Description:

FORM 2
THE PATENTS ACT 1970
(39 of 1970)
&
The Patents Rules, 2003
COMPLETE SPECIFICATION
(See section 10 and rule 13)

1. TITLE OF THE INVENTION: A REAL TIME ROADSIDE COLLISION ALERT SYSTEM AND METHOD THEREOF
2. APPLICANT:
(a) NAME : Nervanik AI Labs Pvt. Ltd.
(b) NATIONALITY : Indian
(c) ADDRESS : A – 1111, World Trade Tower,
Off. S G Road, B/H Skoda Showroom,
Makarba, Ahmedabad 380 051
Gujarat INDIA

3. PREAMBLE TO THE DESCRIPTION
PROVISIONAL
The following specification describes the invention. þ COMPLETE
The following specification particularly describes the invention and the manner in which it is to be performed.

FIELD OF INVENTION
[1] The present disclosure relates to vehicle safety systems, and more particularly to a real-time roadside alert system for headway monitoring and collision warning that utilizes comprehensive sensor data integration, standardized analysis techniques, and intelligent processing methodologies to evaluate driving conditions, calculate potential risks, and provide timely warnings to drivers through an advanced notification framework. The system incorporates multiple data sources from roadside infrastructure and vehicle-mounted sensors to enhance driving safety through proactive hazard detection and alert generation.

BACKGROUND OF THE INVENTION
[2] The transportation industry faces significant technical challenges in road safety despite substantial advancements in intelligent driving monitoring systems (IDMS), driver monitoring systems (DMS), advanced driver assistance systems (ADAS), and autonomous driving technologies. These systems typically operate within a limited detection range of 50-300 meters depending on sensor type and environmental conditions, creating a technical limitation in comprehensive traffic environment assessment beyond the immediate vicinity of the vehicle. This restricted sensing capability results in incomplete situational awareness, particularly in complex traffic scenarios involving multiple vehicles, varying road conditions, and unexpected obstacles.
[3] Existing technical approaches suffer from an inability to effectively integrate multi-source data from roadside infrastructure (such as traffic cameras, weather sensors, road condition monitors, and traffic management systems) and other vehicles (through V2V communications). This integration deficiency creates a technical problem where systems cannot provide comprehensive real-time analysis of traffic conditions and potential hazards across extended roadway segments. The technical consequence is delayed or incomplete information transmission to drivers, compromising timely reaction to developing traffic situations. For instance, current systems typically process data with latencies of 100-500 milliseconds, which at highway speeds can represent several meters of travel distance before a warning is generated. Furthermore, the lack of standardized communication protocols between different manufacturers' systems creates interoperability challenges that fragment the safety ecosystem, resulting in inconsistent alert generation and reduced effectiveness in multi-vehicle scenarios.
[4] Current systems exhibit technical shortcomings in vehicle-specific parameter processing. For example, the technical calculations for braking distance and reaction times vary significantly between commercial trucks (requiring up to 40% longer stopping distances) and passenger cars, yet existing alert systems lack the computational capability to account for these variations in their risk assessment and warning mechanisms. Additionally, conventional systems employ generalized computational procedures that fail to adapt to specific vehicle characteristics such as weight, tire condition, braking system efficiency, and suspension response. This one-size-fits-all approach results in either overly conservative warnings that are potentially ignored by drivers due to alert fatigue, or insufficient warning times that fail to prevent collisions. The technical limitations extend to environmental factor integration, where current systems struggle to dynamically adjust calculations based on road surface conditions, visibility, precipitation, and other weather-related variables that significantly impact vehicle performance and stopping capabilities.
[5] Prior art such as US9453910B2 discloses a vehicle collision warning system that utilizes radar sensors to detect objects in a vehicle's path and calculate time-to-collision values. However, this system is limited to onboard sensors with restricted range and does not incorporate roadside infrastructure data or multi-source integration.
[6] US11327155B2 describes a vehicle-to-vehicle communication system for collision avoidance that exchanges position and velocity data between vehicles, but it primarily relies on direct vehicle communications without integrating roadside infrastructure sensors or environmental data. The system focuses on immediate proximity warnings rather than comprehensive situational awareness across extended roadway segments. Furthermore, it does not address the technical challenges of vehicle-specific parameter processing or environmental factor integration in its collision risk calculations.
[7] There is a critical need for an advanced collision alert system that overcomes these technical limitations by integrating multi-source data from roadside infrastructure and other vehicles, processing this information with minimal latency, and accounting for vehicle-specific parameters and environmental conditions. Such a system would significantly enhance road safety by providing drivers with timely, accurate, and contextually relevant collision warnings based on comprehensive situational awareness beyond the immediate vicinity of the vehicle. The present invention addresses these technical challenges through a real-time roadside collision alert system that utilizes sophisticated data integration, computational analysis techniques, and intelligent processing methodologies to evaluate driving conditions, calculate potential risks, and provide timely warnings to drivers.

SUMMARY OF THE INVENTION
[8] This Summary is provided to introduce a selection of concepts in a simplified form that are further described below in the Detailed Description.
[9] The driver vehicle comprises a comprehensive collision alert system, which is architecturally structured with two distinct but complementary subsystems: a headway monitoring system that continuously analyzes the distance between vehicles and road conditions, and a collision warning system that processes this information to generate timely alerts when potential collision risks are detected.
[10] The collision alert system establishes bidirectional communication interfaces with road infrastructure (including traffic cameras, roadside sensors, traffic management systems, and weather monitoring stations) and other vehicles (through vehicle-to-vehicle communication protocols), which collectively provide real-time input data to the system. The road infrastructure and other vehicles are shown connected to the collision alert system through dedicated connecting lines, representing secure data transmission channels that facilitate continuous information flow between these components, enabling comprehensive situational awareness beyond the immediate vicinity of the vehicle.
[11] The system architecture further incorporates critical external computational and storage components: a high-performance server and scalable cloud storage. The server, depicted as a rectangular processing element with robust connection pathways to the driver vehicle, performs complex computational analysis of the collected data, while the cloud storage which provides extensive remote storage capabilities for historical data analysis, pattern recognition, and system optimization through certain modules.
[12] The arrangement of components and their intricate interconnections within a distributed framework discloses the headway monitoring system and collision warning system are strategically positioned within the collision alert system to optimize data flow and processing efficiency, while road infrastructure and other vehicles are positioned as external input sources that expand the system's detection range beyond conventional vehicle-mounted sensors. The server and cloud storage are connected through high-bandwidth communication channels to enable advanced data processing, analytics, and persistent storage functions that support both real-time operations and continuous system improvement through data-driven refinement.

OBJECT OF THE INVENTION
[13] The main objective of the present disclosure is to provide a real-time roadside collision alert system that enhances vehicle safety through comprehensive sensor data integration, computational analysis techniques, and intelligent processing methodologies.
[14] An additional objective of the present disclosure is to extend the detection range of vehicle safety systems beyond the immediate vicinity of the vehicle by incorporating data from roadside infrastructure and other vehicles.
[15] A further objective of the present disclosure is to reduce latency in data processing and alert generation to provide more timely warnings to drivers in potential collision scenarios.
[16] One more objective of the present disclosure is to improve the accuracy of collision risk assessment by incorporating vehicle-specific parameters and environmental factors into the alert system's calculations.
[17] An alternative objective of the present disclosure is to enhance the interoperability of safety systems across different vehicle manufacturers through standardized communication protocols.
[18] A supplementary objective of the present disclosure is to provide adaptive collision warning parameters that account for different vehicle sizes and models, including variations between commercial trucks, passenger cars, and other vehicle classes.
[19] Yet another objective of the present disclosure is to create a scalable and adaptable collision alert system that can continuously improve through data-driven refinement and certain modules.

BRIEF DESCRIPTION OF FIGURES
[20] Embodiments of the invention will be described, by way of example, with reference to the following drawings, in which:
[21] FIG. 1 illustrates a block diagram of a driver vehicle system, according to aspects of the present disclosure.
[22] FIG. 2 illustrates a block diagram of a headway monitoring system, according to the present disclosure.
[23] FIG. 3 illustrates a block diagram of a collision warning system, in accordance with the present disclosure.
[24] FIG. 4 illustrates a flowchart for a collision alert system process, according to aspects of the present disclosure.
[25] FIG. 5 illustrates a flowchart for a braking distance calculation and deviation analysis process, according to present disclosure.
[26] FIG. 6 illustrates a flowchart for showing interconnection of headway monitoring with collision warning alert process.

DETAILED DESCRIPTION OF THE INVENTION
[27] Before explaining the present invention in detail, it is to be understood that the invention is not limited in its application. The nature of invention and the manner in which it is performed is clearly described in the specification. The invention has various components and they are clearly described in the following pages of the complete specification. It is to be understood that the phraseology and terminology employed herein is for the purpose of description and not of limitation.
[28] As used herein, the term "module" can include a unit implemented in hardware, software, or firmware, and can interchangeably be used with other terms, for example, "logic," "logic block," "part," or "circuitry." A module can be a single integral component, or a minimum unit or part thereof, adapted to perform one or more functions. For example, according to an embodiment of the disclosure, the module can be implemented in a form of an application-specific integrated circuit (ASIC).
[29] The roadside alert system comprises several interconnected modules that work together to enhance road safety and provide timely alerts to drivers. These modules include a driver monitoring system, a headway monitoring system, and a collision warning system.
[30] The driver monitoring system is designed to observe and analyze driver behavior and performance. This system is able to utilize various sensors and cameras to collect data on driver actions, such as eye movement, head position, and steering inputs. The driver monitoring system processes this information to assess driver attentiveness and detect signs of fatigue or distraction.
[31] The headway monitoring system focuses on maintaining safe distances between vehicles. This system employs sensors and cameras to measure the distance between the equipped vehicle and the vehicle ahead. The headway monitoring system calculates the time headway, which represents the time it would take for the equipped vehicle to reach the position of the vehicle in front at its current speed. Based on these calculations, the system can generate alerts when the time headway falls below predetermined safety thresholds.
[32] The collision warning system is responsible for detecting potential collision risks and alerting the driver. This system utilizes a combination of sensors, cameras, and data processing computational procedures to assess the surrounding environment and identify objects or vehicles that pose a collision threat. The collision warning system calculates parameters such as time to collision and evaluates the level of risk based on factors including vehicle speed, distance to objects, and relative motion. When a collision risk is detected, the system generates appropriate warnings to alert the driver.
[33] These modules are able to interact with additional components of the roadside alert system, such as a capturing module for collecting data from roadside infrastructure and other vehicles, a braking distance reaction module for calculating safe stopping distances, and a deviation module for analyzing deviations from safe driving parameters.
[34] The roadside alert system is also able to include a server for processing collected data and cloud storage for storing and retrieving relevant information. These components enable the system to leverage data from multiple sources and provide comprehensive safety monitoring and alerting capabilities.
[35] Figure 1 illustrates a block diagram of a real-time roadside collision alert system. The system comprises a driver vehicle (100), which incorporates a collision alert system (102) having two primary subsystems: a headway monitoring system (104) and a collision warning system (106). The driver vehicle (100) is configured to communicate bidirectionally with external components including road infrastructure (108) and other vehicles (110). The road infrastructure (108) comprises roadside management system (RMS) cameras strategically positioned along roadways to capture continuous streams of visual information. These cameras are configured to monitor traffic conditions and transmit image data to the collision alert system (102).
[36] The system further comprises a server (112) operatively coupled to the driver vehicle (100), road infrastructure (108), and other vehicles (110). The server (112) is configured to receive and process data from these multiple sources, performing computational analysis to extract safety-relevant information. The processed data is then transmitted to cloud storage (114), which is communicatively coupled to the server (112). The cloud storage (114) is configured to store the processed image data in video format, enabling efficient data management and retrieval for historical analysis. This storage component is able to include distributed database architecture to facilitate rapid access to stored information when needed for comparative analysis.
[37] Within the driver vehicle (100), the collision alert system (102) is arranged in a structured arrangement wherein the headway monitoring system (104) and collision warning system (106) operate as complementary subsystems. The headway monitoring system (104) is configured to analyze vehicle spacing and following distances, while the collision warning system (106) is configured to evaluate immediate collision risks. The components of the system are interconnected through secure communication channels, represented by connecting lines in the diagram, which facilitate continuous data flow between all elements. These connections enable comprehensive situational awareness extending beyond the immediate vicinity of the driver vehicle (100).
[38] The system is able to optionally include additional sensor arrays mounted on the driver vehicle (100) to supplement the data received from external sources. These sensors are able to comprise radar units, lidar systems, or additional cameras that provide redundant detection capabilities to enhance system reliability.
[39] Figure 2 illustrates a block diagram of a headway monitoring system. The system comprises multiple interconnected modules arranged in a functional structure to process and analyze vehicle headway data. The headway monitoring system includes a capturing module (202) positioned at the input stage of the system. The capturing module (202) comprises two primary components: a roadside camera (204) and a driver cabin camera (206). The roadside camera (204) is configured to capture external environmental data, while the driver cabin camera (206) is configured to monitor driver behavior and attention levels within the vehicle.
[40] Operatively coupled to the capturing module (202) is a braking distance reaction module (208), which is configured to calculate safe stopping distances, based on multiple parameters including vehicle speed, road conditions, weather conditions, tire traction, vehicle weight, and driver reaction time. The braking distance reaction module (208) comprises two specialized sub-modules: a make-in module (210) and a decelerating module (212). The make-in module (210) is configured to implement a vehicle-specific calculation approach that considers particular characteristics of the vehicle including make, model, weight, brake system efficiency, and tire condition. This module provides customized braking distance calculations tailored to the specific performance capabilities of different vehicle types.
[41] The decelerating module (212) is configured to implement a formula-based calculation approach that applies consistent braking distance calculations across vehicle types at given speeds. This module considers maximum deceleration parameters and environmental variables to determine stopping distances using standardized calculations. A deviation module (214) is communicatively coupled to the braking distance reaction module (208) and positioned at the output stage of the system. The deviation module (214) is configured to calculate delta distance, defined as the difference between calculated stopping distance and actual object distance. The module implements the Pinhole camera model to leverage focal length and image data for estimating distances to objects in the vehicle's path.
[42] The modules are arranged in a sequential processing architecture wherein data flows from the capturing module (202) through the braking distance reaction module (208) to the deviation module (214). This arrangement facilitates efficient data processing and analysis to generate headway monitoring results that are able to be utilized for alert generation. The headway monitoring system is able to optionally include additional environmental sensors to detect precipitation, fog, or other visibility-affecting conditions that influence braking performance and safe following distances. These sensors are able to be integrated with the capturing module (202) to provide supplementary data for more accurate calculations.
[43] Figure 3 illustrates a block diagram of a collision warning system (106). The system comprises multiple functional components arranged in a structured processing organization to detect potential collisions and generate appropriate warnings. At the input level, the collision warning system (106) comprises sensors (301) configured to collect environmental data. These sensors (301) are able to include cameras, radar, lidar, or other sensing technologies capable of detecting objects in the vehicle's vicinity and gathering information about surrounding traffic conditions.
[44] Operatively coupled to the sensors (301) is a lane ROI definer (303), which is configured to process sensor input to establish regions of interest within the driving lane. This component focuses detection efforts on relevant road areas, thereby reducing computational load and minimizing false detections. A determining module (305) is communicatively coupled to the lane ROI definer (303) and positioned at the processing level of the system. The determining module (305) comprises three specialized sub-components arranged in parallel: a speed calculator (307), a distance estimator (309), and a stopping time calculator (311). The speed calculator (307) is configured to determine vehicle and object velocities. The distance estimator (309) is configured to calculate separation distances between the vehicle and potential collision hazards. The stopping time calculator (311) is configured to compute maximum available stopping time based on current speed and obstacle distance parameters.
[45] An evaluating module (312) is operatively coupled to the determining module (305) and positioned at the analysis level of the system. The evaluating module (312) is configured to assess collision risk based on the calculated parameters and determine whether warning activation is necessary. At the output level, a notifying module (313) is communicatively coupled to the evaluating module (312). The notifying module (313) comprises two integrated components: an alert generator (315) and a user interface (317). The alert generator (315) is configured to create warning signals when collision risk exceeds predetermined thresholds. The user interface (317) is configured to present these warnings to the driver through visual, auditory, or haptic means.
[46] The collision warning system (106) is configured to operate with a single alert state designated as "danger." This state is activated when the time to collision, as determined by the stopping time calculator (311) and assessed by the evaluating module (312), falls below a predefined safety threshold. Upon threshold violation, the notifying module (313) generates immediate driver warnings. The system is able to optionally include additional alert states for graduated warning levels or specialized alert mechanisms for different types of collision threats. These optional configurations are able to be implemented through module modifications to the alert generator (315) without requiring hardware changes to the overall system architecture.
[47] Figure 4 illustrates a flowchart of a collision alert system process. The process comprises a sequence of operational steps and decision points that define the system's data flow and functional operation. The process begins with an initialization step (402), wherein the collision alert system is activated and prepared for operation. Following initialization, the process advances to a data capture step (404), wherein information is collected from multiple sources including road infrastructure (108), driver cabin cameras (206), and other vehicles (110). This multi-source data collection provides comprehensive environmental awareness.
[48] The process then reaches a decision point (406), wherein a determination is made regarding whether data processing is required. If processing is necessary (affirmative branch), the process proceeds to step (408), wherein the captured data is transmitted to a server (112) for analysis. The server (112) processes the information to extract relevant features and parameters for safety assessment. Following processing, the data is stored in cloud storage (114) at step (410) for future reference and analysis. If data processing is not required (negative branch from decision point (406)), or after completion of the storage step (410), the process advances to step (412), wherein the headway monitoring system (104) is activated. The headway monitoring system (104) analyzes inter-vehicle distances and assesses risks related to insufficient following distances.
[49] Following headway monitoring activation, the process proceeds to step (414), wherein the collision warning system (106) is activated. The collision warning system (106) evaluates the immediate surroundings to detect potential collision hazards. Based on the information analyzed by both monitoring systems, the process advances to step (416), wherein alerts are generated according to detected risk levels. These alerts are designed to provide timely warnings about potential safety hazards. In the final step (418) of the process, the generated alerts are presented to the driver through an appropriate user interface (317). This interface is able to comprise visual displays, auditory signals, or haptic feedback mechanisms to effectively communicate safety information.
[50] The process is able to optionally include additional steps for system calibration, self-diagnostics, or adaptive learning based on historical alert patterns. These optional steps are able to be integrated at various points in the process flow to enhance system performance and reliability over time.
[51] Figure 5 illustrates a flowchart of a braking distance calculation and deviation analysis process. The process comprises a sequence of operational steps and decision points that define the system's approach to calculating safe stopping distances and analyzing potential deviations. The process begins at step (504), wherein captured data is received from multiple sources including roadside infrastructure (108), driver cabin camera (206), and other vehicles (110). This multi-source data provides comprehensive information about the driving environment.
[52] Following data reception, the process advances to step (506), wherein the received data is processed. During this processing step, object detection module is applied to identify relevant objects within captured images, and Lane Regions of Interest (ROIs) are defined to focus analysis on critical areas. The process then proceeds to decision point (508), wherein the Braking Distance Reaction Module (208) is activated. Following activation, the process reaches decision point (510), wherein a determination is made regarding which calculation method to employ: the Make-in Module (210) or the Decelerating Module (212). If the Make-in Module path is selected (first branch from decision point (510)), the process proceeds to step (512), wherein braking distance is calculated using vehicle-specific parameters. These parameters are able to include vehicle weight, brake system efficiency, tire condition, vehicle make, model, age, maintenance history, suspension system performance, and aerodynamic properties. This approach provides tailored braking distance calculations based on the specific characteristics of the vehicle.
[53] Alternatively, if the Decelerating Module path is selected (second branch from decision point (510)), the process advances to step (514), wherein braking distance is calculated using a deceleration approach. This method applies standardized calculations across vehicle types at given speeds, considering factors such as maximum deceleration and environmental variables. Based on this decision, the process branches into two parallel paths. One path leads to step (512), where braking distance is calculated using the Make-in Module (210), while the other path leads to step (514), where braking distance is calculated using the Decelerating Module (212). Following braking distance calculation through either path, the process converges at step (516), wherein the Deviation Module (214) is activated. This module analyzes the deviation between calculated safe stopping distance and actual distance to objects in the vehicle's path, incorporating speed data from surrounding vehicles to provide contextual risk assessment.
[54] In the final step (518), the process analyzes calculated deviations and generates alert data categorized into four distinct phases based on Time to Headway (THW) values: (a) a safe phase when THW exceeds a first threshold; (b) a warning phase when THW falls between first and second thresholds; (c) a danger phase when THW falls between second and third thresholds; and (d) a critical phase when THW falls below the third threshold. The process concludes at step (518), where the system analyzes deviation, generates data, and sends the accumulated data to a Collision Warning System (106).
[55] Figure 6 illustrates a flowchart of a headway monitoring and collision warning alert process. The process comprises a sequence of operational steps that define the system's approach to generating and delivering collision warnings based on headway monitoring data. The process begins at step (602), wherein accumulated data is received from the headway monitoring system (104). This data includes information about inter-vehicle distances and other parameters relevant to collision risk assessment. Following data reception, the process advances to step (606), wherein speed, distance, and stopping time parameters are determined based on the received data. These calculations provide the fundamental metrics for evaluating collision risk.
[56] The process then proceeds to step (608), wherein collision risk grade is evaluated based on the calculated parameters. This evaluation considers factors including relative vehicle speeds; inter-vehicle distances, and calculated stopping times to assess potential collision severity. Following risk evaluation, the process advances to step (610), wherein the notifying module (313) is activated. This module is responsible for generating and delivering appropriate alerts based on the evaluated risk level.
[57] At step (612), a collision warning alert is generated according to the single "danger" state protocol. This alert is triggered when the calculated time to collision falls below a predefined safety threshold, as determined by the stopping time calculator (311) and evaluated by the evaluating module (312). In the final step (614), the generated alert is presented to the driver through the user interface (317). This interface is able to comprise visual displays, auditory signals, or other appropriate communication means to effectively convey safety information to the driver.
[58] The process is able to optionally include additional steps for alert customization based on driver preferences or vehicle type. These optional steps are able to allow for personalized alert delivery methods, including haptic feedback through the steering wheel or seat, or adjustments to alert timing and intensity based on factors such as vehicle speed, road conditions, or driver responsiveness patterns.
[59] In alternative embodiments, the roadside alert system is able to be integrated with existing vehicle safety systems, such as adaptive cruise control or lane departure warning systems. This integration is able to enable coordinated responses to potential hazards, enhancing overall vehicle safety through synchronized operation of multiple safety mechanisms.
[60] The system is able to be further adapted for different vehicle types, including trucks, buses, or motorcycles, with calculations adjusted to account for specific vehicle characteristics such as mass, dimensions, and braking capabilities. For example, the braking distance calculations for heavy commercial vehicles are able to incorporate longer stopping distances compared to passenger cars operating at equivalent speeds. In yet another embodiment, the system is able to be extended to incorporate dedicated vehicle-to-vehicle (V2V) and vehicle-to-infrastructure (V2I) communication protocols. This extension is able to enable direct exchange of standardized safety information between vehicles and infrastructure components, further enhancing the system's detection and response capabilities.
[61] The system is also able to be configured for various road environments, with specialized detection parameters for urban streets, highways, or rural roads. In urban settings, the system is able to prioritize pedestrian and cyclist detection, while highway configurations are able to focus on high-speed vehicle interactions, and rural implementations are able to emphasize wildlife detection capabilities.
[62] Additional embodiments are able to incorporate weather and road condition data to dynamically adjust risk assessment thresholds. During adverse conditions such as rain, snow, or fog, the system is able to automatically reduce headway thresholds and increase collision warning sensitivity to compensate for reduced traction and visibility. The user interface for displaying alerts is able to be customized according to vehicle type or driver preferences. Interface options are able to include heads-up displays, smartphone integration, or voice-activated controls to ensure optimal alert delivery with minimal driver distraction. The system is able to further include driver behavior analysis capabilities that adapt alert thresholds based on individual driving patterns. This adaptation is able to enable personalized warning delivery tailored to each driver's typical response characteristics.
[63] In a modular implementation, the system is able to be designed with interchangeable components to facilitate upgrades and maintenance. This architecture is able to enable the system to evolve with technological advancements in sensor capabilities, data processing methods, or regulatory requirements.
[64] During operation, the roadside alert system continuously monitors surrounding vehicle positions and speeds to calculate safe following distances. When the system detects unsafe approach conditions, the headway monitoring system (104) activates to analyze time headway between vehicles. If time headway falls below predetermined safety thresholds, the system generates graduated warnings that are able to escalate from initial visual indicators to more prominent audio alerts as risk increases. The system incorporates multiple factors including vehicle speed, road conditions, and relative velocity to determine appropriate warning timing and intensity. This comprehensive approach helps drivers maintain safe following distances and reduces collision risk, particularly in rapidly changing traffic conditions or limited visibility environments.
[65] In an exemplary implementation, a driver operating a sedan at 65 mph on Highway 101 during rush hour encounters suddenly slowing traffic. The roadside alert system activates as follows: RMS cameras mounted on overhead gantries capture real-time video of developing traffic conditions, while the driver cabin camera (206) detects the driver momentarily looking at a navigation display.
[66] The system processes this multi-source data through the server (112) (as shown in step (408) of Figure 4), which identifies rapid deceleration patterns in vehicles 300 meters ahead. This processed data is simultaneously stored in cloud storage (114) (step (410)) and used to activate the vehicle's headway monitoring system (104) (step (412)) and collision warning system (106) (step (414)).
[67] As the driver maintains current speed, the system executes the data processing method illustrated in Figure 5. The captured traffic pattern data (step (504)) is processed (step (506)), with the system focusing specifically on the three vehicles directly ahead in the driver's lane.
[68] The Braking Distance Reaction Module (208) activates (decision point (508)) and, recognizing the vehicle as a 2022 sedan with ABS braking system on wet pavement (it had been raining lightly), selects the Make-in Module (210) path (decision point (510)). The system calculates that at current speed, the vehicle requires 76 meters to stop safely (step (512)), factoring in the specific vehicle's braking capabilities and the wet road conditions. The Deviation Module (214) activates (step (516)) and determines that the current distance to the rapidly decelerating traffic ahead is only 85 meters and closing quickly as the lead vehicles continue to slow. The system calculates that the Time to Headway (THW) is rapidly decreasing below the safe threshold of 2.5 seconds.
[69] Following the process in Figure 6, the system receives this headway data (step (602)) and determines the vehicle's current speed of 65 mph, distance to traffic ahead of 85 meters, and calculates that stopping time is becoming critically short (step (606)). The system evaluates the collision risk as "high" (step (608)) based on the rapidly decreasing distance and the driver's momentary inattention. The notifying module (313) activates (step (610)) and generates a two-stage alert (step (612)). First, a yellow visual warning appears on the heads-up display showing the decreasing distance to traffic ahead. When the THW drops below 1.8 seconds, the alert escalates to a red warning accompanied by an auditory alert stating "Traffic ahead - Brake now!"
[70] The driver, alerted by both visual and audio warnings (step (614)), quickly returns attention to the road and applies the brakes, gradually slowing the vehicle and maintaining a safe distance from the traffic ahead. The system continues monitoring the situation, and as the THW increases back above 2.0 seconds, the alerts are downgraded and eventually cease. Meanwhile, the system has shared this traffic deceleration data with other vehicles (110) in the vicinity through V2V communication, allowing their alert systems to pre-emotively warn their drivers of the slowing traffic ahead, even before their own sensors would have detected the situation.
[71] After the incident, the system uploads anonymized data about the event to the cloud storage (114), where it contributes to ongoing traffic pattern analysis and system improvements. The driver later receives a summary notification through the vehicle's companion app suggesting a route with less stop-and-go traffic for tomorrow's commute.
[72] This exemplary implementation demonstrates how the roadside alert system integrates multiple data sources, processes information in real-time, and provides timely, graduated alerts that effectively prevent potential collisions in everyday driving scenarios.
[73] In another exemplary implementation, a driver is traveling on a rural two-lane highway at dusk. The collision warning system (106) activates when a deer suddenly appears from the roadside. The sensors (301) detect the moving object entering the lane ROI defined by the lane ROI definer (303). The determining module (305) rapidly calculates the vehicle's speed of 55 mph via the speed calculator (307), while the distance estimator (309) determines the deer is only 40 meters ahead. The stopping time calculator (311) computes that the available stopping time is critically short—less than 1.5 seconds.
[74] The evaluating module (312) immediately assesses this as a high-risk collision scenario, and the notifying module (313) generates an urgent danger alert. The user interface (317) displays a bright red warning icon while simultaneously producing a distinctive auditory alert. The driver, warned by this immediate feedback, performs an emergency braking maneuver while maintaining control of the vehicle, successfully avoiding a collision with the deer by a narrow margin. This collision warning example illustrates how the system can detect non-vehicle obstacles and provide critical alerts in situations where driver reaction time alone might be insufficient to prevent a collision. The single-state "danger" alert is designed to trigger immediate driver action in these time-critical scenarios, demonstrating the system's effectiveness in rural environments with wildlife hazards as well as in conventional traffic situations.
[75] The roadside alert system addresses the technical problem of providing real-time driver monitoring and collision prevention in dynamic traffic environments. Traditional driver monitoring systems often rely solely on in-vehicle sensors and cameras, which are able to have limited visibility and context awareness. Additionally, existing collision warning systems are able to not account for rapidly changing road conditions or unexpected obstacles, potentially leading to delayed or inaccurate alerts. The roadside alert system offers a solution to these challenges by integrating multiple data sources and advanced processing techniques. The system combines information from roadside infrastructure cameras, in-vehicle sensors, and other vehicles on the road to create a comprehensive view of the driving environment. This multi-source approach allows for more accurate and timely detection of potential hazards. The system's headway monitoring component continuously analyzes the distances between vehicles and calculates time headway values. By processing this data in real-time, the system can detect when a vehicle is approaching another at an unsafe distance or speed. The collision warning component further enhances safety by rapidly assessing the risk of collision with both vehicles and non-vehicle obstacles. A key aspect of the solution is the system's ability to generate graduated warnings based on the level of risk. As the time headway decreases or collision risk increases, the system escalates the urgency of its alerts. This graduated approach helps prevent alert fatigue while ensuring that drivers receive appropriate warnings in critical situations. The roadside alert system also incorporates advanced data processing techniques to improve accuracy and reduce false alarms. The system defines lane regions of interest to focus on relevant areas of the road, and applies object detection procedures to identify potential hazards. By considering factors such as vehicle-specific characteristics, road conditions, and weather, the system can provide more precise and context-aware alerts. Furthermore, the system's cloud-based architecture allows for continuous improvement and adaptation. By storing and analyzing anonymized data from multiple vehicles and road segments, the system can refine its alert thresholds and risk assessment computational procedures over time. In summary, the roadside alert system addresses the technical problem of real-time driver monitoring and collision prevention by integrating multiple data sources, applying advanced processing techniques, and providing graduated, context-aware alerts. This comprehensive approach enhances road safety and driver awareness in a wide range of driving scenarios.
[76] The systems and methods described herein are able to be implemented in any form of computing or electronic device. The term "computer," as used herein, encompasses any device with processing capabilities sufficient to execute instructions. This includes, but is not limited to, personal computers, servers, mobile devices, personal digital assistants, and similar devices.
[77] Such devices are able to include one or more processors, such as microprocessors, controllers, or other suitable types of processors, capable of executing instructions to control the device's operation. For example, in some implementations using a system-on-a-chip architecture, the processors are able to include fixed-function blocks (hardware accelerators) that perform parts of the method in hardware rather than software or firmware. Platform software, such as an operating system or similar, is able to be installed to support the execution of application software.
[78] The described functionality is able to be implemented in hardware, software, or any combination thereof. When implemented in software, the instructions or code can be stored on or transmitted via a computer-readable medium. Such media include computer-readable storage media, which are able to be volatile or non-volatile, removable or non-removable, and implemented using any technology for storing information such as program code, data structures, or other data. Examples include, but are not limited to, ROM, EEPROM, RAM, magnetic or optical storage, flash memory, or any other storage medium accessible by a computer. Communication media that facilitate the transfer of software, such as via coaxial cables, fiber optics, DSL, or wireless signals, are also able to be considered part of computer-readable media.
[79] Alternatively, or in addition, some or all of the described functionality is able to be implemented using hardware logic components. Examples include, but are not limited to, application-specific integrated circuits, system-on-a-chip systems, field-programmable gate arrays, application-specific standard products, and complex programmable logic devices. In some cases, software instructions are also able to be implemented in dedicated circuits, such as programmable logic arrays or digital signal processors.
[80] The computing device is able to operate as a standalone system or as part of a distributed system, where tasks are performed collectively by multiple devices connected via a network. Such devices are able to communicate over a network connection to perform the described functionality. For instance, software is able to be stored on a remote computer and accessed by a local device, which is able to download and execute portions of the software as needed. Similarly, some instructions are able to be processed locally, while others are able to execute on remote systems or networks. In some cases, the computing device is able to be remote and accessible via a communication interface. Storage of program instructions is also able to be distributed across a network or stored in a combination of local and remote locations. For example, software is able to reside on a remote computer and be accessed by a local terminal, or the system is able to execute some software locally while other components operate on remote servers.
[81] Features of any of the examples or embodiments outlined above are able to be combined to create additional examples or embodiments without losing the intended effect. It should be understood that the description of an embodiment or example provided above is by way of example only, and various modifications could be made by one skilled in the art. Furthermore, one skilled in the art will recognise that numerous further modifications and combinations of various aspects are possible. Accordingly, the described aspects are intended to encompass all such alterations, modifications, and variations that fall within the scope of the appended claims.

, Claims:WE CLAIM:
1. A method for real-time roadside collision alert, the method comprising:
a. receiving, by a capturing module (202), data from roadside infrastructure (108), driver cabin camera (206), and other vehicles (110);
b. processing the received data and activating a braking distance reaction module (208);
c. calculating a braking distance implementing one of a make-in module (210) or a decelerating module (212) based on vehicle-specific parameters;
d. activating a deviation module (214) to analyze deviation based on the calculated braking distance;
e. analyzing deviation involving calculation of a delta distance defined as difference between the calculated braking distance and an actual object distance;
f. transmitting accumulated data from the deviation module (214) to a collision warning system (106);
g. determining speed, distance, and stopping time parameters based on the accumulated data;
h. evaluating a collision risk grade based on the determined parameters;
i. activating a notifying module (313) when the evaluated collision risk grade exceeds a predetermined threshold;
j. generating a collision warning alert based on the evaluated risk grade; and
k. presenting the generated alert to a driver through a user interface (317);
wherein the interconnection between the braking distance calculation and deviation analysis with the collision risk evaluation and alert generation provides comprehensive real-time collision risk assessment with vehicle-specific parameters.

2. The method as claimed in claim 1, wherein the processing step (b) comprises:
i. calculating a braking distance implementing the braking distance reaction module (208);
ii. analyzing deviation based on the calculated braking distance implementing the deviation module (214); and
iii. determining speed, distance, and stopping time parameters implementing the determining module (305).

3. The method as claimed in claim 2, wherein calculating the braking distance comprises selecting between the make-in module (210) calculation and the decelerating module (212) calculation based on vehicle-specific parameters.

4. The method as claimed in any of claims 2 to 3, wherein the method further comprises evaluating a collision risk grade based on the determined speed, distance, and stopping time parameters.

5. The method as claimed in claim 4, wherein generating alerts comprises activating the notifying module (313) when the evaluated collision risk grade exceeds a predetermined threshold.

6. The method as claimed in claim 5, wherein presenting the generated alerts comprises displaying visual warnings on a heads-up display and producing auditory alerts.

7. A real-time roadside collision alert system, comprising:
a. a capturing module (202) configured to capture data from roadside infrastructure (108) and vehicles (110);
b. a processing module configured to process the captured data and determine collision risks, wherein the processing module comprises:
i. a braking distance reaction module (208) configured to calculate a braking distance;
ii. a deviation module (214) configured to analyze deviation based on the calculated braking distance; and
iii. a determining module (305) configured to determine speed, distance, and stopping time parameters; and
c. a notifying module (313) configured to generate and present alerts to a driver based on the determined collision risks.

8. The system as claimed in claim 7, wherein the braking distance reaction module (208) comprises a make-in module (210) and a decelerating module (212), and is configured to select between the make-in module (210) and the decelerating module (212) for braking distance calculation based on vehicle-specific parameters.

9. The system as claimed in claim 7 or 8, wherein the deviation module (214) is configured to calculate a delta distance defined as a difference between the calculated braking distance and an actual object distance.

10. The system as claimed in any of claims 7 to 9, comprising an evaluating module (312) configured to evaluate a collision risk grade based on the determined speed, distance, and stopping time parameters.

11. The system as claimed in claim 10, wherein the notifying module (313) is configured to activate when the evaluated collision risk grade exceeds a predetermined threshold, and generates alerts through the alert generator (315) operatively coupled to a user interface (317), being configured to display visual warnings on a heads-up display and produce auditory alerts.

12. The system as claimed in any of claims 7 to 11, wherein the capturing module (202) comprises a roadside camera (204) and a driver cabin camera (206).

Dated this on 2nd day of August, 2025