Planning a Continuous Vehicle Trajectory for an Automated Lane Change Maneuver by Nonlinear Programming considering Car-Following Rule and Curved Roads
Bibliographic record
Abstract
This study proposes a trajectory planning method for an automated lane change maneuver. We consider that the spatiotemporal trajectory of a controlled vehicle can be represented by a polynomial function and estimate the parameters of the polynomial function through nonlinear programming that does not rely on the assumed time horizon of lane change and the assumed final state of the controlled vehicle. When the controlled vehicle achieves its target lane, the state of this vehicle should be constrained by both the position and the speed of the forward vehicle. We integrate a car-following model into the nonlinear programming to constrain the state of the controlled vehicle at the end of the lane change. Notably, the interaction factors are taken into consideration: the motion of the vehicle that follows the controlled vehicle in the target lane can be influenced by the trajectory planning results of the controlled vehicle. The proposed trajectory planning method can simultaneously estimate the motion of the following vehicle and plan the trajectory for the controlled vehicle. Our proposed model can also work for curved road sections. We represent the curve information in the nonlinear programming by a regression model. We solve the nonlinear programming through the sequential quadratic program (SQP) algorithm and design a method to specify an initial guess for the algorithm. This paper finally demonstrates the effect of the proposed trajectory planning method under different scenarios.
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How this classification was reachedexpand
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 | 0.000 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".