TriPField: A 3D Potential Field Model and Its Applications to Local Path Planning of Autonomous Vehicles
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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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 source (direct Gemma or distilled Codex), 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".