Design And Implementation Of A Data Fusion System For Autonomous Driving

In autonomous driving systems,data quality can have a significant impact on the performance of the system.In practical applications,the quality of sensor data is often affected by a variety of factors,such as weather,road conditions,etc.With the continuous development of technology,the use of multimodal sensors for fusion sensing in autonomous driving systems has become a hot development direction in this field,so the quality of multimodal data needs to be efficiently evaluated and a suitable fusion method needs to be selected based on the data quality.This paper focuses on the design and implementation of a data fusion system for autonomous driving,and the main research contents are as follows:(1)This paper proposes a multidimensional data quality score model,which calculates the comprehensive quality score of data by analyzing the noise dimension,intensity dimension and geometric dimension,so as to evaluate the ability of data to reflect real-world applications.The experimental results show that it can evaluate multiple types of sensor data comprehensively and calculate the real-time data quality score for each data type.(2)Based on the data quality score model,this paper proposes a method for real-time data source selection and target identification algorithm.Firstly,the accuracy prediction of the autonomous driving target recognition task is achieved by using the relevant model training.The target recognition accuracy prediction model is able to predict the accuracy of target recognition in scenarios with different data quality and different algorithms.In target recognition,for dynamic scenarios,it is necessary to choose between different data sources and algorithms in order to achieve the best target recognition accuracy.In this paper,the best data source and target recognition algorithm are selected by considering data quality and target recognition accuracy,so as to achieve the best target recognition accuracy.(3)This thesis designs and implements a data fusion system for autonomous driving by addressing the data fusion problem in autonomous driving systems.The system aims to improve the accuracy and stability of target recognition tasks by fusion processing of multi-sensor data.Starting from the analysis of system requirements,this paper presents the design and implementation process of the system in detail,and discusses the key technologies in depth.Experimental results show that the data fusion system designed in this paper for autonomous driving achieves significant results in improving the accuracy and stability of the target recognition task.Through the fusion processing of multi-sensor data,we are able to effectively improve the performance and reliability of the autonomous driving system.The system has good scalability and ease of use,which provides strong support for the further development of autonomous driving technology.