TriPField: A 3D Potential Field Model and Its Applications to Local Path Planning of Autonomous Vehicles
Why this work is in the frame
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Bibliographic record
Abstract
Potential fields have been integrated with local path-planning algorithms for autonomous vehicles (AVs) to tackle challenging scenarios with dense and dynamic obstacles. Most existing potential fields are isotropic without considering the traffic agent’s geometric shape and could cause failures due to local minima. We propose a three-dimensional potential field (TriPField) model to overcome this drawback by integrating an ellipsoid potential field with a Gaussian velocity field (GVF). Specifically, we model the surrounding vehicles as ellipsoids in corresponding ellipsoidal coordinates, where the formulated Laplace equation is solved with boundary conditions. Meanwhile, we develop a nonparametric GVF to capture the multi-vehicle interactions and then plan the AV’s velocity profiles, reducing the path search space and improving computing efficiency. Finally, a local path-planning framework with our TriPField is developed by integrating model predictive control to consider the constraints of vehicle kinematics. Our proposed approach is verified in three typical scenarios, i.e., active lane change, on-ramp merging, and car following. Experimental results show that our TriPField-based planner obtains a shorter, smoother local path with a slight jerk during control, especially in the scenarios with dense traffic flow, compared with traditional potential field-based planners. Our proposed TriPField-based planner can perform emergent obstacle avoidance for AVs with a high success rate even when the surrounding vehicles behave abnormally.
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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 | 0.000 | 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 it