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.
翻译:智能连通车辆(ICVs)的出现显示了未来智能交通系统的巨大潜力,提高了交通安全和道路效率,但依赖数据驱动的感知和驱动模型的ICV系统面临许多挑战,包括缺乏处理复杂驾驶背景的全面知识。在本文件中,我们积极调查ICV系统的合作知识共享。我们提议了一个安全高效的、定向定向的环流图(DAG)链链式知识共享框架,目的是满足基于小交易的车辆网络的需要。框架可以实现本地和跨区域的知识分享。然后,该框架应用于自主驱动应用程序,其中可以共享基于自动驾驶控制机的学习模型。为基于DAG的知识共享框架提出了轻量级提示选择算法(TSA),以便为跨区域车辆达成共识和身份核查。为了提高模型精度和尽量减少带宽消耗,为模型上传和下载提出了适应性非同步分布学习(ADL)计划。实验结果显示,基于链式知识的共享是安全的,可以共享基于机器学习的自动驾驶模式,并可以阻止现有的恶意用户进行相应的攻击。