AI-DRIVEN AUTONOMOUS VEHICLES AND INTELLIGENT TRANSPORTATION NETWORKS

Abstract

This invention presents a novel AI-driven system for enhancing the safety, efficiency, and sustainability of autonomous vehicles within smart transportation networks. The system utilizes advanced sensor technology, machine learning, and V2X communication to optimize route planning, manage traffic flow, and minimize environmental impact.

Specification

Description:FIELD OF THE INVENTION
This invention relates to the field of autonomous vehicle technology and intelligent transportation systems. It focuses on enhancing the safety, efficiency, and sustainability of autonomous vehicles operating within smart transportation networks through advanced AI-powered decision-making and vehicle-to-everything (V2X) communication.
BACKGROUND OF THE INVENTION
The increasing adoption of autonomous vehicles (AVs) presents both significant opportunities and challenges for urban mobility. While AVs offer the potential to improve traffic flow, reduce accidents, and enhance overall transportation efficiency, realizing this potential requires addressing several critical issues. Existing autonomous vehicle systems often struggle with real-time decision-making in complex and dynamic traffic environments. Many current AV technologies rely on limited sensor data and lack advanced machine learning capabilities, resulting in suboptimal navigation and route planning. The absence of effective vehicle-to-everything (V2X) communication limits the ability of AVs to seamlessly integrate with other vehicles and infrastructure, potentially hindering overall traffic management and increasing the risk of accidents. In addition, energy consumption and environmental impact remain significant concerns for AVs. Current systems frequently fail to optimize energy usage, resulting in inefficient fuel consumption and increased greenhouse gas emissions. There is a pressing need for innovative solutions that can address these limitations by enhancing the capabilities of autonomous vehicle systems, thereby improving safety, efficiency, and sustainability. This invention aims to address these challenges by developing a novel AI-driven system that combines advanced sensor technology, sophisticated machine learning algorithms, and robust V2X communication to enable intelligent decision-making, optimize energy usage, and enhance overall safety within smart transportation networks.
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.
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.
This invention presents an AI-driven system designed to optimize the performance and safety of autonomous vehicles operating within smart transportation networks. The system uses a multi-sensor approach (LiDAR, cameras, radar, GPS, IMU) to acquire real-time environmental data. This data is processed and analyzed using a combination of AI algorithms (neural networks) and machine learning techniques to identify obstacles, predict potential hazards, and optimize driving decisions in real-time. The system facilitates vehicle-to-everything (V2X) communication, enabling seamless interaction between autonomous vehicles and smart infrastructure (traffic lights, other vehicles, traffic management systems). The system dynamically adjusts driving behavior (route, speed, braking) based on real-time conditions and predicted traffic patterns. The system incorporates a continuous learning mechanism, using historical and real-time data to continuously improve its models and enhance overall performance. It optimizes energy consumption through predictive route planning and adaptive driving strategies.
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: PHASES OF AI-DRIVEN AUTONOMOUS VEHICLE AND SMART TRANSPORTATION SYSTEM
FIGURE 2: WORKFLOW OF THE AI-POWERED AUTONOMOUS VEHICLE SYSTEM PROTOTYPE
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 AI-driven autonomous vehicle system comprises five main modules: a sensor and data acquisition unit, a data processing and machine learning engine, a V2X communication module, a navigation and control system, and a user interface. The sensor and data acquisition unit integrates multiple sensors (LiDAR, cameras, radar, GPS, IMU) to collect real-time environmental data, including information on obstacles, traffic conditions, road geometry, and vehicle location. The data processing and machine learning engine uses a combination of AI algorithms (neural networks) and machine learning techniques to process and analyze this data, identifying patterns, predicting potential hazards, and making real-time driving decisions. The V2X communication module facilitates seamless communication between the vehicle and other vehicles, infrastructure, and traffic management systems. This module utilizes established communication protocols (DSRC, C-V2X) to exchange crucial information such as speed, location, and braking status. The navigation and control system utilizes the processed data and communication information to dynamically adjust the vehicle's route, speed, and other driving parameters, optimizing travel paths and enhancing safety. The user interface provides a dashboard display that presents real-time information to the driver, including traffic updates, route information, and other relevant data. The system uses a continuous learning mechanism, continually updating its models based on real-time and historical data to enhance accuracy, efficiency, and adaptability in various traffic conditions. This continuous learning mechanism is key to optimizing energy consumption and ensuring safety by proactively adapting to dynamic traffic situations. By predicting traffic patterns and adjusting routes accordingly, the system reduces fuel consumption, minimizing the overall environmental impact of the vehicle.
, Claims:1. An AI-driven autonomous vehicle system, comprising a sensor and data acquisition unit for collecting real-time environmental data using multiple sensor modalities including LiDAR, cameras, radar, GPS, and IMU.
2. The system, as claimed in Claim 1, further comprising a data processing and machine learning engine that utilizes AI algorithms and machine learning techniques to analyze sensor data and make real-time driving decisions.
3. The system, as claimed in Claim 2, further comprising a V2X communication module for enabling seamless communication with other vehicles and smart infrastructure using established communication protocols.
4. The system, as claimed in Claim 3, further comprising a navigation and control system that dynamically adjusts vehicle route, speed, and other driving parameters in real-time based on sensor data, AI predictions, and V2X communication.
5. The system, as claimed in Claim 4, further comprising a user interface that displays real-time information to the driver, including traffic updates, route information, and other relevant data.
6. The system, as claimed in Claim 5, wherein said system incorporates a continuous learning mechanism to improve its models and optimize performance over time using real-time and historical data.
7. The system, as claimed in Claim 6, wherein said system optimizes energy consumption through predictive route planning and adaptive driving strategies.
8. A method for operating an autonomous vehicle in a smart transportation network, as claimed in Claim 8, comprising the steps of: (a) acquiring real-time environmental data using multiple sensor modalities; (b) processing and analyzing said data using AI algorithms and machine learning techniques; (c) communicating with other vehicles and infrastructure using V2X communication; (d) dynamically adjusting vehicle route, speed, and other driving parameters in real-time; and (e) using historical data to continuously improve system performance and optimize energy consumption.

Documents