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.
翻译:尽管人工智能为车辆内装置提供各种复杂功能,但确保连接和自主车辆的安全驾驶仍是一个普遍关切的问题。各种恶意网络袭击无处不在,同时在世界各地实施车辆互联网,这暴露了对控制卡瓦网络数据管理的一系列可靠性和隐私威胁。加上现有卡瓦网络处理密集计算任务的能力有限,这意味着需要设计一个高效的评估系统,以保障自动驾驶安全,同时又不损害数据安全。在本篇文章中,我们提出了一个新颖的框架,即由 " 闭锁 " 带动的 " 内流安全驾驶评估T " (BEST),它为开展安全驾驶监督提供了明智和可靠的方法,同时保护了特定信息。具体地说,一个利用长期短期记忆模型评估移动的卡瓦网络安全水平的有希望的解决办法。然后,我们调查一个分布式的链如何通过采用一种基于错误的容忍授权的更高共识机制,使CAVAV数据获得足够的信任和稳健。在对目前的安全性作出预测时,要用一种比较准确性的方法来评估,最后要用一种比较最新数据评估的准确性的方法来评估。