  The emergence of Intelligent Connected Vehicles (ICVs) shows great potential
for future intelligent traffic systems, enhancing both traffic safety and road
efficiency. However, the ICVs relying on data driven perception and driving
models face many challenges, including the lack of comprehensive knowledge to
deal with complicated driving context. In this paper, we are motivated to
investigate cooperative knowledge sharing for ICVs. We propose a secure and
efficient directed acyclic graph (DAG) blockchain based knowledge sharing
framework, aiming to cater for the micro-transaction based vehicular networks.
The framework can realize both local and cross-regional knowledge sharing.
Then, the framework is applied to autonomous driving applications, wherein
machine learning based models for autonomous driving control can be shared. A
lightweight tip selection algorithm (TSA) is proposed for the DAG based
knowledge sharing framework to achieve consensus and identity verification for
cross-regional vehicles. To enhance model accuracy as well as minimizing
bandwidth consumption, an adaptive asynchronous distributed learning (ADL)
based scheme is proposed for model uploading and downloading. Experiment
results show that the blockchain based knowledge sharing is secure, and it can
resist attacks from malicious users. In addition, the proposed adaptive ADL
scheme can enhance driving safety related performance compared to several
existing algorithms.
