In recent times, Autonomous Moving Platforms (AMP) have been a vital component for various industrial sectors across the globe as they include a diverse set of aerial, marine, and land-based vehicles. The emergence and the rise of AMP necessitate a precise object-level understanding of the environment, which directly impacts the functioning like decision making, speed control, and direction of the autonomous driving vehicles. Obstacle detection and object classification are the key issues in the AMP. The autonomous vehicle is designed to move in the city roads and it should be bolstered with high-quality object detection/segmentation mechanisms since inaccurate movements and speed limits can prove to be fatal. Motivated from the aforementioned discussion, in this paper, we present $\vartheta$inspect (velocity-inspect), an AI-based 5G enabled object segmentation and speed limit identification scheme for self-driving cars on the city roads. In $\vartheta$inspect, the Convolutional Neural Network (CNN) based semantic image segmentation is carried out to segment the objects as interpreted from the Cityscapes dataset. Then, object clustering is done using the K-Means approach based on the number of unique objects. The semantic segmentation is done over 12 classes and the model outshines concerning state-of-the-art approaches for various parameters like latency, high accuracy of 82.2%, and others. Further, K-Means clustering based Speed Range Analyser (SRA) is proposed to determine the acceptable and safe speed range for the vehicle, which is computed based on the object density of every object in the environment. The results show that the proposed scheme outperforms compared to traditional schemes in terms of latency and accuracy.

近年来，自动移动平台（AMP）已成为全球各种工业领域的重要组成部分，因为它们包括各种空中，海上和陆地车辆。AMP的出现和兴起需要对环境进行精确的对象级了解，这直接影响了自动驾驶车辆的决策，速度控制和方向等功能。障碍物检测和物体分类是AMP中的关键问题。这款自动驾驶汽车旨在在城市道路上行驶，并应采用高质量的物体检测/分割机制，因为不正确的移动和速度限制可能会致命。基于上述讨论的动机，在本文中，我们提出$\vartheta$inspect（velocity-inpect），这是一种基于AI的5G功能的对象分割和城市道路上自动驾驶汽车的速度限制识别方案。在$\vartheta$检查，基于卷积神经网络（CNN）的语义图像分割可对从Cityscapes数据集中解释的对象进行分割。然后，基于唯一对象的数量，使用K-Means方法完成对象聚类。语义分割在12个类上完成，并且模型迟钝地涉及各种参数（例如等待时间，82.2％的高精度）和其他参数的最新方法。此外，提出了基于K均值聚类的速度范围分析器（SRA），以确定车辆的可接受和安全的速度范围，该范围是根据环境中每个对象的对象密度计算得出的。结果表明，与传统方案相比，该方案在时延和准确性方面均优于传统方案。