Mixed local motion planning and tracking control framework for autonomous vehicles based on model predictive control
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
This study proposes a novel mixed motion planning and tracking (MPT) control framework for autonomous vehicles (AVs) based on model predictive control (MPC), which is made up of an MPC‐based longitudinal motion planning module, a feed‐forward longitudinal motion tracking module, and an MPC‐based integrated lateral motion planning and tracking module. First, given the global reference path and the surroundings information obtained from onboard devices and V2X network, the longitudinal motion planning based on a vehicle kinematics model is applied to determine the local target path, the desired longitudinal acceleration, and velocity considering the longitudinal safety priority. Then, based on the planned target path and longitudinal velocity, the integrated lateral MPT module based on a 2 degree‐of‐freedom vehicle model is developed to determine the optimal steering angle while satisfying the multiple kinematics and dynamics constraints. Finally, based on the desired longitudinal acceleration and the steering angle, the longitudinal forces of tires are determined. More importantly, co‐simulations under several typical scenarios between MATLAB/Simulink and CarSim are conducted, and the results demonstrate excellent performance of the proposed mixed framework in both planning and tracking and also its real‐time implementation.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 it