Designing and Implementing AoI-Optimized Scheduling for Autonomous Driving Systems
Bibliographic record
Abstract
Autonomous Driving Systems (ADS) incorporate complex algorithm stacks, including sensing, localization, perception, prediction, planning, and control. To enhance the safety and comfort of ADS, it is crucial to utilize the most recent sensor data and meticulously schedule these algorithm stacks for better system communication. Since the data-flow communication within ADS leverages sensor data for final planning decisions, our objective is to minimize the Age of Information (AoI), a metric that assesses the freshness of the processed information. The optimization of AoI on ADS, however, poses significant challenges due to computing resource limitations and the complicated nature of ADS operations. Moreover, most ADS platforms, such as Autoware, are built on Robot Operating System 2 (ROS 2), whose special execution behavior introduces additional complexities in scheduling algorithm design. To tackle these challenges, we propose an AoI-based scheduling optimization framework specifically for ADS. This involves developing effective algorithms to streamline and enhance the scheduling processes, as well as customizing ROS 2 at the system level to optimize AoI further. Experimental evaluations of our proposed policy against existing state-of-the-art scheduling policies in ROS 2 demonstrate notable improvements in the efficiency and responsiveness of ADS.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.000 | 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".