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Article summary:

1. Autonomous driving systems require minimizing end-to-end latencies for safety and performance.

2. The paper proposes a novel schedule synthesis to reduce probabilistic end-to-end latencies of task sequences in autonomous driving software stacks.

3. The proposed synthesis consists of two stages: baseline schedule generation and task phases optimization, which significantly improve graph schedulability and reduce end-to-end tail latencies.

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