On-road Trajectory Planning with Spatio-temporal RRT* and Always-feasible Quadratic Program
Bibliographic record
Abstract
On-road trajectory planning is a critical module in an autonomous driving system. Instead of using a path-velocity decomposition or longitudinal-lateral decomposition strategy, this work aims to find a trajectory directly. We adopt a sampleand-search planner to get a coarse trajectory and then polish it via numerical optimization. Among the predominant sampleand-search planners, most of the sampling operations are not flexible, which inevitably lead to a solution failure if the sampling density is low, and suffer from the curse of dimensionality if the sampling density is set high. This work proposes a modified RRT* for trajectory search, aiming to promote the sampling flexibility and to get rid of the search randomness. A quadratic program (QP) based smoother is proposed to refine the coarse trajectory. Herein, the scale of the QP problem is fixed and tractable, and the feasibility of the QP problem is always guaranteed.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".