AI赋能区块链驱动的智能安全网联自动驾驶车辆网络：智能安全驾驶评估的下一前沿
Smart and Secure CAV Networks Empowered by AI-Enabled Blockchain: The Next Frontier for Intelligent Safe Driving Assessment
AI总结：
针对CAV安全驾驶监管中的数据隐私、可信性与算力限制问题，本文提出BEST框架，结合LSTM安全评估和基于拜占庭容错的DPoS区块链共识，提升了数据可信度与预测准确率。
摘要：
尽管人工智能为车载设备提供了多种复杂功能，但保障网联自动驾驶车辆（CAV）的安全驾驶仍是一个受到广泛关注的问题。随着车联网在全球范围内的部署，各类恶意网络攻击无处不在，这给CAV网络中的数据管理带来了一系列可靠性和隐私威胁。再加上现有CAV处理密集计算任务的能力有限，这些事实意味着需要设计一种高效的评估系统，以在不损害数据安全的前提下保证自动驾驶安全。本文提出了一种名为Blockchain-enabled intElligent Safe-driving assessmenT（BEST，区块链赋能的智能安全驾驶评估）的新型框架，它提供了一种智能且可靠的方法，用于开展安全驾驶监管并保护车辆信息。具体而言，文中引入了一种利用长短期记忆模型的有前景的解决方案，以评估行驶中CAV的安全等级。随后，我们研究了分布式区块链如何通过采用基于拜占庭容错的委托权益证明共识机制，为CAV数据获得充分的可信性和鲁棒性。仿真结果表明，与现有方案相比，所提出的BEST获得了更好的数据可信性，并在车辆安全评估方面具有更高的预测准确率。最后，我们讨论了未来CAV网络中需要解决的若干开放性挑战。
英文摘要：
Securing safe driving for connected and autonomous vehicles (CAVs) continues to be a widespread concern, despite various sophisticated functions delivered by artificial intelligence for in-vehicle devices. Diverse malicious network attacks are ubiquitous, along with the worldwide implementation of the Internet of Vehicles, which exposes a range of reliability and privacy threats for managing data in CAV networks. Combined with the fact that the capability of existing CAVs in handling intensive computation tasks is limited, this implies a need for designing an efficient assessment system to guarantee autonomous driving safety without compromising data security. In this article we propose a novel framework, namely Blockchain-enabled intElligent Safe-driving assessmenT (BEST), which offers a smart and reliable approach for conducting safe driving supervision while protecting vehicular information. Specifically, a promising solution that exploits a long short-term memory model is introduced to assess the safety level of the moving CAVs. Then we investigate how a distributed blockchain obtains adequate trustworthiness and robustness for CAV data by adopting a byzantine fault tolerance-based delegated proof-of-stake consensus mechanism. Simulation results demonstrate that our presented BEST gains better data credibility with a higher prediction accuracy for vehicular safety assessment when compared with existing schemes. Finally, we discuss several open challenges that need to be addressed in future CAV networks.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
