Scholarlink

Abstract <p>The elements of systems for ensuring autonomous driving of electric vehicles are discussed; primary emphasis is placed on the processing of lidar data using various neural network architectures, such as PointNet, Dynamic Graph CNN, recurrent neural networks, and convolutional neural networks. The benefits of implementing machine learning methods when using lidar are discussed, which allows improving the safety and efficiency of autonomous electric vehicles. The problems of object recognition with noise filtering are considered. The need for further research and development of noise reduction systems to improve the reliability and sustainability of autonomous lidar systems in real-world conditions is substantiated.</p>

错误：搜索内容不能为空，请输入英文关键词
错误：关键词超出字数限制，请精简
高级检索

Application of Machine Learning Methods to Process Lidar Data in Autonomous Driving Systems

  • 期刊论文

  • 发表日期：

    2025年12月24日

查看全文
  • T. V. Chibikova,
  • G. A. Nesterenko

摘要

Abstract

The elements of systems for ensuring autonomous driving of electric vehicles are discussed; primary emphasis is placed on the processing of lidar data using various neural network architectures, such as PointNet, Dynamic Graph CNN, recurrent neural networks, and convolutional neural networks. The benefits of implementing machine learning methods when using lidar are discussed, which allows improving the safety and efficiency of autonomous electric vehicles. The problems of object recognition with noise filtering are considered. The need for further research and development of noise reduction systems to improve the reliability and sustainability of autonomous lidar systems in real-world conditions is substantiated.