Description:FIELD OF DISCLOSURE
[0001] The present disclosure generally relates to hazard prevention systems, more specifically, relates to an adaptive real-time safety alert generation system for vehicle collision prevention at extreme road curves based on AI-enabled proactive risk assessment.
BACKGROUND OF THE DISCLOSURE
[0002] Hazard prevention systems are implemented to protect individuals from potential harm by identifying and controlling hazards before they escalate into harmful or dangerous events. These systems are widely applied across various domains, including industrial operations, environmental monitoring, healthcare, infrastructure management, and public safety. This early warning capability allows for timely intervention, minimizing damage to life, property, and the environment.
[0003] Hazard prevention is critical in road safety due to the high frequency and severe consequences of vehicular accidents worldwide. Road safety remains a critical concern, particularly in regions with complex terrain such as hilly areas, narrow roads, and sharp or blind curves. These areas are highly prone to vehicular accidents due to limited line of sight, sudden changes in elevation, and inadequate warning mechanisms. Collisions at such curves often occur when two or more vehicles approach from opposite directions with minimal visibility and insufficient reaction time. These present unique hazards that require timely detection and intervention to prevent collisions and fatalities.
[0004] Conventional safety alert systems for extreme curves predominantly use static warning signs, reflective boards, or basic visual indicators to caution drivers. These systems lack real-time responsiveness and rely heavily on driver vigilance, which can be compromised by distraction, fatigue, or unfamiliarity with the road. Since the warnings are fixed and passive, they fail to provide dynamic alerts based on actual traffic conditions, making them less effective in preventing sudden collisions, especially when vehicles approach simultaneously from opposite directions.
[0005] Existing road safety alert systems often lack intelligent processing capabilities to accurately differentiate between genuine hazards and non-threatening situations. Without such advanced filtering, these systems may generate frequent false alarms triggered by irrelevant objects, environmental noise, or transient sensor detections. This repetitive and unnecessary alerting can overwhelm drivers, causing them to become desensitized or inattentive to warnings over time.
[0006] Another significant limitation of conventional systems is their poor performance under adverse environmental conditions. Static signs and lights often lose visibility in fog, heavy rain, or low-light situations, severely reducing their ability to alert drivers when it is most critical. Furthermore, many existing sensor-based solutions do not provide comprehensive audio-visual warnings and lack integration of advanced communication technologies such as Vehicle-to-Vehicle (V2V) communication. As a result, early warnings and coordinated alerts that could prevent collisions are absent, leaving drivers unaware of imminent risks beyond their immediate line of sight.
[0007] Additionally, traditional systems generally suffer from issues related to energy efficiency, scalability, and adaptability. Many rely on grid electricity or non-renewable power sources, which may not be practical for remote or hilly locations. High installation and maintenance costs further hinder widespread deployment. Collectively, these disadvantages highlight the need for an intelligent, adaptive, eco-friendly, and cost-effective hazard alert system capable of real-time detection and dynamic risk communication at extreme road curves.
[0008] The present invention solves the limitations of the prior art by providing an adaptive real-time safety alert generation system for vehicle collision prevention at extreme road curves that dynamically detects vehicles approaching from both directions at extreme road curves using advanced sensors and AI-driven analysis. The present invention delivers timely audio-visual alerts calibrated to vehicle speed and environmental conditions, integrates vehicle-to-vehicle (V2V) communication for early warnings, and operates sustainably through solar powered battery backup. This intelligent and eco-friendly solution enhances driver awareness, reduces false alarms, and ensures reliable performance in low-visibility and remote conditions, thereby significantly improving road safety and preventing collisions where traditional static warning systems fail.
[0009] Thus, in light of the above-stated discussion, there exists a need for an adaptive real-time safety alert generation system for vehicle collision prevention at extreme road curves.
SUMMARY OF THE DISCLOSURE
[0010] The following is a summary description of illustrative embodiments of the invention. It is provided as a preface to assist those skilled in the art to more rapidly assimilate the detailed design discussion which ensues and is not intended in any way to limit the scope of the claims which are appended hereto in order to particularly point out the invention.
[0011] According to illustrative embodiments, the present disclosure focuses on an adaptive real-time safety alert generation system for vehicle collision prevention at extreme road curves which overcomes the above-mentioned disadvantages or provide the users with a useful or commercial choice.
[0012] The present invention solves all the above major limitations of an adaptive real-time safety alert generation system for vehicle collision prevention at extreme road curves.
[0013] An objective of the present disclosure is to provide an adaptive real-time safety alert generation system for vehicle collision prevention at extreme road curves.
[0014] Another objective of the present disclosure is to enable simultaneous dual-direction vehicle detection using advanced distance-measuring sensors.
[0015] Another objective of the present disclosure is to implement AI-driven adaptive risk analysis that evaluates vehicle speed, distance, and environmental conditions to determine the level and urgency of alerts.
[0016] Another objective of the present disclosure is to generate dynamic audio and visual alerts that adjust in intensity based on traffic conditions and visibility.
[0017] Another objective of the present disclosure is to integrate vehicle-to-vehicle (V2V) communication technology to send early warnings directly to approaching vehicles beyond the sensor detection range to enhance driver awareness and prevent collisions.
[0018] Yet another objective of the present disclosure is to provide an eco-friendly system that utilizes solar panels in combination with battery backup for sustainable, off-grid operation in remote locations, thereby reducing dependence on non-renewable external power sources.
[0019] Yet another objective of the present disclosure is to ensure reliable operation under various environmental conditions such as fog, rain, low light, and extreme temperatures.
[0020] Yet another objective of the present disclosure is to minimize false alarms through intelligent filtering and adaptive alert thresholds, thereby improving driver response and reducing alert fatigue.
[0021] Yet another objective of the present disclosure is to provide a scalable and cost-effective solution suitable for widespread deployment on highways, rural roads, and challenging terrains.
[0022] Yet another objective of the present disclosure is to offer an automated and autonomous hazard prevention mechanism that requires no manual intervention for activation.
[0023] In light of the above, in one aspect of the present disclosure, a safety alert generation system for vehicle collision prevention at extreme road curves is disclosed herein. The system comprises a pole-mounted device placed on a pole at an extreme road curve and configured to detect vehicles approaching from opposite directions, assess collision risk, and generate real-time audio and visual alerts to warn drivers of potential danger, wherein the pole-mounted device further comprises a plurality of sensor configured to detect vehicles approaching from both directions of an extreme road curve, along with monitoring real-time environmental conditions, an audio alert unit configured to generate audible alerts, a visual alert unit configured to generate visual alerts, a communication network configured to transmit information within the various components of the system, and a microcontroller connected to the plurality of sensor, the audio alert unit, and the visual alert unit via the communication network, the microcontroller configured to analyse sensor data to determine simultaneous bidirectional vehicle presence and trigger safety alerts, wherein the microcontroller further comprises a data input module configured to receive input from the plurality of sensor, a data processing module configured to clean, normalize, and preprocess the received raw input data for further processing, a vehicle identification module configured to detect and identify the presence and direction of multiple approaching vehicles based on the processed sensor data, a distance calculation module configured to calculate the distance of each identified approaching vehicle from the sensor location, a speed calculation module configured to estimate the speed of each identified approaching vehicle using distance-over-time computation, a time calculation module configured to calculate the estimated time of arrival (ETA) of each approaching vehicle at the extreme road curve based on the calculated speed and distance, a collision risk assessment module configured to assess and classify a risk of collision between the identified approaching vehicles by comparing the calculated distance, speed, and estimated time of arrival data against predefined threshold values and utilizing machine learning algorithms, a warning adjustment module configured to dynamically adjust the intensity of audio and visual alerts based on the assessed collision risk level and environmental conditions using machine learning algorithms, an alert activation module configured to trigger audio alerts via the audio alert unit, visual alerts via the plurality of danger light and generate alert notifications upon identification of a collision risk between vehicles, and an output module configured to transmit the processed data and alerts to the identified approaching vehicles. The system also includes a user device placed in a vehicle, connected to the microcontroller via the communication network, and configured to receive and display real-time alerts indicating potential collision risks at extreme road curves.
[0024] In one embodiment, the system further comprises a cloud-database configured to store real-time and historical vehicle detection data, alert logs, and performance metrics of the system for secure access, retrieval, remote monitoring, data analysis, and predictive maintenance.
[0025] In one embodiment, the plurality of sensor comprises sensors, including but not limited to, radar sensors, LiDAR sensors, proximity sensors, ultrasonic sensors, infrared sensors, environmental sensors, and combination thereof.
[0026] In one embodiment, the audio alert unit comprises a buzzer configured to modulate volume based on alert urgency and real-time environmental conditions.
[0027] In one embodiment, the visual alert unit comprises a plurality of danger light configured to modulate brightness based on alert urgency and real-time environmental conditions.
[0028] In one embodiment, the vehicle identification module is configured to detect approaching vehicles within a predefined range of up to 200 meters in both directions from the extreme road curve.
[0029] In one embodiment, the vehicle identification module is configured to place the system in standby mode if only one vehicle is detected approaching the extreme road curve.
[0030] In one embodiment, the output module is further configured to transmit the real-time geographic location coordinates of the pole-mounted device via a location detector for location-based alerting and event data logging.
[0031] In one embodiment, the system further comprises a power unit configured to provide continuous and reliable power to all the components of the system utilizing solar energy with battery backup support.
[0032] In light of the above, in one aspect of the present disclosure, A method for generating safety alerts in real-time for extreme road curves is disclosed herein. The method comprises detecting vehicles approaching from opposite directions, assessing collision risk, and generating real-time audio and visual alerts to warn drivers of potential danger via a pole-mounted device comprising of several components. The method also includes detecting vehicles approaching from both directions of an extreme road curve, along with monitoring real-time environmental conditions via a plurality of sensor. The method also includes generating audible alerts via an audio alert unit. The method also includes generating visual alerts via a visual alert unit. The method also includes transmitting information within the various components of the system via a communication network. The method also includes analysing sensor data to determine simultaneous bidirectional vehicle presence and trigger safety alerts via a microcontroller comprising of several modules. The method also includes receiving input from the plurality of sensor via a data input module. The method also includes cleaning, normalizing, and preprocessing the received raw input data for further processing via a data processing module. The method also includes detecting and identifying the presence and direction of multiple approaching vehicles based on the processed sensor data via a vehicle identification module. The method also includes calculating the distance of each identified approaching vehicle from the sensor location via a distance calculation module. The method also includes estimating the speed of each identified approaching vehicle using distance-over-time computation via a speed calculation module. The method also includes calculating the estimated time of arrival (ETA) of each approaching vehicle at the extreme road curve based on the calculated speed and distance via a time calculation module. The method also includes assessing and classifying a risk of collision between the identified approaching vehicles by comparing the calculated distance, speed, and estimated time of arrival data against predefined threshold values and utilizing machine learning algorithms via a collision risk assessment module. The method also includes dynamically adjusting the intensity of audio and visual alerts based on the assessed collision risk level and environmental conditions using machine learning algorithms via a warning adjustment module. The method also includes triggering audio alerts via the audio alert unit, visual alerts via the visual alert unit and generate alert notifications upon identification of a collision risk between vehicles via an alert activation module. The method also includes transmitting the processed data and alerts to the identified approaching vehicles via an output module. The method also includes receiving and displaying real-time alerts indicating potential collision risks at extreme road curves via a user device.
[0033] These and other advantages will be apparent from the present application of the embodiments described herein.
[0034] The preceding is a simplified summary to provide an understanding of some embodiments of the present invention. This summary is neither an extensive nor exhaustive overview of the present invention and its various embodiments. The summary presents selected concepts of the embodiments of the present invention in a simplified form as an introduction to the more detailed description presented below. As will be appreciated, other embodiments of the present invention are possible utilizing, alone or in combination, one or more of the features set forth above or described in detail below.
[0035] These elements, together with the other aspects of the present disclosure and various features are pointed out with particularity in the claims annexed hereto and form a part of the present disclosure. For a better understanding of the present disclosure, its operating advantages, and the specified object attained by its uses, reference should be made to the accompanying drawings and descriptive matter in which there are illustrated exemplary embodiments of the present disclosure.
BRIEF DESCRIPTION OF THE DRAWINGS
[0036] To describe the technical solutions in the embodiments of the present disclosure or in the prior art more clearly, the following briefly describes the accompanying drawings required for describing the embodiments or the prior art. Apparently, the accompanying drawings in the following description merely show some embodiments of the present disclosure, and a person of ordinary skill in the art can derive other implementations from these accompanying drawings without creative efforts. All of the embodiments or the implementations shall fall within the protection scope of the present disclosure.
[0037] The advantages and features of the present disclosure will become better understood with reference to the following detailed description taken in conjunction with the accompanying drawing, in which:
[0038] FIG. 1 illustrates a block diagram of a safety alert generation system for vehicle collision prevention at extreme road curves, in accordance with an exemplary embodiment of the present disclosure;
[0039] FIG. 2 illustrates a schematic of the pole-mounted device for collision warning and prevention, in accordance with an exemplary embodiment of the present disclosure; and
[0040] FIG. 3 illustrates a flowchart of a method, outlining the sequential steps for generating safety alerts in real-time for extreme road curves, in accordance with an exemplary embodiment of the present disclosure.
[0041] Like reference, numerals refer to like parts throughout the description of several views of the drawing.
[0042] The adaptive real-time safety alert generation system for vehicle collision prevention at extreme road curves is illustrated in the accompanying drawings, which like reference letters indicate corresponding parts in the various figures. It should be noted that the accompanying figure is intended to present illustrations of exemplary embodiments of the present disclosure. This figure is not intended to limit the scope of the present disclosure. It should also be noted that the accompanying figure is not necessarily drawn to scale.
DETAILED DESCRIPTION OF THE DISCLOSURE
[0043] The following is a detailed description of embodiments of the disclosure depicted in the accompanying drawings. The embodiments are in such detail as to communicate the disclosure. However, the amount of detail offered is not intended to limit the anticipated variations of embodiments; on the contrary, the intention is to cover all modifications, equivalents, and alternatives falling within the spirit and scope of the present disclosure.
[0044] In the following description, numerous specific details are set forth in order to provide a thorough understanding of the embodiments of the present disclosure. It may be apparent to one skilled in the art that embodiments of the present disclosure may be practiced without some of these specific details.
[0045] Various terms as used herein are shown below. To the extent a term is used, it should be given the broadest definition persons in the pertinent art have given that term as reflected in printed publications and issued patents at the time of filing.
[0046] The terms “a” and “an” herein do not denote a limitation of quantity, but rather denote the presence of at least one of the referenced items.
[0047] The terms “having”, “comprising”, “including”, and variations thereof signify the presence of a component.
[0048] Referring now to FIG. 1 to FIG. 3 to describe various exemplary embodiments of the present disclosure. FIG. 1 illustrates a block diagram of a safety alert generation system 100 for vehicle collision prevention at extreme road curves, in accordance with an exemplary embodiment of the present disclosure.
[0049] The system 100 may include a pole-mounted device 102 and a user device 104.
[0050] In one embodiment of the present invention, the system 100 further comprises a cloud database 112 configured to store real-time and historical vehicle detection data, alert logs, and performance metrics of the system 100 for secure access, retrieval, remote monitoring, data analysis, and predictive maintenance.
[0051] The pole-mounted device 102 is placed on a pole at an extreme road curve and configured to detect vehicles approaching from opposite directions, assess collision risk, and generate real-time audio and visual alerts to warn drivers of potential danger The pole-mounted device 102 further comprises several components including a plurality of sensor 108, an audio alert unit 116, a visual alert unit 118, a communication network 110, and a microcontroller 114.
[0052] The plurality of sensor 108 are configured to detect vehicles approaching from both directions of an extreme road curve, along with monitoring real-time environmental conditions. The plurality of sensor 108 are capable of capturing various vehicle parameters such as presence, speed, direction of movement, distance from the curve, and relative position with respect to other approaching vehicles. The plurality of sensor 108 are also capable of monitoring real-time environmental conditions such as fog, rain, light intensity, and extreme temperatures.
[0053] In one embodiment of the present invention, the plurality of sensor 108 comprises sensors, including but not limited to, radar sensors, Light Detection and Ranging (LiDAR) sensors, proximity sensors, ultrasonic sensors, infrared sensors, environmental sensors, and combination thereof.
[0054] The audio alert unit 116 is configured to generate audible alerts that serve as real-time warnings to drivers of approaching vehicles near an extreme road curve. The audible alerts are designed to be loud, clear, and attention-grabbing to ensure that drivers are promptly made aware of hazardous conditions ahead.
[0055] In one embodiment of the present invention, the audio alert unit 116 comprises a buzzer 142 configured to modulate volume based on alert urgency and real-time environmental conditions.
[0056] The visual alert unit 118 is configured to generate visual alerts. intended to warn drivers of potential collision risks at extreme road curves.
[0057] In one embodiment of the present invention, the visual alert unit 118 comprises a plurality of danger light 144 configured to modulate brightness based on alert urgency and real-time environmental conditions.
[0058] In one embodiment of the present invention, the plurality of danger light 144 are high-intensity Light Emitting Diode (LED) lights.
[0059] In one embodiment of the present invention, the plurality of danger light 144 emit red coloured light to immediately capture driver attention and signal caution.
[0060] The communication network 110 is configured to transmit information within the various components of the system 100. The communication network 110 transmits information between the plurality of sensor 108, the cloud database 112, the microcontroller 114, the audio alert unit 116, the visual alert unit 118, and the user device 104 for real-time data transmission and coordination.
[0061] In one embodiment of the present invention, the communication network 110 may be both wired and wireless.
[0062] In one embodiment of the present invention, the communication network 104 may include, Wi-Fi, Bluetooth, Ethernet, cellular networks such as 2G, 3G, 4G, and 5G, Wide Area Network (WAN), Local Area Network (LAN), and Virtual Area Network (VAN), serial communication protocols, and universal serial bus (USB) interfaces for an input/output connectivity.
[0063] In one embodiment of the present invention, the communication network 104 may include an antenna supporting long-range wireless communication. Embodiments of the present disclosure are intended to cover all types of communication technologies and networks including, known, related art, and/or later developed technologies.
[0064] The microcontroller 114 is connected to the plurality of sensor 108, the audio alert unit 116, and the visual alert unit 118 via the communication network 110. The microcontroller 114 is configured to analyse and process sensor data to determine simultaneous bidirectional vehicle presence and trigger safety alerts. The microcontroller 114 further comprises several modules including a data input module 122, a data processing module 124, a vehicle identification module 126, a distance calculation module 128, a speed calculation module 130, a time calculation module 132, a collision risk assessment module 134, a warning adjustment module 136, an alert activation module 138, and an output module 140.
[0065] The data input module 122 is configured to receive raw input data from the plurality of sensor 108.
[0066] The data processing module 124 is configured to clean, normalize, and preprocess the received raw input data for further processing.
[0067] The vehicle identification module 126 is configured to detect and identify the presence and direction of multiple approaching vehicles based on the processed sensor data.
[0068] In one embodiment of the present invention, the vehicle identification module 126 is configured to detect approaching vehicles within a predefined range of up to 200 meters in both directions from the extreme road curve. The 200-meter detection range on both sides of the extreme road curve, allows enough time for the vehicles to react and take the necessary preventive measure to avoid the collision.
[0069] In one embodiment of the present invention, the vehicle identification module 126 is configured to place the system 100 in standby mode if only one vehicle is detected approaching the extreme road curve. This helps prevent false alarms and ensures that no unnecessary panic is created.
[0070] The distance calculation module 128 is configured to calculate the distance of each identified approaching vehicle from the sensor location using the processed sensor data. The distance calculation module 128 analyzes positional data captured by the plurality of sensor 108 to determine how far each vehicle is from the pole-mounted device 102.
[0071] The speed calculation module 130 is configured to estimate the speed of each identified approaching vehicle using distance-over-time computation. The speed calculation module 130 continuously monitors the change in the vehicle’s position over successive time intervals based on data received from the plurality of sensor 108. By analyzing how quickly the distance between the vehicle and the sensor location changes over time, the speed calculation module 130 accurately computes the vehicle’s current speed, which is essential for predicting arrival times and evaluating collision risks
[0072] The time calculation module 132 is configured to calculate the estimated time of arrival (ETA) of each approaching vehicle at the extreme road curve based on the calculated speed and distance. The time calculation module 132 computes how long it will take for a vehicle to reach the extreme road curve by dividing the remaining distance to the extreme road curve by the vehicle’s current speed. The ETA information is critical for timely alert generation and for providing advance warnings to drivers approaching the hazardous zone.
[0073] The collision risk assessment module 134 is configured to assess and classify a risk of collision between the identified approaching vehicles by comparing the calculated distance, speed, and ETA data against predefined threshold values and utilizing machine learning algorithms. The collision risk assessment module 134 compares the calculated parameters against predefined threshold values to determine whether the vehicles are likely to come dangerously close to each other at the extreme road curve. Additionally, the collision risk assessment module 134 utilizes machine learning algorithms to improve its predictive accuracy by learning from historical data and real-time inputs. This enables adaptive risk assessment that accounts for varying traffic conditions, environmental factors, and driver behaviours. Based on this analysis, the collision risk assessment module 134 categorizes the collision risk into different levels which helps to trigger appropriate alert responses.
[0074] In an exemplary embodiment of the present invention, the collision risk assessment module categorizes the collision risk into 3 different levels, namely low risk, medium risk, and high risk.
[0075] The warning adjustment module 136 is configured to dynamically adjust the intensity of audio and visual alerts based on the assessed collision risk level and environmental conditions using machine learning algorithms. By utilizing machine learning algorithms, the warning adjustment module 136 analyzes real-time data such as the severity of the collision risk, visibility, weather conditions, and ambient noise levels to optimize alert signals. This adaptive adjustment ensures that the alerts are sufficiently prominent to effectively warn drivers during high-risk situations while minimizing unnecessary disturbance during lower risk scenarios. The machine learning component enables the warning adjustment module 136 to continuously learn from traffic patterns and environmental changes, enhancing its capability to provide context-aware, personalized warnings that improve overall road safety.
[0076] In an exemplary embodiment, the warning adjustment module 136 may increase the volume of the audio alert unit 116 during heavy thunderstorms and enhance the brightness of the visual alert unit 118 in conditions of reduced visibility, such as fog, to ensure effective warning delivery.
[0077] The alert activation module 138 is configured to trigger audio alerts via the audio alert unit 116, visual alerts via the visual alert unit 118 and generate alert notifications upon identification of a collision risk between vehicles. Once the appropriate alert parameters such as volume, brightness, and timing are determined by the warning adjustment module 136, the alert activation module 138 executes the delivery of alerts accordingly, ensuring effective warning to drivers approaching the extreme road curve.
[0078] The output module 140 is configured to transmit the processed data and alerts to the identified approaching vehicles.
[0079] In one embodiment of the present invention, the output module 140 is further configured to transmit the real-time geographic location coordinates of the pole-mounted device 102 via a location detector 120 for location-based alerting and event data logging. The location detector 120 facilitates the generation of proactive alerts, notifying drivers in advance that they are approaching an extreme road curve. The geographic coordinates of the pole-mounted device 102 are displayed on a digital map through the user device 104, enabling drivers to take timely precautionary measures and reduce the risk of potential collisions. Additionally, the event data logging functionality is associated with geotagging, wherein each logged event in the cloud database 112 is tagged with the real-time geographic location coordinates of the pole-mounted device 102. This enables spatially contextualized data analysis for improved monitoring of the system 100, risk zone identification, and historical recordkeeping.
[0080] In one embodiment of the present invention, the location detector 120 is a Global Positioning System (GPS).
[0081] In one embodiment of the present invention, the pole mounted device 102 further comprises a power unit 106 configured to provide continuous and reliable power to all the components of the pole mounted device 102 utilizing solar energy with battery backup support.
[0082] The user device 104 is placed in a vehicle. The user device 104 is connected to the microcontroller 114 via the communication network 110. The user device 104 is configured to receive and display real-time alerts indicating potential collision risks at extreme road curves.
[0083] In one embodiment of the present invention, the user device 104 receives a curve warning notification 5-10 seconds before entering the danger zone, i.e., 200-meter area from the pole-mounted device 102. This helps in preventing late reactions.
[0084] In one embodiment of the present invention, the user device 104 may include a smartphone, a laptop, a tablet, in-vehicle infotainment display, embedded liquid-crystal display (LCD) screen, smart wearable device, heads-up display (HUD), and any other electronic device capable of receiving and displaying real-time alerts.
[0085] In one embodiment of the present invention, the user device 104 comprises a user interface in the form of a mobile or web-based application configured to display real-time alerts, notifications, track extreme road curve location coordinates, and system updates to the user.
[0086] In another embodiment of the present invention, the system 100 enables direct vehicle-to-vehicle (V2V) communication, wherein user devices 104 installed in approaching vehicles are configured to exchange real-time data, including speed, location, and estimated time of arrival (ETA), without relying solely on the pole-mounted device 102.
[0087] FIG. 2 illustrates a schematic 200 of the pole-mounted device 102 for collision warning and prevention, in accordance with an exemplary embodiment of the present disclosure.
[0088] The schematic 200 represents the pole-mounted device 102 installed at an extreme road curve. The schematic shows two vehicles approaching the sharp curve from two opposite directions, one from the east side 202 and the other from the north side 204. At the center of the schematic 200 is a pole-mounted device 102, strategically installed at the curve to monitor and manage potential collision risks. The pole-mounted device 102 is equipped with multiple components, including a plurality of sensor 108, a microcontroller 114, a visual alert unit 118 comprising a plurality of danger light 144 to emit a red-coloured signal light, and an audio alert unit 116 comprising a buzzer 142 to emit a loud sound.
[0089] The plurality of sensor 108 are mounted on the pole-mounted device 102 and are configured to detect the presence and movement of approaching vehicles from both directions within a predefined range of up to 200 meters. When two or more vehicles are detected approaching the curve from opposite directions within this range, the system 100 assesses the potential for a collision with the help of the microcontroller 114. Upon identifying a risk, the pole-mounted device 102 activates audio alerts via the buzzer 142 and visual alerts through the plurality of danger light 144 emitting red-coloured signal light. These alerts are intended to warn the drivers of the imminent danger, prompting them to slow down or proceed with caution. The schematic 200 highlights how this pole-mounted safety mechanism serves to provide proactive, real-time warnings designed to reduce the risk of accidents on blind or sharp curves, particularly in areas with limited visibility.
[0090] FIG. 3 illustrates a flowchart of a method 300, outlining the sequential steps for generating safety alerts in real-time for extreme road curves, in accordance with an exemplary embodiment of the present disclosure.
[0091] The method 300 may include the following steps:
[0092] At step 302, detecting vehicles approaching from opposite directions, assessing collision risk, and generating real-time audio and visual alerts to warn drivers of potential danger via a pole-mounted device 102 comprising of several components.
[0093] At step 304, detecting vehicles approaching from both directions of an extreme road curve, along with monitoring real-time environmental conditions via a plurality of sensor 108.
[0094] At step 306, generating audible alerts via an audio alert unit 116.
[0095] At step 308, generating visual alerts via a visual alert unit 118.
[0096] At step 310, transmitting information within the various components of the system 100 via a communication network 110.
[0097] At step 312, analysing sensor data to determine simultaneous bidirectional vehicle presence and trigger safety alerts via a microcontroller 114 comprising of several modules.
[0098] At step 314, analysing sensor data to determine simultaneous bidirectional vehicle presence and trigger safety alerts via a microcontroller 114 comprising of several modules.
[0099] At step 316, cleaning, normalizing, and preprocessing the received raw input data for further processing via a data processing module 124.
[0100] At step 318, detecting and identifying the presence and direction of multiple approaching vehicles based on the processed sensor data via a vehicle identification module 126.
[0101] At step 320, calculating the distance of each identified approaching vehicle from the sensor location via a distance calculation module 128.
[0102] At step 322, estimating the speed of each identified approaching vehicle using distance-over-time computation via a speed calculation module 130.
[0103] At step 324, calculating the estimated time of arrival (ETA) of each approaching vehicle at the extreme road curve based on the calculated speed and distance via a time calculation module 132.
[0104] At step 326, assessing and classifying a risk of collision between the identified approaching vehicles by comparing the calculated distance, speed, and estimated time of arrival data against predefined threshold values and utilizing machine learning algorithms via a collision risk assessment module 134.
[0105] At step 328, dynamically adjusting the intensity of audio and visual alerts based on the assessed collision risk level and environmental conditions using machine learning algorithms via a warning adjustment module 136.
[0106] At step 330, triggering audio alerts via the audio alert unit 116, visual alerts via the visual alert unit 118 and generate alert notifications upon identification of a collision risk between vehicles via an alert activation module 138.
[0107] At step 332, transmitting the processed data and alerts to the identified approaching vehicles via an output module 140.
[0108] At step 334, receiving and displaying real-time alerts indicating potential collision risks at extreme road curves via a user device 104.
[0109] In the best mode of operation, the system 100 is deployed on a pole-mounted device 102 positioned strategically at an extreme road curve prone to limited visibility and high accident risk. As vehicles approach the curve from either direction, a plurality of sensors 108 mounted on the pole-mounted device 102 capture raw data such as vehicle presence, direction, distance, and environmental conditions. This input data is transmitted via the communication network 110 to the microcontroller 114, which serves as the central processing unit of the system 100. The data input module 122 of the microcontroller 114 receives input from the plurality of sensor 108. The data processing module 124 then cleans, normalizes, and preprocesses the received sensor inputs, ensuring the integrity of the data for subsequent analysis. Next, the vehicle identification module 126 identifies the approaching vehicles and determines their respective directions, then the distance calculation module 128 computes the distance of each identified vehicle from the pole-mounted device 102. Simultaneously, the speed calculation module 130 estimates the vehicles’ speeds by measuring the change in distance over successive time intervals, and the time calculation module 132 calculates the estimated time of arrival (ETA) of each vehicle at the curve.
[0110] Following these measurements, the collision risk assessment module 134 compares the calculated distance, speed, and ETA data against predefined threshold values, and employs machine learning algorithms to assess and classify the risk of collision as low, medium, or high. Based on this classification, the warning adjustment module 136 dynamically adjusts the intensity of audio and visual alerts according to both the assessed risk level and prevailing environmental conditions. Once the appropriate alert parameters are set, the alert activation module 138 triggers real-time alerts by activating the audio alert unit 116 and the visual alert unit 118. These alerts are simultaneously transmitted to the output module 140, which also sends the alerts via the communication network 110 to a user device 104 installed in approaching vehicles. In parallel, the location detector 120 continuously determines the geographic coordinates of the pole-mounted device 102, which are used to alert drivers proactively and to geotag events. This comprehensive, step-by-step process ensures that drivers receive timely and context-aware warnings, enabling them to take appropriate corrective actions to avoid potential collisions at extreme road curves.
[0111] While the invention has been described in connection with what is presently considered to be the most practical and various embodiments, it will be understood that the invention is not to be limited to the disclosed embodiments, but on the contrary, is intended to cover various modifications and equivalent arrangements included within the scope of the appended claims.
[0112] A person of ordinary skill in the art may be aware that, in combination with the examples described in the embodiments disclosed in this specification, units and algorithm steps may be implemented by electronic hardware, computer software, or a combination thereof.
[0113] The foregoing descriptions of specific embodiments of the present disclosure have been presented for purposes of illustration and description. They are not intended to be exhaustive or to limit the present disclosure to the precise forms disclosed, and many modifications and variations are possible in light of the above teaching. The embodiments were chosen and described to best explain the principles of the present disclosure and its practical application, and to thereby enable others skilled in the art to best utilize the present disclosure and various embodiments with various modifications as are suited to the particular use contemplated. It is understood that various omissions and substitutions of equivalents are contemplated as circumstances may suggest or render expedient, but such omissions and substitutions are intended to cover the application or implementation without departing from the scope of the present disclosure.
[0114] Disjunctive language such as the phrase “at least one of X, Y, Z,” unless specifically stated otherwise, is otherwise understood with the context as used in general to present that an item, term, etc., may be either X, Y, or Z, or any combination thereof (e.g., X, Y, and/or Z). Thus, such disjunctive language is not generally intended to, and should not, imply that certain embodiments require at least one of X, at least one of Y, or at least one of Z to each be present.
[0115] In a case that no conflict occurs, the embodiments in the present disclosure and the features in the embodiments may be mutually combined. The foregoing descriptions are merely specific implementations of the present disclosure, but are not intended to limit the protection scope of the present disclosure. Any variation or replacement readily figured out by a person skilled in the art within the technical scope disclosed in the present disclosure shall fall within the protection scope of the present disclosure. Therefore, the protection scope of the present disclosure shall be subject to the protection scope of the claims.
, C , C , Claims:I/We Claim:
1. A safety alert generation system (100) for vehicle collision prevention at extreme road curves, the system (100) comprising:
a pole-mounted device (102) placed on a pole at an extreme road curve and configured to detect vehicles approaching from opposite directions, assess collision risk, and generate real-time audio and visual alerts to warn drivers of potential danger, wherein the pole-mounted device (102) further comprises:
a plurality of sensor (108) configured to detect vehicles approaching from both directions of an extreme road curve, along with monitoring real-time environmental conditions;
an audio alert unit (116) configured to generate audible alerts;
a visual alert unit (118) configured to generate visual alerts;
a communication network (110) configured to transmit information within the various components of the system (100);
a microcontroller (114) connected to the plurality of sensor (108), the audio alert unit (116), and the visual alert unit (118) via the communication network (110), the microcontroller (114) configured to analyse sensor data to determine simultaneous bidirectional vehicle presence and trigger safety alerts, wherein the microcontroller (114) further comprises:
a data input module (122) configured to receive input from the plurality of sensor (108);
a data processing module (124) configured to clean, normalize, and preprocess the received raw input data for further processing;
a vehicle identification module (126) configured to detect and identify the presence and direction of multiple approaching vehicles based on the processed sensor data;
a distance calculation module (128) configured to calculate the distance of each identified approaching vehicle from the sensor location;
a speed calculation module (130) configured to estimate the speed of each identified approaching vehicle using distance-over-time computation;
a time calculation module (132) configured to calculate the estimated time of arrival (ETA) of each approaching vehicle at the extreme road curve based on the calculated speed and distance;
a collision risk assessment module (134) configured to assess and classify a risk of collision between the identified approaching vehicles by comparing the calculated distance, speed, and estimated time of arrival data against predefined threshold values and utilizing machine learning algorithms;
a warning adjustment module (136) configured to dynamically adjust the intensity of audio and visual alerts based on the assessed collision risk level and environmental conditions using machine learning algorithms;
an alert activation module (138) configured to trigger audio alerts via the audio alert unit (116), visual alerts via the visual alert unit (118) and generate alert notifications upon identification of a collision risk between vehicles; and
an output module (140) configured to transmit the processed data and alerts to the identified approaching vehicles; and
a user device (104) placed in a vehicle, connected to the microcontroller (114) via the communication network (110), and configured to receive and display real-time alerts indicating potential collision risks at extreme road curves.
2. The system (100) as claimed in claim 1, wherein the system (100) further comprises a cloud database (112) configured to store real-time and historical vehicle detection data, alert logs, and performance metrics of the system (100) for secure access, retrieval, remote monitoring, data analysis, and predictive maintenance.
3. The system (100) as claimed in claim 1, wherein the plurality of sensor (108) comprises sensors, including but not limited to, radar sensors, LiDAR sensors, proximity sensors, ultrasonic sensors, infrared sensors, environmental sensors, and combination thereof.
4. The system (100) as claimed in claim 1, wherein the audio alert unit (116) comprises a buzzer (142) configured to modulate volume based on alert urgency and real-time environmental conditions.
5. The system (100) as claimed in claim 1, wherein the visual alert unit (118) comprises a plurality of danger light (144) configured to modulate brightness based on alert urgency and real-time environmental conditions.
6. The system (100) as claimed in claim 1, wherein the vehicle identification module (126) is configured to detect approaching vehicles within a predefined range of up to 200 meters in both directions from the extreme road curve.
7. The system (100) as claimed in claim 1, wherein the vehicle identification module (126) is configured to place the system (100) in standby mode if only one vehicle is detected approaching the extreme road curve.
8. The system (100) as claimed in claim 1, wherein the output module (140) is further configured to transmit the real-time geographic location coordinates of the pole-mounted device (102) via a location detector (120) for location-based alerting and event data logging.
9. The system (100) as claimed in claim 1, wherein the pole mounted device (102) further comprises a power unit (106) configured to provide continuous and reliable power to all the components of the pole mounted device (102) utilizing solar energy with battery backup support.
10. A method (300) for generating safety alerts in real-time for extreme road curves, the method (300) comprising:
detecting vehicles approaching from opposite directions, assessing collision risk, and generating real-time audio and visual alerts to warn drivers of potential danger via a pole-mounted device (102) comprising of several components;
detecting vehicles approaching from both directions of an extreme road curve, along with monitoring real-time environmental conditions via a plurality of sensor (108);
generating audible alerts via an audio alert unit (116);
generating visual alerts via a visual alert unit (118);
transmitting information within the various components of the system (100) via a communication network (110);
analysing sensor data to determine simultaneous bidirectional vehicle presence and trigger safety alerts via a microcontroller (114) comprising of several modules;
receiving input from the plurality of sensor (108) via a data input module (122);
cleaning, normalizing, and preprocessing the received raw input data for further processing via a data processing module (124);
detecting and identifying the presence and direction of multiple approaching vehicles based on the processed sensor data via a vehicle identification module (126);
calculating the distance of each identified approaching vehicle from the sensor location via a distance calculation module (128);
estimating the speed of each identified approaching vehicle using distance-over-time computation via a speed calculation module (130);
calculating the estimated time of arrival (ETA) of each approaching vehicle at the extreme road curve based on the calculated speed and distance via a time calculation module (132);
assessing and classifying a risk of collision between the identified approaching vehicles by comparing the calculated distance, speed, and estimated time of arrival data against predefined threshold values and utilizing machine learning algorithms via a collision risk assessment module (134);
dynamically adjusting the intensity of audio and visual alerts based on the assessed collision risk level and environmental conditions using machine learning algorithms via a warning adjustment module (136);
triggering audio alerts via the audio alert unit (116), visual alerts via the visual alert unit (118) and generate alert notifications upon identification of a collision risk between vehicles via an alert activation module (138);
transmitting the processed data and alerts to the identified approaching vehicles via an output module (140); and
receiving and displaying real-time alerts indicating potential collision risks at extreme road curves via a user device (104).