Adaptive Lane Change Trajectory Planning Scheme for Autonomous Vehicles Under Various Road Frictions and Vehicle Speeds
Why this work is in the frame
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Bibliographic record
Abstract
This paper proposes an adaptive lane change trajectory planning scheme to road friction and vehicle speed for autonomous driving, while considering both the maneuver safety and the comfort of occupants. In regard to achieve smooth trajectory, a 7th-order polynomial function is constructed to ensure continuity of the planned trajectory up to the derivative of the curvature (jerk). Unlike traditional planning methods that only consider very limited maneuvering conditions, the proposed scheme adapts to a wide range of road friction and vehicle speed, while ensuring enhanced occupants’ ride comfort and acceptance. The proposed trajectory planning scheme creatively integrates all the dynamic constraints which are defined by road friction, safety, comfort and human-like driving style. It is shown that the proposed lane change planning algorithm reduces to the identification of exclusively the lane change duration given a constant forward speed. Illustrative simulation examples in MATLAB/Simulink have been conducted to demonstrate the validity of the proposed scheme. The acceptable traceability of the planned lane change trajectories is further demonstrated through path tracking analysis of a full-vehicle model in CarSim. Finally, experimental tests have been conducted based on Quanser’s latest self-driving car (QCar) to verify the practical effectiveness of the proposed trajectory planning scheme.
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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 | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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