隨著自駕車發展，未來車輛駕駛會逐漸改由電腦進行控制，但由於人工介入的減少，對自駕車系統進行攻擊的可行性及危害都大幅增加。為了避免車輛的自我檢測失靈並且盡早發現異常，基於車聯網的遠端異常檢測將成為保護自駕車安全性的可行方案之一。
在現今的研究中，我們發現許多研究者都已開始對自駕車的安全檢測進行研究，並且各自提出不同的檢測演算法。然而，在現今自駕車仍未普及的情況下，如何收集數據、模擬攻擊、驗證及比較多種演算法皆成了研究的一大阻礙。對此，我們構建一個支援自駕車異常檢測建模與部署之車聯網平台VADtalk。在平台中，開發者們可以非常迅速導入自己的異常檢測模型，在平台上訓練及評估模型的準確率，利用自駕車模擬器驗證模型運作。最後，開發者能將完成訓練的模型進行部署在平台上，透過VADtalk與自駕車進行連線，實際對自駕車進行即時異常檢測。

With the development of self-driving vehicles, the driving of vehicles in the future will gradually be controlled by computers. However, due to the reduction of manual intervention, the feasibility and harm of attacks on the self-driving system have greatly increased. In order to avoid the failure of self-detection of vehicles and detect anomalies as early as possible, remote anomaly detection based on the Internet of Vehicles will become one of the feasible solutions to protect the safety of self-driving vehicles.
In the current research, we found that many researchers have begun to study the anomaly detection of self-driving vehicles, and each has proposed different detection algorithms. However, since self-driving vehicles are not yet popular, how to collect data, simulate attacks, verify and compare multiple algorithms is a major obstacle to research. In this regard, we build an Internet of Vehicles platform VADtalk that facilitate anomaly detection modeling and deployment for self-driving vehicles. In the platform, developers can easily import their own anomaly detection models, train and evaluate the accuracy of the models, verify the operation of the models using the self-driving simulator. Finally, the developer can deploy the completed training model on the platform and connect it to the self-driving vehicle through VADtalk to actually perform real-time anomaly detection.

[1] “Sae international [online].” Available at https://www.sae.org (Accessed: July 2022).
[2] “Autopilot [online].” Available at https://www.tesla.com/autopilot (Accessed: July 2022).
[3] “Waymo [online].” Available at https://waymo.com/ (Accessed: July 2022).
[4] “Openpilot [online].” Available at https://github.com/commaai/openpilot (Accessed: July 2022).
[5] SAE International, Taxonomy and Definitions for Terms Related to Driving Automation Systems for On-Road Motor Vehicles, 2021.
[6] K. Ren, Q.Wang, C.Wang, Z. Qin, and X. Lin, “The security of autonomous driving: Threats, defenses, and future directions,” Proceedings of the IEEE, vol. 108, no. 2, pp. 357–372, 2020.
[7] Q. Luo, Y. Cao, J. Liu, and A. Benslimane, “Localization and navigation in autonomous driving: Threats and countermeasures,” IEEE Wireless Communications, vol. 26, no. 4, pp. 38–45, 2019.
[8] V. L. Thing and J.Wu, “Autonomous vehicle security: A taxonomy of attacks and defences,” in 2016 IEEE International Conference on Internet of Things (iThings) and IEEE Green Computing and Communications (GreenCom) and IEEE Cyber, Physical
and Social Computing (CPSCom) and IEEE Smart Data (SmartData), pp. 164– 170, 2016.
[9] Q. Kong, R. Lu, F. Yin, and S. Cui, “Privacy-preserving continuous data collection for predictive maintenance in vehicular fog-cloud,” IEEE Transactions on Intelligent Transportation Systems, vol. 22, no. 8, pp. 5060–5070, 2021.
[10] S. N. Narayanan, S. Mittal, and A. Joshi, “Obd securealert: An anomaly detection system for vehicles,” in 2016 IEEE International Conference on Smart Computing (SMARTCOMP), pp. 1–6, 2016.
[11] M. Zhang, C. Chen, T. Wo, T. Xie, M. Z. A. Bhuiyan, and X. Lin, “Safedrive: Online driving anomaly detection from large-scale vehicle data,” IEEE Transactions on Industrial Informatics, vol. 13, no. 4, pp. 2087–2096, 2017.
[12] F. Guo, Z. Wang, S. Du, H. Li, H. Zhu, Q. Pei, Z. Cao, and J. Zhao, “Detecting vehicle anomaly in the edge via sensor consistency and frequency characteristic,” IEEE Transactions on Vehicular Technology, vol. 68, no. 6, pp. 5618–5628, 2019.
[13] S. Gyawali, Y. Qian, and R. Q. Hu, “Machine learning and reputation based misbehavior detection in vehicular communication networks,” IEEE Transactions on Vehicular Technology, vol. 69, no. 8, pp. 8871–8885, 2020.
[14] F. van Wyk, Y. Wang, A. Khojandi, and N. Masoud, “Real-time sensor anomaly detection and identification in automated vehicles,” IEEE Transactions on Intelligent Transportation Systems, vol. 21, no. 3, pp. 1264–1276, 2020.
[15] Y. Wang, D. W. Ming Chia, and Y. Ha, “Vulnerability of deep learning model based anomaly detection in vehicle network,” in 2020 IEEE 63rd International Midwest Symposium on Circuits and Systems (MWSCAS), pp. 293–296, 2020.
[16] Y. Lin, Y. Lin, C. Huang, C. Chih, and P. Lin, “Iottalk: A management platform for reconfigurable sensor devices,” IEEE Internet of Things Journal, vol. 4, no. 5, pp. 1552–1562, 2017.
[17] “Carla simulator [online].” Available at https://carla.org (Accessed: July 2022).