摘 要
随着自动驾驶技术的快速发展，环境感知作为其核心环节之一，对车辆的安全性和可靠性起着决定性作用。本研究针对复杂交通场景下单一传感器性能局限的问题，提出了一种基于多传感器融合的环境感知技术方案。通过结合激光雷达、摄像头和毫米波雷达等多源传感器数据，构建了以深度学习和贝叶斯估计为核心的融合算法框架，实现了目标检测、分类与跟踪的高精度处理。研究中设计了一种自适应权重分配机制，能够根据传感器特性及环境变化动态调整数据融合策略，从而显著提升系统的鲁棒性和适应性。实验结果表明，该方法在多种典型场景下的目标识别准确率较单一传感器方案提升了25%以上，同时有效降低了误检率和漏检率。本研究的主要贡献在于提出了一种高效的数据融合架构，并验证了其在复杂动态环境中的优越性能，为自动驾驶车辆的实际应用提供了重要技术支持。
关键词：多传感器融合；自动驾驶；环境感知
Abstract
With the rapid development of autonomous driving technology, environmental perception, as one of its core components, plays a decisive role in the safety and reliability of vehicles. This study addresses the issue of performance limitations of single sensors in complex traffic scenarios by proposing a multi-sensor fusion-based environmental perception technology scheme. By integrating data from multiple sources such as LiDAR, cameras, and millimeter-wave radars, a fusion algorithm fr amework centered on deep learning and Bayesian estimation is constructed, achieving high-precision processing for ob ject detection, classification, and tracking. An adaptive weight allocation mechanism is designed in this research, which dynamically adjusts the data fusion strategy according to sensor characteristics and environmental changes, thereby significantly enhancing the robustness and adaptability of the system. Experimental results indicate that the proposed method improves target recognition accuracy by more than 25% in various typical scenarios compared to single-sensor solutions, while effectively reducing false detection and missed detection rates. The primary contribution of this study lies in the proposal of an efficient data fusion architecture, whose superior performance in complex dynamic environments has been validated, providing crucial technical support for the practical application of autonomous driving vehicles.
Keywords: Multi-Sensor Fusion；Autonomous Driving；Environmental Perception
目 录
引言 1
一、多传感器融合技术概述 1
（一）传感器类型与功能分析 1
（二）融合算法的基本原理 2
（三）技术挑战与研究意义 2
二、环境感知中的数据处理方法 3
（一）数据采集与预处理技术 3
（二）数据同步与校准策略 3
（三）异常数据的检测与处理 4
三、融合算法在环境感知中的应用 4
（一）基于Kalman滤波的融合方法 4
（二）基于深度学习的融合模型 4
（三）实时性与精度的平衡分析 5
四、自动驾驶场景下的性能评估 5
（一）测试场景的设计与构建 5
（二）性能指标体系的建立 6
（三）实验结果与优化建议 6
结 论 7
致 谢 8
参考文献 9
