基于多源融合的智能网联汽车环境感知系统优化路径  

Optimization Path of Intelligent Networked Vehicle Environmental Perception System Based on Multi-source Fusion

作　　者：李琨 Li Kun(Huanggang Polytechnic College,Huanggang 438000,China)

机构地区：[1]黄冈职业技术学院,湖北黄冈438000

出　　处：《专用汽车》2025年第4期81-83,共3页Special Purpose Vehicle

摘　　要：随着我国智能网联汽车技术的不断发展,环境感知系统对整车安全性和智能化程度起着越来越重要的影响。当前智能网联汽车环境感知系统普遍存在传感器单一、数据利用率低等问题,制约了其性能发挥。据此,在分析现有系统问题的基础上,提出了基于多源融合的优化路径,包括构建多源异构传感器集成系统、引入多模态深度学习算法处理多元环境数据等关键技术,旨在提升系统的环境感知精准度和决策智能化水平,为智能网联汽车的安全高效运行提供有力支撑。With the continuous development of intelligent connected vehicle technology in China,the environmental perception system has a crucial impact on the safety and intelligence of the entire vehicle.The current intelligent connected vehicle environment perception system generally has problems such as single sensors and low data utilization,which restrict its performance.Based on the analysis of existing system problems,this article proposes an optimization path based on multi-source fusion,including the construction of a multi-source heterogeneous sensor integration system,the introduction of multimodal deep learning algorithms to process diverse environmental data,and other key technologies.The aim is to improve the system's environmental perception accuracy and decision-making intelligence level,providing strong support for the safe and efficient operation of intelligent connected vehicles.

关 键 词：智能网联汽车 环境感知系统 多源融合 异构传感器 深度学习 云边端协同 

分 类 号：U461[机械工程—车辆工程]