Fast Trajectory Planning in Cartesian rather than Frenet Frame: A Precise Solution for Autonomous Driving in Complex Urban Scenarios
Why this work is in the frame
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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.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it