machine learning (ML) model,
whether one or more impediments exists along the road of travel within a predefined distance from the position of the two-wheeled vehicle; and
generating, by the processing unit, an early warning to alert a driver of the two-
wheeled vehicle about the one or more impediments along the road of travel upon
15 determining that the one or more impediments exist along the road of travel within the
predefined distance from the position of the two-wheeled vehicle.
7. The method of claim 6, wherein upon determining that the one or more impediments
exists on the road within the predefined distance from the position of the two-wheeled
20 vehicle, the method further comprises:
automatically reducing speed of the two-wheeled vehicle to a safe driving speed limit to safely cross the one or more impediments; and
controlling acceleration of the two-wheeled vehicle by restricting increase of the speed of the two-wheeled vehicle beyond the safe driving speed limit. 25
8. The method of claim 6, wherein the one or more sensors are selected from a group of:
impediment measuring sensor and positioning sensor.
9. The method of claim 6, wherein the ML model is trained based on at least one of: real-
30 time data when the two-wheeled vehicle is in motion, and data related to impediments
on roads stored in a cloud database, wherein the data stored in the cloud database is shared by other two-wheeled vehicles.
10. The method of claim 6, further comprising:
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generating the early warning to alert the driver of the two-wheeled vehicle based on a severity level of the one or more impediments; and
outputting the early warning via an instrument cluster in the two-wheeled vehicle.