Vakilsearch LogoIs NowZolvit Logo
close icon
image
image
user-login
Patent search/

SYSTEMS AND METHODS FOR AI-DRIVEN RIDE VEHICLE OPERATIONS

search

Patent Search in India

  • tick

    Extensive patent search conducted by a registered patent agent

  • tick

    Patent search done by experts in under 48hrs

₹999

₹399

Talk to expert

SYSTEMS AND METHODS FOR AI-DRIVEN RIDE VEHICLE OPERATIONS

ORDINARY APPLICATION

Published

date

Filed on 11 November 2024

Abstract

ABSTRACT Systems and Methods for AI-Driven Ride Vehicle Operations The present disclosure introduces systems and methods for AI-driven ride vehicle operations 100 designed to optimize ride-sharing and autonomous vehicle performance through real-time data integration and machine learning. The system incorporates data collection module 102 to gather real-time inputs, enhanced by crowdsourcing data collection submodule 104. Collected data is analyzed by data processing and analytics module 106, which informs the AI decision-making module 108 to generate real-time routing and dispatch adjustments. User interface module 110 provides passengers and drivers with updates and feedback options, while dynamic fleet management system 126 optimizes vehicle assignment based on demand. Feedback-driven learning system 116 integrates user feedback to refine operational algorithms, and environmental impact monitoring dashboard 120 tracks emissions, supporting sustainability. Security is maintained through blockchain-based identity verification module 124 and security and privacy module 122, ensuring a secure, efficient, and environmentally conscious ride vehicle operation system. Reference Fig 1

Patent Information

Application ID202441086929
Invention FieldELECTRONICS
Date of Application11/11/2024
Publication Number46/2024

Inventors

NameAddressCountryNationality
K RaghavendraAnurag University, Venkatapur (V), Ghatkesar (M), Medchal Malkajgiri DT. Hyderabad, Telangana, IndiaIndiaIndia

Applicants

NameAddressCountryNationality
Anurag UniversityVenkatapur (V), Ghatkesar (M), Medchal Malkajgiri DT. Hyderabad, Telangana, IndiaIndiaIndia

Specification

Description:DETAILED DESCRIPTION

[00022] The following detailed description illustrates embodiments of the present disclosure and ways in which they can be implemented. Although some modes of carrying out the present disclosure have been disclosed, those skilled in the art would recognise that other embodiments for carrying out or practising the present disclosure are also possible.

[00023] The description set forth below in connection with the appended drawings is intended as a description of certain embodiments of system and methods for AI - driven ride vehicle operations and is not intended to represent the only forms that may be developed or utilised. The description sets forth the various structures and/or functions in connection with the illustrated embodiments; however, it is to be understood that the disclosed embodiments are merely exemplary of the disclosure that may be embodied in various and alternative forms. The figures are not necessarily to scale; some features may be exaggerated or minimised to show details of particular components. Therefore, specific structural and functional details disclosed herein are not to be interpreted as limiting, but merely as a representative basis for teaching one skilled in the art to variously employ the present invention.

[00024] While the disclosure is susceptible to various modifications and alternative forms, specific embodiment thereof has been shown by way of example in the drawings and will be described in detail below. It should be understood, however, that it is not intended to limit the disclosure to the particular forms disclosed, but on the contrary, the disclosure is to cover all modifications, equivalents, and alternatives falling within the scope of the disclosure.

[00025] The terms "comprises", "comprising", "include(s)", or any other variations thereof, are intended to cover a non-exclusive inclusion, such that a setup, or system that comprises a list of components or steps does not include only those components or steps but may include other components or steps not expressly listed or inherent to such setup or system. In other words, one or more elements in a system or apparatus preceded by "comprises... a" does not, without more constraints, preclude the existence of other elements or additional elements in the system or apparatus.

[00026] In the following detailed description of the embodiments of the disclosure, reference is made to the accompanying drawings and which are shown by way of illustration-specific embodiments in which the disclosure may be practiced. These embodiments are described in sufficient detail to enable those skilled in the art to practice the disclosure, and it is to be understood that other embodiments may be utilized and that changes may be made without departing from the scope of the present disclosure. The following description is, therefore, not to be taken in a limiting sense.

[00027] The present disclosure will be described herein below with reference to the accompanying drawings. In the following description, well-known functions or constructions are not described in detail since they would obscure the description with unnecessary detail.

[00028] Referring to Fig. 1, system and methods for AI - driven ride vehicle operations 100 is disclosed, in accordance with one embodiment of the present invention. It comprises of data collection module 102, crowdsourcing data collection submodule 104, data processing and analytics module 106, ai decision-making module 108, user interface module 110, communication system with traffic management 112, multi-modal integration system 114, feedback-driven learning system 116, vehicle health monitoring and maintenance predictor 118, environmental impact monitoring dashboard 120, security and privacy module 122, blockchain-based identity verification module 124, dynamic fleet management system 126, contextual awareness engine 128, automated incident response system 130, adaptive learning mechanism 132, interoperability framework 134, driver performance and behavioral analytics module 136, energy-efficient routing for electric vehicles (evs) 138, augmented reality (ar) navigation assistance module 140, intelligent vehicle-to-vehicle (v2v) communication module 142, behavioral pricing module 144, integrated driver health monitoring system 146, data visualization and reporting module 148.

[00029] Referring to Fig. 1, the present disclosure provides details of system and methods for AI - driven ride vehicle operations 100 . This system is designed to optimize ride-sharing and autonomous vehicle efficiency, safety, and environmental sustainability through real-time data analytics, predictive routing, and demand forecasting. In one embodiment, the AI-driven ride vehicle operations system may include key components such as data collection module 102, crowdsourcing data collection submodule 104, and data processing and analytics module 106, enabling dynamic routing and resource management. The system incorporates the AI decision-making module 108 and feedback-driven learning system 116 to adapt to traffic and user feedback in real time. It also features blockchain-based identity verification module 124 and security and privacy module 122 to ensure secure data handling. Additional components such as energy-efficient routing for electric vehicles 138 and environmental impact monitoring dashboard 120 enhance sustainability and operational effectiveness.

[00030] Referring to Fig. 1, systems and methods for AI-driven ride vehicle operations 100 is provided with data collection module 102, which gathers data from multiple sources, including GPS signals, traffic sensors, user requests, and historical ride patterns. This module operates continuously to capture real-time information on current transportation conditions, serving as the primary data source for the entire system. The data collection module 102 works closely with the data processing and analytics module 106 to ensure that all collected data is accurately analyzed and ready for decision-making. Additionally, this module collaborates with crowdsourcing data collection submodule 104 to incorporate real-time traffic insights from users, improving overall system responsiveness.

[00031] Referring to Fig. 1, systems and methods for AI-driven ride vehicle operations 100 is provided with crowdsourcing data collection submodule 104, which utilizes data from users' mobile devices to capture real-time traffic conditions, incidents, and road closures. This submodule enables the system to make more precise predictions by including user-generated information, thereby supplementing data collection module 102 for enhanced traffic analysis. The crowdsourcing data collection submodule 104 shares its insights with the data processing and analytics module 106 for refined data analysis and prediction accuracy. Working together with the AI decision-making module 108, it ensures that route adjustments reflect the latest real-world conditions.

[00032] Referring to Fig. 1, systems and methods for AI-driven ride vehicle operations 100 is provided with data processing and analytics module 106, which processes incoming data through machine learning algorithms to identify patterns and predict demand surges. This module evaluates data received from data collection module 102 and crowdsourcing data collection submodule 104 to enable informed routing and scheduling decisions. It also directly supports the AI decision-making module 108 by providing it with real-time and historical insights, thereby enhancing the accuracy of route optimization. Additionally, data insights are shared with the environmental impact monitoring dashboard 120 to track and report sustainability metrics.

[00033] Referring to Fig. 1, systems and methods for AI-driven ride vehicle operations 100 is provided with AI decision-making module 108, which is responsible for predictive modeling and real-time decision-making regarding routing and dispatching. This module utilizes processed data from data processing and analytics module 106 to dynamically adjust vehicle paths based on current traffic, demand levels, and environmental factors. Working with the feedback-driven learning system 116, it adapts its algorithms based on user feedback to continually improve service efficiency. The AI decision-making module 108 is critical in directing resources across the fleet and integrates seamlessly with the multi-modal integration system 114 for optimal route planning.

[00034] Referring to Fig. 1, systems and methods for AI-driven ride vehicle operations 100 is provided with user interface module 110, which enables interaction between users and the system, providing real-time updates, estimated arrival times, and navigation instructions. It integrates seamlessly with AI decision-making module 108 to relay dynamic route information and updates based on the latest data processing results. The user interface module 110 also collects feedback from users, which it relays to the feedback-driven learning system 116 to improve user satisfaction and enhance overall service quality. Additionally, it supports user preferences and customization, making rides more personalized and responsive to individual needs.

[00035] Referring to Fig. 1, systems and methods for AI-driven ride vehicle operations 100 is provided with communication system with traffic management 112, which connects the ride vehicle operations with city traffic management systems. This module receives live updates on traffic signals, road closures, and construction zones, allowing the system to make informed routing adjustments. Working closely with the AI decision-making module 108, it provides real-time data that enhances route optimization. The communication system 112 also interfaces with the environmental impact monitoring dashboard 120 to adjust routes in ways that minimize congestion and reduce emissions.

[00036] Referring to Fig. 1, systems and methods for AI-driven ride vehicle operations 100 is provided with multi-modal integration system 114, which facilitates seamless connectivity with public transit schedules and other transportation options. This system enables users to receive multi-modal travel recommendations, combining ride-sharing with public transit for more efficient routes. By collaborating with the AI decision-making module 108, the multi-modal integration system 114 optimizes routes across various transport modes. Additionally, it shares data with the user interface module 110 to present users with convenient, integrated travel options.

[00037] Referring to Fig. 1, systems and methods for AI-driven ride vehicle operations 100 is provided with feedback-driven learning system 116, which collects user feedback after each ride to refine algorithms and enhance the overall user experience. This system processes feedback from the user interface module 110 and uses it to improve the data processing and analytics module 106. Working in conjunction with the AI decision-making module 108, it adjusts system behavior based on user preferences and historical patterns, ensuring continuous optimization of service quality.

[00038] Referring to Fig. 1, systems and methods for AI-driven ride vehicle operations 100 is provided with vehicle health monitoring and maintenance predictor 118, which tracks vehicle performance data to anticipate and prevent mechanical issues. This system uses AI algorithms to predict maintenance needs and alerts operators to potential issues before they impact service. Interfacing with the data processing and analytics module 106, it allows for more accurate predictions on vehicle reliability, while also contributing to the dynamic fleet management system 126 by optimizing vehicle availability based on health status.

[00039] Referring to Fig. 1, systems and methods for AI-driven ride vehicle operations 100 is provided with environmental impact monitoring dashboard 120, which tracks metrics such as fuel consumption and emissions to assess the sustainability of ride operations. This dashboard aggregates data from the data processing and analytics module 106 and works with the AI decision-making module 108 to prioritize eco-friendly routes. The dashboard also provides operators with reports on environmental impact, assisting in decision-making that aligns with sustainable practices.

[00040] Referring to Fig. 1, systems and methods for AI-driven ride vehicle operations 100 is provided with security and privacy module 122, which protects user data through encryption, secure communication protocols, and anonymization techniques. This module ensures that data collected by data collection module 102 is securely handled across all system components. Working with the blockchain-based identity verification module 124, it enhances trust and compliance with privacy regulations, ensuring that all user information remains confidential.

[00041] Referring to Fig. 1, systems and methods for AI-driven ride vehicle operations 100 is provided with blockchain-based identity verification module 124, which leverages blockchain technology to securely verify the identities of drivers and passengers. This module helps prevent fraud and enhances transaction transparency by working alongside the security and privacy module 122. It interfaces with the user interface module 110 to facilitate secure, trust-based interactions between users and the ride-sharing platform.

[00042] Referring to Fig. 1, systems and methods for AI-driven ride vehicle operations 100 is provided with dynamic fleet management system 126, which employs AI algorithms to optimize vehicle assignment based on real-time demand, availability, and location. This system utilizes insights from the data processing and analytics module 106 to reduce idle times and maximize vehicle utilization. It works closely with vehicle health monitoring and maintenance predictor 118 to ensure that only operationally sound vehicles are deployed, improving service efficiency.

[00043] Referring to Fig. 1, systems and methods for AI-driven ride vehicle operations 100 is provided with contextual awareness engine 128, which uses sensor data and machine learning to analyze the surrounding environment. This engine takes into account nearby pedestrians, cyclists, road conditions, and traffic signals, enhancing vehicle safety and optimizing route decisions. It interacts with the AI decision-making module 108 to incorporate real-time environmental awareness, ensuring safer and more accurate route adjustments.

[00044] Referring to Fig. 1, systems and methods for AI-driven ride vehicle operations 100 is provided with automated incident response system 130, which detects incidents such as accidents, delays, or unexpected route obstructions during rides. This system can automatically reroute vehicles and notify passengers of changes, improving safety and reliability. It coordinates with the user interface module 110 to deliver timely updates to users and integrates with the AI decision-making module 108 to determine optimal rerouting strategies.

[00045] Referring to Fig. 1, systems and methods for AI-driven ride vehicle operations 100 is provided with adaptive learning mechanism 132, which continuously adjusts AI algorithms based on user interactions, feedback, and operational data. This mechanism interfaces with the feedback-driven learning system 116 to enhance system adaptability, ensuring the AI decision-making module 108 evolves over time to meet changing user expectations. The adaptive learning mechanism 132 promotes ongoing improvement in service efficiency and user experience.

[00046] Referring to Fig. 1, systems and methods for AI-driven ride vehicle operations 100 is provided with interoperability framework 134, which allows the system to integrate with third-party applications, including payment processors and navigation apps. This framework improves user convenience by supporting external interactions, while also linking with the communication system with traffic management 112 for broader transportation ecosystem integration. It enables seamless connectivity with smart city infrastructure, enhancing the overall functionality of the system.

[00047] Referring to Fig. 1, systems and methods for AI-driven ride vehicle operations 100 is provided with driver performance and behavioral analytics module 136, which monitors and evaluates driver behavior in real time. This module analyzes driving patterns and provides feedback for performance improvement, working with the vehicle health monitoring and maintenance predictor 118 to maintain safe driving practices. It also interfaces with the adaptive learning mechanism 132 to refine behavioral analytics based on observed trends.

[00048] Referring to Fig. 1, systems and methods for AI-driven ride vehicle operations 100 is provided with energy-efficient routing for electric vehicles (EVs) 138, which considers battery levels, charging station locations, and optimal energy usage routes to enhance the usability of EVs within the fleet. This component interacts with the AI decision-making module 108 for energy-efficient path planning, contributing to sustainability goals by reducing energy consumption and prioritizing green routes.

[00049] Referring to Fig. 1, systems and methods for AI-driven ride vehicle operations 100 is provided with augmented reality (AR) navigation assistance module 140, which displays real-time navigation overlays for drivers, improving situational awareness in complex urban environments. The AR navigation assistance module 140 works in conjunction with the user interface module 110 to deliver intuitive navigation instructions, helping drivers make informed decisions quickly and accurately.

[00050] Referring to Fig. 1, systems and methods for AI-driven ride vehicle operations 100 is provided with intelligent vehicle-to-vehicle (V2V) communication module 142, which enables communication between vehicles in the fleet, allowing them to share information on traffic conditions, hazards, and nearby incidents. This module enhances fleet coordination and safety, working with the contextual awareness engine 128 and communication system with traffic management 112 to facilitate a connected transportation network.

[00051] Referring to Fig. 1, systems and methods for AI-driven ride vehicle operations 100 is provided with behavioral pricing module 144, which adjusts ride fares based on demand trends, user behavior, and real-time conditions. This module optimizes revenue while incentivizing off-peak usage, leveraging data from the data processing and analytics module 106 for pricing decisions. It integrates with the user interface module 110 to communicate fare adjustments to users.

[00052] Referring to Fig. 1, systems and methods for AI-driven ride vehicle operations 100 is provided with integrated driver health monitoring system 146, which tracks driver wellness indicators like fatigue and stress through biometric sensors or mobile app inputs. This system promotes safe driving practices by alerting drivers to take breaks when needed. It works with the driver performance and behavioral analytics module 136 to provide a comprehensive view of driver health and performance.

[00053] Referring to Fig. 1, systems and methods for AI-driven ride vehicle operations 100 is provided with data visualization and reporting module 148, which provides fleet operators with insights into performance metrics, user behavior, and environmental impact. This module aggregates data from the environmental impact monitoring dashboard 120 and data processing and analytics module 106 to create comprehensive reports. It supports strategic decision-making by offering actionable data on fleet efficiency and sustainability.

[00054] Referring to Fig 4, there is illustrated method 200 for systems and methods for AI-driven ride vehicle operations 100. The method comprises:

At step 202, method 200 includes data collection module 102 gathering real-time information from GPS, traffic sensors, and user requests;
At step 204, method 200 includes crowdsourcing data collection submodule 104 adding real-time traffic updates from users' devices to enrich data insights;
At step 206, method 200 includes data processing and analytics module 106 analyzing data to identify patterns, forecast demand, and support routing optimization;
At step 208, method 200 includes AI decision-making module 108 generating real-time routing and dispatch decisions based on traffic, demand, and environmental data;
At step 210, method 200 includes user interface module 110 delivering ride status updates and optimized navigation instructions to passengers and drivers;
At step 212, method 200 includes communication system with traffic management 112 receiving live traffic updates for route adjustments through AI decision-making module 108;
At step 214, method 200 includes multi-modal integration system 114 providing combined travel recommendations with public transit, coordinated by AI decision-making module 108;
At step 216, method 200 includes feedback-driven learning system 116 incorporating user feedback to refine algorithms in data processing and analytics module 106;
At step 218, method 200 includes vehicle health monitoring and maintenance predictor 118 forecasting maintenance needs to ensure operational vehicles are assigned by dynamic fleet management system 126;
At step 220, method 200 includes environmental impact monitoring dashboard 120 tracking emissions and fuel data to promote eco-friendly route decisions;
At step 222, method 200 includes security and privacy module 122 securing data across all modules to protect user privacy;
At step 224, method 200 includes blockchain-based identity verification module 124 verifying identities to enhance trust;
At step 226, method 200 includes dynamic fleet management system 126 assigning vehicles based on demand and availability to reduce idle time;
At step 228, method 200 includes contextual awareness engine 128 adjusting routes dynamically by analyzing surrounding conditions;
At step 230, method 200 includes automated incident response system 130 detecting incidents and rerouting vehicles while notifying users;
At step 232, method 200 includes adaptive learning mechanism 132 updating algorithms to adapt to user needs based on ongoing feedback;
At step 234, method 200 includes interoperability framework 134 integrating with third-party applications for seamless user experience;
At step 236, method 200 includes driver performance and behavioral analytics module 136 monitoring driver behavior for safe practices;
At step 238, method 200 includes energy-efficient routing for electric vehicles 138 optimizing routes based on battery levels and charging stations;
At step 240, method 200 includes augmented reality (AR) navigation assistance module 140 providing drivers with real-time visual navigation overlays;
At step 242, method 200 includes intelligent vehicle-to-vehicle (V2V) communication module 142 enabling real-time traffic and hazard sharing between vehicles;
At step 244, method 200 includes behavioral pricing module 144 adjusting fares dynamically based on demand and conditions;
At step 246, method 200 includes integrated driver health monitoring system 146 tracking wellness indicators for driver safety;
At step 248, method 200 includes data visualization and reporting module 148 offering fleet operators insights on performance and environmental impact.

[00055] In the description of the present invention, it is also to be noted that, unless otherwise explicitly specified or limited, the terms "fixed" "attached" "disposed," "mounted," and "connected" are to be construed broadly, and may for example be fixedly connected, detachably connected, or integrally connected, either mechanically or electrically. They may be connected directly or indirectly through intervening media, or they may be interconnected between two elements. The specific meaning of the above terms in the present invention can be understood in specific cases to those skilled in the art.

[00056] Modifications to embodiments of the present disclosure described in the foregoing are possible without departing from the scope of the present disclosure as defined by the accompanying claims. Expressions such as "including", "comprising", "incorporating", "have", "is" used to describe and claim the present disclosure are intended to be construed in a non- exclusive manner, namely allowing for items, components or elements not explicitly described also to be present. Reference to the singular is also to be construed to relate to the plural where appropriate.

[00057] Although embodiments have been described with reference to a number of illustrative embodiments thereof, it should be understood that numerous other modifications and embodiments can be devised by those skilled in the art that will fall within the spirit and scope of the principles of this disclosure. More particularly, various variations and modifications are possible in the component parts and/or arrangements of the subject combination arrangement within the scope of the present disclosure, the drawings and the appended claims. In addition to variations and modifications in the component parts and/or arrangements, alternative uses will also be apparent to those skilled in the art.
, Claims:WE CLAIM:
1. A systems and methods for AI-driven ride vehicle operations 100 comprising of
data collection module 102 to gather real-time information from GPS, traffic sensors, and user requests;
crowdsourcing data collection submodule 104 to add real-time traffic updates from users' devices for enriched insights;
data processing and analytics module 106 to analyze data, identify patterns, and support routing optimization;
ai decision-making module 108 to generate real-time routing and dispatch decisions based on analyzed data;
user interface module 110 to deliver ride status updates and navigation instructions to passengers and drivers;
communication system with traffic management 112 to receive live traffic updates for route adjustments;
multi-modal integration system 114 to provide combined travel recommendations with public transit;
feedback-driven learning system 116 to incorporate user feedback for continual refinement of algorithms;
vehicle health monitoring and maintenance predictor 118 to forecast maintenance needs for operational readiness;
environmental impact monitoring dashboard 120 to track emissions and fuel data for eco-friendly routes;
security and privacy module 122 to secure data and ensure user privacy across all modules;
blockchain-based identity verification module 124 to verify identities and enhance trust between users and system;
dynamic fleet management system 126 to assign vehicles based on demand and reduce idle time;
contextual awareness engine 128 to adjust routes dynamically based on surrounding conditions;
automated incident response system 130 to detect incidents and reroute vehicles while notifying users;
adaptive learning mechanism 132 to update algorithms based on ongoing user feedback;
interoperability framework 134 to integrate third-party applications for a seamless experience;
driver performance and behavioral analytics module 136 to monitor driver behavior for safety;
energy-efficient routing for electric vehicles 138 to optimize routes based on battery levels and charging stations;
augmented reality (AR) navigation assistance module 140 to provide drivers with real-time visual navigation overlays;
intelligent vehicle-to-vehicle (V2V) communication module 142 to enable real-time traffic and hazard sharing;
behavioral pricing module 144 to adjust fares dynamically based on demand and real-time conditions;
integrated driver health monitoring system 146 to track wellness indicators for driver safety; and
data visualization and reporting module 148 to offer fleet operators insights on performance and environmental impact.
2. The systems and methods for AI-driven ride vehicle operations 100 as claimed, wherein data collection module 102 is configured to aggregate real-time data from diverse sources, including GPS, traffic sensors, and user inputs, providing a comprehensive and dynamic dataset for route optimization and demand prediction.

3. The systems and methods for AI-driven ride vehicle operations 100 as claimed, wherein data processing and analytics module 106 is configured to analyze collected data through machine learning algorithms, identifying traffic patterns, forecasting demand surges, and dynamically supporting the AI decision-making module 108 for optimal routing and dispatching.

4. The systems and methods for AI-driven ride vehicle operations 100 as claimed, wherein AI decision-making module 108 is configured to generate real-time decisions on routing and resource allocation, using predictive modeling to adjust routes based on traffic, demand, and environmental data, thus enhancing operational efficiency and minimizing idle time.

5. The systems and methods for AI-driven ride vehicle operations 100 as claimed, wherein user interface module 110 is configured to deliver real-time updates, optimized navigation, and personalized feedback options, enhancing user experience and enabling continuous interaction with the system's adaptive learning mechanism.

6. The systems and methods for AI-driven ride vehicle operations 100 as claimed, wherein dynamic fleet management system 126 is configured to allocate vehicles based on real-time demand and vehicle availability, using optimization algorithms to reduce idle time and maximize resource utilization across the fleet.

7. The systems and methods for AI-driven ride vehicle operations 100 as claimed, wherein feedback-driven learning system 116 is configured to incorporate user feedback and real-time performance data into machine learning models, continuously refining routing algorithms and improving service quality based on evolving user preferences.

8. The systems and methods for AI-driven ride vehicle operations 100 as claimed, wherein environmental impact monitoring dashboard 120 is configured to track emissions and fuel consumption in real-time, providing actionable insights to adjust routing for minimized environmental impact and alignment with sustainability goals.

9. The systems and methods for AI-driven ride vehicle operations 100 as claimed, wherein blockchain-based identity verification module 124 is configured to securely verify identities of both drivers and passengers, employing blockchain technology to ensure transaction transparency and prevent identity fraud within the ride vehicle ecosystem

10. The systems and methods for AI-driven ride vehicle operations 100 as claimed, wherein method comprises of
data collection module 102 gathering real-time information from GPS, traffic sensors, and user requests;
crowdsourcing data collection submodule 104 adding real-time traffic updates from users' devices to enrich data insights;
data processing and analytics module 106 analyzing data to identify patterns, forecast demand, and support routing optimization;
AI decision-making module 108 generating real-time routing and dispatch decisions based on traffic, demand, and environmental data;
user interface module 110 delivering ride status updates and optimized navigation instructions to passengers and drivers;
communication system with traffic management 112 receiving live traffic updates for route adjustments through AI decision-making module 108;
multi-modal integration system 114 providing combined travel recommendations with public transit, coordinated by AI decision-making module 108;
feedback-driven learning system 116 incorporating user feedback to refine algorithms in data processing and analytics module 106;
vehicle health monitoring and maintenance predictor 118 forecasting maintenance needs to ensure operational vehicles are assigned by dynamic fleet management system 126;
environmental impact monitoring dashboard 120 tracking emissions and fuel data to promote eco-friendly route decisions;
security and privacy module 122 securing data across all modules to protect user privacy;
blockchain-based identity verification module 124 verifying identities to enhance trust;
dynamic fleet management system 126 assigning vehicles based on demand and availability to reduce idle time;
contextual awareness engine 128 adjusting routes dynamically by analyzing surrounding conditions;
automated incident response system 130 detecting incidents and rerouting vehicles while notifying users;
adaptive learning mechanism 132 updating algorithms to adapt to user needs based on ongoing feedback;
interoperability framework 134 integrating with third-party applications for seamless user experience;
driver performance and behavioral analytics module 136 monitoring driver behavior for safe practices;
energy-efficient routing for electric vehicles 138 optimizing routes based on battery levels and charging stations;
augmented reality (AR) navigation assistance module 140 providing drivers with real-time visual navigation overlays;
intelligent vehicle-to-vehicle (V2V) communication module 142 enabling real-time traffic and hazard sharing between vehicles;
behavioral pricing module 144 adjusting fares dynamically based on demand and conditions;
integrated driver health monitoring system 146 tracking wellness indicators for driver safety;
data visualization and reporting module 148 offering fleet operators insights on performance and environmental impact.

Documents

NameDate
202441086929-COMPLETE SPECIFICATION [11-11-2024(online)].pdf11/11/2024
202441086929-DECLARATION OF INVENTORSHIP (FORM 5) [11-11-2024(online)].pdf11/11/2024
202441086929-DRAWINGS [11-11-2024(online)].pdf11/11/2024
202441086929-EDUCATIONAL INSTITUTION(S) [11-11-2024(online)].pdf11/11/2024
202441086929-EVIDENCE FOR REGISTRATION UNDER SSI [11-11-2024(online)].pdf11/11/2024
202441086929-EVIDENCE FOR REGISTRATION UNDER SSI(FORM-28) [11-11-2024(online)].pdf11/11/2024
202441086929-FIGURE OF ABSTRACT [11-11-2024(online)].pdf11/11/2024
202441086929-FORM 1 [11-11-2024(online)].pdf11/11/2024
202441086929-FORM FOR SMALL ENTITY(FORM-28) [11-11-2024(online)].pdf11/11/2024
202441086929-FORM-9 [11-11-2024(online)].pdf11/11/2024
202441086929-POWER OF AUTHORITY [11-11-2024(online)].pdf11/11/2024
202441086929-REQUEST FOR EARLY PUBLICATION(FORM-9) [11-11-2024(online)].pdf11/11/2024

footer-service

By continuing past this page, you agree to our Terms of Service,Cookie PolicyPrivacy Policy  and  Refund Policy  © - Uber9 Business Process Services Private Limited. All rights reserved.

Uber9 Business Process Services Private Limited, CIN - U74900TN2014PTC098414, GSTIN - 33AABCU7650C1ZM, Registered Office Address - F-97, Newry Shreya Apartments Anna Nagar East, Chennai, Tamil Nadu 600102, India.

Please note that we are a facilitating platform enabling access to reliable professionals. We are not a law firm and do not provide legal services ourselves. The information on this website is for the purpose of knowledge only and should not be relied upon as legal advice or opinion.