To meet the extended ultra-low latency and high reliability (xURLLC) requirements for autonomous driving sys-tems, multiple access schemes must operate reliably in high-mobility and complex propagation environments. Recently, rate-splitting multiple access (RSMA) has emerged as a promis-ing multi-user transmission framework, showing robustness in dynamic situations where imperfect and outdated channel state information (CSI) is prevalent. Moreover, the advanced sensing, localization, and on-board computation capabilities of autonomous driving vehicles facilitate the construction of a channel knowledge map (CKM), which is a key enabler for environment-aware communications in future 6G networks. In this work, we propose a CKM-aware finite-blocklength (FBL) RSMA for a downlink autonomous driving system. The location-dependent large-scale channel information provided by CKM is exploited to guide the common/private power split and common-rate allocation in RSMA. Specifically, we derive a new and tight closed-form bound for the private-stream ergodic rate. Combined with the closed-form expression for the common-stream ergodic rate, an optimization design of splitting ratios is formulated to maximize the min-rate performance among multiple users. Numerical results show that the proposed scheme outperforms the considered space-division multiple access (SDMA) and non-orthogonal multiple access (NOMA) schemes in high-mobility scenarios. Comparisons across different CKMs further highlight the benefit of accurate channel information, demonstrating how environment-aware channel knowledge complements flexible in-terference management of RSMA in high-mobility short-packet communications.

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