Motion Planning and Tracking Control of Autonomous Vehicle Based on Improved <a:math xmlns:a="http://www.w3.org/1998/Math/MathML" id="M1"> <a:msup> <a:mrow> <a:mi>A</a:mi> </a:mrow> <a:mi>∗</a:mi> </a:msup> </a:math> Algorithm
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
The traditional <a:math xmlns:a="http://www.w3.org/1998/Math/MathML" id="M2"> <a:msup> <a:mrow> <a:mi>A</a:mi> </a:mrow> <a:mi>∗</a:mi> </a:msup> </a:math> algorithm, applied to the motion planning of autonomous vehicles, easily causes high computational costs and excessive turning points generated in the planning path. In addition, the vehicle cannot track the path due to the unsmooth inflection point. To overcome these potential limitations, an improved <c:math xmlns:c="http://www.w3.org/1998/Math/MathML" id="M3"> <c:msup> <c:mrow> <c:mi>A</c:mi> </c:mrow> <c:mi>∗</c:mi> </c:msup> </c:math> algorithm-based motion planning algorithm and a tracking control strategy based on model predictive control theory were proposed in this work. The method of expanding the search neighborhood is adopted to improve the planning efficiency of <e:math xmlns:e="http://www.w3.org/1998/Math/MathML" id="M4"> <e:msup> <e:mrow> <e:mi>A</e:mi> </e:mrow> <e:mi>∗</e:mi> </e:msup> </e:math> algorithm. The artificial potential field method is also incorporated into the proposed <g:math xmlns:g="http://www.w3.org/1998/Math/MathML" id="M5"> <g:msup> <g:mrow> <g:mi>A</g:mi> </g:mrow> <g:mi>∗</g:mi> </g:msup> </g:math> algorithm. The resultant force generated by each potential field is further introduced into the evaluation function of <i:math xmlns:i="http://www.w3.org/1998/Math/MathML" id="M6"> <i:msup> <i:mrow> <i:mi>A</i:mi> </i:mrow> <i:mi>∗</i:mi> </i:msup> </i:math> algorithm to plan the driving path, which could be suitable for autonomous vehicles. The sharp nodes in the path are smoothed by cubic quasi-uniform B-spline curve. The tracking control strategy is designed based on model predictive control theory to realize the accurate tracking of the planned path. Typical obstacle avoidance conditions were selected for co-simulation test verification. The experimental results show that the proposed motion planning algorithm and tracking control strategy can effectively plan the obstacle avoidance path and accurately track the path in different environments.
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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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.002 |
| 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