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. 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 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. Motivated by
this, in this article, we propose a novel framework, namely Blockchain-enabled
intElligent Safe-driving assessmenT (BEST), that 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.

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