Enhancing the Safety of Autonomous Driving Systems via AoI-Optimized Task Scheduling
Bibliographic record
Abstract
An Autonomous Driving System (ADS) uses various sensors and deep learning to improve navigation and control tasks. Maintaining road safety requires that these tasks are seamlessly synchronized and consistently utilize the most recent sensing data. This synchronization poses challenges due to 1) various sensing periods across different sensors, 2) the interdependency of tasks, and 3) constraints on computational resources. Our research pioneers the use of the Age of Information (AoI) to measure task scheduling performance within ADS. With theoretical analysis, we disclose that optimizing AoI simultaneously minimizes response time and maximizes throughput. We then offer a formal definition of the AoI-centric task scheduling problem. Given the NP-hardness of this problem, we design a 4-approximation algorithm. To enhance the practicality of our solutions, we propose an extended formulation optimizing AoI-centric scheduling over a specified cycle and correspondingly develop a reinforcement learning-based approach. Experimental simulations, benchmarked against the Apollo driving system, demonstrate that our AoI-optimized task scheduling outperforms Apollo's scheduling mechanisms in terms of AoI, throughput, and worst-case response time. Notably, our proposed solution operating on four cores yields a maximum AoI lower than Apollo's schedulers running on eight cores.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".