SCHED: Safe CPU Scheduling Framework with Reinforcement Learning and Decision Trees for Autonomous Vehicles

Autonomous vehicles (AVs) require consistently low-latency computations; however, operating system (OS) CPU scheduler can lead to high tail latency that threatens timely decision-making and safety. General-purpose OS schedulers prioritize fairness and throughput over individual task deadlines, posing challenges for complex AV workloads with mixed-criticality tasks. Despite extensive research on CPU scheduling, reinforcement learning (RL) has not been explored at the OS level due to feasibility concerns. This paper presents SCHED, the first RL-based CPU scheduling framework optimized for OS integration. SCHED learns a scheduling policy via reinforcement learning and deploys it as a lightweight, decision-tree-based scheduler. We demonstrate that SCHED remains sufficiently fast for OS-level integration compared to existing OS schedulers. Furthermore, we validate SCHED on a realistic autonomous vehicle pipeline, demonstrating its practical potential in maintaining low latency and ensuring safer, more responsive AV operations.