Description:FIELD OF THE INVENTION
The present invention relates to vehicle-to-everything (V2X) communication systems designed to enhance road safety. More specifically, it integrates vehicles, road infrastructure, pedestrians, cyclists, and external networks into a real-time communication network using advanced technologies such as 5G, artificial intelligence (AI), and machine learning (ML) to predict and prevent road hazards.
BACKGROUND OF THE INVENTION
Road safety has always been a concern, with millions of accidents occurring every year due to various factors, including driver error, lack of timely information, and road hazards. As urbanization increases, the number of vehicles on roads grows, exacerbating congestion, delays, and the risk of accidents. Current safety mechanisms—traffic lights, road signs, and human judgment—are insufficient in providing the real-time, comprehensive data needed to avoid accidents. Additionally, pedestrians, cyclists, and infrastructure are not actively integrated into a communication network with vehicles, leaving room for potential incidents due to miscommunication or lack of visibility.
In essence, the problem revolves around the following:
• Delayed information flow regarding road hazards, accidents, and traffic conditions.
• Inability to react quickly to real-time events, such as sudden vehicle stops, pedestrians crossing, or road obstructions.
• Limited interaction between vehicles, infrastructure, and vulnerable road users (pedestrians, cyclists).
Several existing solutions attempt to address road safety by providing basic communication capabilities or enhancing vehicle awareness:
1. Vehicle-to-Vehicle (V2V) communication: Some systems allow vehicles to share speed, location, and direction with nearby vehicles. Used dedicated short-range communication (DSRC) technologies. This helps in collision avoidance by providing drivers with proximity warnings.
2. Vehicle-to-Infrastructure (V2I) systems: These involve the interaction between vehicles and road infrastructure, such as traffic signals or toll booths. The infrastructure relays traffic light changes or roadwork alerts to vehicles.
3. Vehicle-to-Pedestrian (V2P) technologies: Some V2P systems enable vehicles to communicate with smart phones or wearable devices of pedestrians to warn them about potential collisions.
4. General V2X platforms: Some patents address broader V2X frameworks integrating all forms of communication (V2V, V2I, V2P, and V2N - vehicle to network). However, they remain limited in scope, lacking advanced AI-driven coordination and fail-safe systems.
Despite these advancements, current solutions are often siloed (focusing on one mode of communication) and reactive rather than predictive. They do not fully utilize emerging technologies such as artificial intelligence, machine learning, or 5G communication for more comprehensive safety measures.
SUMMARY OF THE INVENTION
This summary is provided to introduce a selection of concepts, in a simplified format, that are further described in the detailed description of the invention.
This summary is neither intended to identify key or essential inventive concepts of the invention and nor is it intended for determining the scope of the invention.
The V2X Communication System for Enhanced Road Safety is an intelligent and integrated network that facilitates real-time communication between vehicles, infrastructure, pedestrians, and external networks. By utilizing AI-powered predictive analytics, the system enhances decision-making and ensures improved road safety. The system enables multi-channel communication, reducing the reaction time for drivers and traffic control systems.
The Vehicle-to-Vehicle (V2V) component enables vehicles to share speed, location, and direction data, allowing AI algorithms to assess risks and issue warnings about potential collisions. This interconnectivity enhances situational awareness and proactive accident prevention. Additionally, the Vehicle-to-Infrastructure (V2I) component provides real-time updates from traffic signals, road signs, and construction zones, allowing vehicles to adjust speed or routes dynamically.
The Vehicle-to-Pedestrian (V2P) module enhances pedestrian safety by utilizing smartphone or wearable device signals to detect pedestrians near roadways. The system alerts both vehicles and pedestrians of potential hazards, preventing collisions in blind spots or low-visibility conditions. Finally, the Vehicle-to-Network (V2N) component aggregates information from IoT devices, external weather databases, and mapping services to provide real-time adjustments and predictive traffic analysis.
The system’s novelty lies in its predictive intelligence, which employs machine learning models to anticipate potential road hazards based on historical and real-time data. Unlike traditional systems that react to detected threats, this invention enables proactive safety interventions. By integrating AI-driven analytics and low-latency 5G connectivity, the system minimizes delays in safety-critical situations, significantly reducing accidents and optimizing traffic flow.
To further clarify advantages and features of the present invention, a more particular description of the invention will be rendered by reference to specific embodiments thereof, which is illustrated in the appended drawings. It is appreciated that these drawings depict only typical embodiments of the invention and are therefore not to be considered limiting of its scope. The invention will be described and explained with additional specificity and detail with the accompanying drawings.
The proposed V2X Communication System for Enhanced Road Safety is an integrated solution that brings together vehicles, road infrastructure, pedestrians, cyclists, and external networks into a unified, real-time communication network. It operates by leveraging cutting-edge technologies like 5G, artificial intelligence (AI), and machine learning (ML) to predict and prevent potential collisions or hazards.
BRIEF DESCRIPTION OF THE DRAWINGS
The illustrated embodiments of the subject matter will be understood by reference to the drawings, wherein like parts are designated by like numerals throughout. The following description is intended only by way of example, and simply illustrates certain selected embodiments of devices, systems, and methods that are consistent with the subject matter as claimed herein, wherein:
FIGURE 1: SYSTEM ARCHITECTURE
FIGURE 2: FLOW DIAGRAM
The figures depict embodiments of the present subject matter for the purposes of illustration only. A person skilled in the art will easily recognize from the following description that alternative embodiments of the structures and methods illustrated herein may be employed without departing from the principles of the disclosure described herein.
DETAILED DESCRIPTION OF THE INVENTION
The detailed description of various exemplary embodiments of the disclosure is described herein with reference to the accompanying drawings. It should be noted that the embodiments are described herein in such details as to clearly communicate the disclosure. However, the amount of details provided herein 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 scope of the present disclosure as defined by the appended claims.
It is also to be understood that various arrangements may be devised that, although not explicitly described or shown herein, embody the principles of the present disclosure. Moreover, all statements herein reciting principles, aspects, and embodiments of the present disclosure, as well as specific examples, are intended to encompass equivalents thereof.
The terminology used herein is for the purpose of describing particular embodiments only and is not intended to be limiting of example embodiments. As used herein, the singular forms “a",” “an” and “the” are intended to include the plural forms as well, unless the context clearly indicates otherwise. It will be further understood that the terms “comprises,” “comprising,” “includes” and/or “including,” when used herein, specify the presence of stated features, integers, steps, operations, elements and/or components, but do not preclude the presence or addition of one or more other features, integers, steps, operations, elements, components and/or groups thereof.
It should also be noted that in some alternative implementations, the functions/acts noted may occur out of the order noted in the figures. For example, two figures shown in succession may, in fact, be executed concurrently or may sometimes be executed in the reverse order, depending upon the functionality/acts involved.
In addition, the descriptions of "first", "second", “third”, and the like in the present invention are used for the purpose of description only, and are not to be construed as indicating or implying their relative importance or implicitly indicating the number of technical features indicated. Thus, features defining "first" and "second" may include at least one of the features, either explicitly or implicitly.
Unless otherwise defined, all terms (including technical and scientific terms) used herein have the same meaning as commonly understood by one of ordinary skill in the art to which example embodiments belong. It will be further understood that terms, e.g., those defined in commonly used dictionaries, should be interpreted as having a meaning that is consistent with their meaning in the context of the relevant art and will not be interpreted in an idealized or overly formal sense unless expressly so defined herein.
The V2X Communication System for Enhanced Road Safety consists of a multi-layered architecture incorporating communication modules, predictive analytics, and adaptive response mechanisms. The system features onboard vehicle sensors that capture data such as speed, direction, proximity to other vehicles, and road conditions. These sensors continuously transmit data to the V2X network, enabling real-time communication with infrastructure, pedestrians, and external networks.
The V2V module facilitates data exchange between vehicles through direct wireless communication. Using AI-driven algorithms, the system analyzes vehicle trajectories and predicts collision risks, issuing timely warnings to drivers or autonomous driving systems. The V2I module establishes connectivity between vehicles and smart road infrastructure, allowing traffic signals and road signs to transmit situational updates to vehicles. This feature enables real-time route optimization and adaptive speed control to prevent congestion and accidents.
The V2P component relies on pedestrian detection mechanisms enabled through smartphones or wearable technology. When a pedestrian approaches a crossing, the system assesses potential risks based on vehicle trajectories and issues warnings to both drivers and pedestrians. If an imminent collision is detected, automated safety measures such as vehicle deceleration can be activated.
The V2N module extends the system’s capabilities by integrating cloud-based AI processing and third-party data sources, such as weather updates, traffic density, and emergency response networks. This component allows vehicles to make dynamic driving adjustments based on evolving road conditions, significantly improving safety and mobility.
The core AI-driven predictive model continuously analyzes historical and real-time traffic data to identify patterns associated with high-risk driving conditions. Through deep learning techniques, the system refines its hazard detection accuracy over time, making it increasingly effective at preventing accidents. The 5G-enabled low-latency communication framework ensures that safety alerts and system interventions occur with minimal delay.
The system architecture is designed for interoperability with existing transportation infrastructure and modern vehicle communication protocols. The modular nature of the invention allows for easy integration with various automotive platforms, enhancing its scalability and widespread adoption. The implementation of edge computing ensures efficient processing of real-time data at the local level, reducing dependency on centralized cloud resources and improving response times in safety-critical scenarios.
By offering a predictive, AI-enhanced V2X communication network, the invention improves traffic safety, reduces congestion, and enhances pedestrian protection. The system’s ability to anticipate and mitigate road hazards before they occur marks a transformative shift in vehicle communication technology.
The proposed V2X Communication System for Enhanced Road Safety is an integrated solution that brings together vehicles, road infrastructure, pedestrians, cyclists, and external networks into a unified, real-time communication network. It operates by leveraging cutting-edge technologies like 5G, artificial intelligence (AI), and machine learning (ML) to predict and prevent potential collisions or hazards.
• Vehicle-to-Vehicle (V2V): Vehicles share information about their speed, location, and direction. The system uses AI algorithms to assess risks and issue warnings about potential collisions or unsafe conditions.
• Vehicle-to-Infrastructure (V2I): Vehicles receive real-time updates from traffic lights, road signs, or nearby construction sites, adjusting speed or routes based on road conditions.
• Vehicle-to-Pedestrian (V2P): Pedestrians with smart phones or wearable devices are alerted when they cross a street with an oncoming vehicle, while vehicles are similarly warned of nearby pedestrians in blind spots.
• Vehicle-to-Network (V2N): The system connects to the cloud and broader networks, aggregating information from IoT devices, external databases (e.g., weather conditions), and third-party services (e.g., mapping tools) for real-time adjustments and predictions.
The V2X Communication System for Enhanced Road Safety represents a significant leap forward in vehicle communication technologies. By integrating AI-driven predictive analytics and comprehensive real-time communication between vehicles, infrastructure, and pedestrians, the system enhances road safety, reduces accidents, and improves traffic management. Unlike current systems, which are reactive and fragmented, this invention offers a unified, intelligent platform capable of preventing accidents before they happen, making roads safer for all users.
The proposed invention is unique because it incorporates a predictive element using machine learning models, enabling vehicles to not just react to but also anticipate road hazards, improving decision-making and safety measures. Real-time data from multiple sources allows for dynamic, self-adaptive communication and response systems.
The novelty of this invention lies in its predictive intelligence and comprehensive integration of all road users (vehicles, infrastructure, and pedestrians) in a seamless V2X ecosystem. This system will:
• Use machine learning to predict potential accidents based on data patterns from vehicle sensors, infrastructure, and historical traffic data.
• Enable multi-channel communication between vehicles, infrastructure, and pedestrians in a more holistic manner, something current systems don’t fully offer.
• Employ real-time data aggregation and low-latency 5G connectivity for instant communication between different elements, reducing delays in safety-critical situations.
The impact will be profound in terms of reducing accidents, improving traffic flow, and enhancing urban mobility. The ability to predict and prevent accidents will save lives, while real-time routing adjustments will reduce congestion. 
, Claims:1. A V2X communication system for enhanced road safety, comprising:
a vehicle communication module configured to facilitate real-time Vehicle-to-Vehicle (V2V) data exchange;
an infrastructure communication interface enabling Vehicle-to-Infrastructure (V2I) interactions;
a pedestrian detection module allowing Vehicle-to-Pedestrian (V2P) communication;
a cloud-based network connection for Vehicle-to-Network (V2N) data aggregation;
an AI-driven predictive analytics unit for real-time hazard detection and accident prevention.
2. The system as claimed in claim 1, wherein the V2V module enables direct wireless communication between vehicles for collision risk assessment.
3. The system as claimed in claim 1, wherein the V2I interface facilitates adaptive traffic management and route optimization based on infrastructure signals.
4. The system as claimed in claim 1, wherein the V2P module detects pedestrian locations using smartphone or wearable device signals.
5. The system as claimed in claim 1, wherein the AI-driven predictive analytics unit continuously processes historical and real-time data to forecast potential road hazards.
6. The system as claimed in claim 1, wherein the low-latency 5G communication framework ensures real-time transmission of safety alerts.
7. The system as claimed in claim 1, wherein the system dynamically adjusts vehicle speed and route based on real-time data from external sources.
8. The system as claimed in claim 1, wherein cloud-based processing aggregates data from IoT devices and third-party services for enhanced decision-making.
9. The system as claimed in claim 1, wherein edge computing is utilized to process time-sensitive data locally, reducing response time in safety-critical scenarios.
10. The system as claimed in claim 1, wherein AI-enhanced traffic flow optimization reduces congestion and improves urban mobility.