Real-Time Optimization-Based Path Planning for Autonomous Semi-Trailer Trucks*
Bibliographic record
Abstract
Autonomous semi-trailer trucks have great potential to offer safer and more efficient transportation. Path planning is an important part of autonomous driving, which aims to generate an optimal path for vehicles to avoid collision risks and keep them as centered in the lane as possible. However, achieving an optimal path for semi-trailer trucks is challenging due to the complex kinematics, the large vehicle dimensions and the trade-off between model complexity and real-time capability. In this work, we propose a novel real-time optimization-based path planning method to address these problems. This approach involves modeling the entire tractor-trailer system with positioning of all axles and corners. This detailed model enables more accurate path planning, allowing for full utilization of the drivable space while satisfying all physical constraints. The modeling is approximated and simplified by assuming equal curvature and using the law of cosines, which greatly reduces computation burden with slightly sacrificing modeling accuracy. Then we construct an optimization problem with strict collision avoidance constraints and soft lane centering preferences. This allows the truck's wheels to temporarily exceed the lane boundaries in certain scenarios like tight bends or narrow roads, improving its passing ability. The optimization problem is solved using high-efficient Augmented Lagrange Multiplier method. We demonstrate the performance of the proposed method with simulations and real semi-trailer truck experiments. The results show that the proposed method is efficient and accurate for real-time application. It can significantly improve the vehicle behavior in terms of obstacle avoidance and lane centering.
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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 | 0.001 | 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".