SYSTEM AND METHOD FOR SOFTWARE-DEFINED VEHICLE CONTROL AND OPTIMIZATION IN AUTONOMOUS AND CONNECTED ENVIRONMENTS

Abstract

The invention relates to a software-defined system and method for controlling and optimizing autonomous and connected vehicles. The system comprises a centralized control unit executing modular software modules for managing vehicle functions, including propulsion, navigation, and safety mechanisms. It integrates a sensor array, vehicle-to-everything (V2X) communication interfaces, and a machine learning-based optimization engine to process real-time data from the environment, traffic, and infrastructure. The system dynamically adjusts vehicle operations based on real-time conditions to enhance safety, efficiency, and adaptability. Key features include predictive analytics for driving conditions, adaptive control strategies, secure communication protocols, and continuous learning through cloud-based feedback mechanisms. The invention is designed for diverse use cases such as urban traffic management, highway driving, and off-road navigation, providing a scalable and future-proof solution for next-generation autonomous vehicles.

Specification

Description:The embodiments of the present invention generally relates to the field of vehicle automation and connected systems, specifically focusing on a software-defined architecture for autonomous vehicle control and optimization. It integrates modular software frameworks, real-time decision-making algorithms, and vehicle-to-everything (V2X) communication technologies to enhance adaptability, safety, and efficiency in autonomous and connected driving environments.
BACKGROUND OF THE INVENTION
The following description of related art is intended to provide background information pertaining to the field of the disclosure. This section may include certain aspects of the art that may be related to various features of the present disclosure. However, it should be appreciated that this section be used only to enhance the understanding of the reader with respect to the present disclosure, and not as admissions of prior art.

Autonomous and connected vehicles are revolutionizing the transportation industry by offering innovative solutions for improved safety, efficiency, and user experience. Traditional control systems in vehicles rely heavily on hardware-centric designs, which are often rigid, difficult to upgrade, and unable to adapt to dynamic driving scenarios or regulatory changes. This limitation poses challenges in implementing advanced functionalities required for autonomous vehicles operating in diverse environments.

The rapid advancements in communication technologies, including 5G and dedicated short-range communication (DSRC), have enabled real-time data exchange between vehicles, infrastructure, and cloud systems. Such vehicle-to-everything (V2X) communication systems hold significant potential for improving traffic management, reducing accidents, and enabling cooperative driving. However, integrating these systems into existing control architectures remains complex and resource-intensive.

The rise of machine learning and artificial intelligence has opened new opportunities for predictive analytics and dynamic decision-making in autonomous systems. These technologies can process large volumes of data from sensors, infrastructure, and the cloud to adapt vehicle operations in real-time. Despite their potential, current implementations often lack a unified framework to seamlessly incorporate these capabilities.

There is a growing demand for software-defined solutions that can address the challenges of modularity, scalability, and real-time adaptability. Such solutions need to be flexible enough to handle diverse use cases, such as urban driving, highway cruising, and off-road navigation, while ensuring robust performance and compliance with regulatory standards.

OBJECTIVE OF THE INVENTION

Some of the objects of the present disclosure, which at least one embodiment herein satisfies are listed herein below.

An objective of the present invention is to provide a system and method for software-defined vehicle control that enables dynamic and adaptive management of autonomous and connected vehicles.

Another objective of the present invention is overcome the limitations of hardware-centric designs by employing a modular software architecture that supports flexible and scalable operation.

Another objective of the present invention is to integrate advanced vehicle-to-everything (V2X) communication capabilities, allowing vehicles to interact with other vehicles, infrastructure, and cloud systems for enhanced situational awareness and decision-making.

Another objective of the present invention is to improve traffic coordination and safety across connected transportation networks.

Another objective of the present invention is to use machine learning algorithms to process real-time data from sensors and external sources, enabling predictive analytics and adaptive vehicle control.
Another objective of the present invention is to to optimize vehicle performance metrics such as energy efficiency, route planning, and braking systems through continuous feedback loops and cloud-based optimization engines.

Another objective of the present invention is to ensures real-time adaptability and long-term learning.

Another objective of the present invention is to ensure compliance with cybersecurity standards by integrating robust encryption and authentication protocols for all communication channels, safeguarding vehicle data from unauthorized access.

Another objective of the present invention is to offering an intuitive interface that allows operators to monitor performance, adjust configurations, and provide manual interventions when required.

Another objective of the present invention is to establish a framework that can seamlessly integrate with evolving technologies and regulatory frameworks, ensuring that the solution remains future-proof and versatile across various operational scenarios.

SUMMARY OF THE INVENTION
This section is provided to introduce certain objects and aspects of the present disclosure in a simplified form that are further described below in the detailed description. This summary is not intended to identify the key features or the scope of the claimed subject matter.

In an aspect, the invention provides a software-defined system and method for vehicle control and optimization in autonomous and connected environments. The system features a centralized control unit that employs modular software architecture to manage vehicle operations, including propulsion, navigation, and safety systems. It integrates real-time data from sensors and V2X communication interfaces, enabling adaptive responses to environmental and traffic conditions.

A key component of the system is a machine learning-based optimization engine that processes data to refine vehicle operations continuously. The solution also includes a secure communication framework to protect data integrity and a user interface for monitoring and manual overrides. Together, these components deliver a scalable, adaptive, and efficient solution for next-generation autonomous vehicles.
BRIEF DESCRIPTION OF DRAWINGS
The accompanying drawings, which are incorporated herein, and constitute a part of this invention, illustrate exemplary embodiments of the disclosed methods and systems in which like reference numerals refer to the same parts throughout the different drawings. Components in the drawings are not necessarily to scale, emphasis instead being placed upon clearly illustrating the principles of the present invention. Some drawings may indicate the components using block diagrams and may not represent the internal circuitry of each component. It will be appreciated by those skilled in the art that invention of such drawings includes the invention of electrical components, electronic components or circuitry commonly used to implement such components.

FIG. 1 illustrates an exemplary software-defined system for vehicle control and optimization in autonomous and connected environments, in accordance with an embodiment of the present disclosure.

DETAILED DESCRIPTION OF THE INVENTION

In the following description, for the purposes of explanation, various specific details are set forth in order to provide a thorough understanding of embodiments of the present disclosure. It will be apparent, however, that embodiments of the present disclosure may be practiced without these specific details. Several features described hereafter can each be used independently of one another or with any combination of other features. An individual feature may not address all of the problems discussed above or might address only some of the problems discussed above. Some of the problems discussed above might not be fully addressed by any of the features described herein.

The ensuing description provides exemplary embodiments only and is not intended to limit the scope, applicability, or configuration of the disclosure. Rather, the ensuing description of the exemplary embodiments will provide those skilled in the art with an enabling description for implementing an exemplary embodiment. It should be understood that various changes may be made in the function and arrangement of elements without departing from the spirit and scope of the disclosure as set forth.

Specific details are given in the following description to provide a thorough understanding of the embodiments. However, it will be understood by one of ordinary skill in the art that the embodiments may be practiced without these specific details. For example, circuits, systems, networks, processes, and other components may be shown as components in block diagram form in order not to obscure the embodiments in unnecessary detail. In other instances, well-known circuits, processes, algorithms, structures, and techniques may be shown without unnecessary detail to avoid obscuring the embodiments.

Also, it is noted that individual embodiments may be described as a process that is depicted as a flowchart, a flow diagram, a data flow diagram, a structure diagram, or a block diagram. Although a flowchart may describe the operations as a sequential process, many of the operations can be performed in parallel or concurrently. In addition, the order of the operations may be re-arranged. A process is terminated when its operations are completed but could have additional steps not included in a figure. A process may correspond to a method, a function, a procedure, a subroutine, a subprogram, etc. When a process corresponds to a function, its termination can correspond to a return of the function to the calling function or the main function.

The word "exemplary" and/or "demonstrative" is used herein to mean serving as an example, instance, or illustration. For the avoidance of doubt, the subject matter disclosed herein is not limited by such examples. In addition, any aspect or design described herein as "exemplary" and/or "demonstrative" is not necessarily to be construed as preferred or advantageous over other aspects or designs, nor is it meant to preclude equivalent exemplary structures and techniques known to those of ordinary skill in the art. Furthermore, to the extent that the terms "includes," "has," "contains," and other similar words are used in either the detailed description or the claims, such terms are intended to be inclusive in a manner similar to the term "comprising" as an open transition word without precluding any additional or other elements.

Reference throughout this specification to "one embodiment" or "an embodiment" or "an instance" or "one instance" means that a particular feature, structure, or characteristic described in connection with the embodiment is included in at least one embodiment of the present disclosure. Thus, the appearances of the phrases "in one embodiment" or "in an embodiment" in various places throughout this specification are not necessarily all referring to the same embodiment. Furthermore, the particular features, structures, or characteristics may be combined in any suitable manner in one or more embodiments.

The terminology used herein is for the purpose of describing particular embodiments only and is not intended to be limiting of the disclosure. As used herein, the singular forms "a", "an", and "the" are intended to include the plural forms as well, unless the context indicates otherwise. It will be further understood that the terms "comprises" and/or "comprising," when used in this specification, 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. As used herein, the term "and/or" includes any and all combinations of one or more of the associated listed items.

The present invention relates to a software-defined system and method for vehicle control and optimization, designed specifically for autonomous and connected vehicles operating in diverse environments. The system employs a modular software architecture that allows dynamic reconfiguration of vehicle operations based on real-time sensor data, environmental conditions, and traffic scenarios. The modular design ensures scalability and adaptability, enabling seamless integration of new functionalities without overhauling the system.

The system includes a centralized control unit responsible for orchestrating various vehicle subsystems such as propulsion, steering, braking, and navigation. This control unit executes software modules designed for specific functionalities, which can be updated or replaced independently. This eliminates the dependency on hardware-centric architectures, enabling faster adoption of new technologies and regulatory changes.

A key feature of the invention is its vehicle-to-everything (V2X) communication interface, which allows the vehicle to exchange data with other vehicles, infrastructure, and cloud systems. This interface supports various communication protocols, including Dedicated Short-Range Communications (DSRC) and 5G, to ensure robust connectivity in urban, rural, and high-speed environments. Real-time data from V2X communication is combined with sensor inputs from devices like LIDAR, cameras, GPS, and inertial measurement units (IMUs), forming a comprehensive data set for decision-making.

The invention also incorporates a machine learning-based optimization engine hosted either in the cloud or on edge servers. This engine analyzes real-time data to predict and adapt to changing driving conditions, enabling functionalities like predictive braking, adaptive cruise control, and dynamic route optimization. Feedback from the optimization engine is used to refine vehicle operations continuously, improving performance metrics such as fuel efficiency, safety, and ride comfort.

To ensure data integrity and security, the system includes robust encryption protocols and cybersecurity measures. These safeguards protect V2X communications from unauthorized access and ensure compliance with industry standards. A user interface is provided for operators to monitor vehicle performance, adjust configurations, and intervene manually if necessary. The interface is designed to be intuitive, allowing seamless interaction between the user and the system.

In one embodiment, the system is configured for urban driving scenarios, where traffic congestion and frequent stops are common. The centralized control unit uses real-time traffic data from V2X communication to dynamically adjust vehicle speed and optimize route selection. The system integrates with smart traffic lights to anticipate signal changes and reduce idling times at intersections. Machine learning algorithms predict congestion patterns, enabling the vehicle to take alternate routes to minimize travel time and fuel consumption. This embodiment ensures compliance with city traffic regulations while enhancing passenger safety and ride comfort.

The urban optimization framework also incorporates pedestrian and cyclist detection capabilities. Sensor data from cameras and LIDAR is processed in real-time to identify vulnerable road users, and the control unit adjusts braking and steering operations to prevent collisions. By leveraging V2X communication, the system can receive alerts from nearby connected devices, further improving situational awareness.

In another embodiment, the system focuses on highway scenarios, where vehicles often travel at high speeds and require precise lane management. The centralized control unit integrates data from GPS, lane-keeping cameras, and radar sensors to maintain optimal lane positioning and safe following distances. Adaptive cruise control is implemented, where machine learning algorithms predict the behavior of surrounding vehicles and adjust speed accordingly.

The system also supports cooperative adaptive cruise control (CACC) through V2X communication, allowing multiple vehicles to form platoons. In a platoon, vehicles maintain a synchronized speed and distance, significantly improving aerodynamics and fuel efficiency. This embodiment is particularly beneficial for long-haul transportation, where energy optimization is critical.

The highway driving framework includes a predictive engine for detecting potential hazards, such as sudden lane changes by other drivers or debris on the road. The system can perform automated lane changes or emergency braking in response to such hazards, ensuring passenger safety.

A third embodiment addresses off-road driving scenarios, where environmental conditions can vary widely, and standard navigation systems may not suffice. The centralized control unit integrates data from high-resolution terrain mapping, IMUs, and proximity sensors to navigate uneven or unpredictable terrains. Machine learning models trained on off-road datasets enable the system to classify terrain types and adjust vehicle operations accordingly.

For example, the system can modify suspension settings, traction control, and power distribution to optimize performance on gravel, mud, or snow. V2X communication is used to receive updates from other off-road vehicles or infrastructure, such as trail markers or weather stations, providing additional context for decision-making.

The off-road embodiment also supports autonomous convoying, where multiple vehicles coordinate their movements using V2X communication. This feature is particularly useful for industries like mining, forestry, or military operations, where vehicles often operate in challenging environments and need to maintain precise coordination.

While considerable emphasis has been placed herein on the preferred embodiments, it will be appreciated that many embodiments can be made and that many changes can be made in the preferred embodiments without departing from the principles of the invention. These and other changes in the preferred embodiments of the invention will be apparent to those skilled in the art from the disclosure herein, whereby it is to be distinctly understood that the foregoing descriptive matter to be implemented merely as illustrative of the invention and not as limitation.
, Claims:1. A software-defined system for vehicle control and optimization in autonomous and connected environments, comprising:
a centralized control unit configured to execute modular software modules for managing vehicle functions including propulsion, navigation, steering, and braking;
a communication interface enabling vehicle-to-everything (V2X) connectivity to receive and transmit real-time data from external sources including other vehicles, infrastructure, and cloud systems;
a sensor array comprising LIDAR, cameras, GPS, and inertial measurement units (IMUs) for collecting real-time environmental and operational data;
a machine learning-based optimization engine configured to process data from the sensor array and V2X communication to generate adaptive control strategies; and
a feedback mechanism configured to dynamically adjust vehicle control parameters based on real-time conditions to optimize performance, safety, and efficiency.

2. The system of claim 1, wherein the centralized control unit is further configured to dynamically load and unload modular software components based on driving scenarios, such as urban traffic, highway driving, and off-road navigation.

3. The system of claim 1, wherein the communication interface supports multiple communication protocols, including Dedicated Short-Range Communications (DSRC) and 5G, to ensure reliable data exchange in diverse environments.

4. The system of claim 1, wherein the optimization engine incorporates machine learning algorithms, including reinforcement learning and neural networks, to predict driving conditions and enhance vehicle operations.

5. The system of claim 1, wherein the sensor array includes proximity sensors for detecting obstacles and generating collision avoidance signals for the centralized control unit.

6. The system of claim 1, further comprising a user interface configured to display real-time vehicle performance metrics and allow manual control or intervention by the operator.

7. The system of claim 1, wherein the feedback mechanism includes a cloud-based component for storing historical vehicle data to refine future control strategies through continuous learning.

8. The system of claim 1, further comprising an encryption and authentication protocol for securing all data transmitted through the communication interface.

9. The system of claim 1, wherein the centralized control unit is programmed to coordinate with other vehicles in a convoy or platoon, synchronizing speed and positioning through V2X communication for fuel efficiency and safety.

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