A Novel Motion Planning for Autonomous Vehicles Using Point Cloud Based Potential Field
Bibliographic record
Abstract
Ensuring accurate and efficient perception and motion planning is critical for the safety of autonomous vehicles. Addressing these pivotal challenges, this paper introduces a novel motion planning method employing a Lidar point cloud-based potential field (PF). Our approach innovatively extracts the drivable area boundary from point cloud, enhancing computational efficiency and reducing common perception errors, such as missed detections and inaccurate obstacle shape estimation. Built upon this drivable area boundary, the PF effectively represents the cost of traversing diverse areas. The PF is integrated into a model predictive control (MPC) framework to generate control commands considering vehicle dynamics, constraints, collision avoidance, and passenger comfort. Given the highly nonlinear nature of simultaneous longitudinal and lateral motion planning, an efficient Frenet frame-based trajectory sampling method is developed to provide an initial guess of the optimal trajectory for this complex motion planning task. The perception module has been validated in real bus tests, confirming its reliability and efficiency, and the entire motion planning methodology has been rigorously tested through simulations. These simulations show that our method efficiently generates smooth and safe control commands, even in challenging scenarios where the obstacle vehicle suddenly changes its lane, and remains robust under considerable state observation noise.
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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) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".