  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.
