DESC:RISK DETECTION SYSTEM FOR A CONNECTED VEHICLE
CROSS REFERENCE TO RELATED APPLICTIONS
The present application claims priority from Indian Provisional Patent Application No. 202421021042 filed on 19/03/2024, the entirety of which is incorporated herein by a reference.
TECHNICAL FIELD
The present disclosure generally relates to vehicular risk detection systems. Further, the present disclosure particularly relates to a risk detection system for a connected vehicle.
BACKGROUND
Urban development and rapid advancements in transportation infrastructure have led to the proliferation of connected vehicles, significantly transforming modern mobility systems. The integration of advanced sensing, communication, and computing technologies has enabled vehicles to interact with other road users, infrastructure, and cloud-based services through vehicle-to-everything (V2X) communication. Such connectivity has facilitated the development of intelligent transportation systems that optimize traffic management, reduce congestion, and enhance road safety. Additionally, connected vehicles incorporate electronic control units (ECUs), sensor networks, and real-time data analytics to monitor vehicle performance and affirm operational efficiency. However, the increasing complexity of vehicle systems and urban environments has necessitated robust risk detection mechanisms capable of identifying abnormal events and mitigating hazards in real time.
Various conventional risk detection systems have been developed to monitor vehicle conditions and detect anomalies that may indicate safety risks. One commonly employed technique involves the use of onboard diagnostics (OBD) systems integrated with vehicle sensors to track parameters such as engine health, braking efficiency, fuel consumption, tire pressure, and emission levels. Upon detecting deviations from predefined thresholds, alerts are generated to notify drivers or fleet operators about potential issues. However, such conventional approaches are often limited by their reliance on static rule-based algorithms, which lack adaptability to dynamic driving conditions, environmental changes, and evolving vehicle behaviors. Furthermore, sensor inaccuracies, false alarms, and delayed responses reduce the effectiveness of such systems, leading to compromised risk detection and delayed interventions.
Another widely recognized approach involves machine learning-based risk assessment techniques, wherein predictive models analyze historical and real-time vehicle data to detect patterns indicative of hazards. Such models leverage adaptive learning mechanisms to refine risk predictions over time by incorporating newly acquired data. However, machine learning-based techniques require extensive training datasets and significant computational resources, making real-time implementation challenging, particularly in edge computing environments. Additionally, such techniques are susceptible to sensor noise, data inconsistencies, and connectivity disruptions, which can compromise the accuracy and timeliness of risk assessment.
Apart from onboard diagnostics and machine learning-based risk detection, various other techniques have been explored to enhance vehicle safety. One such technique involves cloud-based risk assessment platforms that collect, process, and analyze data from multiple connected vehicles to identify trends and emerging risk factors. While such platforms enable large-scale risk evaluation and predictive analytics, they are heavily dependent on continuous network connectivity, introducing challenges related to data transmission delays, bandwidth constraints, and cybersecurity vulnerabilities. Another approach integrates biometric sensors and camera-based driver monitoring systems to assess driver fatigue, distraction, or impairment. However, such techniques primarily focus on internal driver behavior and do not account for external risk factors such as road conditions, mechanical failures, and unpredictable environmental hazards.
In addition to technical challenges, existing risk detection techniques often struggle with real-time event assessment and decision-making. The increasing complexity of modern vehicle architectures, coupled with diverse operational environments, necessitates the collection of additional event-specific data to enable risk analysis. However, conventional techniques lack dynamic data acquisition capabilities, resulting in incomplete assessments and limited situational awareness. Moreover, delays in processing and transmitting risk-related information to external monitoring systems or cloud servers further hinder timely risk mitigation efforts.
In light of the above discussion, there exists an urgent need for solutions that overcome the problems associated with conventional systems and/or techniques for risk detection in connected vehicles.
SUMMARY
The aim of the present disclosure is to provide a risk detection system for a connected vehicle to enable real-time identification and assessment of abnormal events occurring within the connected vehicle.
The present disclosure relates to a risk detection system for a connected vehicle. A detection unit detects an abnormal event occurring within the connected vehicle based on sensor data and generates an abnormal event signal. A receiving unit receives the generated abnormal event signal from the detection unit via a vehicle networking terminal. A first-generation unit generates data demand information based on the received abnormal event signal using a pre-trained frequent pattern model, wherein the generated data demand information instructs the vehicle networking terminal to collect additional event data associated with the detected abnormal event. An acquisition unit obtains the additional event data corresponding to the generated data demand information from the vehicle networking terminal. A second-generation unit generates a risk analysis result for the vehicle networking terminal based on the abnormal event signal and the additional event data. A transmission unit transmits the generated risk analysis result to a server based on the detected abnormal event. Further, such a system enables real-time detection of abnormal events and enhances risk assessment accuracy through additional data acquisition. Moreover, such a system improves overall vehicular safety by facilitating the transmission of risk analysis results to a server for further analysis.
In another aspect, the present disclosure provides a risk detection system further comprising an anomaly detection unit. Such an anomaly detection unit compares the abnormal event signal with the previously recorded abnormal event signals to classify a severity level of an anomaly. Further, such an anomaly detection unit assigns a confidence score to the detected abnormal event based on deviation from the previously recorded abnormal event signals. Moreover, such an anomaly detection unit improves risk classification accuracy by dynamically analyzing historical event patterns.
In another aspect, the present disclosure provides a risk detection system further comprising a vehicular event simulation unit. Such a vehicular event simulation unit reconstructs the detected abnormal event using sensor data to validate the risk assessment conclusions. Further, such a vehicular event simulation unit generates an event replay model to enable post-event forensic analysis. Moreover, such a vehicular event simulation unit enhances risk assessment reliability by providing a data-driven event reconstruction mechanism.
In another aspect, the present disclosure provides a risk detection system wherein the first-generation unit further refines the data demand information. Such refinement is based on a correlation between the previously recorded abnormal event signals and previously collected additional event data. Further, such refinement enables more targeted and efficient data collection by adapting to historical event correlations.
In another aspect, the present disclosure provides a risk detection system further comprising a vehicular behavior profiling unit. Such a vehicular behavior profiling unit analyzes the driving patterns in response to the abnormal event signal to refine detection accuracy. Further, such a vehicular behavior profiling unit enhances the contextual understanding of driver responses to detected risks.
In another aspect, the present disclosure provides a risk detection system further comprising an emergency response coordination unit. Such an emergency response coordination unit communicates the risk analysis result to the connected vehicles within a predefined radius to improve hazard awareness. Further, such an emergency response coordination unit enhances collective vehicular safety by disseminating risk information to nearby vehicles.
In another aspect, the present disclosure provides a risk detection system wherein the receiving unit further verifies authenticity of the abnormal event signal. Such verification is performed by applying a validation mechanism based on the previously recorded abnormal event signals. Further, such verification reduces false alarms by making sure that detected anomalies are genuine before initiating risk response actions.
In another aspect, the present disclosure provides a risk detection system further comprising a predictive risk assessment unit. Such a predictive risk assessment unit generates a probability estimate of the future abnormal event occurrences based on the previously recorded abnormal event signals. Further, such a predictive risk assessment unit refines the probability estimate by integrating the vehicular motion parameters. Moreover, such a predictive risk assessment unit enhances proactive risk management by forecasting future abnormalities.
In another aspect, the present disclosure provides a risk detection system further comprising a geo-tagging unit. Such a geo-tagging unit assigns a location identifier to the abnormal event signal for spatial risk mapping. Further, such a geo-tagging unit links the location identifier with traffic data to optimize event response prioritization. Moreover, such a geo-tagging unit improves situational awareness by correlating detected risks with geographical locations.
In another aspect, the present disclosure provides a method for detecting risks in a connected vehicle. A detection unit detects an abnormal event occurring within the connected vehicle based on sensor data and generates an abnormal event signal. A receiving unit receives the abnormal event signal from the detection unit via a vehicle networking terminal. A first-generation unit generates data demand information based on the received abnormal event signal using a pre-trained frequent pattern model, wherein the generated data demand information instructs the vehicle networking terminal to collect additional event data associated with the detected abnormal event. An acquisition unit obtains the additional event data corresponding to the generated data demand information from the vehicle networking terminal. A second-generation unit generates a risk analysis result for the vehicle networking terminal based on the abnormal event signal and the additional event data. A sending unit transmits the generated risk analysis result to a server based on the detected abnormal event. Further, such a method enables a structured approach for real-time risk assessment and event-driven data collection.
In another aspect, the present disclosure provides a method further comprising displaying recommendations based on the generated risk analysis result. A driver assistance interface displays such recommendations and transmits recommended safety manoeuvres to an external vehicle control system. Further, such a driver assistance interface enhances driver response efficiency by providing actionable safety recommendations.
In another aspect, the present disclosure provides a method further comprising generating a structured log entry for the detected abnormal event. An automated incident logging unit generates such a log entry and categorizes the log entry based on event severity and a resolution status. Further, such an automated incident logging unit facilitates systematic incident tracking and documentation.
In another aspect, the present disclosure provides a method further comprising identifying external environmental factors contributing to the detected abnormal event. A road hazard detection unit identifies such external environmental factors and integrates detected hazards with navigation data. Further, such a road hazard detection unit improves real-time route optimization by correlating risk factors with navigation parameters.
In another aspect, the present disclosure provides a method further comprising prioritizing the risk analysis result based on severity levels derived from a predefined risk classification model. A second-generation unit performs such prioritization to streamline risk response efforts. Further, such prioritization enables more effective resource allocation for addressing high-risk events.
In another aspect, the present disclosure provides a method further comprising adjusting data processing parameters of the pre-trained frequent pattern model. An adaptive event correlation unit performs such adjustments based on the risk analysis result. Further, such an adaptive event correlation unit improves model adaptability by refining data interpretation based on evolving risk patterns.
BRIEF DESCRIPTION OF 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 illustrates a risk detection system 100 for a connected vehicle, in accordance with the embodiments of the present disclosure.
FIG. 2 illustrates a method 200 for detecting the risks in a connected vehicle, in accordance with the embodiments of the present disclosure.
FIG. 3 illustrates a data flow diagram for detecting risks in a connected vehicle, in accordance with the embodiments of the present disclosure.
FIG. 4 illustrates a decision flow diagram for detecting risks in a connected vehicle, in accordance with the embodiments 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
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 recognise that other embodiments for carrying out or practising the present disclosure are also possible.
The description set forth below in connection with the appended drawings is intended as a description of certain embodiments of a risk detection system for a connected vehicle and is not intended to represent the only forms that may be developed or utilised. The description sets forth the various structures and/or functions in connection with the illustrated embodiments; however, it is to be understood that the disclosed embodiments are merely exemplary of the disclosure that may be embodied in various and alternative forms. The figures are not necessarily to scale; some features may be exaggerated or minimised to show details of particular components. Therefore, specific structural and functional details disclosed herein are not to be interpreted as limiting, but merely as a representative basis for teaching one skilled in the art to variously employ the present invention.
While the disclosure is susceptible to various modifications and alternative forms, specific embodiment thereof has been shown by way of example in the drawings and will be described in detail below. It should be understood, however, that it is not intended to limit the disclosure to the particular forms disclosed, but on the contrary, the disclosure is to cover all modifications, equivalents, and alternatives falling within the scope of the disclosure.
In the following detailed description of the embodiments of the disclosure, reference is made to the accompanying drawings, and which are shown by way of illustration specific embodiments in which the disclosure may be practiced. These embodiments are described in sufficient detail to enable those skilled in the art to practice the disclosure, and it is to be understood that other embodiments may be utilized and that changes may be made without departing from the scope of the present disclosure. The following description is, therefore, not to be taken in a limiting sense.
The present disclosure will be described herein below with reference to the accompanying drawings. In the following description, well known functions or constructions are not described in detail since they would obscure the description with unnecessary detail.
As used herein, the term "risk detection system" refers to a system associated with identifying and analyzing potential risks occurring in a monitored environment. Such a system comprises multiple components that work collectively to detect, assess, and communicate abnormal conditions that may require intervention. The implementation of such a system is observed in various domains, including automotive safety, industrial monitoring, and healthcare diagnostics. In an automotive application, such a system comprises sensors, data processing units, and communication interfaces that monitor real-time operational parameters of a vehicle. Risk identification is performed based on predefined patterns, statistical models, or machine learning-based analysis.
As used herein, the term "connected vehicle" refers to a vehicle that utilizes embedded communication technologies to exchange data with internal and external systems. Such a vehicle interacts with other vehicles, infrastructure, cloud servers, and user interfaces to optimize safety, navigation, and operational performance. Connected vehicle technologies comprise short-range wireless communication, cellular networks, and satellite-based navigation systems. For example, in a vehicle-to-vehicle communication setup, such a vehicle transmits real-time movement data to nearby vehicles to facilitate collision avoidance. In a vehicle-to-infrastructure scenario, such a vehicle receives traffic signal information to optimize driving behavior based on signal timings. The applications of connected vehicles extend to fleet management, remote diagnostics, and automated driving support systems.
As used herein, the term "detection unit" refers to a component of a system responsible for identifying specific conditions based on input data. Such a detection unit processes signals received from sensors, analyzing variations that indicate predefined events or anomalies. Detection mechanisms vary based on the monitored parameter, including optical detection, thermal sensing, acoustic analysis, or electromagnetic measurements.
As used herein, the term "abnormal event" refers to an occurrence that deviates from expected or predefined operational conditions. Such an event is characterized by parameters exceeding predefined thresholds or showing patterns inconsistent with historical data. The classification of abnormal events depends on the specific application, including mechanical failures, environmental hazards, or behavioral anomalies.
As used herein, the term "sensor data" refers to information acquired from sensors monitoring physical, chemical, or environmental parameters. Such sensor data includes numerical readings, signal waveforms, or binary status indicators, depending on the type of sensor used. Sensors that generate such data comprise accelerometers, gyroscopes, thermocouples, piezoelectric sensors, and pressure transducers. In an automotive system, such sensor data comprises acceleration values from an inertial measurement unit, tire pressure levels from pressure sensors, and braking force measurements from force transducers. Such sensor data is processed, analyzed, and transmitted to relevant components for further evaluation and response.
As used herein, the term "abnormal event signal" refers to an electronically generated indicator that signifies the occurrence of an abnormal event. Such an abnormal event signal is produced when monitored parameters exceed predefined limits or match specific risk patterns. Such an abnormal event signal is transmitted to processing units, alert mechanisms, or external monitoring systems for further assessment.
As used herein, the term "receiving unit" refers to a component responsible for acquiring and processing incoming data or signals. Such a receiving unit is utilized in various communication and control systems to facilitate information exchange. In automotive applications, such a receiving unit processes sensor inputs from distributed monitoring devices and forwards relevant information to processing units. For example, in a vehicle networking system, such a receiving unit acquires traffic data from external sources and integrates the acquired traffic data into navigation control mechanisms. The operation of such a receiving unit involves decoding, filtering, and error-checking incoming transmissions to make sure data integrity before further processing.
As used herein, the term "detect" refers to the process of identifying the presence, occurrence, or deviation of a specific condition based on acquired data. Such a process is fundamental to systems requiring continuous monitoring and analysis of physical or environmental parameters. Various methods are used to achieve such detection, including threshold-based comparisons, statistical analysis, and pattern recognition techniques. In automotive applications, such detection involves analyzing acceleration data to determine whether a vehicle is experiencing excessive lateral forces indicative of a stability risk. Detection mechanisms depend on sensor accuracy, data processing algorithms, and real-time computational capabilities to identify relevant conditions effectively.
As used herein, the term "vehicle networking terminal" refers to a communication interface within a vehicle that enables data exchange between onboard systems, external infrastructure, and cloud-based services. Such a vehicle networking terminal facilitates real-time transmission and reception of data, supporting applications such as remote diagnostics, traffic monitoring, and vehicular safety mechanisms. The connectivity of such a vehicle networking terminal is established through wired or wireless communication technologies, including cellular networks, dedicated short-range communication (DSRC), Bluetooth, or Wi-Fi.
As used herein, the term "first-generation unit" refers to a processing unit responsible for generating initial data insights based on received input signals. Such a first-generation unit analyzes detected anomalies or abnormalities and determines further data acquisition requirements based on pre-defined criteria. The operation of such a first-generation unit involves statistical analysis, pattern recognition, and correlation-based decision-making.
As used herein, the term "data demand information" refers to a set of parameters specifying additional data collection requirements based on an initial event analysis. Such data demand information comprises specific sensor inputs, environmental readings, or diagnostic parameters necessary to refine the accuracy of risk assessment. The structure of such data demand information is determined by predefined models that correlate historical trends with real-time sensor outputs.
As used herein, the term "additional event data" refers to supplementary information collected in response to a detected abnormal event. Such additional event data enhances the accuracy of risk evaluation by providing contextual insights beyond the initially detected parameters. The nature of such additional event data varies depending on the application, including secondary sensor readings, environmental conditions, or historical operational trends.
As used herein, the term "acquisition unit" refers to a component responsible for retrieving data from sources based on specified collection parameters. Such an acquisition unit interacts with sensors, external databases, or remote monitoring systems to acquire relevant data necessary for further analysis. The implementation of such an acquisition unit varies based on the data source, including direct sensor interfacing, communication with external servers, or retrieval from cloud-based storage.
As used herein, the term "second-generation unit" refers to a processing unit responsible for generating refined analytical outputs based on primary event detection and supplementary data acquisition. Such a second-generation unit evaluates multiple data sources to derive an assessment of detected risks. The operation of such a second-generation unit involves statistical correlation, machine learning-based inference, or rule-based decision-making.
As used herein, the term "risk analysis result" refers to an analytical outcome derived from evaluating detected abnormal events, supplementary data inputs, and predefined assessment models. Such a risk analysis result provides a structured representation of the detected risk, indicating severity levels, potential impact, and recommended response actions. The format of such a risk analysis result varies depending on the domain, including numerical risk scores, categorical classifications, or visualized trend reports.
As used herein, the term "transmission unit" refers to a component responsible for communicating processed data or analytical results to designated recipients. Such a transmission unit interacts with wired or wireless communication interfaces to deliver information securely and efficiently. The implementation of such a transmission unit varies depending on the communication medium, including radio frequency transmission, satellite communication, or cloud-based data exchange.
As used herein, the term "server" refers to a computing system that processes, stores, or distributes data within a networked environment. Such a server hosts analytical models, archives historical data, and facilitates communication between multiple connected entities. The configuration of such a server depends on the application, including cloud-based, on-premises, or edge-computing architectures.
As used herein, the term "transmit" refers to the process of sending data, signals, or information from one entity to another through a communication channel. Such transmission occurs through wired connections, wireless networks, or optical communication pathways. Transmission mechanisms comprise signal encoding, data encryption, and error correction techniques to enable reliable information delivery.
FIG. 1 illustrates a risk detection system 100 for a connected vehicle, in accordance with the embodiments of the present disclosure. The risk detection system 100 comprises a detection unit 102 that detects an abnormal event occurring within a connected vehicle based on sensor data and generates an abnormal event signal. The detection unit 102 receives input from multiple sensors integrated within the connected vehicle, where such sensors monitor operational parameters including but not limited to acceleration, braking force, engine performance, tire pressure, and environmental conditions. The detection unit 102 continuously analyzes the sensor data to identify deviations from predefined operational thresholds or learned behavioral patterns indicative of abnormal conditions. Such deviations comprise abrupt deceleration, excessive lateral forces, unusual engine temperature fluctuations, or erratic steering behavior. Upon detecting such an abnormal event, the detection unit 102 generates the abnormal event signal representing the detected deviation. The detection unit 102 associates the abnormal event signal with corresponding sensor data, timestamp information, and vehicle status indicators. The abnormal event signal is then transmitted to a receiving unit 104 for further processing. The detection unit 102 operates using predefined detection parameters stored in an onboard database or adaptive learning mechanisms based on historical sensor data. In certain implementations, the detection unit 102 incorporates multiple detection methodologies, including threshold-based comparisons, statistical anomaly detection, and pattern recognition models. The detection unit 102 functions autonomously within the connected vehicle and processes sensor data in real-time to assure immediate identification of abnormal events.
In an embodiment, the risk detection system 100 comprises a receiving unit 104 that receives the generated abnormal event signal from the detection unit 102 via a vehicle networking terminal 106. The receiving unit 104 processes the abnormal event signal to validate the integrity and format before forwarding the abnormal event signal to subsequent processing units. The receiving unit 104 establishes a communication interface with the vehicle networking terminal 106, enabling data exchange between onboard systems and external processing units. The receiving unit 104 verifies the authenticity of the abnormal event signal by cross-referencing the abnormal event signal with previously recorded event patterns or applying digital signature validation. The receiving unit 104 filters redundant or low-priority abnormal event signals based on predefined event severity criteria. The receiving unit 104 is implemented as a dedicated electronic component or as part of an integrated vehicular data processing system. The receiving unit 104 supports multiple data communication standards, including wired and wireless transmission technologies, to facilitate efficient information exchange. Upon validation, the receiving unit 104 forwards the abnormal event signal to a first-generation unit 108 for further processing. The receiving unit 104 is configured to handle multiple incoming abnormal event signals simultaneously, affirming uninterrupted data flow within the risk detection system 100.
In an embodiment, the first-generation unit 108 generates data demand information based on the received abnormal event signal using a pre-trained frequent pattern model, wherein the generated data demand information instructs the vehicle networking terminal 106 to collect additional event data associated with the detected abnormal event. The first-generation unit 108 processes the abnormal event signal received from the receiving unit 104 to determine additional data collection requirements. The first-generation unit 108 analyzes the abnormal event characteristics using a pre-trained frequent pattern model stored within a local or remote database. The first-generation unit 108 identifies correlations between the detected abnormal event and previously recorded abnormal events to refine data collection criteria. The first-generation unit 108 generates the data demand information specifying the type and source of additional event data required to enhance risk assessment accuracy. The first-generation unit 108 transmits the data demand information to the vehicle networking terminal 106, enabling targeted data acquisition from relevant sensors, onboard logs, or external monitoring systems. The first-generation unit 108 dynamically adjusts data demand parameters based on environmental conditions, vehicle status, or operational requirements. The first-generation unit 108 optimizes data demand requests by prioritizing data sources while minimizing unnecessary data retrieval.
In an embodiment, the risk detection system 100 comprises an acquisition unit 110 that obtains the additional event data corresponding to the generated data demand information from the vehicle networking terminal 106. The acquisition unit 110 retrieves sensor data, diagnostic logs, or external input signals specified in the data demand information. The acquisition unit 110 interacts with multiple data sources, including onboard vehicle control systems, remote servers, and cloud-based analytics platforms. The acquisition unit 110 retrieves real-time data streams or historical event logs based on the requirements defined by the first-generation unit 108. The acquisition unit 110 affirms accurate data retrieval by performing integrity checks and validation procedures before processing the acquired data. The acquisition unit 110 filters irrelevant or redundant data to optimize processing efficiency. The acquisition unit 110 supports multiple data acquisition methodologies, including direct sensor interfacing, remote server queries, and event-driven data logging. The acquisition unit 110 processes acquired data in raw or structured formats before transmitting the acquired data to a second-generation unit 112 for further risk analysis.
In an embodiment, the risk detection system 100 comprises a second-generation unit 112 that generates a risk analysis result for the vehicle networking terminal 106 based on the abnormal event signal and the additional event data. The second-generation unit 112 processes the acquired additional event data in conjunction with the abnormal event signal to derive an assessment of potential risks. The second-generation unit 112 applies predefined risk assessment models, statistical correlation techniques, or pattern recognition methodologies to analyze the acquired data. The second-generation unit 112 assigns a severity rating to the detected abnormal event based on the evaluated parameters. The second-generation unit 112 integrates multiple data inputs to refine the accuracy of risk analysis results. The second-generation unit 112 dynamically adjusts risk analysis models based on historical event patterns or updated environmental conditions. The second-generation unit 112 generates a structured risk analysis result, including key risk indicators, severity classifications, and potential impact assessments. The second-generation unit 112 stores generated risk analysis results in a local memory for reference in subsequent assessments or system calibrations. The second-generation unit 112 transmits the generated risk analysis result to a transmission unit 114 for further dissemination.
In an embodiment, the risk detection system 100 comprises a transmission unit 114 that transmits the generated risk analysis result to a server 116 based on the detected abnormal event. The transmission unit 114 establishes a communication link with the server 116 to enable secure and reliable data transmission. The transmission unit 114 formats the risk analysis result into a standardized data structure suitable for remote processing and storage. The transmission unit 114 applies data encryption, compression, or authentication mechanisms to maintain transmission integrity. The transmission unit 114 supports multiple transmission protocols, including cellular networks, Wi-Fi, satellite communication, and vehicle-to-infrastructure (V2I) communication channels. The transmission unit 114 makes sure real-time transmission of risk analysis results to facilitate timely response actions. The transmission unit 114 includes error correction mechanisms to address data transmission inconsistencies. The transmission unit 114 dynamically adjusts transmission priorities based on network availability, bandwidth limitations, or operational urgency. The transmission unit 114 stores transmission logs in a local memory to track communication history and affirm data consistency.
In an embodiment, the risk detection system 100 may comprise an anomaly detection unit that compares an abnormal event signal with previously recorded abnormal event signals to classify a severity level of an anomaly. The anomaly detection unit receives input from a historical event database containing records of previously detected abnormal events, including sensor readings, environmental conditions, and vehicle operational states. The anomaly detection unit applies comparative analysis techniques to determine whether the detected abnormal event exhibits similar characteristics to past events or represents an outlier requiring further assessment. A confidence score is assigned to the detected abnormal event based on deviation from previously recorded abnormal event signals. The confidence score quantifies the likelihood that the detected abnormal event aligns with a known anomaly pattern or represents a distinct occurrence requiring special attention. The anomaly detection unit refines severity classification by integrating factors such as sensor accuracy, external environmental influences, and temporal trends. The anomaly detection unit communicates the assigned severity level and confidence score to subsequent processing units to facilitate informed risk assessment and mitigation measures.
In an embodiment, the risk detection system 100 may comprise a vehicular event simulation unit that reconstructs a detected abnormal event using sensor data to validate risk assessment conclusions. The vehicular event simulation unit receives input from real-time sensor data, historical records, and predefined vehicular models to generate a simulated representation of the detected abnormal event. The vehicular event simulation unit applies computational modeling techniques to recreate event conditions, including vehicle speed, acceleration, braking force, and external environmental parameters. The vehicular event simulation unit generates an event replay model to facilitate post-event forensic analysis, providing insights into the sequence of events leading to the detected abnormality. The vehicular event simulation unit enables identification of contributing factors such as driver behavior, road surface conditions, and mechanical failures. The event replay model incorporates multi-sensor fusion techniques to enhance accuracy, combining inputs from cameras, accelerometers, gyroscopes, and GPS data. The vehicular event simulation unit stores reconstructed event data in a structured format for review by external monitoring entities or regulatory authorities.
In an embodiment, the first-generation unit 108 may refine data demand information based on a correlation between previously recorded abnormal event signals and previously collected additional event data. The first-generation unit 108 evaluates patterns within historical abnormal event records to identify commonalities and refine subsequent data collection requests. The first-generation unit 108 determines whether previously acquired additional event data provides sufficient contextual understanding of a detected abnormal event or whether further data acquisition is necessary. The first-generation unit 108 establishes correlation metrics by analyzing sensor outputs, operational states, and environmental conditions associated with past events. The first-generation unit 108 dynamically adjusts data demand information parameters to optimize the selection of relevant data sources while minimizing redundant data retrieval. The first-generation unit 108 applies predefined correlation thresholds to filter unnecessary data acquisition requests, enabling efficient processing of abnormal event signals. The first-generation unit 108 transmits refined data demand information to a vehicle networking terminal 106 to facilitate targeted data collection. The first-generation unit 108 updates correlation parameters based on continuous learning from newly recorded abnormal event signals and additional event data.
In an embodiment, the risk detection system 100 may comprise a vehicular behavior profiling unit that analyzes driving patterns in response to an abnormal event signal to refine detection accuracy. The vehicular behavior profiling unit receives input from sensors monitoring vehicle acceleration, steering response, braking force, and driver interactions with control interfaces. The vehicular behavior profiling unit evaluates deviations from normal driving patterns to determine whether an abnormal event results from external factors or driver-initiated actions. The vehicular behavior profiling unit identifies recurring patterns of driver responses to abnormal event signals, classifying behavioral trends such as sudden braking, evasive manoeuvres, or prolonged inactivity. The vehicular behavior profiling unit integrates historical driving behavior records to differentiate between expected and anomalous responses. The vehicular behavior profiling unit applies classification techniques to associate specific driving patterns with potential risks, enhancing the reliability of risk assessment. The vehicular behavior profiling unit transmits analyzed behavioral insights to processing units responsible for generating risk analysis results. The vehicular behavior profiling unit continuously updates profiling parameters based on evolving driver behavior trends and environmental influences.
In an embodiment, the risk detection system 100 may comprise an emergency response coordination unit that communicates a risk analysis result to connected vehicles within a predefined radius to improve hazard awareness. The emergency response coordination unit receives the risk analysis result from a second-generation unit 112 and determines an appropriate broadcast range based on severity classification and geographic context. The emergency response coordination unit establishes a communication link with a vehicle networking terminal 106 to facilitate real-time transmission of hazard alerts. The emergency response coordination unit transmits warning notifications to connected vehicles using vehicle-to-vehicle (V2V) communication protocols, allowing nearby vehicles to implement precautionary measures. The emergency response coordination unit integrates traffic density data and environmental conditions to optimize dissemination of risk information. The emergency response coordination unit applies prioritization logic to determine whether additional entities, such as traffic management centers or emergency response teams, should receive the transmitted risk analysis result. The emergency response coordination unit logs transmitted alerts in a database for audit and review purposes.
In an embodiment, the receiving unit 104 may verify authenticity of the abnormal event signal by applying a validation mechanism based on previously recorded abnormal event signals. The receiving unit 104 processes the abnormal event signal received from a detection unit 102 and compares characteristics of the received signal with historical data stored in a memory or database. The receiving unit 104 evaluates multiple parameters, including signal structure, frequency of occurrence, and correlation with past abnormal events, to determine authenticity. The receiving unit 104 applies a validation mechanism that includes statistical analysis, pattern recognition, or a checksum-based integrity verification to identify inconsistencies or anomalies in the received signal. If discrepancies are detected, the receiving unit 104 flags the abnormal event signal for further review or requests additional sensor data before proceeding with risk assessment. The receiving unit 104 operates in real-time to make sure that only authentic abnormal event signals are processed by subsequent units. The receiving unit 104 maintains a record of validation results to refine future signal verification processes. The receiving unit 104 adapts validation criteria based on environmental conditions, sensor reliability metrics, and frequency of detected abnormal events to minimize false alarms and prevent incorrect data processing.
In an embodiment, the risk detection system 100 may comprise a predictive risk assessment unit that generates a probability estimate of future abnormal event occurrences based on previously recorded abnormal event signals and refines the probability estimate by integrating vehicular motion parameters. The predictive risk assessment unit analyzes historical abnormal event signals stored in a database and applies statistical models to determine patterns indicative of future events. The predictive risk assessment unit evaluates factors such as frequency of past events, environmental conditions, and variations in vehicle operational parameters to compute probability estimates. The predictive risk assessment unit integrates vehicular motion parameters, including acceleration, braking force, yaw rate, and lateral stability, to refine the probability estimate by incorporating real-time driving dynamics. The predictive risk assessment unit dynamically adjusts probability thresholds based on observed trends and deviations from normal vehicle behavior. The predictive risk assessment unit processes data continuously and updates probability estimates at predefined intervals or upon detection of significant changes in vehicle behavior. The predictive risk assessment unit transmits probability estimates to subsequent processing units or external monitoring systems to facilitate proactive risk assessment and incident prevention.
In an embodiment, the risk detection system 100 may comprise a geo-tagging unit that assigns a location identifier to the abnormal event signal for spatial risk mapping and links the location identifier with traffic data to optimize event response prioritization. The geo-tagging unit retrieves real-time geographic coordinates from a vehicle positioning system and associates such coordinates with the detected abnormal event. The geo-tagging unit assigns a unique location identifier based on the detected position and timestamps the abnormal event signal to maintain an accurate event history. The geo-tagging unit retrieves and integrates traffic data, including congestion levels, road conditions, and historical incident reports, to contextualize the detected abnormal event within the broader transportation network. The geo-tagging unit prioritizes event response based on spatial risk mapping, making sure that events occurring in high-risk areas receive timely analysis and intervention. The geo-tagging unit continuously updates spatial data and recalibrates location-based risk assessments based on evolving traffic conditions. The geo-tagging unit transmits geo-tagged abnormal event data to remote servers or emergency response systems for further processing. The geo-tagging unit maintains a repository of location-tagged events to enhance predictive analytics and route optimization.
FIG. 2 illustrates a method 200 for detecting the risks in a connected vehicle, in accordance with the embodiments of the present disclosure. At step 202, a detection unit 102 detects an abnormal event occurring within a connected vehicle based on sensor data and generates an abnormal event signal. The detection unit 102 continuously receives input from multiple sensors monitoring vehicle parameters including acceleration, braking force, tire pressure, engine performance, and external environmental conditions. The detection unit 102 evaluates sensor readings against predefined thresholds or historical data patterns to identify deviations indicative of potential risks. When an abnormal condition such as excessive deceleration, instability, or abnormal engine behavior is detected, the detection unit 102 generates an abnormal event signal. The detection unit 102 associates the abnormal event signal with relevant metadata, including timestamp, vehicle speed, geographic location, and sensor-specific readings. The detection unit 102 then forwards the abnormal event signal to a receiving unit 104 for further processing.
At step 204, the receiving unit 104 receives the abnormal event signal from the detection unit 102 via a vehicle networking terminal 106. The receiving unit 104 establishes a communication link with the vehicle networking terminal 106 to facilitate data transfer. Upon receiving the abnormal event signal, the receiving unit 104 verifies signal integrity by performing checks on data format, completeness, and consistency. The receiving unit 104 compares the abnormal event signal with previously recorded abnormal event signals to determine correlations. If the abnormal event signal meets predefined validation criteria, the receiving unit 104 forwards the signal to a first-generation unit 108 for further processing. The receiving unit 104 also logs received abnormal event signals in a memory database for future reference and analysis.
At step 206, the first-generation unit 108 generates data demand information based on the received abnormal event signal using a pre-trained frequent pattern model. The first-generation unit 108 processes the abnormal event signal to determine additional data collection requirements that improve event assessment accuracy. The first-generation unit 108 evaluates event characteristics such as frequency, location, and environmental impact to refine data demand parameters. Based on detected event patterns, the first-generation unit 108 generates data demand information specifying the type of additional data required from relevant sensors or external sources. The first-generation unit 108 transmits the data demand information to the vehicle networking terminal 106, instructing the terminal to initiate targeted data collection processes. The first-generation unit 108 also updates internal records with newly generated data demand parameters to refine future event analysis.
At step 208, an acquisition unit 110 obtains the additional event data corresponding to the generated data demand information from the vehicle networking terminal 106. The acquisition unit 110 retrieves requested sensor readings, system logs, or external monitoring data as specified by the first-generation unit 108. The acquisition unit 110 interacts with onboard vehicle control systems, cloud-based storage platforms, and external traffic management databases to obtain relevant information. The acquisition unit 110 validates retrieved additional event data by performing integrity checks and eliminating redundant or low-relevance data.
At step 210, the second-generation unit 112 generates a risk analysis result for the vehicle networking terminal 106 based on the abnormal event signal and the additional event data. The second-generation unit 112 processes the acquired data to assess the severity, probability, and consequences of the detected abnormal event. The second-generation unit 112 integrates multiple data sources, including real-time sensor inputs, historical event records, and environmental conditions, to produce an assessment. The second-generation unit 112 applies predefined risk evaluation techniques, such as statistical correlation or pattern recognition, to refine event classification. The second-generation unit 112 assigns a risk level to the detected abnormal event and generates a structured risk analysis result that comprises key event attributes and associated impact assessments. The second-generation unit 112 prepares the risk analysis result for transmission to external monitoring systems.
At step 212, a transmission unit 114 transmits the generated risk analysis result to a server 116 based on the detected abnormal event. The transmission unit 114 formats the risk analysis result into a structured data package for efficient transmission. The transmission unit 114 establishes a communication link with the server 116 via a wired or wireless network connection. The transmission unit 114 applies data security measures, such as encryption or authentication, to maintain transmission integrity. The transmission unit 114 affirms that the risk analysis result is successfully received by the server 116 by implementing acknowledgment-based transmission protocols. The transmission unit 114 logs transmission details, including timestamps, destination addresses, and data packet identifiers, for future reference. The transmission unit 114 dynamically adjusts transmission priority based on network conditions, server availability, or the severity of the detected abnormal event.
FIG. 3 illustrates a data flow diagram for detecting risks in a connected vehicle, in accordance with the embodiments of the present disclosure. Sensor inputs provide real-time data related to vehicle operations, including acceleration, braking force, engine performance, and environmental conditions. Detection unit 102 analyzes sensor data to identify deviations from predefined thresholds and generates an abnormal event signal. Receiving unit 104 receives the abnormal event signal and transmits such signal to first-generation unit 108 for further processing. First-generation unit 108 generates data demand information specifying additional event data required to refine risk assessment. Vehicle networking terminal 106 facilitates communication between onboard components and external data sources to collect additional event data. Acquisition unit 110 retrieves such additional event data and forwards such data to second-generation unit 112. Second-generation unit 112 processes abnormal event signals and additional event data to generate a structured risk analysis result. Transmission unit 114 transmits the risk analysis result to server 116 for further assessment and response coordination.
FIG. 4 illustrates a decision flow diagram for detecting risks in a connected vehicle, in accordance with the embodiments of the present disclosure. The decision flow begins with determining whether an abnormal event has been detected. If no abnormal event is detected, no further action is required. If an abnormal event is detected, an evaluation is performed to determine whether additional event data is required. If additional event data is needed, acquisition unit 110 fetches the required data from relevant sources, including onboard sensors, vehicle networking terminal 106, and historical event records. Once the required data is available, risk analysis is performed to assess the severity of the detected abnormal event. Based on the analysis, the abnormal event is categorized into high-risk, medium-risk, or low-risk classifications. High-risk events are prioritized for immediate reporting, medium-risk events are scheduled for standard reporting, and low-risk events are recorded for future reference. The final risk analysis result is transmitted to server 116.
In an embodiment, a driver assistance interface may display recommendations based on the generated risk analysis result and transmits recommended safety manoeuvres to an external vehicle control system. The driver assistance interface processes the risk analysis result received from the second-generation unit 112 and presents visual or auditory cues to assist in real-time decision-making. The driver assistance interface interprets severity levels and contextual factors of the detected abnormal event to determine an appropriate response. The driver assistance interface generates adaptive safety recommendations by analyzing vehicle dynamics, road conditions, and surrounding traffic. The recommendations are presented through a graphical display, heads-up display, or voice-guided instructions. The driver assistance interface transmits the recommended safety manoeuvres to the external vehicle control system for automated intervention. Communication between the driver assistance interface and the external vehicle control system occurs via the vehicle networking terminal 106, allowing real-time execution of corrective actions. The driver assistance interface facilitates interaction between onboard systems and autonomous driving mechanisms to optimize vehicle response. The communication supports multiple standards, including vehicle-to-infrastructure (V2I) and vehicle-to-vehicle (V2V) interfaces. Prioritization of recommended manoeuvres is based on risk severity, affirming timely intervention for high-risk scenarios.
In an embodiment, an automated incident logging unit may generate a structured log entry for the detected abnormal event and categorizes a log entry based on event severity and resolution status. The automated incident logging unit receives the abnormal event signal from the detection unit 102 and associates the detected event with timestamp data, sensor readings, and vehicle operating parameters. The log entry is structured into predefined categories, enabling systematic record-keeping for risk assessment and compliance monitoring. Severity levels are assigned based on predefined classification models stored in a local or cloud-based database. The resolution status indicators are updated as corrective actions are taken in response to the detected abnormal event. The automated incident logging unit communicates with the server 116 to synchronize event logs for centralized tracking and analysis. The recorded data supports multiple formats, including structured event logs, diagnostic reports, and statistical summaries. Integration with vehicle maintenance systems enables the triggering of service notifications based on recorded abnormal events. Forensic analysis is facilitated through chronological records of detected events and corresponding system responses.
In an embodiment, a road hazard detection unit may identify external environmental factors contributing to the detected abnormal event and integrates detected hazards with navigation data. The road hazard detection unit receives sensor data from onboard environmental monitoring systems, including cameras, LiDAR, radar, and weather sensors. The analysis of external conditions comprises road surface quality, weather disturbances, traffic congestion, and visibility impairments. The detected hazards are correlated with real-time vehicle behavior to determine their impact on operational safety. Hazard classification labels are assigned based on predefined risk parameters stored in a database. The integration of detected hazards with navigation data enables adaptive route optimization and dynamic hazard avoidance. Communication of identified hazards occurs through the vehicle networking terminal 106 for transmission to connected vehicles and traffic management systems. Prioritization of hazard alerts is based on severity levels and proximity to the detected abnormal event. Mapping databases are updated with real-time hazard information, enabling continuous refinement of route planning strategies. The road hazard detection unit interacts with predictive risk assessment units to enhance hazard forecasting based on historical event patterns. Hazard detection methodologies comprise image recognition, acoustic analysis, and meteorological data processing. Transient anomalies are filtered to minimize false alarms and optimize hazard reporting accuracy.
In an embodiment, the second-generation unit 112 may prioritize the risk analysis result based on severity levels derived from a predefined risk classification model. The second-generation unit 112 processes acquired event data, evaluates abnormal event characteristics, and assigns priority ratings based on severity classification criteria. Stored classification models are referenced to determine appropriate prioritization thresholds. Dynamic adjustments in prioritization levels occur based on environmental conditions, vehicle operational status, and historical event trends. The assignment of priority rankings assures that high-risk conditions receive immediate attention. The prioritized risk analysis results are transmitted to the vehicle networking terminal 106 for dissemination to relevant entities. Severity classifications are updated based on continuous data analysis and real-time system inputs. Multi-tiered classification frameworks support scalable risk assessment strategies. Refinements in prioritization parameters occur through adaptive learning mechanisms based on previous event outcomes. The prioritized risk analysis results are stored in a local memory unit for reference in future assessments.
In an embodiment, an adaptive event correlation unit may adjust the data processing parameters of the pre-trained frequent pattern model based on the risk analysis result. The adaptive event correlation unit receives the generated risk analysis result from the second-generation unit 112 and evaluates deviations from expected event patterns. Modifications in predefined correlation parameters refine event classification accuracy. Updates to data processing models occur based on historical risk assessment data and real-time sensor inputs. Adjustments in correlation weightings improve predictive capabilities and minimize false event detections. Synchronization of correlation adjustments occurs with stored event history databases to assure consistency in event classification methodologies. The adaptive event correlation unit interacts with vehicle control systems to align adjusted parameters with operational safety frameworks. Refinements in correlation parameters integrate with predictive risk assessment models to enhance forecasting accuracy. Communication of updated correlation parameters occurs through the vehicle networking terminal 106 for remote synchronization with cloud-based analytical frameworks. Automated recalibration mechanisms periodically adjust correlation settings based on evolving risk patterns. Statistical anomalies that do not contribute to meaningful risk assessment improvements are filtered.
In an embodiment, detection unit 102 detects an abnormal event occurring within the connected vehicle based on sensor data and generates an abnormal event signal. Detection unit 102 processes sensor data obtained from various onboard sensors, including accelerometers, gyroscopes, pressure sensors, and temperature sensors, to identify deviations from expected operating conditions. Detection unit 102 continuously monitors variations in sensor readings and applies predefined event classification criteria to determine whether detected deviations qualify as abnormal events. Upon detecting an abnormal event, detection unit 102 generates an abnormal event signal representing the identified deviation and associates such signal with relevant vehicle parameters, including timestamp information, location data, and vehicle operational state. Detection unit 102 transmits the abnormal event signal to receiving unit 104 for further processing. Detection unit 102 dynamically adjusts detection sensitivity based on environmental conditions and historical event patterns to improve identification accuracy. Detection unit 102 processes multiple data streams simultaneously, allowing real-time detection of multiple event types.
In an embodiment, receiving unit 104 receives the generated abnormal event signal from detection unit 102 via vehicle networking terminal 106. Receiving unit 104 establishes a communication interface with vehicle networking terminal 106 to facilitate transmission of detected events from detection unit 102 to subsequent processing units. Receiving unit 104 verifies received abnormal event signals by applying predefined validation criteria based on signal format, timestamp consistency, and historical event references. Receiving unit 104 filters redundant abnormal event signals based on predefined prioritization rules to minimize unnecessary data transmission. Receiving unit 104 supports communication over multiple data transmission standards, including wired and wireless interfaces, to affirm reliable event signal reception. Receiving unit 104 synchronizes received abnormal event signals with stored historical data to enable contextual event comparison. Receiving unit 104 forwards validated abnormal event signals to first-generation unit 108 for further assessment and data demand generation.
In an embodiment, first-generation unit 108 generates data demand information based on the received abnormal event signal using a pre-trained frequent pattern model, wherein the generated data demand information instructs vehicle networking terminal 106 to collect additional event data associated with the detected abnormal event. First-generation unit 108 analyzes the abnormal event signal to determine relevant supplementary data sources required for risk assessment. First-generation unit 108 references stored event pattern models to identify missing contextual information that may improve analysis accuracy. First-generation unit 108 generates data demand information specifying the type, source, and priority level of additional event data required. First-generation unit 108 transmits generated data demand information to vehicle networking terminal 106 to facilitate real-time data acquisition from onboard sensors, external monitoring systems, or historical event logs. First-generation unit 108 dynamically refines data demand parameters based on event classification severity and previously recorded event characteristics.
In an embodiment, acquisition unit 110 obtains the additional event data corresponding to the generated data demand information from vehicle networking terminal 106. Acquisition unit 110 retrieves supplementary data from specified sources, including onboard sensors, diagnostic logs, and vehicle telemetry systems. Acquisition unit 110 processes acquired data to make sure consistency, completeness, and relevance to the detected abnormal event. Acquisition unit 110 filters redundant or unnecessary data points to optimize processing efficiency and reduce computational overhead. Acquisition unit 110 timestamps retrieved data entries and associates such data with corresponding abnormal event signals. Acquisition unit 110 transmits processed additional event data to second-generation unit 112 for further analysis.
In an embodiment, second-generation unit 112 generates a risk analysis result for vehicle networking terminal 106 based on the abnormal event signal and the additional event data. Second-generation unit 112 integrates multiple data sources to perform a risk assessment of the detected abnormal event. Second-generation unit 112 applies statistical correlation techniques and stored event classification models to derive a structured risk analysis result. Second-generation unit 112 assigns severity ratings and likelihood estimations to detected risks based on evaluated parameters. Second-generation unit 112 formats the generated risk analysis result into a structured output for further transmission.
In an embodiment, transmission unit 114 transmits the generated risk analysis result to server 116 based on the detected abnormal event. Transmission unit 114 formats the risk analysis result for secure and efficient transmission. Transmission unit 114 supports multiple data transmission protocols, including cellular, satellite, and vehicle-to-cloud communication channels. Transmission unit 114 applies data encryption and authentication mechanisms to maintain transmission security. Transmission unit 114 verifies successful delivery of the risk analysis result to server 116 before marking the event as transmitted.
In an embodiment, an anomaly detection unit compares the abnormal event signal with previously recorded abnormal event signals to classify a severity level of an anomaly, wherein the anomaly detection unit assigns a confidence score to the detected abnormal event based on deviation from previously recorded abnormal event signals. The anomaly detection unit references historical abnormal event patterns to determine the likelihood of false detections. The anomaly detection unit dynamically adjusts confidence scoring parameters based on evolving detection trends.
In an embodiment, a vehicular event simulation unit reconstructs the detected abnormal event using sensor data to validate the risk assessment conclusions, wherein the vehicular event simulation unit generates an event replay model to enable post-event forensic analysis. The vehicular event simulation unit integrates multiple data inputs to recreate real-time event conditions for validation. The vehicular event simulation unit provides comparative analysis between detected events and simulated reconstructions to refine risk assessment accuracy.
In an embodiment, first-generation unit 108 further refines the data demand information based on a correlation between previously recorded abnormal event signals and previously collected additional event data. First-generation unit 108 dynamically adjusts data acquisition parameters based on past event outcomes to improve information relevance.
In an embodiment, a vehicular behavior profiling unit analyzes driving patterns in response to the abnormal event signal to refine detection accuracy. The vehicular behavior profiling unit evaluates historical and real-time driving behaviors to assess deviations indicative of risk.
In an embodiment, an emergency response coordination unit communicates the risk analysis result to connected vehicles within a predefined radius to improve hazard awareness. The emergency response coordination unit disseminates structured risk notifications to surrounding vehicles to facilitate preemptive safety measures.
In an embodiment, receiving unit 104 further verifies authenticity of the abnormal event signal by applying a validation mechanism based on previously recorded abnormal event signals. Receiving unit 104 cross-references received signals with historical event logs to detect signal inconsistencies.
In an embodiment, a predictive risk assessment unit generates a probability estimate of future abnormal event occurrences based on previously recorded abnormal event signals, wherein the predictive risk assessment unit refines probability estimates by integrating vehicular motion parameters. The predictive risk assessment unit continuously updates probability models based on evolving vehicular conditions.
In an embodiment, a geo-tagging unit assigns a location identifier to the abnormal event signal for spatial risk mapping, wherein the geo-tagging unit links the location identifier with traffic data to optimize event response prioritization. The geo-tagging unit enables regional risk assessment through dynamic spatial data integration.
In an embodiment, a driver assistance interface displays recommendations based on the generated risk analysis result and transmits recommended safety manoeuvres to an external vehicle control system. The driver assistance interface provides visual or auditory feedback to enhance vehicle control responses.
In an embodiment, an automated incident logging unit generates a structured log entry for the detected abnormal event and categorizes the log entry based on event severity and resolution status. The automated incident logging unit maintains an event record database for compliance tracking.
In an embodiment, a road hazard detection unit identifies external environmental factors contributing to the detected abnormal event and integrates detected hazards with navigation data. The road hazard detection unit enhances adaptive route planning based on real-time hazard assessments.
In an embodiment, second-generation unit 112 prioritizes the risk analysis result based on severity levels derived from a predefined risk classification model. The second-generation unit 112 dynamically adjusts risk categorization based on evolving hazard assessments.
In an embodiment, an adaptive event correlation unit adjusts data processing parameters of the pre-trained frequent pattern model based on the risk analysis result. The adaptive event correlation unit refines predictive accuracy through continuous parameter tuning.
In the description of the present invention, it is also to be noted that, unless otherwise explicitly specified or limited, the terms “disposed,” “mounted,” and “connected” are to be construed broadly, and may for example be fixedly connected, detachably connected, or integrally connected, either mechanically or electrically. They may be connected directly or indirectly through intervening media, or they may be interconnected between two elements. The specific meaning of the above terms in the present invention can be understood in specific cases to those skilled in the art.
Modifications to embodiments and combination of different 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 “comprising”, “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 where appropriate.
Although embodiments have been described with reference to a number of illustrative embodiments thereof, it should be understood that numerous other modifications and embodiments can be devised by those skilled in the art that will fall within the spirit and scope of the principles of this disclosure. More particularly, various variations and modifications are possible in the component parts and/or arrangements of the subject combination arrangement within the scope of the present disclosure, the drawings and the appended claims. In addition to variations and modifications in the component parts and/or arrangements, alternative uses will also be apparent to those skilled in the art.
,CLAIMS:WE CLAIM:
1. A risk detection system 100 for a connected vehicle, wherein the risk detection system 100 comprises:
a detection unit 102, detects an abnormal event occurring within the connected vehicle based on sensor data and generates an abnormal event signal;
a receiving unit 104, receives the generated abnormal event signal from the detection unit 102 via a vehicle networking terminal 106;
a first-generation unit 108, generates data demand information based on the received abnormal event signal using a pre-trained frequent pattern model, wherein the generated data demand information instructs the vehicle networking terminal 106 to collect additional event data associated with the detected abnormal event;
an acquisition unit 110, obtains the additional event data corresponding to the generated data demand information, from the vehicle networking terminal 106;
a second-generation unit 112, generates a risk analysis result for the vehicle networking terminal 106 based on the abnormal event signal and the additional event data; and
a transmission unit 114, transmits the generated risk analysis result to a server 116 based on the detected abnormal event.
2. The risk detection system 100 of claim 1, further comprising an anomaly detection unit to compare the abnormal event signal with the previously recorded abnormal event signals to classify a severity level of an anomaly, and wherein the anomaly detection unit assigns a confidence score to the detected abnormal event based on deviation from the previously recorded abnormal event signals.
3. The risk detection system 100 of claim 1, further comprising a vehicular event simulation unit that reconstructs the detected abnormal event using sensor data to validate the risk assessment conclusions, and wherein the vehicular event simulation unit generates an event replay model to enable post-event forensic analysis.
4. The risk detection system 100 of claim 1, wherein the first-generation unit 108 further refines the data demand information based on a correlation between the previously recorded abnormal event signals and previously collected additional event data.
5. The risk detection system 100 of claim 1, further comprising a vehicular behavior profiling unit that analyzes the driving patterns in response to the abnormal event signal to refine detection accuracy.
6. The risk detection system 100 of claim 1, further comprising an emergency response coordination unit that communicates the risk analysis result to the connected vehicles within a predefined radius to improve hazard awareness.
7. The risk detection system 100 of claim 1, wherein the receiving unit 104 further verifies authenticity of the abnormal event signal by applying a validation mechanism based on the previously recorded abnormal event signals.
8. The risk detection system 100 of claim 1, further comprising a predictive risk assessment unit that generates a probability estimate of the future abnormal event occurrences based on the previously recorded abnormal event signals, and wherein the predictive risk assessment unit refines probability estimate by integrating the vehicular motion parameters.
9. The risk detection system 100 of claim 1, further comprising a geo-tagging unit that assigns a location identifier to the abnormal event signal for spatial risk mapping, and wherein the geo-tagging unit links the location identifier with traffic data to optimize event response prioritization.
10. A method 200 for detecting the risks in a connected vehicle, the method comprising:
detecting, by a detection unit 102, an abnormal event occurring within the connected vehicle based on sensor data and generating an abnormal event signal;
receiving, by a receiving unit 104, the abnormal event signal from the detection unit 102 via a vehicle networking terminal 106;
generating, by a first-generation unit 108, data demand information based on the received abnormal event signal using a pre-trained frequent pattern model, wherein the generated data demand information instructs the vehicle networking terminal 106 to collect additional event data associated with the detected abnormal event;
obtaining, by an acquisition unit 110, the additional event data corresponding to the generated data demand information from the vehicle networking terminal 106;
generating, by a second-generation unit 112, a risk analysis result for the vehicle networking terminal 106 based on the abnormal event signal and the additional event data; and
transmitting, by a sending unit 114, the generated risk analysis result to a server 116 based on the detected abnormal event.
11. The method 200 of claim 10, further comprising:
displaying, by a driver assistance interface, the recommendations based on the generated risk analysis result; and
transmitting, by the driver assistance interface, the recommended safety maneuvers to an external vehicle control system.
12. The method 200 of claim 10, further comprising:
generating, by an automated incident logging unit, a structured log entry for the detected abnormal event; and
categorizing, by the automated incident logging unit, the log entry based on event severity and a resolution status.
13. The method 200 of claim 10, further comprising:
identifying, by a road hazard detection unit, the external environmental factors contributing to the detected abnormal event; and
integrating, by the road hazard detection unit, detected the hazards with navigation data.
14. The method 200 of claim 10, further comprising prioritizing, by the second-generation unit 112, the risk analysis result based on the severity levels derived from a predefined risk classification model.
15. The method 200 of claim 10, further comprising adjusting, by an adaptive event correlation unit, the data processing parameters of the pre-trained frequent pattern model based on the risk analysis result.