Safety-Aware AoI Scheduling for LEO Satellite-Assisted Autonomous Driving

TL;DR

This paper introduces a safety-aware AoI scheduling framework for LEO satellite-assisted autonomous driving, addressing satellite-vehicle Doppler effects, handover outages, and heterogeneous freshness needs.

Contribution

It proposes a unified two-timescale AoI model with safety constraints, and develops SafeScale-MATD3, a MARL-based proactive handover scheduler that suppresses ping-pong oscillations.

Findings

SafeScale-MATD3 satisfies the 1% collision-alert violation budget.

It reduces violation rate by 4 to 5.5 times compared to baselines.

It achieves 35% lower collision-alert AoI and better energy-freshness tradeoff.

Abstract

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.