Description:SYSTEM AND METHOD FOR QUANTIFYING RIDER SAFETY LEVEL
TECHNICAL FIELD
[0001] The present subject matter generally relates to vehicle safety. More 5 particularly, but not exclusively to a system and method for quantifying rider safety level.
BACKGROUND
[0002] A landscape of vehicle safety has seen significant advancements over 10 years, with a focus on mitigating risks associated with collisions and accidents. Traditional safety systems often rely on detecting changes in velocity to identify collision events. However, such systems have limitations, particularly in detecting minor collisions where a transferred force may not be significant enough to trigger alerts. 15
[0003] Existing safety systems primarily rely on force thresholds to detect collisions. However, they often fail to detect minor collisions, such as low-speed rear-endings or sideswipes, where the impact force may not exceed predefined thresholds. Conventional systems lack an ability to assess specific safety thresholds for individual riders. Riders are often unaware of their safety 20 levels during the course of a ride. Thus, the riders may unknowingly exceed safe limits, increasing the risk of accidents and injuries.
[0004] Conventional safety systems are unable to provide insights into optimizing vehicle handling based on rider safety thresholds. Without this information, vehicle suspension systems, handlebars, and seating 25 arrangements may not be optimized to minimize discomfort and enhance passenger safety during sudden changes in speed or direction.
[0005] Vehicle manufacturers design safety features such as airbags, crumple zones, and seat belts to mitigate the impact of collisions. Safety features are designed based on generalized assumptions and may not effectively address 30 the specific needs or characteristics of individual riders. Vehicle safety
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features provide passive protection during collisions but do not offer real-time feedback to riders to prevent accidents or adjust driving behaviour [0006] Riders receive education and training on safe riding practices, defensive driving techniques, and the importance of following traffic rules. Rider education relies on the rider's ability to recognize and respond to 5 potential risks, which may vary depending on factors such as experience level and situational awareness. Once riders complete their training, there is limited ongoing monitoring or feedback on their adherence to safe riding practices.
[0007] Therefore, there is a need in the art for a system and method for emergency vehicle notification which addresses at least the aforementioned 10 problems and other problems of known art.
[0008] Further limitations and disadvantages of conventional and traditional approaches will become apparent to one of skill in the art, through comparison of described systems with some aspects of the present disclosure, as set forth in the remainder of the present application and with reference to 15 the drawings.
SUMMARY OF THE INVENTION
[0009] According to embodiments illustrated herein, the present invention provides a system and method for quantifying rider safety level. In one aspect, 20 the present invention provided the method for quantifying rider safety level. The method comprises determining, by a processor, a speed of a vehicle. The method comprises comparing, by the processor, the determined speed of the vehicle with a predefined speed limit. The method comprises receiving, by the processor, first information associated with the vehicle from an IMU 25 sensor. Herein, the first information comprises acceleration data, location data, and orientation data of the vehicle. The method comprises determining, by the processor, one or more forces acting on a rider of the vehicle based on the received first information. The method comprises comparing, by the processor, each of the determined one or more forces with a predefined 30 threshold associated with each of the one or more forces. The method
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comprises determining a value based on the comparison. Herein, the value is indicative of the rider safety level. The method comprises providing, by the processor, the determined value to a rider of the vehicle. [00010] In one aspect, the present invention provided the system for quantifying rider safety level. The system comprises an inertial measurement 5 unit (IMU) associated with a vehicle. Herein, the IMU is configured to determine acceleration data and orientation data of the vehicle. The system comprises a location sensor configured to determine location data of the vehicle. The system comprises a processor configured to comparing, by the processor, a speed of the vehicle with a predefined speed limit. The processor 10 is configured to determine, by the processor, one or more forces acting on a rider of the vehicle based on first information. Herein, the first information comprises the determined acceleration data, the determined orientation data, and the determined location data. The processor is configured to compare, by the processor, each of the determined one or more forces with a predefined 15 threshold associated with each of the one or more forces. The processor is configured to determine a value based on the comparison, wherein the value is indicative of the rider safety level. The processor is configured to provide, by the processor, the determined value to a rider of the vehicle.
[00011] It is to be understood that both the foregoing general description and 20 the following detailed description are exemplary and explanatory only and are not restrictive of the invention, as claimed.
BRIEF DESCRIPTION OF THE DRAWINGS
[00012] The details are described with reference to an embodiment of a 25 system and a method for quantifying rider safety level along with the accompanying diagrams. The same numbers are used throughout the drawings to reference similar features and components.
[00013] Figure 1 exemplarily illustrates a system for quantifying rider safety level, in accordance with an embodiment of the present disclosure. 30
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[00014] Figure 2 exemplarily illustrates a flowchart of a method for quantifying rider safety level, in accordance with an embodiment of the present disclosure.
DETAILED DESCRIPTION
[00015] Exemplary embodiments are described with reference to the 5 accompanying drawings. Wherever convenient, the same reference numbers are used throughout the drawings to refer to the same or like parts. While examples and features of disclosed principles are described herein, modifications, adaptations, and other implementations are possible without departing from the scope of the disclosed embodiments. It is intended that the 10 following detailed description be considered as exemplary only, with the true scope and spirit being indicated by the following claims.
[00016] The terms “an embodiment”, “embodiment”, “embodiments”, “the embodiment”, “the embodiments”, “one or more embodiments”, “some embodiments”, and “one embodiment” mean “one or more (but not all) 15 embodiments of the invention(s)” unless expressly specified otherwise. The terms “including”, “comprising”, “having” and variations thereof mean “including but not limited to”, unless expressly specified otherwise. The terms “a”, “an” and “the” mean “one or more”, unless expressly specified otherwise. 20
[00017] The embodiments of the present invention will now be described in detail with reference to a system and a method for quantifying rider safety level with the accompanying drawings. However, the present invention is not limited to the present embodiments. The present subject matter is further described with reference to accompanying figures. It should be noted that the 25 description and figures merely illustrate principles of the present subject matter. Various arrangements may be devised that, although not explicitly described or shown herein, encompass the principles of the present subject matter. Moreover, all statements herein reciting principles, aspects, and examples of the present subject matter, as well as specific examples thereof, 30 are intended to encompass equivalents thereof.
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[00018] A person with ordinary skills in the art will appreciate that the systems, modules, and sub-modules have been illustrated and explained to serve as examples and should not be considered limiting in any manner. It will be further appreciated that the variants of the above disclosed system elements, modules, and other features and functions, or alternatives thereof, 5 may be combined to create other different systems or applications.
[00019] The present subject matter is described using the system and the method for quantifying rider safety level, whereas the claimed subject matter can be used in any other type of application employing above-mentioned method for quantifying rider safety level, with required changes and without 10 deviating from the scope of invention. Further, it is intended that the disclosure and examples given herein be considered as exemplary only.
[00020] An objective of the present invention is to provide a method for quantifying rider safety level. The method comprises determining, by a processor, a speed of a vehicle. The method comprises comparing, by the 15 processor, the determined speed of the vehicle with a predefined speed limit. The method comprises receiving, by the processor, first information associated with the vehicle from an IMU sensor. Herein, the first information comprises acceleration data, location data, and orientation data of the vehicle. The method comprises determining, by the processor, one or more forces 20 acting on a rider of the vehicle based on the received first information. The method comprises comparing, by the processor, each of the determined one or more forces with a predefined threshold associated with each of the one or more forces. The method comprises determining a value based on the comparison. Herein, the value is indicative of the rider safety level. The 25 method comprises providing, by the processor, the determined value to a rider of the vehicle.
[00021] Another objective of the present invention is to provide a system for quantifying rider safety level. The system comprises an inertial measurement unit (IMU) associated with a vehicle. Herein, the IMU is configured to 30 determine acceleration data and orientation data of the vehicle. The system
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comprises a location sensor configured to determine location data of the vehicle. The system comprises a processor configured to comparing, by the processor, a speed of the vehicle with a predefined speed limit. The processor is configured to determine, by the processor, one or more forces acting on a rider of the vehicle based on first information. Herein, the first information 5 comprises the determined acceleration data, the determined orientation data, and the determined location data. The processor is configured to compare, by the processor, each of the determined one or more forces with a predefined threshold associated with each of the one or more forces. The processor is configured to determine a value based on the comparison, wherein the value 10 is indicative of the rider safety level. The processor is configured to provide, by the processor, the determined value to a rider of the vehicle. [00022] It may be appreciated that traditional safety systems often rely on detecting changes in velocity to identify collision events. However, such systems have limitations, particularly in detecting minor collisions where a 15 transferred force may not be significant enough to trigger alerts. Existing safety systems primarily rely on force thresholds to detect collisions. However, they often fail to detect minor collisions, such as low-speed rear-endings or sideswipes, where the impact force may not exceed predefined thresholds. Conventional systems lack an ability to assess specific safety 20 thresholds for individual riders. Riders are often unaware of their safety levels during the course of a ride. Thus, the riders may unknowingly exceed safe limits, increasing the risk of accidents and injuries.
[00023] In order to mitigate the aforesaid issues, disclosed is method and the system for quantifying rider safety level. The method comprises determining, 25 by a processor, a speed of a vehicle.
[00024] The method comprises comparing, by the processor, the determined speed of the vehicle with a predefined speed limit. The method comprises receiving, by the processor, first information associated with the vehicle from an IMU sensor. Herein, the first information comprises acceleration data, 30 location data, and orientation data of the vehicle.
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[00025] In an embodiment, the acceleration data of the vehicle comprises a first acceleration along a first axis, a second acceleration along a second axis, and a third acceleration along a third axis. Herein, the first acceleration along the first axis corresponds to roll, the second acceleration along the second axis corresponds to pitch, and the third acceleration along the third axis 5 corresponds to yaw.
[00026] In an embodiment, the first information comprises magnetometer data. Herein, the magnetometer data comprises a first magnetometer reading along the first axis, second magnetometer reading along the second axis, and a third magnetometer reading along the third axis. 10
[00027] In an embodiment, the first information associated with the vehicle comprise information associated with a road topography of a route followed by the vehicle. The method comprises determining, by the processor, one or more forces acting on a rider of the vehicle based on the received first information. In an embodiment, the one or more forces is at least one of: a 15 gravitation force, a wind force, an impact force. In an embodiment, the one or more forces is determined along the first axis, the second axis, and the third axis.
[00028] The method comprises comparing, by the processor, each of the determined one or more forces with a predefined threshold associated with 20 each of the one or more forces. In an embodiment, the method comprises receiving, by the processor, historical driving pattern information associated with the rider of the vehicle. The method comprises receiving, by the processor, body proportion information corresponding to a height and a weight of the rider of the vehicle. The method comprises receiving, by the 25 processor, historical reaction time information associated with the rider of the vehicle. The method comprises applying, by the processor, a machine learning model on the received historical driving pattern information, the received body proportion information, and the received historical reaction time information. Herein, the predefined threshold associated with each of the 30
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one or more forces is determined based on the application of the machine learning model. [00029] The method comprises determining a value based on the comparison. Herein, the value is indicative of the rider safety level. The method comprises providing, by the processor, the determined value to the rider of the vehicle. 5
[00030] In an embodiment, the method comprises determining, by the processor, a crash state of the vehicle based on a speed of the vehicle in a predefined time duration and inputs of one or more sensors. Herein, the one or more sensors comprises at least one of: an accelerometer, a gyroscope, an impact sensor, a wheel speed sensor, a vehicle speed sensor, or an ultrasonic 10 sensor. The method comprises transmitting, by the processor, one or more notifications to one or more user devices associated with one or more emergency contacts of the vehicle based on the determination that the crash state of the vehicle is a positive state.
[00031] In an embodiment, the method comprises determining, by the 15 processor, whether the value indicative of the rider safety level is greater than a first threshold level. The method comprises notifying, by the processor, the rider about the value based on the determination that the value indicative of the rider safety level is greater than the first threshold level.
[00032] In an embodiment, the method comprises determining, by the 20 processor, whether the value indicative of the rider safety level is greater than a second threshold level. The method comprises controlling, by the processor, an acceleration and braking mechanism of the vehicle if the determined value is greater than the second threshold level.
[00033] In an embodiment, the method comprises rendering the value 25 indicative of the rider safety level on an audio device and/or a display device associated with the vehicle, wherein the audio device is a speaker of a head wearable device.
[00034] In an embodiment, the system for quantifying a rider safety level is provided. The system comprises an inertial measurement unit (IMU) 30 associated with a vehicle. Herein, the IMU is configured to determine
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acceleration data and orientation data of the vehicle. The system comprises a location sensor configured to determine location data of the vehicle. The system comprises a processor configured to compare a speed of the vehicle with a predefined speed limit. The processor is configured to determine one or more forces acting on a rider of the vehicle based on first information. 5 Herein, the first information comprises the determined acceleration data, the determined orientation data, and the determined location data. The processor is configured to compare each of the determined one or more forces with a predefined threshold associated with each of the one or more forces. The processor is configured to determine a value based on the comparison, 10 wherein the value is indicative of the rider safety level. The processor is configured to provide the determined value to a rider of the vehicle. [00035] In an embodiment, the system comprises one or more force sensors positioned on a handlebar of the vehicle. Herein, the one or more force sensor is configured to determine information associated with force applied by the 15 user, information associated with a grip of the rider, and information associated with steering preferences for the rider. Herein, the predefined threshold associated with each of the one or more forces is dynamically varied based on the determined information associated with force applied by the rider, the determined information associated with the grip of the rider, and the 20 determined information associated with steering preferences for the rider.
[00036] In an embodiment, the system comprises a height sensor configured to determine a height of the user in a seated position and a weight sensor configured to measure a weight of the user, wherein the height sensor and/or the weight sensor is positioned at a first region corresponding to a seat, or a 25 second region corresponding to a footrest area of the vehicle. In an example, the height sensor may be a camera, an infrared sensor, and the like.
[00037] In an embodiment, the processor is configured to receive historical driving pattern information associated with a user of the vehicle. The processor is configured to receive historical reaction time information 30 associated with the user of the vehicle. The processor is configured to apply
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a machine learning model on the received historical driving pattern information, the determined height of the rider, the determined weight of the rider, and the received historical reaction time information. Herein, the predefined gravitational force is determined based on the application of the machine learning model. 5 [00038] Figure 1 exemplarily illustrates a system for quantifying rider safety level, in accordance with an embodiment of the present disclosure. Figure 1 depicts a system (100). The system (100) comprises an inertial measurement unit (IMU) (102), a location sensor (104), a processor (106), one or more force sensors (108), a height sensor (110), and a weight sensor (112). 10
[00039] The IMU (102) is configured to determine acceleration data and orientation data of the vehicle. In an embodiment, the acceleration data of the vehicle comprises a first acceleration along a first axis, a second acceleration along a second axis, and a third acceleration along a third axis. In an embodiment, each of the one or more forces is determined along the first axis, 15 the second axis, and the third axis.
[00040] The location sensor (104) is configured to determine location data of the vehicle. The processor (106) is configured to compare a speed of the vehicle with a predefined speed limit.
[00041] The processor (106) is configured to determine one or more forces 20 acting on a rider of the vehicle based on first information. Herein, the first information comprises the determined acceleration data, the determined orientation data, and the determined location data.
[00042] In an embodiment, the first information associated with the vehicle comprise information associated with a road topography of a route followed 25 by the vehicle. In an embodiment, the first information comprises magnetometer data, wherein the magnetometer data comprises a first magnetometer reading along the first axis, second magnetometer reading along the second axis, and a third magnetometer reading along the third axis.
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[00043] The processor (106) is configured to compare each of the determined one or more forces with a predefined threshold associated with each of the one or more forces.
[00044] In an embodiment, the system (100) comprises a height sensor (110) configured to determine a height of the rider in a seated position and a weight 5 sensor (112) configured to measure a weight of the rider. Herein, the height sensor (110) and/or the weight sensor (112) is positioned at a first region corresponding to a seat, or a second region corresponding to a footrest area of the vehicle.
[00045] In an embodiment, the processor (106) is configured to receive 10 historical driving pattern information associated with a user of the vehicle. The processor (106) is configured to receive historical reaction time information associated with the user of the vehicle. The processor (106) is configured to apply a machine learning model on the received historical driving pattern information, the determined height of the rider, the determined 15 weight of the rider, and the received historical reaction time information. Herein, the predefined gravitational force is determined based on the application of the machine learning model.
[00046] In an embodiment, the one or more force sensor (108) is configured to determine information associated with force applied by the rider, 20 information associated with a grip of the rider, and information associated with steering preferences for the rider, wherein the predefined threshold associated with each of the one or more forces is dynamically varied based on the determined information associated with force applied by the rider, the determined information associated with the grip of the rider, and the 25 determined information associated with steering preferences for the rider.
[00047] In an embodiment, the one or more forces is at least one of: a gravitation force, a wind force, an impact force.
[00048] The processor (106) is configured to determine a value based on the comparison. Herein, the value is indicative of the rider safety level. 30
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[00049] The processor (106) is configured to provide the determined value to a rider of the vehicle. In an embodiment, the processor (106) is configured to render the value indicative of the rider safety level on an audio device and/or a display device associated with the vehicle, wherein the audio device is a speaker of a head wearable device. 5
[00050] In an embodiment, the processor (106) is configured to determine a crash state of the vehicle based on a speed of the vehicle in a predefined time duration and inputs of one or more sensors. Herein, the one or more sensors comprises at least one of: an accelerometer, a gyroscope, an impact sensor, a wheel speed sensor, a vehicle speed sensor, or an ultrasonic sensor. The 10 processor (106) is configured to transmit one or more notifications to one or more user devices associated with one or more emergency contacts of the vehicle based on the determination that the crash state of the vehicle is a positive state.
[00051] In an embodiment, the processor (106) is configured to determine 15 whether the value indicative of the rider safety level is greater than a first threshold level. In an embodiment, the processor (106) is configured to notify the rider about the value based on the determination that the value indicative of the rider safety level is greater than the first threshold level.
[00052] In an embodiment, the processor (106) is configured to determine 20 whether the value indicative of the rider safety level is greater than a second threshold level. The processor (106) is configured to control an acceleration and braking mechanism of the vehicle if the determined value is greater than the second threshold level.
[00053] Figure 2 exemplarily illustrates a flowchart (200) of a method for 25 quantifying rider safety level, in accordance with an embodiment of the present disclosure. The flowchart (200) comprises blocks from 202 to 214.
[00054] At 202, a speed of a vehicle is determined. The processor (106) is configured to determine the speed of the vehicle. At 204, the determined speed of the vehicle is compared with the predefined speed limit. At 206, first 30
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information associated with the vehicle is received from the IMU sensor (102). Herein, the first information comprises acceleration data, location data, and orientation data of the vehicle. At 208, one or more forces acting on a rider of the vehicle is determined based on the received first information. At 210, each of the determined one or more forces is compared with a predefined 5 threshold associated with each of the one or more forces. At 212, a value is determined based on the comparison. Herein, the value is indicative of the rider safety level. At 214, the determined value is provided to a rider of the vehicle. [00055] In a scenario, a two wheeler is equipped with the proposed system, 10 comprising various sensors (IMU, force sensors, height, and weight sensors), an in-vehicle control unit (IVCU), and a smart helmet integrated with a speaker for voice alerts. The sensors are strategically placed to capture relevant data such as vehicle orientation, acceleration, rider grip, and physical attributes. As the rider starts the journey, the sensors continuously gather real-15 time data on the vehicle's movements, the rider's interactions (such as steering and braking forces), and the surrounding environment (via location sensors). The data is transmitted to the IVCU, which processes it to calculate G-forces experienced by the rider and assess their safety level based on predefined thresholds. The IVCU utilizes historical data on the rider's driving patterns, 20 body proportions, and reactions, along with machine learning algorithms, to determine personalized safety thresholds. For example, if a rider has a shorter reaction time or lower tolerance for G-forces, the system adjusts the safety thresholds accordingly to provide adequate warnings and interventions. If the calculated G-force exceeds the rider's personalized safety threshold, the 25 IVCU triggers a warning. The warning is relayed to the rider via voice command through the smart helmet's integrated speaker, alerting them to the potential risk of a collision or exceeding safe limits. Simultaneously, a visual alert is displayed on the vehicle's cluster to ensure the rider is aware of the safety concern. Depending on the severity of the safety violation, the system 30 may initiate adaptive responses such as: adjusting vehicle acceleration and braking mechanisms to mitigate risks, sending notifications to emergency
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contacts if a crash is imminent or has occurred, and providing suggestions for safer driving practices based on real-time data analysis. Throughout the journey, the system continues to collect data and adapt its safety thresholds based on the rider's behaviour and environmental conditions. This adaptive learning process ensures that the system remains effective in providing 5 personalized protection and enhancing the rider's overall safety experience. [00056] In another scenario, a motorcycle is equipped with the proposed system. The system includes sensors such as an IMU (Inertial Measurement Unit), force sensors on the handlebars, a height sensor near the seat, a weight sensor on the footrests, and global positioning system (GPS) for location data. 10 An in-vehicle control unit (IVCU) processes sensor data and controls safety alerts. A smart helmet worn by the rider is equipped with a speaker for voice alerts. During a ride, the IMU continuously measures acceleration along three axes: x (forward/backward), y (left/right), and z (up/down). Force sensors on the handlebars detect grip strength and steering preferences. The height 15 sensor measures the rider's height, and the weight sensor measures their weight. GPS provides real-time location data. The IVCU analyses historical data on the rider's driving patterns, body proportions, and reactions. For example, let's say the rider has a reaction time of 0.5 seconds and a maximum G-force tolerance of 4 g. Based on this data, the system sets personalized 20 safety thresholds for the rider. As the rider accelerates, brakes, and steers, the system continuously monitors sensor data. Let's assume the rider accelerates rapidly, causing the motorcycle to experience a peak acceleration of 3 m/s² (equivalent to approximately 0.3 g). The IVCU calculates the G-force experienced by the rider, considering their weight and the motorcycle's 25 acceleration. Since the calculated G-force (0.3 g) is below the rider's threshold (4 g), no immediate warning is triggered. If the rider suddenly brakes hard, causing a deceleration of 5 m/s² (approximately 0.5 g), the system detects this rapid change in acceleration. The IVCU compares the calculated G-force (0.5 g) to the rider's threshold (4 g). Since the G-force exceeds the threshold, the 30 system triggers a warning. The smart helmet emits a voice alert, notifying the rider of the high G-force and prompting them to adjust their driving
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behaviour. Throughout the ride, the system continues to collect data and adjust safety thresholds based on the rider's behaviour. If the rider consistently exceeds certain thresholds, the system may recommend additional training or provide personalized tips for safer riding. Thus, by leveraging advanced sensor technology, real-time data processing, and 5 personalized safety assessments, the system aims to enhance rider safety and prevent accidents effectively. [00057] The disclosed system and the method provide personalized safety assessment. The disclosed system and the method consider individual rider characteristics such as driving patterns, body proportions, and reaction times 10 to determine personalized safety thresholds. This customization ensures that safety alerts are tailored to the specific capabilities and limitations of each rider, enhancing the effectiveness of accident prevention measures.
[00058] The disclosed system and the method provide real-time feedback. Unlike traditional safety systems that provide passive protection, the 15 disclosed system and the method offers real-time feedback to riders during their journey. Riders are promptly alerted to potential safety risks, allowing them to adjust their driving behaviour and mitigate the likelihood of accidents before they occur.
[00059] The disclosed system and the method provide adaptive learning. The 20 disclosed system and the method incorporate machine learning algorithms that continuously analyse rider behaviour and adjust safety thresholds accordingly. Over time, the system learns from the rider's interactions and adapts its response to provide increasingly accurate and effective safety assessments. 25
[00060] The disclosed system and the method provide comprehensive sensor integration. By integrating multiple sensors such as IMUs, force sensors, and GPS, the disclosed system and the method captures a wide range of data points related to vehicle dynamics, rider behaviour, and environmental conditions. This comprehensive sensor integration enables the system to 30
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make informed safety judgments based on a holistic understanding of the riding context. [00061] The disclosed system and the method provide enhanced rider experience. The inclusion of a smart helmet with voice alerts enhances the rider's experience by providing clear and intuitive safety warnings. Riders feel 5 more informed and empowered to make safer decisions, contributing to a more confident and enjoyable riding experience.
[00062] The disclosed system and the method provide dynamic safety response. The disclosed system and the method allow for dynamic adjustments to safety thresholds based on real-time conditions and rider 10 feedback. This flexibility ensures that safety measures remain effective across different riding scenarios and environmental conditions, maximizing overall safety outcomes.
[00063] The disclosed system and the method provide continuous monitoring and optimization. Through continuous data collection and analysis, the 15 disclosed system and the method enables ongoing monitoring of rider safety and performance. Any deviations from established safety thresholds are promptly identified and addressed, ensuring that riders receive the highest level of protection throughout their journey. Thus, the disclosed system and the method offers a comprehensive and adaptable approach to rider safety, 20 leveraging advanced sensor technology, real-time feedback mechanisms, and adaptive learning algorithms to provide personalized protection and enhance overall safety outcomes.
[00064] The objectives of the claimed invention collectively aim to address the technical challenges associated with rider safety and provide a 25 comprehensive solution that for quantifiers a rider safety level.
[00065] In light of the above-mentioned advantages and the technical advancements provided by the disclosed system and the method, the claimed steps as discussed above are not routine, conventional, or well understood in the art, as the claimed steps enable the following solutions to the existing 30 problems in conventional technologies. Further, the claimed steps clearly
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bring an improvement in the functioning of the configuration itself as the claimed steps provide a technical solution to a technical problem. [00066] A description of an embodiment with several components in communication with another does not imply that all such components are required, On the contrary, a variety of optional components are described to 5 illustrate the wide variety of possible embodiments of the invention.
[00067] Finally, the language used in the specification has been principally selected for readability and instructional purposes, and it may not have been selected to delineate or circumscribe the inventive subject matter and is therefore intended that the scope of the invention be limited not by this 10 detailed description, but rather by any claims that issue on an application based here on. Accordingly, the embodiments of the present invention are intended to be illustrative, but not limiting, of the scope of the invention, which is set forth in the following claims.
[00068] While various aspects and embodiments have been disclosed herein, 15 other aspects and embodiments will be apparent to those skilled in the art. The various aspects and embodiments disclosed herein are for purposes of illustration and are not intended to be limiting, with the true scope and spirit being indicated by the following claims.
[00069] While the present disclosure has been described with reference to 20 certain embodiments, it will be understood by those skilled in the art that various changes may be made, and equivalents may be substituted without departing from the scope of the present disclosure. In addition, many modifications may be made to adapt a particular situation or material to the teachings of the present disclosure without departing from its scope. 25 Therefore, it is intended that the present disclosure not be limited to the particular embodiment disclosed, but that the present disclosure will include all embodiments falling within the scope of the appended claims.
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Reference Numerals:
100– System
102-One or more microphones
104– Speaker
106-Controller 5
108-Power supply
110-Set of vehicle parameters
200-Vehicle
300-Flowchart , Claims:We Claim:
1.A method for quantifying a rider safety level, the method comprising: 5
determining, by a processor (106), a speed of a vehicle;
comparing, by the processor (106), the determined speed of the
vehicle with a predefined speed limit;
receiving, by the processor (106), first information associated with the vehicle from an inertial measurement unit (IMU) sensor 10 (102), wherein the first information comprises acceleration data, location data, and orientation data of the vehicle;
determining, by the processor (106), one or more forces acting on a rider of the vehicle based on the received first information;
comparing, by the processor (106), each of the determined one 15 or more forces with a predefined threshold associated with each of the one or more forces;
determining, by the processor (106), a value based on the comparison, wherein the value is indicative of the rider safety level; and 20
providing, by the processor (106), the determined value to a rider of the vehicle.
2.The method for quantifying the rider safety level as claimed in claim1, wherein the first information associated with the vehicle comprise25 information associated with a road topography of a route followed bythe vehicle.
3.The method for quantifying the rider safety level as claimed in claim1, wherein the acceleration data of the vehicle comprises a first30 acceleration along a first axis, a second acceleration along a secondaxis, and a third acceleration along a third axis.
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4.The method for quantifying the rider safety level as claimed in claim3, wherein the first information comprises magnetometer data,wherein the magnetometer data comprises a first magnetometerreading along the first axis, second magnetometer reading along the5 second axis, and a third magnetometer reading along the third axis.
5.The method for quantifying the rider safety level as claimed in claim3, wherein each of the one or more forces is determined along the firstaxis, the second axis, and the third axis.10
6.The method for quantifying the rider safety level as claimed in claim1 comprising:
receiving, by the processor (106), historical driving pattern information associated with the rider of the vehicle; 15
receiving, by the processor (106), body proportion information corresponding to a height and a weight of the rider of the vehicle;
receiving, by the processor (106), historical reaction time information associated with the rider of the vehicle; and
applying, by the processor (106), a machine learning model on 20 the received historical driving pattern information, the received body proportion information, and the received historical reaction time information, wherein
the predefined threshold associated with each of the one or more forces is determined based on the application of 25 the machine learning model.
7.The method for quantifying the rider safety level as claimed in claim1 comprising:
determining, by the processor (106), a crash state of the 30 vehicle based on a speed of the vehicle in a predefined time duration and inputs of one or more sensors, wherein the one or more sensors
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comprises at least one of: an accelerometer, a gyroscope, an impact sensor, a wheel speed sensor, a vehicle speed sensor, or an ultrasonic sensor; and
transmitting, by the processor (106), one or more notifications to one or more user devices associated with one or more emergency 5 contacts of the vehicle based on the determination that the crash state of the vehicle is a positive state.
8.The method for quantifying the rider safety level as claimed in claim1, wherein the one or more forces is at least one of: a gravitation force,10 a wind force, an impact force.
9.The method for quantifying the rider safety level as claimed in claim1 comprising:
determining, by the processor (106), whether the value 15 indicative of the rider safety level is greater than a first threshold level; and
notifying, by the processor (106), the rider about the value based on the determination that the value indicative of the rider safety level is greater than the first threshold level. 20
10.The method for quantifying the rider safety level as claimed in claim1 comprising:
determining, by the processor (106), whether the value
indicative of the rider safety level is greater than a second threshold 25 level; and
controlling, by the processor (106), an acceleration and braking mechanism of the vehicle if the determined value is greater than the second threshold level.
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11.The method for quantifying the rider safety level as claimed in claim1 comprising rendering, by the processor (106), the value indicative
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of the rider safety level on an audio device and/or a display device associated with the vehicle, wherein the audio device is a speaker of a head wearable device.
12. A system (100) for quantifying a rider safety level comprising: 5
an inertial measurement unit (IMU) (102) associated with a vehicle, wherein the IMU (102) is configured to determine acceleration data and orientation data of the vehicle;
a location sensor (104) configured to determine location data of the vehicle; and 10
a processor (106) configured to:
compare a speed of the vehicle with a predefined speed limit;
determine one or more forces acting on a rider of the vehicle
based on first information, wherein the first information comprises the determined acceleration data, the determined orientation data, and the 15 determined location data;
compare each of the determined one or more forces with a predefined threshold associated with each of the one or more forces;
determine a value based on the comparison, wherein the value is indicative of the rider safety level; and 20
provide the determined value to a rider of the vehicle.
13.The system (100) for quantifying the rider safety level as claimed inclaim 12 comprising one or more force sensors (108) positioned on ahandlebar of the vehicle, wherein the one or more force sensor (108)25 is configured to determine information associated with force appliedby the rider, information associated with a grip of the rider, andinformation associated with steering preferences for the rider, whereinthe predefined threshold associated with each of the one or moreforces is dynamically varied based on the determined information30 associated with force applied by the rider, the determined information
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associated with the grip of the rider, and the determined information associated with steering preferences for the rider.
14.The system (100) for quantifying the rider safety level as claimed inclaim 12 comprising a height sensor (110) configured to determine a5 height of the rider in a seated position and a weight sensor (112)configured to measure a weight of the rider, wherein the height sensor(110)and/or the weight sensor (112) is positioned at a first regioncorresponding to a seat, or a second region corresponding to a footrestarea of the vehicle.10
15.The system for quantifying the rider safety level claimed in claim 14,wherein the processor (106) is configured:
receive historical driving pattern information associated with a user of the vehicle; 15
receive historical reaction time information associated with the user of the vehicle; and
apply a machine learning model on the received historical driving pattern information, the determined height of the rider, the determined weight of the rider, and the received historical reaction 20 time information, wherein
the predefined gravitational force is determined based on the application of the machine learning model.