摘 要
随着智能驾驶技术的快速发展，汽车智能驾驶辅助系统的可靠性与安全性成为研究热点。本研究旨在针对智能驾驶辅助系统中故障检测与维修的关键问题，提出一种基于多源传感器数据融合的故障诊断方法，以提升系统运行的稳定性和可维护性。通过构建深度学习模型对传感器数据进行实时分析，结合传统信号处理技术提取特征，实现了对系统潜在故障的精准定位与预警。研究采用实车测试数据验证了所提方法的有效性，结果表明该方法能够显著提高故障检测的准确率，并缩短维修响应时间。此外，本研究创新性地引入了自适应阈值调整机制，增强了算法在复杂工况下的鲁棒性。总体而言，本研究为智能驾驶辅助系统的故障检测与维修提供了新的思路和技术支持，对推动相关技术的实际应用具有重要意义。
关键词：智能驾驶辅助系统；故障诊断；多源传感器数据融合；深度学习；自适应阈值调整机制
Abstract
With the rapid development of intelligent driving technology, the reliability and safety of automotive intelligent driving assistance systems have become research hotspots. This study focuses on the critical issues of fault detection and maintenance in intelligent driving assistance systems and proposes a fault diagnosis method based on multi-source sensor data fusion to enhance the stability and maintainability of system operation. By constructing a deep learning model for real-time analysis of sensor data and integrating traditional signal processing techniques for feature extraction, this method achieves precise localization and early warning of potential system faults. The effectiveness of the proposed approach is validated using real-world vehicle test data, and the results demonstrate that it significantly improves the accuracy of fault detection while reducing maintenance response time. Additionally, this study innovatively introduces an adaptive threshold adjustment mechanism, which enhances the robustness of the algorithm under complex operating conditions. Overall, this research provides new insights and technical support for fault detection and maintenance in intelligent driving assistance systems, contributing significantly to the practical application of related technologies.
Keywords: Intelligent Driving Assistance System；Fault Diagnosis；Multi-Source Sensor Data Fusion；Deep Learning；Adaptive Threshold Adjustment Mechanism
目 录
引言 1
一、智能驾驶辅助系统概述 1
（一）系统组成与功能分析 1
（二）核心技术发展现状 2
（三）故障检测研究意义 2
二、故障检测方法与技术 2
（一）常见故障类型分析 3
（二）数据采集与诊断技术 3
（三）人工智能在故障检测中的应用 3
三、维修策略与实施路径 4
（一）故障定位与优先级评估 4
（二）维修工具与技术支持 4
（三）实际维修案例分析 5
四、系统优化与未来展望 5
（一）提高故障检测准确性的方法 5
（二）维修流程的标准化建设 5
（三）新技术对未来发展的影响 6
结 论 6
致 谢 8
参考文献 9
随着智能驾驶技术的快速发展，汽车智能驾驶辅助系统的可靠性与安全性成为研究热点。本研究旨在针对智能驾驶辅助系统中故障检测与维修的关键问题，提出一种基于多源传感器数据融合的故障诊断方法，以提升系统运行的稳定性和可维护性。通过构建深度学习模型对传感器数据进行实时分析，结合传统信号处理技术提取特征，实现了对系统潜在故障的精准定位与预警。研究采用实车测试数据验证了所提方法的有效性，结果表明该方法能够显著提高故障检测的准确率，并缩短维修响应时间。此外，本研究创新性地引入了自适应阈值调整机制，增强了算法在复杂工况下的鲁棒性。总体而言，本研究为智能驾驶辅助系统的故障检测与维修提供了新的思路和技术支持，对推动相关技术的实际应用具有重要意义。
关键词：智能驾驶辅助系统；故障诊断；多源传感器数据融合；深度学习；自适应阈值调整机制
Abstract
With the rapid development of intelligent driving technology, the reliability and safety of automotive intelligent driving assistance systems have become research hotspots. This study focuses on the critical issues of fault detection and maintenance in intelligent driving assistance systems and proposes a fault diagnosis method based on multi-source sensor data fusion to enhance the stability and maintainability of system operation. By constructing a deep learning model for real-time analysis of sensor data and integrating traditional signal processing techniques for feature extraction, this method achieves precise localization and early warning of potential system faults. The effectiveness of the proposed approach is validated using real-world vehicle test data, and the results demonstrate that it significantly improves the accuracy of fault detection while reducing maintenance response time. Additionally, this study innovatively introduces an adaptive threshold adjustment mechanism, which enhances the robustness of the algorithm under complex operating conditions. Overall, this research provides new insights and technical support for fault detection and maintenance in intelligent driving assistance systems, contributing significantly to the practical application of related technologies.
Keywords: Intelligent Driving Assistance System；Fault Diagnosis；Multi-Source Sensor Data Fusion；Deep Learning；Adaptive Threshold Adjustment Mechanism
目 录
引言 1
一、智能驾驶辅助系统概述 1
（一）系统组成与功能分析 1
（二）核心技术发展现状 2
（三）故障检测研究意义 2
二、故障检测方法与技术 2
（一）常见故障类型分析 3
（二）数据采集与诊断技术 3
（三）人工智能在故障检测中的应用 3
三、维修策略与实施路径 4
（一）故障定位与优先级评估 4
（二）维修工具与技术支持 4
（三）实际维修案例分析 5
四、系统优化与未来展望 5
（一）提高故障检测准确性的方法 5
（二）维修流程的标准化建设 5
（三）新技术对未来发展的影响 6
结 论 6
致 谢 8
参考文献 9
