An Enabling Trajectory Planning Scheme for Lane Change Collision Avoidance on Highways
Bibliographic record
Abstract
This paper presents a hierarchical three-layer trajectory planning framework to realize real-time collision avoidance under complex driving conditions. This is mainly ascribed to the generation of a collision-free trajectory cluster based on the speed and the path re-planning. The upper-layer controller is to generate a reference quintic polynomial trajectory based on the Sequential Quadratic Programming by assuming mild speed and acceleration variations of the surrounding vehicles. The waypoints and time stamps can be obtained via the reference trajectory. When the assumption is invalid under complex driving conditions, the middle-layer controller would generate a Quadratic Programming-based trajectory cluster to assign different time stamps to each waypoint through time-based sampling methods. The lower-layer controller would be triggered to create a new feasible trajectory based on the path sampling if the collision avoidance requirements are not satisfied. The host vehicle will return to its original lane if no feasible time window is available to perform a lane change maneuver under the vehicle kinematics and lane change time/displacement constraints. The effectiveness of the proposed scheme is verified under various scenarios through comprehensive simulations.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".