Autonomous platoons traversing infrastructure gaps increasingly depend on LEO satellite backhaul for safety-critical updates, yet no existing framework jointly addresses compound Doppler from simultaneous satellite and vehicle motion, sub-slot handover outages that exceed collision-alert deadlines, and heterogeneous freshness requirements across three vehicular priority classes. The core challenge is a \emph{timescale mismatch}: coarse control slots hide sub-slot outages, which makes both AoI spike analysis and safety verification ill-posed. Ping-pong handover oscillations further compound AoI cost in a way that purely reactive schedulers cannot mitigate. We address these challenges through a unified framework that couples a two-timescale AoI model with tiered time-average safety constraints enforced by virtual queues. A closed-form ping-pong AoI envelope reveals that cumulative penalty grows quadratically in oscillation length, analytically justifying oscillation suppression as the highest-leverage safety mechanism. The resulting drift-plus-penalty template is instantiated as SafeScale-MATD3 with proactive handover timing and multi-task dual-critic MARL. A key finding is that suppressing brief but repeated ping-pong oscillations yields larger safety returns than shortening any single outage, and that tick-level AoI accounting is a necessary condition for verifiable collision-alert guarantees under LEO handovers. Simulations show that SafeScale-MATD3 is the only method satisfying the strict 1 % collision-alert violation budget, reducing violation rate by 4 to 5.5 times versus baselines, while achieving 35 % lower collision-alert AoI and strict Pareto dominance on the energy and freshness tradeoff.

翻译：跨越基础设施间隙的自主车队日益依赖低地球轨道卫星回传链路传输安全关键更新，然而现有框架未能联合解决以下问题：卫星与车辆同步运动导致的复合多普勒效应、超过碰撞预警截止时间的子时隙切换中断、以及三类车辆优先级异构新鲜度需求。核心挑战在于\emph{时间尺度失配}：粗粒度控制时隙隐藏了子时隙中断，导致AoI峰值分析与安全验证均病态。乒乓切换振荡进一步加剧了纯粹反应式调度器无法缓解的AoI代价。我们通过统一框架应对这些挑战，该框架将双时间尺度AoI模型与虚拟队列强制执行的层级化时间平均安全约束相结合。封闭形式的乒乓AoI包络揭示累积惩罚随振荡长度呈二次方增长，从理论上证明抑制振荡是最高杠杆的安全机制。所生成的漂移加惩罚模板被实例化为SafeScale-MATD3，具备主动切换时序与多任务双批评者多智能体强化学习。关键发现是：抑制短暂但重复的乒乓振荡比缩短任何单次中断能带来更大的安全收益，而时钟级AoI核算是实现LEO切换下可验证碰撞预警保证的必要条件。仿真表明，SafeScale-MATD3是唯一满足严格1%碰撞预警违规预算的方法，相较于基线将违规率降低4至5.5倍，同时实现碰撞预警AoI降低35%，并在能量与新鲜度权衡上严格帕累托占优。