REAL-TIME OBJECT DETECTION IN AUTONOMOUS VEHICLES

Abstract

The invention relates to a real-time object detection system for autonomous vehicles, utilizing sensor fusion and artificial intelligence (AI) to detect, classify, and track objects in the vehicle’s environment. The system integrates data from multiple sensors, including cameras, LiDAR, radar, and ultrasonic sensors, to provide a comprehensive and accurate understanding of the surroundings. AI-based processing algorithms, such as convolutional neural networks (CNNs), analyze the fused sensor data to identify and classify objects such as pedestrians, vehicles, and obstacles in real-time. The system is capable of adapting to dynamic driving conditions through continuous learning, ensuring robust performance in various environmental conditions. This real-time detection and tracking system is crucial for safe and efficient navigation of autonomous vehicles.

Specification

Description:The embodiments of the present invention generally relate to real-time object detection systems for autonomous vehicles. Specifically, it pertains to a system and method for detecting, classifying, and tracking objects in the vehicle's environment using a combination of multiple sensor technologies, such as cameras, LiDAR, radar, and ultrasonic sensors, along with artificial intelligence (AI) and sensor fusion techniques. The invention aims to enhance the safety, reliability, and efficiency of autonomous driving systems by providing accurate, real-time object detection under various environmental conditions.

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 pr , Claims:1. A real-time object detection system for an autonomous vehicle, comprising:
a plurality of sensors selected from the group consisting of cameras, LiDAR, radar, and ultrasonic sensors, configured to collect environmental data around the vehicle;
a fusion module that combines the data from the plurality of sensors to generate a unified environment map;
an AI-based processor configured to analyze the unified environment map and detect objects in real-time;
a classification module that categorizes the detected objects into predefined classes including but not limited to pedestrians, vehicles, traffic signs, and obstacles;
a tracking module that continuously tracks the detected objects' positions and movements over time.

2. The system of claim 1, wherein the AI-based processor utilizes machine learning algorithms, including convolutional neural networks (CNN), for object detection and classification.

3. The system of claim 1, wherein the fusion module uses sensor fusion techniques to enhance detection accuracy

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