Trajectory Planning for Autonomous Vehicles at Intersections Based on the Spatial Structure
Bibliographic record
Abstract
Intersection trajectory planning constitutes a fundamental challenge for autonomous vehicles, with spatial structure elements such as geometry, static obstacles, and conflict points critically determining both path geometry and speed profiles. Distinguished from existing studies applied in simple crossroad scenarios where the spatial structure is often overlooked, this study proposes a flexible and effective method for intersection that explicitly incorporates spatial structure. Our approach decomposes the problem into path planning and speed planning. First, we analyze and classify the intersection’s spatial structure components that affect planning into visible and invisible elements. Then, the cubic quasiuniform Basis‐spline (B‐spline) curve and cost function are applied to generate the optimal, static‐obstacle‐free path. In the subsequent speed planning stage, the traffic rule of conflict points is used to generate the reference speed, and quadratic programming is applied to generate the dynamic‐obstacle‐free speed curve in the spatiotemporal (ST) graph. To validate the effectiveness of this approach, experiments are conducted in four static intersections and six dynamic scenarios with different spatial structures. Compared with other methods like A ∗ and dynamic programming, this approach is more applicable and efficient. The results demonstrate the flexibility and practicality of this method, which can govern the continuity and smoothness of road connection points and ensure safety under the constraints of different intersection elements.
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How this classification was reachedexpand
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 | 0.000 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".