Autonomous Path Planning for Road Vehicles in Narrow Environments: An Efficient Continuous Curvature Approach
Why this work is in the frame
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Bibliographic record
Abstract
In this paper we introduce a novel method for obtaining good quality paths for autonomous road vehicles (e.g., cars or buses) in narrow environments. There are many traffic situations in urban scenarios where nontrivial maneuvering in narrow places is necessary. Navigating in cluttered parking lots or having to avoid obstacles blocking the way and finding a detour even in narrow streets are challenging, especially if the vehicle has large dimensions like a bus. We present a combined approximation-based approach to solve the path planning problem in such situations. Our approach consists of a global planner which generates a preliminary path consisting of straight and turning-in-place primitives and a local planner which is used to make the preliminary path feasible to car-like vehicles. The approximation methodology is well known in the literature; however, both components proposed in this paper differ from existing similar planning methods. The approximation process with the proposed local planner is proven to be convergent for any preliminary global paths. The resulting path has continuous curvature which renders our method well suited for application on real vehicles. Simulation experiments show that the proposed method outperforms similar approaches in terms of path quality in complicated planning tasks.
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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 | 0.001 |
| 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