In order to save computing power yet enhance safety, there is a strong intention for autonomous vehicles (AVs) in future to drive collaboratively by sharing sensory data and computing results among neighbors. However, the intense collaborative computing and data transmissions among unknown others will inevitably introduce severe security concerns. Aiming at addressing security concerns in future AVs, in this paper, we develop SPAD, a secured framework to forbid free-riders and {promote trustworthy data dissemination} in collaborative autonomous driving. Specifically, we first introduce a publish/subscribe framework for inter-vehicle data transmissions{. To defend against free-riding attacks,} we formulate the interactions between publisher AVs and subscriber AVs as a vehicular publish/subscribe game, {and incentivize AVs to deliver high-quality data by analyzing the Stackelberg equilibrium of the game. We also design a reputation evaluation mechanism in the game} to identify malicious AVs {in disseminating fake information}. {Furthermore, for} lack of sufficient knowledge on parameters of {the} network model and user cost model {in dynamic game scenarios}, a two-tier reinforcement learning based algorithm with hotbooting is developed to obtain the optimal {strategies of subscriber AVs and publisher AVs with free-rider prevention}. Extensive simulations are conducted, and the results validate that our SPAD can effectively {prevent free-riders and enhance the dependability of disseminated contents,} compared with conventional schemes.

翻译：为在节省算力的同时提升安全性，未来自动驾驶车辆（AVs）具有通过共享传感器数据和计算结果实现协同驾驶的强烈需求。然而，未知实体间频繁的协同计算和数据传输不可避免地会引发严重的安全隐患。针对未来自动驾驶车辆的安全问题，本文提出SPAD安全框架，旨在禁止搭便车行为并促进协同自动驾驶中的可信数据传播。具体而言，我们首先引入用于车辆间数据传输的发布/订阅框架。为防御搭便车攻击，将发布者AV与订阅者AV之间的交互建模为车辆发布/订阅博弈，并通过分析Stackelberg均衡激励AV提供高质量数据。同时在该博弈中设计声誉评估机制，以识别传播虚假信息的恶意AV。进一步，针对动态博弈场景中网络模型参数和用户成本模型参数知识不足的问题，提出基于热启动的两层强化学习算法，用于获取具有搭便车预防能力的订阅者AV与发布者AV的最优策略。通过大量仿真实验，结果表明与传统方案相比，SPAD可有效禁止搭便车行为并增强传播内容的可信赖性。