用LSTM检测车辆通信中的异常消息，实时触发安全协议防止事故。
A neural-network based anomaly detection system and a safety protocol to protect vehicular network
- 用LSTM模型离线训练，实时检测车辆网络中的错误信息。
- 在车队场景中可避免近99%由恶意行为引发的事故。
- 适合关注车联网安全与自动驾驶可靠性的研究者。
本论文针对协同智能交通系统（CITS）提升道路安全与效率的需求，提出基于机器学习的车辆通信异常检测系统（MDS），利用长短期记忆网络（LSTM）识别车载网络中不准确或误导性消息。该模型在VeReMi数据集上离线训练，并在车队编队场景中实测，结果显示其能有效触发防御协议，在检测到异常时解散车队，从而几乎完全避免因误行为导致的事故。尽管系统对一般性误行为检测准确率高，但因交通条件差异，难以精确分类具体类型，表明通用自适应协议仍具挑战。论文认为，通过增加数据和优化模型，该MDS有望应用于实际CITS，显著降低协同驾驶网络中的安全风险。
原文摘要 · Abstract (English)
This thesis addresses the use of Cooperative Intelligent Transport Systems (CITS) to improve road safety and efficiency by enabling vehicle-to-vehicle communication, highlighting the importance of secure and accurate data exchange. To ensure safety, the thesis proposes a Machine Learning-based Misbehavior Detection System (MDS) using Long Short-Term Memory (LSTM) networks to detect and mitigate incorrect or misleading messages within vehicular networks. Trained offline on the VeReMi dataset, the detection model is tested in real-time within a platooning scenario, demonstrating that it can prevent nearly all accidents caused by misbehavior by triggering a defense protocol that dissolves the platoon if anomalies are detected. The results show that while the system can accurately detect general misbehavior, it struggles to label specific types due to varying traffic conditions, implying the difficulty of creating a universally adaptive protocol. However, the thesis suggests that with more data and further refinement, this MDS could be implemented in real-world CITS, enhancing driving safety by mitigating risks from misbehavior in cooperative driving networks.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。