Securing a safe-driving circumstance for connected and autonomous vehicles (CAVs) continues to be a widespread concern despite various sophisticated functions delivered by artificial intelligence for in-vehicle devices. Besides, diverse malicious network attacks become 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 another fact that CAVs are now limited in handling intensive computation tasks, it thus renders a pressing demand of designing an efficient assessment system to guarantee autonomous driving safety without compromising data security. To this end, we propose in this article a novel framework of Blockchain-enabled intElligent Safe-driving assessmenT (BEST) to offer a smart and reliable approach for conducting safe driving supervision while protecting vehicular information. Specifically, a promising solution of exploiting a long short-term memory algorithm is first introduced in detail for an intElligent Safe-driving assessmenT (EST) scheme. To further facilitate the EST, we demonstrate how a distributed blockchain obtains adequate efficiency, trustworthiness and resilience with an adopted byzantine fault tolerance-based delegated proof-of-stake consensus mechanism. Moreover, several challenges and discussions regarding the future research of this BEST architecture are presented.
翻译:尽管人工智能为车内装置提供了各种尖端功能,但确保连接和自主车辆的安全驾驶环境仍然是一个普遍问题,此外,各种恶意网络袭击随着全球实施车辆互联网而变得无处不在,这暴露了对管理CAV网络数据的一系列可靠性和隐私威胁。此外,由于CAV目前处理密集计算任务的能力有限,加上CAV目前对处理密集计算任务的能力有限,因此迫切需要设计一个高效的评估系统,以保障自主驾驶安全,同时又不损害数据安全。为此,我们提议在本条中建立一个新的框架,即由封闭链驱动的Int Elligent安全驾驶评估T(BEST)来提供一个聪明和可靠的方法,用以进行安全驾驶监督,同时保护车辆信息。具体地说,利用长期短期记忆算法这一有希望的办法首先被详细引入,用于一个内在安全驾驶评估T(EST)计划。为了进一步便利EST,我们展示分布的块链如何获得足够的效率、可信赖性和弹性,并具有抵御能力,同时采用一种由BZANTE进行的安全驾驶式容忍研究的多种挑战。