2 nd generation autonomous navigation using advance v2x and infrastructure

Abstract

The invention relates to an autonomous vehicle navigation system utilizing infrastructure-mounted sensors (100), such as cameras, LiDAR, radar, thermal, and infrared sensors, to monitor road conditions. These sensors (100) are mounted on street poles (101) and ceilings (102), transmitting real-time environmental data to a centralized AI server (108). The AI server (108) processes the data using machine learning algorithms to make navigation decisions and provide autonomous vehicles with continuous updates. The system includes a V2X connectivity box in each vehicle that enables communication with infrastructure via GSM, Wi-Fi, 5G, 6G and satellite networks, reducing the need for onboard sensors and computational load. The present invention is compatible with Advanced Driver-Assistance Systems (ADAS) technologies. The present invention enhances vehicle safety, reduces manufacturing costs, and enables real-time hazard detection and adaptive routing. The system is scalable across urban and rural environments, contributing to smarter traffic management and sustainability.

Description

[0001] FORM 2

[0002] THE PATENTS ACT, 1970 (39 of 1970) &

[0003] The Patents Rules, 2003 COMPLETE SPECIFICATION (See section 10 and rule 13) TITLE OF THE INVENTION

[0004] 2ndGENERATION AUTONOMOUS NAVIGATION USING ADVANCE

[0005] V2X AND INFRASTRUCTURE APPLICANT(S) a. Name : Bhargava S Hegde b. Nationality : INDIAN c. Address : # 199, Bangari, 1st main, Gattegere, BEML-3rdstage,

[0006] Rajarajeshwari Nagar, Bangalore-560098, Karnataka,

[0007] India a. Name : Pramod Kumar V Manderwad b. Nationality : INDIAN c. Address : # 220, 1st Main, 6th Cross, Ideal Homes Township,

[0008] Rajarajeshwari Nagar, Bangalore- 560098,

[0009] Karnataka, India a. Name : Vaishnavi B b. Nationality : INDIAN c. Address : 15, Kulakkarai Steet, Katpadi, Vellore- 632007, Tamil

[0010] Nadu, India a. Name : G K Prasad b. Nationality : INDIAN c. Address : # 124, “Anugrah”, 3rd Main, Sampige Layout, Near

[0011] Vijaynagar, Bengaluru-560079, Karnataka, India

[0012] 3. PREAMBLE TO THE DESCRIPTION

[0013] COMPLETE

[0014] The following specification particularly describes the invention and the manner in which it has to be performed.

[0015] 4. DESCRIPTION

[0016] Technical Field of the Invention

[0017] This invention relates to autonomous vehicle navigation systems. More specifically, it focuses on using sensors mounted on infrastructure to gather real-time data about road conditions.

[0018] Background of the Invention

[0019] Autonomous vehicle technology has rapidly advanced over the past decade, with promises of revolutionizing the way we travel. However, despite significant progress, fully autonomous driving systems still face considerable technical and practical challenges that prevent widespread adoption. The core problem lies in the reliance on onboard vehicle sensors for environmental perception, decision-making, and navigation. Autonomous vehicles today are equipped with complex sensor arrays, including cameras, LiDAR, radar, ultrasonic sensors, and various processing units. While these systems allow vehicles to perceive their surroundings, they are inherently limited by factors such as cost, complexity, weather conditions, and real-time data processing capacity.

[0020] LiDAR’ s effectiveness decreases significantly in fog or heavy rain, while cameras and visual sensors struggle with low light and visibility issues. These shortcomings pose a serious safety risk, as the autonomous vehicle may not detect obstacles, pedestrians, or road conditions accurately, resulting in potential accidents or operational failures.

[0021] Another significant issue is the cost and complexity of the current sensor-based autonomous driving systems. Vehicles are outfitted with an array of high-cost sensors and processors to handle complex real-time data processing, environmental perception, and decision-making tasks. The development and production of these sensor arrays, particularly LiDAR and radar systems, are expensive, making the vehicles prohibitively costly for mass-market adoption. Furthermore, the maintenance of these complex systems can be both difficult and expensive, as even minor damages to sensors can severely impair a vehicle’s ability to operate autonomously. This financial burden hampers the commercial viability of autonomous vehicles and limits their accessibility to a broader consumer base.

[0022] The computational load on onboard systems is another challenge. Each vehicle must process vast amounts of data from its sensors in real-time to make safe driving decisions. This requires advanced onboard computing power, which increases both the energy consumption and cost of autonomous vehicles. Furthermore, the vehicle’s capacity to process data quickly can be overwhelmed by sudden changes in the environment, leading to slower reaction times and potential safety hazards. Current systems often struggle to cope with the high-speed decision-making required for safe and efficient driving in complex traffic conditions.

[0023] Lastly, the lack of real-time communication between vehicles and infrastructure further compounds these problems. Without dynamic communication, autonomous vehicles operate in isolation, relying solely on their onboard sensors and data processing capabilities. This isolation can lead to inefficiencies in traffic management, such as bottlenecks, unnecessary stopping, or abrupt acceleration, as vehicles are not equipped to react in a coordinated manner to real-time traffic changes or unexpected events.

[0024] Several prior art technologies have attempted to address the issues mentioned above by enhancing the sensor capabilities of autonomous vehicles. Early models of autonomous vehicles primarily used camera-based systems combined with basic radar for object detection and path planning. While these systems performed well under clear weather conditions, they quickly encountered limitations in poor visibility and in environments with unpredictable obstacles.

[0025] Subsequent innovations introduced LiDAR (Light Detection and Ranging), which allowed autonomous vehicles to map their surroundings in three dimensions by measuring the distance to objects using laser pulses. LiDAR significantly improved object detection accuracy and enabled vehicles to perceive their surroundings more comprehensively than camera-based systems alone. However, LiDAR’ s limitations in adverse weather conditions, such as fog and rain, soon became apparent. Furthermore, the cost of LiDAR remains extremely high, with some systems adding tens of thousands of dollars to the price of a vehicle. The fragility of LiDAR sensors also makes them prone to damage, increasing long-term maintenance costs.

[0026] Other prior art attempted to enhance the computational power of onboard systems to manage the massive amounts of data collected by sensors. Autonomous vehicles were equipped with high-powered processors capable of real-time data fusion, which integrates data from multiple sensors to create a comprehensive understanding of the environment. While this improved the vehicle’s decision-making abilities, it also increased the vehicle’s energy consumption and production costs. Moreover, the reliance on real-time data processing within the vehicle still left autonomous systems vulnerable to sensor failures or blind spots in certain conditions.

[0027] Efforts to introduce Vehicle-to-Vehicle (V2V) and Vehicle-to-Infrastructure (V2I) communication systems have made progress in addressing the isolation of vehicles. V2V communication allows autonomous vehicles to share information about their speed, location, and intended path, thereby reducing the likelihood of collisions and improving traffic efficiency. V2I communication involves vehicles communicating with roadside infrastructure, such as traffic lights or road signs, to receive updates on road conditions or traffic signals. However, these systems are still in their infancy and are limited in their deployment. In practice, they have not yet been widely implemented, especially in less developed regions. Furthermore, V2X systems have struggled with standardization and communication bandwidth issues, limiting their efficacy.

[0028] The prior art solutions described above suffer from several inherent disadvantages that prevent them from fully addressing the core challenges facing autonomous driving.

[0029] Firstly, sensor dependency remains a significant issue. Most autonomous vehicles still rely heavily on onboard sensors like cameras, LiDAR, and radar to perceive their environment. While these sensors work well under optimal conditions, they remain vulnerable to weather conditions, lighting changes, and other environmental factors. Additionally, the sheer complexity of managing and integrating data from multiple sensors adds to the system’s computational and financial burden.

[0030] Secondly, the high cost of production and maintenance continues to hinder the scalability of autonomous vehicles. LiDAR and radar systems, while improving perception, contribute significantly to the vehicle’s overall cost, making autonomous driving technologies inaccessible to the mass market. Moreover, these systems are delicate and susceptible to damage from accidents or harsh environments, leading to increased maintenance costs and operational downtime.

[0031] Another disadvantage is the lack of efficient traffic coordination. Despite advances in V2V and V2I communication, autonomous vehicles today operate largely in isolation, relying on their onboard systems to make decisions. This lack of coordination results in inefficiencies in traffic flow, such as unnecessary braking or acceleration, traffic jams, and increased fuel consumption. Without real-time communication with other vehicles or infrastructure, autonomous systems cannot fully optimize their driving behavior in response to dynamic traffic conditions.

[0032] Finally, computational load and energy inefficiency continue to plague onboard systems. Autonomous vehicles must process vast amounts of data in real-time to make critical decisions, requiring high-powered processors and advanced software. This leads to high energy consumption, which reduces the vehicle’s operational efficiency and increases the cost of maintaining these systems.

[0033] Given these persistent challenges and limitations, there is a pressing need for an improved autonomous vehicle navigation system. The industry requires a solution that not only addresses the shortcomings of current technologies but also provides a scalable, cost-effective, and reliable platform for the future of autonomous driving.

[0034] Firstly, there is a need to reduce the reliance on onboard sensors by shifting the perception and data processing tasks to external infrastructure. A system that uses infrastructure-based sensors would alleviate the cost and complexity associated with equipping every vehicle with expensive sensor arrays. By leveraging external infrastructure, such as street poles and traffic lights, vehicles can receive comprehensive environmental data without the need for high-powered onboard sensors.

[0035] Secondly, there is a dire need for a centralized data processing system that can manage real-time data from multiple vehicles and infrastructure sensors. Such a system would offload the computational burden from individual vehicles and centralize decisionmaking in an Al-driven server that can process vast amounts of data simultaneously. This would improve response times, optimize traffic flow, and reduce the energy consumption of autonomous vehicles. Thirdly, a system that integrates V2X communication with centralized Al processing would provide a more coordinated and efficient traffic management solution. By allowing vehicles to communicate not only with each other but also with a centralized Al that controls the infrastructure, the entire traffic ecosystem could be optimized for efficiency and safety. This system would reduce traffic jams, improve fuel efficiency, and enhance road safety by making real-time adjustments based on traffic conditions, weather, and potential hazards.

[0036] Finally, there is an urgent need for a system that can operate reliably in adverse environmental conditions. Infrastructure-based sensors that include thermal cameras, infrared sensors, and weather-resistant technologies could ensure consistent and reliable perception of the environment, regardless of weather conditions or lighting. This would address one of the most critical limitations of current autonomous vehicles, ensuring safe operation in all environments.

[0037] The present invention addresses all these needs by providing a scalable, reliable, and cost-effective autonomous vehicle navigation system that leverages infrastructurebased sensors, centralized Al processing, and real-time V2X communication. By shifting the sensing and decision-making tasks to infrastructure, this system offers a more accessible and reliable solution for the future of autonomous driving.

[0038] Brief Summary of the Invention

[0039] The following presents a simplified summary of the disclosure in order to provide a basic understanding to the reader. This summary is not an extensive overview of the disclosure, and it does not identify key / critical elements of the invention or delineate the scope of the invention. Its sole purpose is to present some concepts disclosed herein in a simplified form as a prelude to the more detailed description that is presented later.

[0040] The primary object of the present invention is to provide a robust and cost-effective autonomous vehicle navigation system that relies on infrastructure-based sensors to enhance safety, efficiency, and scalability in autonomous driving. This invention aims to shift the burden of sensing and data processing from individual vehicles to a network of strategically placed sensors in the infrastructure. By doing so, the cost and complexity of autonomous vehicles can be significantly reduced, making the technology more accessible.

[0041] A key object of the invention is to improve road safety. The system incorporates infrastructure-mounted sensors, such as cameras, LiDAR, radar, thermal, and infrared sensors, which continuously monitor road conditions, traffic flow, obstacles, and other environmental factors. These sensors feed data into a centralized Al system that processes the information in real-time, making quick decisions to guide vehicles safely through different driving conditions. The Al system can also anticipate hazards and take corrective actions, such as issuing emergency braking instructions or rerouting vehicles to avoid accidents.

[0042] Another object of the invention is to reduce the manufacturing and operational costs associated with autonomous vehicles. Because the infrastructure handles the majority of the sensing and data processing tasks, vehicles no longer require high-end sensor arrays or complex onboard processing systems. This reduction in vehicle-side hardware not only lowers production costs but also simplifies vehicle maintenance, making autonomous driving more economical for manufacturers and consumers alike. The invention also aims to provide scalability and adaptability across a variety of environments, including dense urban areas, rural roads, and indoor settings such as airports or industrial facilities. The system is designed to operate effectively in these different landscapes by placing sensors on street poles, ceilings, or other infrastructure elements. The modular nature of the system enables incremental deployment, allowing for gradual adaptation based on the specific requirements of the location.

[0043] Another important object of the invention is to enhance traffic efficiency and reduce congestion. The system utilizes real-time data from the infrastructure sensors to manage traffic flow dynamically. Autonomous vehicles receive continuous updates on traffic conditions and are able to adjust their speeds, routes, and behaviors to optimize traffic patterns. By reducing the frequency of sudden stops, accelerations, and inefficient driving practices, the system contributes to smoother traffic flow, reduced congestion, and lower fuel consumption, thus supporting sustainability.

[0044] A further object of the invention is to improve the reliability and performance of autonomous vehicles in adverse conditions. Traditional autonomous vehicles often face difficulties in challenging environments, such as in poor visibility conditions caused by fog, heavy rain, or nighttime driving. By using infrastructure-based sensors, including thermal cameras and infrared sensors, the present system is capable of functioning effectively even in these conditions. This reliability helps to ensure safe and consistent autonomous driving regardless of environmental challenges.

[0045] Moreover, the invention seeks to facilitate seamless communication between vehicles and the surrounding infrastructure. By employing V2X (Vehicle-to-Every thing) and V2I (Vehicle-to-Infrastructure) communication protocols, the system ensures that vehicles receive real-time updates on road conditions, hazards, and navigation commands. This communication network allows vehicles to coordinate with one another, further enhancing safety and improving traffic management efficiency. The present invention introduces a novel autonomous vehicle navigation system that transforms how autonomous vehicles interact with their surroundings. Instead of relying solely on vehicle-mounted sensors, this system leverages infrastructure-based sensors that are strategically placed along roadways, highways, and indoor environments. These sensors continuously gather real-time data about road conditions, traffic flow, obstacles, and other environmental factors.

[0046] A key aspect of the invention is the use of a centralized Al server that processes the data collected from the infrastructure sensors. This Al server is equipped with advanced machine learning algorithms that analyze the incoming data and generate real-time navigation commands for the vehicles. The centralized nature of the Al system allows for comprehensive processing, reducing the need for each vehicle to perform complex onboard computations. The Al server can handle data from multiple sources, ensuring that decisions are made quickly and accurately to guide vehicles safely.

[0047] Another significant aspect of the invention is its ability to reduce the reliance on expensive onboard sensors. While traditional autonomous vehicles require high-cost sensor arrays, such as LiDAR, radar, and cameras, the present system offloads most of the sensing duties to the infrastructure. This not only reduces the manufacturing costs of autonomous vehicles but also simplifies their design and maintenance, making the technology more affordable and easier to implement.

[0048] The V2X communication system forms another critical aspect of the invention. Through V2X and V2I communication protocols, vehicles and infrastructure are able to communicate seamlessly, ensuring that vehicles receive up-to-the-minute information about their surroundings. This communication network enables vehicles to adapt to changing road conditions, such as traffic congestion or obstacles, in real time. The system also allows vehicles to communicate with each other through V2V (Vehicle-to- Vehicle) protocols, facilitating coordinated driving and improving overall road safety.

[0049] The invention also emphasizes real-time safety interventions. By constantly monitoring the environment, the system can issue critical safety commands, such as emergency braking, lane-keeping assistance, or collision avoidance, in response to detected hazards. These safety features are essential for preventing accidents and ensuring that autonomous vehicles operate safely, even in complex or unpredictable environments.

[0050] In addition to safety and cost benefits, the present invention provides scalability across various environments. The infrastructure-based sensors can be installed incrementally in urban, rural, and indoor locations, allowing for a flexible deployment strategy. Whether deployed on city streets, highways, rural roads, or indoor facilities such as airports and factories, the system is designed to adapt to the specific needs of the environment. This flexibility ensures that the system can be expanded over time, providing widespread support for autonomous driving technologies.

[0051] Another aspect of the invention is its ability to function reliably in adverse environmental conditions. Unlike traditional autonomous systems, which may struggle in poor visibility or extreme weather conditions, the present system utilizes specialized sensors, such as thermal cameras and infrared sensors, that can detect obstacles and monitor road conditions even in fog, heavy rain, or low-light environments. This capability makes the system highly reliable, ensuring safe autonomous navigation in a wide range of weather and lighting conditions.

[0052] The invention further addresses the issue of traffic congestion and inefficiency. By using real-time data from infrastructure sensors, the system optimizes traffic flow by instructing vehicles to adjust their speed and route based on current conditions. The centralized Al can predict and prevent traffic jams by rerouting vehicles or managing the speed of multiple vehicles to smooth traffic patterns. This not only improves the efficiency of road systems but also reduces vehicle emissions by minimizing stop-and- go driving and sudden acceleration.

[0053] A final aspect of the present invention is its ability to enhance sustainability. By improving traffic flow and reducing unnecessary acceleration and braking, the system helps to lower fuel consumption and emissions. Moreover, by reducing the need for high-cost vehicle sensors and complex onboard systems, the invention contributes to more environmentally friendly vehicle production and maintenance practices. The system’s reliance on existing infrastructure, such as GSM towers and existing power lines, further reduces the need for extensive new infrastructure development, making it an eco-friendly solution for modern transportation challenges.

[0054] Further scope of applicability of the present invention will become apparent from the detailed description given hereinafter. However, the detailed description and specific examples, while indicating preferred embodiments of the invention, will be given by way of illustration along with complete specification.

[0055] Brief Description of the Drawings

[0056] The invention will be further understood from the following detailed description of a preferred embodiment taken in conjunction with an appended drawing, in which:

[0057] Fig. 1 illustrates the comprehensive overview of the infrastructure-driven autonomous vehicle system, in accordance with an exemplary embodiment of the present invention.

[0058] Fig. 2 [a & b] illustrates the sequential flow of information through the autonomous driving system, in accordance with an exemplary embodiment of the present invention. Fig. 3 illustrates the intricate data flow within the autonomous driving system, in accordance with an exemplary embodiment of the present invention.

[0059] Fig. 4 illustrates the example map of infrastructure driven autonomous driving system, in accordance with an exemplary embodiment of the present invention.

[0060] Detailed Description of the Invention

[0061] It is to be understood that the present disclosure is not limited in its application to the details of construction and the arrangement of components set forth in the following description or illustrated in the drawings. The present disclosure is capable of other embodiments and of being practiced or of being carried out in various ways. In addition, it is to be understood that the phraseology and terminology used herein is for the purpose of description and should not be regarded as limiting.

[0062] The use of “including”, “comprising” or “having” and variations thereof herein is meant to encompass the items listed thereafter and equivalents thereof as well as additional items. 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. Further, the use of terms “first”, “second”, and “third”, and the like, herein do not denote any order, quantity, or importance, but rather are used to distinguish one element from another.

[0063] The present invention relates to an autonomous vehicle navigation system that introduces an innovative infrastructure-based approach for autonomous driving. Traditional autonomous vehicles rely heavily on onboard sensors, such as cameras, LiDAR, radar, and others, which increases vehicle cost and complexity. In contrast, the system proposed in this invention shifts much of the sensing and data processing burden to infrastructure elements, such as street poles, road signs, and buildings. Sensors mounted on these infrastructure elements gather environmental data, including road conditions, traffic flow, and potential hazards. This data is transmitted to a centralized Al server, which processes it in real-time using machine learning algorithms to make navigation decisions. The centralized Al then sends these navigation commands to autonomous vehicles equipped with V2X (Vehicle-to- Everything) communication technology. This approach reduces the reliance on expensive onboard vehicle sensors and enhances the overall safety, cost-efficiency, and scalability of autonomous driving systems.

[0064] In an exemplary embodiment of the present invention, a network of sensors, including cameras, LiDAR, radar, thermal cameras, and infrared sensors, are mounted on infrastructure such as street poles and ceilings. These sensors capture data on environmental conditions such as traffic patterns, obstacles, and weather conditions. The data is transmitted via a communication network to a centralized Al processing unit, which uses machine learning techniques, such as convolutional neural networks (CNN), artificial neural networks (ANN), and large language models (LLM), to process the data in real-time. The Al processing unit continuously updates its models based on historical and real-time data, enabling it to make highly accurate navigation decisions. The processed data is then transmitted back to the vehicles via a V2X communication system, which allows vehicles to adjust their navigation routes and speeds accordingly.

[0065] The system is designed to be scalable across various environments, including urban areas, rural regions, highways, and even indoor spaces like airports or industrial facilities. The infrastructure sensors can be customized to suit the needs of specific environments, ensuring full coverage and real-time monitoring of all relevant conditions. The modular nature of this invention allows for incremental deployment, making it adaptable to both developed and developing regions. Additionally, the centralized Al server can handle data from multiple sources simultaneously, allowing for efficient management of complex traffic systems and large numbers of autonomous vehicles.

[0066] The invention will now be described in further detail, with reference to specific components and their corresponding reference numerals. As illustrated in Fig. 1, the autonomous vehicle navigation system comprises a network of infrastructure sensors (100) mounted on street poles (101), ceilings (102), and other strategic locations. These sensors include cameras (103) for visual data, LiDAR (104) for creating three- dimensional maps, radar (105) for measuring the speed and distance of objects, thermal cameras (106) for detecting temperature variations, and infrared sensors for night-time or low-visibility conditions. These sensors capture real-time data from the environment, including information about traffic conditions, road obstacles, pedestrians, and other vehicles.

[0067] The captured data is transmitted through a communication system that supports Vehicle- to-Infrastructure (V2I) and Vehicle-to-Everything (V2X) communication protocols. The communication system uses technologies such as GSM, Wi-Fi, 4G, 5G, and satellite networks to transmit the data wirelessly between the infrastructure sensors and the centralized Al server.

[0068] Fig. 2(a) and Fig. 2 (b) illustrated a centralized Al processing unit (108), uses advanced machine learning algorithms to process the data in real-time. The Al server makes navigation decisions based on the incoming data and sends real-time instructions back to the vehicles. The Al continuously improves its decision-making capabilities by updating its models with new data, ensuring better performance over time. The vehicle communication system that integrates various components for sensing, data transmission, and vehicle navigation. The process begins with sensing hardware (sensor devices) (110) that collect data from the environment or vehicles. This data is transmitted to communication towers (116), which act as intermediary nodes, sending and receiving data between vehicles and the network through either physical wired connections (112) or wireless means, such as cellular networks. Once the communication towers receive the data, it undergoes data transmission to a processing unit (114), typically driven by Al or machine learning models, which analyze and process the raw information collected from the sensors. The processed data is then transmitted (118) to the vehicles via a V2X (Vehicle-to-Everything) system (120), enabling communication between vehicles, the network, and infrastructure. Finally, the processed data guides the navigation of vehicles (122s), allowing for real-time adjustments such as optimal routing, collision avoidance, and improved driving safety and efficiency.

[0069] Fig. 3 illustrates each autonomous vehicle is equipped with a V2X connectivity box (120), which includes a GSM modem (124), Wi-Fi adapters (126), antennas (128), and Bluetooth modules (130) for communication with the infrastructure. This connectivity box receives navigation instructions from the centralized Al and adjusts the vehicle’s route, speed, and other operational parameters accordingly. The V2X connectivity box (120) also communicates with other nearby vehicles through Vehicle-to- Vehicle (V2V) protocols, allowing the vehicles to coordinate their movements and avoid collisions. The onboard sensors in the vehicle provide supplementary data, such as information on the vehicle’s internal status and close-proximity obstacles, but the majority of the navigation relies on the infrastructure-based sensors and centralized Al (108). The autonomous vehicle navigation system leverages a network of infrastructure sensors (100) mounted on structures such as street poles (101) and ceilings (102). These sensors, including cameras (103), LiDAR (104), radar (105), thermal cameras (106), and infrared sensors, are strategically positioned to monitor environmental data such as road conditions, traffic flow, and obstacles. The data captured by these sensors is critical for enabling real-time navigation of autonomous vehicles.

[0070] To enhance the efficiency of data transmission and processing, the system incorporates processing hubs, which are distributed along the roadways. These processing hubs serve as intermediate data aggregation points that collect data from nearby infrastructure sensors (100) and perform preliminary processing. By aggregating sensor data locally, the system reduces the burden on the centralized Al server (108) and minimizes communication latency, especially in high-traffic environments.

[0071] Each processing hub is equipped with computational resources that allow it to process raw sensor data into structured information, such as vehicle speeds, traffic densities, and obstacle positions. Once the data is processed, it is transmitted to the centralized Al server (108) for further analysis and real-time decision-making. This distributed processing architecture ensures that the system can handle large volumes of data while maintaining low-latency communication between the infrastructure and autonomous vehicles.

[0072] In environments where communication bandwidth may be limited or where real-time response is critical, the processing hubs play an essential role in optimizing the overall performance of the autonomous vehicle navigation system. By pre-processing data locally, these hubs help reduce the volume of data sent to the Al server, allowing for quicker response times and more efficient use of communication resources. The system includes additional safety mechanisms to enhance vehicle operation. For example, automatic emergency braking systems can be triggered by the Al when it detects an imminent collision, and ensures that the ego vehicle maintains to safe distance from the target vehicle in front. Furthermore, lane departure warnings alert the driver or autonomous control system if the vehicle is straying out of its lane. These safety features are critical in mitigating risks and ensuring the reliable operation of autonomous vehicles in various conditions, including dense urban traffic and highspeed highway travel.

[0073] The method of manufacturing the autonomous vehicle navigation system begins with the fabrication of infrastructure sensors (100), which include cameras (103), LiDAR (104), radar (105), thermal cameras (106), and infrared sensors. These sensors are specifically designed to capture various types of environmental data, including road conditions, traffic flow, and obstacles. Once fabricated, these sensors are assembled onto street poles (101), ceilings (102), or other strategic structures in diverse environments such as urban streets, highways, rural roads, and indoor settings like airports or industrial facilities. The positioning of these sensors ensures optimal data coverage to enhance the system’s effectiveness.

[0074] Next, processing hubs are manufactured to serve as intermediate data aggregation points. These hubs are equipped with computational resources that allow them to pre- process the data collected by the infrastructure sensors (100) before transmitting the data to the centralized Al server (108). The Al server (108) is then developed and configured with advanced machine learning algorithms capable of processing large volumes of real-time data from multiple infrastructure sensors (100) to make quick and accurate navigation decisions. Additionally, V2X connectivity boxes are produced for installation in autonomous vehicles. These connectivity boxes include GSM modems, Wi-Fi adapters, antennas, and Bluetooth modules, which enable seamless communication between the vehicles and the centralized Al server (108). The V2X connectivity boxes are then installed into vehicles, allowing them to receive real-time navigation commands from the Al server (108) and transmit feedback back to the server. Finally, the entire system, including the infrastructure sensors (100), processing hubs, centralized Al server (108), and V2X connectivity boxes, is calibrated to ensure accurate data transmission and efficient vehicle operation in various environments.

[0075] The method of operation for the autonomous vehicle navigation system begins with the capture of environmental data using infrastructure sensors (100). These sensors, mounted on street poles (101) and ceilings (102), include cameras (103) for visual data, LiDAR (104) for 3D mapping, radar (105) for measuring the speed and distance of objects, thermal cameras (106) for temperature variations, and infrared sensors for low- light and night-time conditions. The data collected by these sensors includes information about road conditions, traffic patterns, potential obstacles, and other environmental factors.

[0076] Once the data is captured, it is transmitted to processing hubs that are strategically distributed along the roadways. The processing hubs perform initial aggregation and pre-processing of the sensor data to reduce communication latency and the burden on the centralized Al server. The pre-processed data is then transmitted to the centralized Al server (108), where it is further analyzed using machine learning algorithms, such as convolutional neural networks (CNN) and artificial neural networks (ANN). The Al server makes real-time navigation decisions based on the incoming data and continuously updates its models to enhance its decision-making capabilities. The centralized Al server (108) then generates navigation commands based on the processed data, which are transmitted to the vehicles via a communication system that supports V2X (Vehicle-to-Everything) and V2I (Vehicle-to-Infrastructure) communication protocols. The V2X connectivity box installed in each vehicle receives these navigation commands and adjusts the vehicle’s speed, route, and other parameters accordingly. The vehicles are also capable of communicating with other vehicles using V2V (Vehicle-to- Vehicle) communication, which ensures coordinated and safe movement.

[0077] In addition to navigation, the system includes safety interventions, such as automatic emergency braking, lane departure warnings, and adaptive cruise control, which are triggered by the centralized Al server (108) in response to real-time data from the infrastructure sensors (100). These interventions enhance the vehicle’s safety by preventing collisions and ensuring that the vehicle remains on the correct path. The method of operation ensures seamless coordination between the infrastructure sensors, processing hubs, centralized Al server, and vehicles, providing a robust, scalable, and safe solution for autonomous vehicle navigation in various environments.

[0078] The autonomous vehicle navigation system is not limited to outdoor environments. It can be applied in indoor scenarios, such as airports or industrial facilities, where autonomous vehicles need to navigate complex pathways. In such environments, sensors can be mounted on ceilings or walls to monitor the movement of people and objects. The centralized Al processes this information and provides the vehicles with real-time navigation data to optimize their movement through the facility. This indoor application allows the system to improve efficiency and safety in areas where human- driven vehicles or manually operated equipment may struggle to perform. The present invention provides a versatile solution that can be applied in various domains, ranging from urban traffic systems to rural road networks, industrial facilities, and even indoor environments such as shopping malls or airports. Its primary application lies in autonomous vehicle navigation on roads and highways. By reducing the reliance on expensive vehicle-mounted sensors and transferring much of the sensing and processing responsibility to the infrastructure, the system enables a more cost-effective and scalable deployment of autonomous vehicles. It is especially useful in densely populated cities where real-time traffic management and safety are of utmost importance.

[0079] The system is also well-suited for rural and less developed areas, where upgrading infrastructure may be more feasible than outfitting individual vehicles with expensive sensor arrays. In these environments, infrastructure-based sensors can track traffic flow, road conditions, and weather-related hazards, enabling safe and efficient autonomous driving even in regions where conventional navigation technologies might be ineffective. Additionally, the present invention is compatible with Advanced Driver- Assistance Systems (ADAS) technologies.

[0080] One of the key benefits of the present invention is its ability to significantly enhance safety. The centralized Al processes environmental data in real time, providing vehicles with instructions to avoid obstacles, regulate traffic flow, and prevent accidents. By utilizing a combination of sensors such as LiDAR, radar, and thermal cameras, the system can function effectively under diverse weather conditions, including fog, rain, and snow, where conventional vehicle sensors often fall short. Additionally, the Al's capability to continuously learn and improve through data feedback loops ensures increased reliability over time. The invention is suitable for use in both human-driven and autonomous vehicles, as well as Al-powered robotic cars. According to one exemplary embodiment, the present invention is not limited to bots and Al-powered robotic cars but is also applicable to transport vehicles, commercial vehicles, vans, buses, trucks, and any other road-going vehicles.

[0081] In addition to safety, the system offers environmental benefits. By optimizing traffic flow and reducing the need for sudden braking or acceleration, the system helps lower fuel consumption and vehicle emissions. The reduction in onboard sensors and computational hardware also contributes to more sustainable vehicle production and operation. The ability to leverage existing communication networks, such as GSM towers, further reduces the need for additional infrastructure investment, making the system both economical and environmentally friendly.

[0082] To validate the performance of the autonomous vehicle navigation system, rigorous testing was conducted under various conditions, including urban and rural environments, highways, and indoor settings. The system was tested for its ability to process data from multiple infrastructure sensors, make real-time navigation decisions, and ensure vehicle safety. The tests adhered to industry standards such as ISO 26262 for functional safety in road vehicles, IEC 61508 for electrical, electronic, and programmable systems, and ISO 21448 for safety of the intended functionality in automated driving.

[0083] In urban settings, the system demonstrated high accuracy in detecting pedestrians, traffic lights, road signs, and vehicles in real-time. The centralized Al processed data from multiple infrastructure sensors with a latency of fewer than 100 milliseconds, allowing vehicles to react to dynamic changes in the environment with minimal delay. The system successfully prevented collisions by providing timely emergency braking signals and ensuring vehicles maintained safe distances from each other through adaptive cruise control. In rural environments, where communication networks may be less robust, the system continued to perform effectively by utilizing satellite networks and longer-range sensors. The infrastructure-based sensors detected road hazards, such as fallen trees or animals, and transmitted this data to the vehicles in real-time. The V2X communication system ensured that vehicles received navigation instructions even in areas with limited connectivity.

[0084] Indoor testing was conducted in an airport environment, where the system was tasked with guiding autonomous vehicles through complex pathways while avoiding obstacles such as pedestrians and luggage carts. The ceiling-mounted sensors captured real-time data, and the centralized Al efficiently processed this information to guide the vehicles. The system was able to optimize vehicle routes, reducing congestion and improving the overall efficiency of the airport's transportation network.

[0085] In all tested environments, the autonomous vehicle navigation system met or exceeded the required safety and performance standards, demonstrating its ability to operate reliably in a wide range of conditions.

[0086] In one exemplary scenario, the present invention can be applied to indoor mobility at airports, where efficient passenger and luggage movement between terminals, gates, and other facilities is essential. Large airports often rely on human-driven shuttles or carts, which can be inefficient and prone to delays during peak hours. To address this issue, the invention proposes the deployment of autonomous vehicles that leverage infrastructure-based sensors, such as cameras installed throughout the terminal on ceilings, walls, and at key junctions. These sensors provide comprehensive coverage of the environment by tracking passenger flow, obstacles, and luggage movement in real time. The collected data is transmitted to a centralized Al system that processes the information to enable the autonomous shuttles and carts to navigate efficiently. This configuration minimizes the need for expensive, vehicle-specific sensor arrays, as the centralized system utilizes existing infrastructure components, such as closed-circuit cameras (CC Cameras) and GSM networks, to achieve cost-effective operations. The Vehicle- to-Infrastructure (V2I) and Vehicle-to-Everything (V2X) communication systems allow the centralized Al to communicate directly with the autonomous vehicles, providing them with updated routes, avoiding obstacles, and optimizing paths for efficient passenger pick-up, drop-off, and luggage handling. Additionally, the system can dynamically adjust routes in response to airport operations, such as gate changes or security alerts, ensuring seamless coordination and improved mobility within the airport environment.

[0087] In another exemplary scenario, as illustrated in Fig. 4, the present invention is applied to enable autonomous vehicle movement on highways equipped with a series of interconnected "Sensing Poles," "Processing Hubs," and "Connectivity Towers" that create a smart infrastructure network, providing continuous coverage and data exchange across the highway. The infrastructure is set up as follows: Sensing Poles are installed along the highway to collect real-time data on traffic flow, vehicle speed, and potential obstacles, utilizing sensors such as LiDAR, cameras, and radar. These Sensing Poles are connected to local Processing Hubs that aggregate the data and make real-time decisions. Connectivity Towers, placed at strategic locations, facilitate seamless Vehicle-to-Everything (V2X) communication, allowing vehicles to exchange data with both the infrastructure and other vehicles within the defined connectivity range.

[0088] When an autonomous vehicle equipped with a V2X box enters the highway, it connects to the nearest Connectivity Tower to receive initial data from the Processing Hub. As the vehicle travels, it continuously exchanges information with Connectivity Towers along the route. In the event of an obstacle, such as a stalled vehicle or debris, the nearest Sensing Poles detect the anomaly and relay the information to the Processing Hub, which then sends rerouting instructions to the vehicle through the Connectivity Towers. This enables the vehicle, whether driven by an Advanced Driver- Assistance System (ADAS) or operating in a fully autonomous mode, to navigate safely and efficiently.

[0089] Furthermore, as the vehicle moves across different connectivity ranges, it experiences a seamless handover between Connectivity Towers, ensuring uninterrupted communication and continuous data exchange throughout its journey on the highway.

[0090] This scenario illustrates how the present invention enhances autonomous vehicle performance and safety by leveraging a robust, infrastructure-based sensor network and V2X communication.

Claims

5. CLAIMSI / We Claim:

1. An autonomous vehicle navigation system, comprising: a network of infrastructure sensors (100), including cameras, LiDAR, radar, thermal cameras, and infrared sensors, mounted on structures such as street poles (101) or ceilings (102) to capture real-time environmental data related to roads, pathways, and traffic conditions; a centralized Al server (108) configured to receive and process data from the infrastructure sensors (100), using machine learning algorithms to generate real-time navigation decisions for autonomous vehicles; a communication system enabling Vehicle-to-Infrastructure (V2I) and Vehicle- to-Everything (V2X) communication, utilizing GSM, Wi-Fi, 4G, 5G, 6G and satellite networks to transmit data between the infrastructure (100) and vehicles; and a V2X connectivity box installed in each vehicle, comprising a GSM modem, Wi-Fi adaptors, 5G, 6Gand antennas to receive navigation commands from the centralized Al server (108) and execute them for autonomous navigation;Characterized in that, the infrastructure sensors (100) are distributed across urban and rural environments to reduce the need for high-end onboard sensors in the vehicles; the centralized Al server (108) continuously updates its machine learning algorithms based on real-time data from the sensors (100) and feedback from the vehicles, optimizing navigation decisions; the V2X connectivity box enables real-time communication with the infrastructure (100), reducing the computational load on the vehicle's onboard systems.The autonomous vehicle navigation system according to claim 1 , wherein the infrastructure sensors (100) further include night vision cameras (103) and quantum sensors (104) for enhanced detection of environmental conditions in low-light or adverse weather conditions.

2. The autonomous vehicle navigation system according to claim 1, wherein the centralized Al server (108) utilizes convolutional neural networks (CNN), artificial neural networks (ANN), and large language models (LLM), Vision Language Models (VLMs) for processing data from the infrastructure sensors (100).

3. The autonomous vehicle navigation system according to claim 1, wherein the communication system further supports Dedicated Short-Range Communications (DSRC) to enable low-latency data transmission between the infrastructure and vehicles.

4. The autonomous vehicle navigation system according to claim 1 , wherein the V2X connectivity box further includes Bluetooth, 5G, 6G and satellite communication modules for additional redundancy in data transmission.

5. The autonomous vehicle navigation system according to claim 1, wherein the infrastructure sensors (100) are strategically positioned to optimize data coverage for specific environments such as highways, parking lots, and indoor facilities like airports.

6. The autonomous vehicle navigation system according to claim 1, wherein the centralized Al server (108) is configured to process data from multiple infrastructure sensors (100) in real-time to provide continuous navigation assistance and hazard detection for autonomous vehicles.

7. The autonomous vehicle navigation system according to claim 1 , wherein the V2X connectivity box provides Level- 1 to Level-5 Advanced driver assistance systems ADAS Features braking based on real-time data from the centralized Al server (108).

8. The autonomous vehicle navigation system according to claim 1, wherein the infrastructure sensors (100) communicate with processing hubs located along roadways or within a cloud computing resource, and the processing hubs aggregate data before transmitting it to the centralized Al server (108).

9. A method for manufacturing the autonomous vehicle navigation system as claimed in claim 1, comprising the steps of: fabricating infrastructure sensors (100), including cameras (103), LiDAR (104), radar (105), thermal cameras (106), and infrared sensors, to capture environmental data related to road conditions, traffic flow, and obstacles; assembling the infrastructure sensors (100) onto designated structures, including street poles (101) and ceilings (102), in locations to optimize coverage across urban, rural, and indoor environments; manufacturing processing hubs that aggregate data from infrastructure sensors (100), and equipping them with computational resources for pre-processing sensor data before transmission to the centralized Al server (108); developing and configuring the centralized Al server (108) to process data from multiple infrastructure sensors (100) using machine learning algorithms for real-time navigation decision-making; producing V2X connectivity boxes equipped with GSM modems, Wi-Fi, 5G, 6G adapters, antennas, and Bluetooth modules, to be installed in vehicles for communication with the infrastructure and centralized Al server (108);installing the V2X connectivity boxes into vehicles for receiving navigation commands from the Al server (108) and transmitting feedback to update Al models; and calibrating the entire system, including the sensors (100), processing hubs, centralized Al server (108), and V2X connectivity boxes, to ensure accurate real-time data exchange and efficient vehicle navigation.

10. The method as claimed in claim 1, wherein the method includes V2X Sensors, environmental sensors, ultra-sonic sensors and infrastructure sensors.

11. A method as claimed in claim 1 , wherein the V2X box present in a vehicle includes odometry sensor, inertial measurement unit and V2X sensors.

6. DATE AND SIGNATUREDated this the 03rdday of October 2024Signature(Mr. Anugu Vijaya Bhaskar Reddy)IN / PA-2420Agent for applicant

Images