ENHANCED DEEP LEARNING ALGORITHM FOR AUTONOMOUS VEHICLES

Abstract

The present invention relates to an enhanced deep learning algorithm for autonomous vehicles, designed to improve performance, safety, and adaptability in dynamic driving environments. The algorithm integrates multi-modal sensor data from LIDAR, radar, and cameras, enabling more accurate perception and decision-making. It incorporates edge-computing for real-time processing, reducing latency and enhancing responsiveness. Additionally, the system features a self-improving model that learns continuously from real-time driving data, allowing the vehicle to adapt to new environments without the need for frequent retraining. The invention also ensures robustness under adverse conditions, such as low visibility and inclement weather, offering a comprehensive solution for reliable autonomous vehicle operation.

Specification

Description:The embodiments of the present invention generally relates to the field of artificial intelligence (AI) and machine learning (ML), specifically to deep learning algorithms used in autonomous vehicles. The invention aims to enhance the performance, safety, and adaptability of deep learning models employed in autonomous driving systems by integrating multiple sensor data sources, improving real-time processing capabilities, and enabling continuous learning in dynamic 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 vehicles (AVs) rely on advanced deep learning algorithms to , Claims: 1. A deep learning algorithm for autonomous vehicles, comprising:
a multi-modal learning system integrating data from at least one of LIDAR, radar, and camera sensors;
an edge-processing unit configured to handle real-time data processing and reduce latency;
a self-improving learning mechanism that dynamically adjusts the deep learning model based on new driving data, improving performance over time.

2. The deep learning algorithm of claim 1, wherein the multi-modal system further includes fusion techniques to synchronize and optimize sensor data for enhanced object detection, tracking, and scene understanding.

3. The deep learning algorithm of claim 1, wherein the self-improving learning mechanism uses feedback from the vehicle's environment to continuously update the model without requiring full retraining.

4. The deep learning algorithm of claim 1, wherein the edge-processing unit comprises an on-board processor that performs preliminary data analysis and only transmits essential information to a centr

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