Description:TECHNICAL FIELD
The present disclosure relates to electric vehicles. Moreover, the present disclosure pertains to an incident monitoring system for a two-wheeler electric vehicle.
BACKGROUND
Two-wheeler electric vehicles represent a rapidly growing segment in the global transportation market, driven by the need for sustainable mobility solutions. With this growth, there has been a parallel need to enhance the safety features and emergency response capabilities of the two-wheeler electric vehicles. Vehicle safety systems typically include basic features such as anti-lock braking systems (ABS), traction control, and emergency alert systems. Traditional emergency response systems in vehicles often rely on manual activation by the rider or basic crash sensors that trigger emergency notifications containing only location data and basic incident information.
For the existing two-wheeler electric vehicle, first, the accuracy of incident detection can be compromised by false positives or missed detections when relying on single-sensor data. Second, when incidents occur, emergency responders often lack immediate visual information about the incident scene, which can delay appropriate response assessment and deployment. Current systems also typically lack the capability to provide comprehensive incident documentation. While some two-wheeler electric vehicles may be equipped with dash cameras or similar recording devices, such two-wheeler electric vehicle generally require manual activation.
Therefore, in light of the foregoing discussion, there exists a need to overcome the aforementioned drawbacks.
SUMMARY
The present disclosure provides an incident monitoring system for a two-wheeler electric vehicle. The present disclosure provides a solution to the technical problem of how to ensure rider safety in the two-wheeler electric vehicle. An aim of the present disclosure is to provide a solution that overcomes at least partially the problems encountered in the prior art and offers an improved incident monitoring system for the two-wheeler electric vehicle. The improved incident monitoring system generates an alert message upon occurrence of a vehicle incident and transmits an alert message to a recipient based on the severity of the vehicle incident.
One or more objectives of the present disclosure are achieved through the solutions outlined in the independent claims, while further advantageous features are described in the dependent claims.
In one aspect, the present disclosure provides an incident monitoring system for a two-wheeler electric vehicle, the incident monitoring system comprising:
at least one processor configured to:
receive sensor data indicating a vehicle status;
determine, based on the sensor data, occurrence of a vehicle incident;
automatically activate, responsive to determining the occurrence of the vehicle incident, at least one multimedia capture device;
obtain, from the at least one multimedia capture device, multimedia content associated with the vehicle incident;
determine a location of the two-wheeler electric vehicle;
generate an alert message comprising the multimedia content, the location of the two-wheeler electric vehicle, and incident information derived from the sensor data; and
transmit the alert message to at least one recipient device.
The incident monitoring system enables comprehensive, automated incident documentation and emergency response for the two-wheeler electric vehicle by integrating the plurality of sensors with the multimedia capture device. Through continuous analysis of the sensor data, the incident monitoring system, may instantly detect the vehicle incident and trigger an automated response sequence. When the vehicle incident occurs, an immediate activation of the multimedia capture device ensures visual documentation of the vehicle incident, while simultaneous location determination provides precise positioning information. By automatically compiling such multimedia content, location data, and incident information derived from the sensor data into a structured alert message and transmitting the alert message to the recipient device, the incident monitoring system eliminates manual intervention and minimizes response time for generation of the alert message. The automation and integration of the sensor data, the multimedia content, and the location results in faster emergency response times, more accurate incident documentation, and improved rider safety through immediate notification of relevant parties. The ability of the incident monitoring system to combine the sensor data with the multimedia content and tracking of the location creates a reliable, self-contained incident monitoring and reporting solution. Further, the incident monitoring system operates independently of a rider input, ensuring each of the sensor data, the multimedia content, and the location is captured and transmitted even in situations where the rider is unable to request assistance manually.
It is to be appreciated that all the aforementioned implementation forms can be combined. All steps that are performed by the various entities described in the present application, as well as the functionalities described to be performed by the various entities, are intended to mean that the respective entity is adapted to or configured to perform the respective steps and functionalities. It will be appreciated that features of the present disclosure are susceptible to being combined in various combinations without departing from the scope of the present disclosure as defined by the appended claims.
Additional aspects, advantages, features, and objects of the present disclosure would be made apparent from the drawings and the detailed description of the illustrative implementations construed in conjunction with the appended claims that follow.
BRIEF DESCRIPTION OF THE DRAWINGS
The summary above, as well as the following detailed description of illustrative embodiments, is better understood when read in conjunction with the appended drawings. For the purpose of illustrating the present disclosure, exemplary constructions of the disclosure are shown in the drawings. However, the present disclosure is not limited to specific methods and instrumentalities disclosed herein. Moreover, those in the art will understand that the drawings are not to scale. Wherever possible, like elements have been indicated by identical numbers.
Embodiments of the present disclosure will now be described, by way of example only, with reference to the following diagrams wherein:
FIG. 1 is a diagram illustrating a side view of a two-wheeler electric vehicle, in accordance with an embodiment of the present disclosure;
FIG. 2 is a block diagram of an incident monitoring system of the two-wheeler electric vehicle, in accordance with an embodiment of the present disclosure; and
FIG. 3 is a diagram illustrating an exemplary scenario of working of the incident monitoring system of the two-wheeler electric vehicle, in accordance with an embodiment of the present disclosure.
In the accompanying drawings, an underlined number is employed to represent an item over which the underlined number is positioned or an item to which the underlined number is adjacent. A non-underlined number relates to an item identified by a line linking the non underlined number to the item. When a number is non-underlined and accompanied by an associated arrow, the non-underlined number is used to identify a general item at which the arrow is pointing.
DETAILED DESCRIPTION OF EMBODIMENTS
The following detailed description illustrates embodiments of the present disclosure and ways in which they can be implemented. Although some modes of carrying out the present disclosure have been disclosed, those skilled in the art would recognize that other embodiments for carrying out or practicing the present disclosure are also possible.
FIG. 1 is a diagram illustrating a side view of a two-wheeler electric vehicle, in accordance with an embodiment of the present disclosure. With reference to FIG. 1, there is shown a two-wheeler electric vehicle 100 with enhanced rider safety. The two-wheeler electric vehicle 100 includes a chassis 102. The chassis 102 serves as a primary structural framework, forming a backbone of the two-wheeler electric vehicle 100 and extending from front to rear. The chassis 102 is operatively coupled to ground engaging members 104. In the illustrated embodiment of FIG. 1, the ground engaging members 104 (hereinafter referred as wheels 104) includes two wheels.
The two-wheeler electric vehicle 100 includes an electric powertrain for moving the two-wheeler electric vehicle 100. Specifically, the electric powertrain is securely mounted to the chassis 102 of the two-wheeler electric vehicle 100 to create a robust structural integration, allowing the powertrain to transmit torque to the wheels 104. The electric powertrain includes an electric motor (not shown) connected to a power source, specifically a battery pack 108 housed within a carrier 110. The carrier 110 is integrated into the chassis 102 of the two-wheeler electric vehicle 100. The electric powertrain may further include a controller for managing motor output, a drive shaft to transfer torque, and other known drive components for transmission of motive power from the electric motor to the wheels 104. It should be appreciated that while the illustrated embodiment of FIG. 1 describes the two-wheeler electric vehicle 100, the principles of the battery system 106 may be applied to various types of electric vehicles, including but not limited to electric scooters, electric motorcycles, and other light electric vehicles designed for urban mobility.
The two-wheeler electric vehicle 100 includes a seat 112 mounted on an upper portion of the chassis 102, positioned above the battery system 106, and is securely fastened with front mounting brackets and rear support connecting to the backrest 114. The seat 112 and the backrest 114 are securely fastened to the chassis 102 for stability and safety. The backrest 114 is connected to the rear portion of the seat 112 and is supported by vertical frame members that attach to the rear structure of the chassis 102, ensuring proper rider support and comfort.
The two-wheeler electric vehicle 100 includes a front section secured through mounting brackets and a support structure. At the front section of the two-wheeler electric vehicle 100, handlebars 116 are operatively connected to the chassis 102 for steering through a head tube and a steering column. The two-wheeler electric vehicle 100 includes a front storage compartment 118 integrated with the chassis 102 and mounted to an upper portion of the handlebars 116. The handlebars 116 are operatively connected to the chassis 102 for steering. The integration of the front storage compartment 118 with the chassis 102 enhances the strength and rigidity of the front section of the two-wheeler electric vehicle 100. The integration also simplifies the assembly process and reduces the number of individual components required. By integrating the front storage compartment 118 into the chassis 102, the design of the two-wheeler electric vehicle 100 improves weight distribution and ensures a cleaner, more aerodynamic profile for better handling and efficiency during rides.
The front storage compartment 118 includes a set of front assembly components. The set of front assembly components includes a cluster gauge 120, at least one mirror 122 for providing a rear view, a visor 124, a horn 126 for providing audio signalling and a headlamp 128. The cluster gauge 120 is used for displaying vehicle information. The cluster gauge 120 is a dashboard display panel and includes a speedometer (shows vehicle speed), odometer (shows total distance travelled), battery charge level indicator, various warning lights and indicators, clock and other vehicle status information. The visor 124 is a protective or shielding component for display protection. The visor 124 protects the cluster gauge 120 from sunlight glare. The visor 124 has a curved or angled design to improve the aerodynamics of the two-wheeler electric vehicle 100. The visor 124 is mounted above the headlamp 128. The headlamp 128 in the two-wheeler electric vehicle 100 is the front lighting system and provides forward illumination. The front storage compartment 118 includes mounting features integrally formed within the structure of the front storage compartment 118 for direct attachment of the set of front assembly components, enabling tool-free service access to the components. The mounting features are formed as part of the front storage compartment 118, thus ensuring no separate brackets or attachments are to be added later, reducing the number of parts in the two-wheeler electric vehicle 100.
FIG. 2 is a block diagram of an incident monitoring system of the two-wheeler electric vehicle, in accordance with an embodiment of the present disclosure. FIG. 2 is described in conjunction with elements of FIG. 1. With reference to FIG. 2, there is shown the incident monitoring system 200 installed in the two-wheeler electric vehicle 100 to generate an alert message in case of occurrence of a vehicle incident. The incident monitoring system 200 includes at least one processor 202, a memory 206, a multimedia capture device 208, a plurality of sensors 210, a legroom heating system 212, and a fog lamp system 214. The incident monitoring system 200 is communicatively coupled with a communication network 216. The incident monitoring system 200 communicates with at least one recipient device 218 through the communication network.
The processor 202 refers to a computational element that is operable to respond to and process instructions that drive the incident monitoring system 200. The processor 202 may refer to one or more individual processors, processing devices, and various elements associated with a processing device that may be shared by other processing devices. Additionally, the one or more individual processors, processing devices, and elements are arranged in various architectures for responding to and processing the instructions that drive the incident monitoring system 200. In some implementations, the processor 202 may be an independent unit and may be located outside the incident monitoring system 200. Examples of the processor 202 may include but are not limited to, a hardware processor, a digital signal processor (DSP), a microprocessor, a microcontroller, a complex instruction set computing (CISC) processor, an application-specific integrated circuit (ASIC) processor, a reduced instruction set (RISC) processor, a very long instruction word (VLIW) processor, a state machine, a data processing unit, a graphics processing unit (GPU), and other processors or control circuitry.
The memory 206 refers to a volatile or persistent medium, such as an electrical circuit, magnetic disk, virtual memory, or optical disk, in which a computer can store data or software for any duration. Optionally, the memory 206 is a non-volatile mass storage, such as a physical storage media. Furthermore, a single memory may encompass and, in a scenario, and the incident monitoring system 200 is distributed, the processor 202, the memory 206 and/or storage capability may be distributed as well. Examples of implementation of the memory 206 may include, but are not limited to, an Electrically Erasable Programmable Read-Only Memory (EEPROM), Dynamic Random-Access Memory (DRAM), Random Access Memory (RAM), Read-Only Memory (ROM), Hard Disk Drive (HDD), Flash memory, a Secure Digital (SD) card, Solid-State Drive (SSD), and/or CPU cache memory.
The multimedia capture device 208 refers to an integrated system capable of capturing both still photographs and video footage while the two-wheeler electric vehicle 100 is in motion. The multimedia capture device 208 includes safety-oriented features to enable hands-free operation during riding, to ensure the safety of the rider.
The plurality of sensors 210 refers to the comprehensive network of electronic detection and measurement devices integrated throughout the two-wheeler electric vehicle 100. The plurality of sensors 210 monitor various vehicle parameters and environmental conditions to ensure optimal performance and safety. The plurality of sensors 210 includes, but is not limited to, impact sensors for emergency alerts, temperature sensors for the legroom heating system 212, visibility sensors for the fog lamp system 214, and motion sensors for fall detection. The plurality of sensors 210 work in concert to provide real-time data to the processor 202 of the two-wheeler electric vehicle 100, enabling automated responses to changing conditions and emergencies while also supporting the safety and comfort features of the two-wheeler electric vehicle 100.
The legroom heating system 212 comprises temperature-controlled heating elements specifically designed to provide warmth to the lower extremities of the rider during cold weather conditions. The legroom heating system 212 consists of heating elements strategically positioned in a legroom area of the two-wheeler electric vehicle 100. In an implementation, the legroom heating system 212 incorporates temperature regulation capabilities to maintain optimal comfort levels, featuring simple ON/OFF functionality for convenience of the rider. The legroom heating system 212 is designed to operate efficiently within the power management framework of the two-wheeler electric vehicle 100, ensuring effective heating without significantly impacting the performance of the two-wheeler electric vehicle 100.
The fog lamp system 214 is a lighting solution integrated into the two-wheeler electric vehicle 100, specifically designed to improve visibility during foggy or adverse weather conditions. The fog lamp system 214 consists of fog lamps for providing enhanced ground illumination and reduced glare in low-visibility conditions. The fog lamp system 214 is designed to complement a lighting setup of the two-wheeler electric vehicle 100, providing additional illumination when needed while minimizing power consumption. The positioning and beam pattern of the fog lamps are optimized to maximize visibility without causing distraction to other riders on the road.
The communication network 216 includes a medium (e.g., a communication channel) through which a recipient device 218 of a plurality of recipient devices communicates with the incident monitoring system 200. The communication network 216 may be a wired or wireless communication network. Examples of the communication network 216 may include, but are not limited to, Internet, a Local Area Network (LAN), a wireless personal area network (WPAN), a Wireless Local Area Network (WLAN), a wireless wide area network (WWAN), a cloud network, a Long-Term Evolution (LTE) network, a plain old telephone service (POTS), a Metropolitan Area Network (MAN), and/or the Internet.
In operations, the processor 202 is configured to receive the sensor data 206B indicating a vehicle status. The vehicle status is derived from the analysis of the sensor data 206B received from the plurality of sensors 210. The processor 202 monitors the sensor data 206B received from the plurality of sensors 210 to determine the vehicle status. For example, the motion sensor captures the movement of the two-wheeler electric vehicle 100 indicating to the processor 202 that the two-wheeler electric vehicle 100 is moving swiftly.
In an implementation, the sensor data 206B comprises at least one of acceleration data, orientation data, impact data, speed data, or location data of the two-wheeler electric vehicle 100. The sensor data 206B includes data from the plurality of sensors 210, including accelerometers for measuring sudden changes in motion, gyroscopes for orientation tracking, impact sensors for collision detection, speedometers for velocity monitoring, and GPS modules for location tracking.
In another implementation, the processor 202 is configured to receive acceleration data from at least one accelerometer, orientation data from at least one gyroscope. The processor 202 interfaces with the accelerometer that continuously monitor the movement and angular orientation of the two-wheeler electric vehicle 100. For example, the accelerometer detects changes in the acceleration of the two-wheeler electric vehicle along three axes. The at least one gyroscope measures rotational movement and orientation of the two-wheeler electric vehicle 100. The data from the accelerometer and the gyroscope enables the incident monitoring system 200 to assess the dynamic state of the two-wheeler electric vehicle 100 and contributes to identifying any irregularities in the movement, such as sudden instability.
In addition, the processor 202 is further configured to receive speed data from at least one speed sensor and impact force data from at least one impact sensor. The speed sensor provides real-time information about the velocity of the two-wheeler electric vehicle 100, while the impact sensor measures the force exerted on the two-wheeler electric vehicle 100 during a collision or abrupt impact. The data from the speed sensor and the impact force data allows the processor 202 to evaluate critical incidents, such as sudden stops or crashes, thereby facilitating the generation of accurate incident alerts for enhanced rider safety.
The processor 202 receives the sensor data 206B through a dedicated data acquisition system 204 that continuously monitors the vehicle status of the two-wheeler electric vehicle 100. The data acquisition system 204 employs multiple input channels connected to the plurality of sensors 210 throughout the two-wheeler electric vehicle 100, each equipped with signal conditioning circuits to ensure data quality. The sensor data 206B streams are processed through analog-to-digital converters and buffered in the memory 206 for immediate analysis. The continuous monitoring of the two-wheeler electric vehicle 100 enables the data acquisition system 204 to maintain current awareness of the operational state and detect any anomalies of the two-wheeler electric vehicle 100 instantly.
The processor 202 is further configured to determine, based on the sensor data 206B, occurrence of the vehicle incident. The processor 202 analyses the inputs from the accelerometer, gyroscope, speed sensor, and impact sensor to identify patterns indicative of an abnormal event, such as a crash. For instance, a sudden deceleration combined with a significant impact force might be used to classify a situation as the vehicle incident. The determination of the occurrence of the vehicle incident helps the incident monitoring system 200 take immediate action, such as activating multimedia capture devices or sending alerts.
In some implementations, the processor 202 is configured to compare the received sensor data 206B with predetermined threshold values. The processor 202 analyses the sensor data 206B using sophisticated pattern recognition operation. The processor 202 employs multiple threshold comparisons and sensor fusion techniques to identify incident patterns. For example, when the sensor data 206B indicates a sudden deceleration combined with an abnormal tilt angle, the incident monitoring system 200 cross-references the value of such sudden deceleration and the abnormal tilt angle against predetermined values of deceleration and tilt angle. The analysis of the sensor data 206B ensures accurate detection of the vehicle incident while minimizing false detections of the vehicle incident.
In some implementations, the processor 202 is configured to detect at least one of a sudden deceleration exceeding a first threshold value. The processor 202 analyses data from the accelerometer to identify abrupt reductions in the speed of the two-wheeler electric vehicle 100. For example, if the two-wheeler electric vehicle 100 decelerates at a rate greater than 8 meter per second square (m/s²), the incident monitoring system 200 recognizes it as an unusual event. The processor 202 is further configured to detect a tilt angle exceeding a second threshold value, an impact force exceeding a third threshold value or a combination of sensor values indicating loss of vehicle stability. For instance, if the two-wheeler electric vehicle 100 tilts beyond 45 degrees and simultaneously experiences a significant impact, the processor 202 determines that the two-wheeler electric vehicle 100 is no longer stable. Such detection ensures timely responses to potential accidents or unsafe driving conditions.
The processor 202 monitors vehicle stability through a combination of sensor values. The processor 202 analyses the accelerometer, gyroscope, and speed data to create a real-time stability model. Such a stability model considers multiple factors such as lean angle, acceleration forces, and speed to determine if the two-wheeler electric vehicle 100 is maintaining stable operation. When the stability model detects anomalous behaviour, such as unusual combinations of lean angle and acceleration, the stability model flags such unusual combinations for analysis of the vehicle incident.
In some implementations, the processor 202 is configured to confirm the vehicle incident when at least two different types of sensor data 206B indicate anomalous vehicle behaviour within a predetermined time window. In such implementations, the processor 202 confirms vehicle incidents through a multi-sensor validation approach by analysing data from the plurality of sensors 210 within a specific time window. When at least two different types of sensors (such as accelerometer, gyroscope, speed sensor, or impact sensor) detect anomalous readings beyond the respective thresholds within the same predetermined time period, the incident monitoring system 200 validates the detected anomalous readings as a genuine incident. For example, if both the accelerometer detects a sudden deceleration and the gyroscope detects an unusual tilt angle within a few seconds, the incident monitoring system 200 confirms the sudden deceleration and the unusual tilt angle as the vehicle incident. The multi-sensor confirmation approach facilitates in eliminating false positives that might occur from single sensor anomalies and ensures more accurate incident detection by requiring corroborating evidence from different sensor types, ultimately providing more reliable incident monitoring and emergency response triggering.
The processor 202 is further configured to automatically activate, responsive to determining the occurrence of the vehicle incident, at least one multimedia capture device 208. Upon incident detection, the processor 202 of the incident monitoring system 200 automatically activates the multimedia capture device 208. The activation of the multimedia capture device 208 occurs through dedicated control signals sent from the processor 202 to the multimedia capture device 208. The multimedia capture device 208 includes both photo and video capturing capabilities, with image stabilization and automatic exposure adjustment. Such activation of the multimedia capture device 208 ensures that a visual data is captured as soon as the vehicle incident occurs.
In an implementation, at least one processor 202 is further configured to maintain a continuous buffer of the multimedia content 206A from the at least one multimedia capture device 208 during the operation of the two-wheeler electric vehicle 100 and include pre-incident multimedia content 206A from the continuous buffer in the alert message. The incident monitoring system 200 implements a circular buffer memory architecture to continuously store the multimedia content 206A during the operation of the two-wheeler electric vehicle 100. The incident monitoring system 200 allocates dedicated blocks in the memory 206 to maintain a rolling window of the latest footage. In an example, the incident monitoring system 200 stores the last 4 to 6 minutes of video and photos. When the vehicle incident occurs, the processor 202 stores the multimedia content 206A before the vehicle incident in the memory 206.
The at least one processor 202 is configured to obtain from the at least one multimedia capture device 208, multimedia content 206A associated with the vehicle incident. The multimedia capture device 208 obtains high quality multimedia content 206A using image processing techniques. The multimedia capture device 208 captures both still images and video footage, employing continuous buffer to preserve pre-incident multimedia content and post-incident multimedia content. The captured multimedia content 206A undergoes real-time processing for quality optimization and storage efficiency. In another implementation, the at least one processor 202 is configured to store pre-incident photos from the continuous buffer of the multimedia content 206A. The at least one processor 202 is further configured to capture post-incident photos from the continuous buffer of the multimedia content 206A. In addition, the at least one processor 202 is configured to include both pre-incident and post-incident photos in the alert message.
Upon the vehicle incident detection, the processor 202 executes a two-phase photo capture sequence. First, the processor 202 immediately locks the pre-incident photos from the continuous buffer. In an example, the processor 202 stores the last 5-10 seconds before the incident. Then, the processor 202 initiates a new capture sequence for post-incident photos. The processor 202 of the incident monitoring system 200 employs image stabilization and auto-exposure adjustment to ensure clear captures despite a movement of the two-wheeler electric vehicle 100. The two-phase photo capture approach creates a comprehensive visual timeline of the vehicle incident, aiding in accurate incident reconstruction and analysis.
In yet another implementation, upon detecting the vehicle incident, the processor 202 is further configured to preserve pre-incident video footage from a first predetermined time period before the vehicle incident. The processor 202 is further configured to continue recording for a second predetermined time period after the vehicle incident to capture post-incident video footage. In addition, the processor 202 is further configured to include the pre-incident video footage and the post-incident video footage in the alert message. The processor 202 implements a video preservation protocol using predetermined time windows. Upon the detection of the vehicle incident, the processor 202 automatically preserves video footage from the first predetermined time period (for example, 30 seconds before the vehicle incident) while continuing to record for the second predetermined time period (for example, 60 seconds after the vehicle incident). The processor 202 uses high-efficiency video encoding to maintain quality while optimizing storage space. The pre-incident video footage and the post-incident video footage enable detailed analysis of the circumstances of the vehicle incident.
The processor 202 is further configured to determine a location of the two-wheeler electric vehicle 100. The processor 202 of the incident monitoring system 200 determines the location of the two-wheeler electric vehicle 100 using a location sensor of the plurality of sensors 210 installed in the two-wheeler electric vehicle 100. In some implementations, the two-wheeler electric vehicle 100 uses a combination of GPS receivers and additional positioning technologies to determine the location of the two-wheeler electric vehicle 100. The location sensor is connected to the processor 202 to maintain continuous tracking of the position of the two-wheeler electric vehicle. In another implementation, the location sensor uses satellite triangulation and, when available, cellular network positioning as a backup to determine the location of the two-wheeler electric vehicle 100.
In an implementation, the at least one processor 202 is further configured to analyse the multimedia content 206A to determine incident severity and select the at least one recipient based on the determined incident severity. The processor 202 employs computer vision operations to analyse the multimedia content 206A and determine incident severity levels. The analysis includes impact force assessment, vehicle orientation changes, and visual damage detection. Based on the incident severity level, the processor 202 selects the appropriate recipient from a predetermined hierarchy (for example, emergency services for severe incidents and family members for minor incidents). Such analysis of the multimedia content 206A by the processor 202 ensures a right recipient is notified based on incident severity.
The processor 202 is further configured to generate an alert message comprising the multimedia content 206A, the location of the two-wheeler electric vehicle 100, and incident information derived from the sensor data 206B. The processor 202 generates the alert message by combining multiple data streams into a structured format. The multiple data streams include but are not limited to pre-incident photos, post-incident photos, pre-incident videos, post-incident videos, and the location of the two-wheeler electric vehicle 100. The processor 202 integrates the multimedia content 206A, precise location coordinates, and a comprehensive incident information derived from the sensor data 206B. Such integration of the multimedia content 206A, location coordinates and incident information creates a complete incident report that provides emergency responders with comprehensive situational awareness.
In an implementation, the at least one processor 202 is further configured to compress the multimedia content 206A based on available network bandwidth; and prioritize transmission of critical incident data within the alert message. The processor 202 implements dynamic content compression based on network conditions. The processor 202 analyses available network bandwidth and applies appropriate compression operations to ensure reliable transmission to the recipient device 218. The processor 202 sends the pre-incident data and post-incident data within a predetermined time parameter as a priority in a compression queue. Such priority of pre-incident data and post-incident data ensures that essential information reaches the recipient device 218, even in poor network conditions. Such an intelligent compression strategy optimizes data transmission while preserving crucial incident information.
The processor 202 is further configured to transmit the alert message to at least one recipient device 218. The processor 202 transmits the alert message through multiple communication channels to ensure reliable delivery to the at least one recipient device 218. The processor 202 employs cellular networks as primary channels, with fallback options for areas with limited connectivity. The processor 202 implements priority-based transmission protocols, ensuring pre-incident data and post-incident data of the predetermined time parameter is sent first. Such multiple communication channel approach to transmit the alert message maximizes the likelihood of successful alert delivery even in challenging network conditions.
In another implementation, the at least one processor 202 is further configured to receive user preferences for incident detection sensitivity for the two-wheeler electric vehicle 100. In such implementations, the processor 202 enables customizable incident detection sensitivity by allowing users to adjust detection thresholds according to their riding style and comfort level. The adjustments modify various sensor thresholds like acceleration limits, tilt angles, or impact force levels within predefined safety bounds. For example, if a user prefers higher sensitivity, the system adjusts to trigger alerts at lower impact forces or smaller tilt angles, while maintaining minimum safety parameters to ensure critical incidents are never missed. The customization capability balances individual riding preferences with essential safety requirements, allowing riders to personalize their incident detection settings while the system maintains its core protective functions for a safer riding experience.
The processor 202 is further configured to adjust incident detection thresholds based on the user preferences while maintaining minimum safety parameters. The incident monitoring system 200 allows riders to set personal preferences for incident detection sensitivity while maintaining core safety parameters. The processor 202 implements a bounded adjustment system where the rider can modify incident detection thresholds within pre-defined safety limits. For example, the rider can adjust incident detection sensitivity between 80% and 120% of a base threshold. Such modification in the incident detection sensitivity ensures comfort while maintaining essential safety features.
In yet another implementation, the at least one processor 202 is further configured to store incident patterns and corresponding response effectiveness data for the two-wheeler electric vehicle 100. In such implementation, the processor 202 stores and analyzes incident patterns and corresponding response effectiveness data for the two-wheeler electric vehicle through a dedicated incident learning system. The dedicated incident learning system continuously collects and stores data about various incident occurrences, including the type of incident (like falls, collisions, or near-misses), the conditions under which they occurred, and the effectiveness of the system's response to each incident. The processor 202 utilizes this historical data to build a knowledge base that helps improve incident detection accuracy and response optimization. For example, if certain patterns of sensor readings consistently precede actual incidents, the dedicated incident learning system refines its detection parameters to better identify similar situations in the future. Similarly, if specific response actions (like alert message formats or emergency buzzer patterns) prove more effective in particular types of incidents, the dedicated incident learning system adapts its response protocols accordingly. The continuous learning and adaptation process enhances the overall safety system's effectiveness over time by leveraging real-world incident data to optimize both detection and response mechanisms.
The processor 202 is further configured to modify incident detection parameters based on the stored data. The processor 202 maintains a database of incident patterns and corresponding response effectiveness data. The processor 202 continuously analyses historical incident data to identify patterns and correlations. In some implementations, the processor 202 uses machine learning operations to modify incident detection parameters to improve accuracy over time. Such modifications in the incident detection parameters help reduce false positives while ensuring the reliable detection of genuine incidents.
In another implementation, the at least one processor 202 is further configured to generate multiple formats of the alert message based on recipient type and transmit each format to corresponding recipients. The processor 202 generates alerts in multiple formats tailored to different recipient types. For emergency services, the processor 202 creates detailed technical reports with location coordinates and the sensor data 206B. For family members, the processor 202 generates simplified alerts with the location coordinates and status information. For insurance purposes, the processor 202 prepares comprehensive incident reports with multimedia content 206A. In some implementations, the alert message can be a phone call, an email, an SMS, and a voice message.
In another implementation, at least one processor 202 is further configured to control an electronically actuated latch mechanism of the front storage compartment 118 of the two-wheeler electric vehicle 100. In such implementation, the processor 202 controls the electronically actuated latch mechanism of the front storage compartment 118 through an electronic control unit that interfaces with a motorized or solenoid-based latch system. When triggered through the vehicle's interface (via dedicated switches or control panel), the processor 202 sends an electrical signal to activate the latch actuator, which mechanically releases the lock mechanism of the front storage compartment 118. The system typically employs a small electric motor or solenoid that pulls or rotates the electronically actuated latch mechanism to disengage it from the catch, similar to modern automotive trunk release mechanisms. For safety, the electronically actuated latch mechanism includes position sensors to confirm proper latch operation and prevent accidental opening while the vehicle is in motion. A practical example would be when a rider needs to access the frunk to store items - they can simply press the designated release button on the vehicle's control panel, which signals the processor 202 to activate the latch mechanism, smoothly opening the storage compartment without requiring manual key operation.
The processor 202 is configured to authenticate a user request before enabling access to the front storage compartment 118. The processor 202 controls access to the front storage compartment 118 through the electronically actuated latch mechanism. The processor 202 implements a multi-factor authentication system, verifying a user identity through methods such as RFID key fobs, smartphone authentication, or PIN codes.
In yet another implementation, at least one processor 202 is further configured to control an electronically actuated access mechanism of a charging port of the two-wheeler electric vehicle 100. In such implementations, the processor 202 manages the charging port access mechanism of the two-wheeler electric vehicle 100 through an electronic control unit connected to an actuator system. When a charging request is initiated through the user interface, the processor 202 sends an electrical signal to the actuator mechanism, which engages a servo motor or solenoid to unlatch and open the charging port cover. In some examples, the electronically actuated access mechanism includes position sensors that provide feedback to the processor 202 about the cover's status (open/closed), allowing precise control over the opening and closing operations. The processor 202 is also automatically trigger the actuator to close the port cover once charging is complete, utilizing the same mechanical system operating in reverse to ensure secure closure of the charging port.
The processor 202 is configured to verify vehicle status meets charging safety conditions before enabling access of the charging port. The processor 202 is configured to manage charging port access through a safety verification. Before enabling the charging port access, the processor 202 checks multiple parameters, including a vehicle stationary status, battery temperature, voltage levels, and connection point integrity. The electronically actuated access mechanism only activates when all safety conditions are met. Such comprehensive safety check prevents potential charging-related incidents and protect both the two-wheeler electric vehicle 100 and the rider.
In another implementation, at least one processor 202 is further configured to activate an emergency safety buzzer of the two-wheeler electric vehicle 100 responsive to determining the occurrence of the vehicle incident and adjust a buzzer activation threshold based on a historical incident data. The emergency safety buzzer (not shown) operates through a microcontroller-based circuit triggering high-decibel audio alerts upon incident detection by the processor 202. The processor 202 analyses historical incident data for adjusting buzzer activation threshold based on past false incident detection and real incident detection. For example, if, in a case, certain movement patterns of the two-wheeler electric vehicle 100 consistently trigger false alarms. In such cases, the incident monitoring system 200 automatically adjusts the buzzer activation threshold while maintaining the safety parameters.
In another implementation, at least one processor 202 is configured to control the legroom heating system 212 of the two-wheeler electric vehicle 100. In such implementation, the processor 202 controls the legroom heating system 212 in the two-wheeler electric vehicle 100 through an integrated temperature control module. Such module receives input from temperature sensors positioned in the legroom area that continuously monitor ambient temperature conditions. When the temperature falls below a predetermined threshold, the processor 202 activates the heating elements embedded within the vehicle's leg shield area through pulse width modulation (PWM) control signals.
The processor 202 is further configured to receive temperature data from at least one temperature sensor communicatively connected with at least one processor 202 and automatically adjust the legroom heating system 212 based on the temperature data. The legroom heating system 212 utilizes the heating elements controlled through a closed-loop feedback system of the temperature sensors. The temperature sensors continuously monitor ambient and surface temperatures, feeding data to the processor 202. The processor 202 employs pulse width modulation control to regulate heating output based on temperature differentials precisely.
In yet another implementation, at least one processor 202 is further configured to control a fog lamp system 214 of the two-wheeler electric vehicle 100. The processor 202 is configured to receive ambient light and weather condition data. In addition, the processor 202 is configured to automatically activate the fog lamp system 214 based on the ambient light and weather condition data. The fog lamp system 214 integrates ambient light sensors of the plurality of sensors 210 and monitors weather conditions to determine visibility conditions. The processor 202 analyses sensor data 206B using threshold detection operations to identify poor visibility situations. The fog lamp system 214 automatically activates fog lamps when conditions meet predetermined criteria, enhancing visibility and safety in adverse weather conditions.
FIG. 3 is a diagram illustrating an exemplary scenario to explain the working of the incident monitoring system of the two-wheeler electric vehicle, in accordance with an embodiment of the present disclosure. FIG. 3 is described in conjunction with elements of FIGs. 1 and 2. With reference to FIG. 3, there is shown the diagram 300 illustrating a first exemplary scenario 302. The first exemplary scenario 302 depicts the two-wheeler electric vehicle 100 involved in a collision with another vehicle. Upon detecting the vehicle incident through the sensor data 206B, the incident monitoring system 200 activates the multimedia capture device 208 to record details of the vehicle incident. A first rider driving the two-wheeler electric vehicle 100 is constantly being monitored by the plurality of sensors 210. Upon occurrence of the vehicle incident, the incident monitoring system 200 also determines the location of the two-wheeler electric vehicle 100 and generates the alert message containing the multimedia content 206A, location, and other incident-specific information. The alert message is transmitted via the communication network 216 to a first recipient device 308 for emergency response or assistance.
With reference to FIG. 3, there is shown the diagram 300 illustrating a second exemplary scenario 304. The second exemplary scenario 304 involves a collision between the two-wheeler electric vehicle 100 and a stationary obstacle, such as a tree. The incident monitoring system 200 detects the vehicle incident by analysing data from the plurality of sensors 210, such as the accelerometers, gyroscopes, and impact sensors. Following incident detection, the incident monitoring system 200 captures the multimedia content 206A before and after the collision, determines the severity of the vehicle incident, and sends the alert message to a second recipient device 310 through the communication network 216. The alert message ensures timely action based on the nature and severity of the vehicle incident.
With reference to FIG. 3, there is shown the diagram 300 illustrating a third exemplary scenario 306. The third exemplary scenario 306 illustrates a situation where a third rider loses balance or the two-wheeler electric vehicle 100 becomes unstable, possibly resulting in a fall. The incident monitoring system 200 utilizes orientation data from gyroscopes, speed data, and tilt angle measurements to detect such vehicle incidents. Upon incident detection, the multimedia content 206A is captured, and the alert message is generated, including pre- and post-incident details, location, and severity of the vehicle incident. The alert is transmitted to a third recipient device 312, through the communication network 216, ensuring assistance is dispatched promptly.
Modifications to embodiments of the present disclosure described in the foregoing are possible without departing from the scope of the present disclosure as defined by the accompanying claims. Expressions such as "including", "comprising", "incorporating", "have", "is" used to describe and claim the present disclosure are intended to be construed in a non-exclusive manner, namely allowing for items, components or elements not explicitly described also to be present. Reference to the singular is also to be construed to relate to the plural. The word "exemplary" is used herein to mean "serving as an example, instance or illustration". Any embodiment described as “exemplary” is not necessarily to be construed as preferred or advantageous over other embodiments and/or to exclude the incorporation of features from other embodiments. The word "optionally" is used herein to mean "is provided in some embodiments and not provided in other embodiments". It is appreciated that certain features of the present disclosure, which are, for clarity, described in the context of separate embodiments, may also be provided in combination in a single embodiment. Conversely, various features of the present disclosure, which are, for brevity, described in the context of a single embodiment, may also be provided separately or in any suitable combination or as suitable in any other described embodiment of the disclosure. , Claims:CLAIMS
We claim:
1. An incident monitoring system (200) for a two-wheeler electric vehicle (100), the incident monitoring system (200) comprising:
at least one processor (202) configured to:
receive sensor data (206B) indicating a vehicle status;
determine, based on the sensor data (206B), occurrence of a vehicle incident;
automatically activate, responsive to determining the occurrence of the vehicle incident, at least one multimedia capture device (208);
obtain, from the at least one multimedia capture device (208), multimedia content (206A) associated with the vehicle incident;
determine a location of the two-wheeler electric vehicle (100);
generate an alert message comprising the multimedia content (206A), the location of the two-wheeler electric vehicle (100), and incident information derived from the sensor data (206B); and
transmit the alert message to at least one recipient device (218).
2. The incident monitoring system (200) as claimed in claim 1, wherein the sensor data (206B) comprises at least one of: acceleration data, orientation data, impact data, speed data, or location data of the two-wheeler electric vehicle (100).
3. The incident monitoring system (200) as claimed in claim 1, wherein the at least one processor (202) is further configured to: activate an emergency safety buzzer of the two-wheeler electric vehicle (100) responsive to determining the occurrence of the vehicle incident; and adjust a buzzer activation threshold based on historical incident data.
4. The incident monitoring system (200) as claimed in claim 1, wherein the at least one processor (202) is further configured to: control a legroom heating system (212) of the two-wheeler electric vehicle (100); receive temperature data from at least one temperature sensor communicatively connected with the at least one processor (202); and automatically adjust the legroom heating system (212) based on the temperature data.
5. The incident monitoring system (200) as claimed in claim 1, wherein the at least one processor (202) is further configured to: maintain a continuous buffer of the multimedia content (206A) from the at least one multimedia capture device (208) during operation of the two-wheeler electric vehicle (100); and include pre-incident multimedia content (206A) from the continuous buffer in the alert message.
6. The incident monitoring system (200) as claimed in claim 5, wherein the at least one processor (202) is further configured to:
upon detecting the vehicle incident:
store pre-incident photos from the continuous buffer of the multimedia content (206A);
capture post-incident photos from the continuous buffer of the multimedia content (206A); and
include both pre-incident and post-incident photos in the alert message.
7. The incident monitoring system (200) as claimed in claim 1, wherein the at least one processor (202) is further configured to:
upon detecting the vehicle incident:
preserve pre-incident video footage from a first predetermined time period before the vehicle incident;
continue recording for a second predetermined time period after the vehicle incident to capture post-incident video footage; and
include the pre-incident video footage and the post-incident video footage in the alert message.
8. The incident monitoring system (200) as claimed in claim 1, wherein the at least one processor (202) is further configured to: control a fog lamp system (214) of the two-wheeler electric vehicle (100); receive ambient light and weather condition data; and automatically activate the fog lamp system (214) based on the ambient light and weather condition data.
9. The incident monitoring system (200) as claimed in claim 1, wherein the at least one processor (202) is further configured to: control an electronically actuated latch mechanism of a front storage compartment (118) of the two-wheeler electric vehicle (100); and authenticate a user request before enabling access to the front storage compartment (118).
10. The incident monitoring system (200) as claimed in claim 1, wherein the at least one processor (202) is further configured to: control an electronically actuated access mechanism of a charging port of the two-wheeler electric vehicle (100); and verify vehicle status meets charging safety conditions before enabling access of the charging port.
11. The incident monitoring system (200) as claimed in claim 1, wherein the at least one processor (202) is further configured to: analyse the multimedia content (206A) to determine incident severity; and select the at least one recipient based on the determined incident severity.
12. The incident monitoring system (200) as claimed in claim 1, wherein the at least one processor (202) is further configured to: store incident patterns and corresponding response effectiveness data for the two-wheeler electric vehicle (100); and modify incident detection parameters based on the stored data.
13. The incident monitoring system (200) as claimed in claim 1, wherein the at least one processor (202) is further configured to: compress the multimedia content (206A) based on available network bandwidth; and prioritize transmission of critical incident data within the alert message.
14. The incident monitoring system (200) as claimed in claim 1, wherein the at least one processor (202) is further configured to: receive user preferences for incident detection sensitivity for the two-wheeler electric vehicle (100); and adjust incident detection thresholds based on the user preferences while maintaining minimum safety parameters.
15. The incident monitoring system (200) as claimed in claim 1, wherein the at least one processor (202) is further configured to: generate multiple formats of the alert message based on recipient type; and transmit each format to corresponding recipients
16. The incident monitoring system (200) as claimed in claim 1, wherein the at least one processor (202) is configured to determine the occurrence of the vehicle incident by:
receiving, from a plurality of sensors (210) of the two-wheeler electric vehicle (100):
acceleration data from at least one accelerometer;
orientation data from at least one gyroscope;
speed data from at least one speed sensor; and
impact force data from at least one impact sensor;
comparing the received sensor data (206B) with predetermined threshold values;
detecting at least one of:
a sudden deceleration exceeding a first threshold value;
a tilt angle exceeding a second threshold value;
an impact force exceeding a third threshold value; or
a combination of sensor values indicating loss of vehicle stability; and
confirming the vehicle incident when at least two different types of sensor data (206B) indicate anomalous vehicle behaviour within a predetermined time window.