Parking Trajectory Planning for Autonomous Vehicles Under Narrow Terminal Constraints
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Trajectory planning in tight spaces presents a significant challenge due to the complex maneuvering required under kinematic and obstacle avoidance constraints. When obstacles are densely distributed near the target state, the limited connectivity between the feasible states and terminal state can further decrease the efficiency and success rate of trajectory planning. To address this challenge, we propose a novel Dual-Stage Motion Pattern Tree (DS-MPT) algorithm. DS-MPT decomposes the trajectory generation process into two stages: merging and posture adjustment. Each stage utilizes specific heuristic information to guide the construction of the trajectory tree. Our experimental results demonstrate the high robustness and computational efficiency of the proposed method in various parallel parking scenarios. Additionally, we introduce an enhanced driving corridor generation strategy for trajectory optimization, reducing computation time by 54% to 84% compared to traditional methods. Further experiments validate the improved stability and success rate of our approach.
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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 | 0.000 |
| 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