AI-Powered Autonomous Navigation System for Vehicles

Abstract

The invention relates to an AI-powered autonomous navigation system for vehicles, utilizing advanced machine learning algorithms and sensor fusion techniques to enable real-time decision-making for safe, efficient, and adaptable navigation. The system integrates a sensor suite, including LIDAR, cameras, radar, and GPS, to collect environmental data, which is processed by onboard AI models for object detection, path planning, and dynamic decision-making. The system continuously adapts to changing road conditions, traffic patterns, and obstacles, enabling vehicles to autonomously navigate both familiar and unfamiliar environments without human intervention, improving safety and operational efficiency across various vehicle types.

Specification

Description:The present invention relates to autonomous navigation systems for vehicles, specifically to an AI-powered system that enables vehicles to navigate autonomously through various environments. This invention utilizes artificial intelligence, machine learning algorithms, and advanced sensor technologies to provide real-time decision-making capabilities, optimizing navigation efficiency, safety, and adaptability for a wide range of vehicle types, including cars, trucks, and drones.
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 navigation systems have seen significant advancements in recent year , Claims:1. A method for autonomous navigation of a vehicle, comprising the steps of:
collecting real-time environmental data via a sensor suite, including LIDAR, cameras, radar, and GPS;
processing the environmental data using one or more AI models to detect objects, predict potential hazards, and assess road conditions;
generating a navigation plan based on the processed data;
controlling the vehicle's steering, acceleration, and braking based on the generated navigation plan, wherein the navigation plan is adapted in real-time based on continuous environmental updates.

2. The method of claim 1, wherein the AI models include a combination of deep learning algorithms for object detection and reinforcement learning algorithms for decision-making in dynamic environments.

3. A system for autonomous navigation of a vehicle, comprising:
a sensor suite including LIDAR, cameras, radar, and GPS configured to collect environmental data;
an onboard computing platform comprising a processor and memory, the processor being con

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