Fast Trajectory Planning in Cartesian rather than Frenet Frame: A Precise Solution for Autonomous Driving in Complex Urban Scenarios
Bibliographic record
Abstract
On-road trajectory planning is a direct reflection of an autonomous vehicle’s intelligence level when traveling on an urban road. The prevalent on-road trajectory planners include the spline-based, sample-and-search-based, and optimal-control-based methods. Path-velocity decomposition and Frenet frame have been widely adopted in the aforementioned methods, which, nonetheless, largely degrade the trajectory planning quality when the road curvature is large and/or the scenario is complex. This paper aims to plan precise and high-quality on-road trajectories, thus we choose to describe the concerned scheme as an optimal control problem, wherein the urban road scenario is described completely in the Cartesian frame rather than in the Frenet frame. The formulated optimal control problem should be numerically solved in real-time. To that end, a light-weighted iterative computation architecture is built. In each iteration, a tunnel construction strategy tractablely models the collision-avoidance constraints, and a constraint softening strategy helps to find an intermediate trajectory for constructing the tunnels in the next iteration. Efficacy of the proposed on-road trajectory planner is validated by simulations on a high-curvature urban road wherein the ego vehicle is surrounded by multiple social vehicles at various speeds.
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 | 0.000 | 0.001 |
| 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 | 0.001 | 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".