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：为节省计算资源并提升安全性，未来自动驾驶车辆强烈倾向于通过共享感知数据和计算结果实现协同驾驶。然而，与未知车辆进行密集协作计算和数据传输不可避免地会引发严重的安全问题。针对未来自动驾驶车辆中的安全挑战，本文提出SPAD安全框架，旨在禁止搭便车行为并促进协作自动驾驶中的可信数据分发。具体而言，我们首先构建面向车辆间数据传输的发布/订阅框架。为抵御搭便车攻击，将发布者与订阅者之间的交互建模为车辆发布/订阅博弈，并通过分析Stackelberg均衡激励车辆提供高质量数据。我们在博弈中设计声誉评估机制，以识别恶意车辆传播虚假信息的行为。此外，针对动态博弈场景中网络模型参数与用户成本模型认知不足的问题，我们提出一种基于热启动的两层强化学习算法，实现订阅者与发布者策略的最优解并有效防止搭便车行为。大量仿真结果表明，与传统方案相比，SPAD能显著抑制搭便车行为并提升发布内容的可信度。